User:WeijiBaikeBianji/HumanGeneticsquotations

Quotations from various books for improving Wikipedia articles

Feel free to use these quotations to update Wikipedia articles, for example articles on human genetics and related topics. The quotation collection will grow over the next year or more as I obtain reliable sources by interlibrary loan and read to verify and transcription-type the quotations. You are very welcome to discuss article improvements on the article talk page of any article where you plan to use these quotations.

Bazzett, Terence J. (2008). An Introduction to Behavior Genetics. Sunderland (MA): Sinauer. pp. 241–242. ISBN 978-0-87893-049-4. Taken together, these findings suggest that about 50% of the variation seen in IQ scores is accounted for by genetics and a nearly equal percentage is accounted for by environment. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Castles, Elaine E. (6 June 2012). Inventing Intelligence. ABC-CLIO. p. 71. ISBN 978-1-4408-0338-3. Retrieved 31 August 2013. Binet termed his new scale a test of intelligence. It is interesting to note that the primary connotation of the French term l'intelligence in Binet's time is what we might call "school brightness," and Binet himself claimed no function for his scales beyond that of measuring academic aptitude. Unlike many of his contemporaries, he did not believe that heredity was the primary determinant of test performance. Comparing the test scores of two individuals, Binet asserted, was meaningful only if they had been provided equal educational opportunities and environmental stimulation. (citing White, "Conceptual Foundations of IQ Testing" Psychology, Public Policy, and Law 6 (2000):33–43) {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Castles, Elaine E. (6 June 2012). Inventing Intelligence. ABC-CLIO. p. 102. ISBN 978-1-4408-0338-3. Retrieved 31 August 2013. these groups, he was convinced, were the primary cause of the 'enormous amount of crime, pauperism, and industrial inefficiency' then plaguing the nation. As we have seen, it was to describe this supposed danger that he coined the infamous term 'menace of the feeble-minded.' Terman uncritically accepted Carl Brigham's claims that the army testing data demonstrated the inferior intelligence of non-Nordic immigrants, and he used these data to advocate vociferously for the restriction of immigration. His concern about the threat posed by individuals of low intelligence (whether immigrant or native-born) also led Terman to join many of his fellow reformers as an active member of the Human Betterment Foundation, an organization founded to promote eugenic sterilization of the feebleminded. He wrote several popular articles in support of this cause, proudly claiming in one of them that intelligence testing served as 'the beacon light of the eugenics movement.' (citing "The Measurement of Intelligence (1916)" in The Bell Curve Debate: History, Documents, Opinions, ed. Russell Jacoby and Naomi Glauberman (New York, NY: Times Books, 1995), 545. ) (citing For a history of the phrase, see J. W. Trent, Jr., Inventing the Feeble Mind: A History of Mental Retardation in the United States (Berkeley, CA: University of California Press, 1994). ) {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Castles, Elaine E. (6 June 2012). Inventing Intelligence. ABC-CLIO. p. 135. ISBN 978-1-4408-0338-3. Retrieved 31 August 2013. Probably the most contentious of the issues fueling the IQ Wars is the extent to which heredity determines psychometric intelligence. The emotion with which even supposedly hardheaded scientists approach this question suggests that its social and political implications are far more potent than its actual scientific importance. (citing Because we have yet to develop any reliable measures of more broadly defined adaptive intelligence, this discussion of necessity excludes any exploration of possible genetic effects on its expression. In his famous studies of Hereditary Genius, published in 1869, Sir Francis Galton did in fact attempt to demonstrate the hereditary nature of broadly defined intelligence; in the absence of any objective measure of IQ, he used eminence as his mark of genius. Unfortunately, Galton completely ignored any possible contribution of social status or other environmental factors to eminence in Victorian England. Therefore, although fascinating as social history, his study is virtually worthless as science. Sir Francis Galton, Hereditary Genius: An Inquiry into Its Laws and Consequences (New York, NY: St. Martin's Press, 1978). ) {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Castles, Elaine E. (6 June 2012). Inventing Intelligence. ABC-CLIO. p. 137. ISBN 978-1-4408-0338-3. Retrieved 31 August 2013. Let us start with the hereditarians. They draw on three main types of evidence to support their position. First, research suggests that heredity accounts for somewhere between 20 and 80 percent of the variation in IQ scores within groups, at least within the range of Western cultures that have been studied. (Most hereditarians argue for values at the higher rather than the lower end of this range.) Therefore, they maintain, it is logical to assume that genetic factors account for at least some of the difference between racial and ethnic groups as well. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Castles, Elaine E. (6 June 2012). Inventing Intelligence. ABC-CLIO. p. 138. ISBN 978-1-4408-0338-3. Retrieved 31 August 2013. Although it seems compelling at first glance, the hereditarians' first argument is actually based on a logical fallacy. The existence of genetic variation within groups in no way proves genetic differences between groups. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Castles, Elaine E. (6 June 2012). Inventing Intelligence. ABC-CLIO. p. 138. ISBN 978-1-4408-0338-3. Retrieved 31 August 2013. But, argue the hereditarians, we already know that there are genetic differences between the races. After all, members of different racial groups do look different, and we agree that physical traits like eye and skin color, hair color and texture, facial features, and body shape are largely under genetic control. These particular characteristics, however, are generally determined by only a few discrete genes. Intelligence, on the other hand, is highly complex and involves many different brain functions; its genetic underpinnings must be multidetermined as well. Regardless of race, human beings have in common about 99.9 percent of their DNA; indeed, many researchers now argue that there is no genetic basis at all for our socially constructed categories of race. (citing David C. Rowe, 'Under the Skin: On the Impartial Treatment of Genetic and Environmental Hypotheses of Racial Differences,' American Psychologist 60 (2005): 60-70.) {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Castles, Elaine E. (6 June 2012). Inventing Intelligence. ABC-CLIO. p. 139. ISBN 978-1-4408-0338-3. Retrieved 31 August 2013. Psychologist Claude Steele has approached this issue from a different and very interesting perspective. Around the world, he reports, 'caste-like minorities' (for example, the Maoris in New Zealand, Oriental Jews in Israel, the Bakaru in Japan, and the Harijans, or untouchables, of India) score about 15 points lower on IQ tests than do memebers of the dominant group. (Caste-like minorities are groups whose members are shunted into inferior social positions solely on the basis of group membership, regardless of their own individual talents. In our own country, there is little argument that African Americans have historically been relegated to this status.) In some cases, these minority groups are racially distinct from the majority; in others, however, the disenfranchised minority and the dominant group are racially indistinguishable. In the latter situation, racially based genetic differences cannot possibly account for the observed disparities in IQ scores. And if genetic racial differences between the groups cannot explain the disparities in these cases, it stretches credulity to argue that they account for gaps of an identical size among groups that do differ racially. (citing "A Threat in the Air: How Stereotypes Shape the Intellectual Identities and Performance of Women and African Americans," American Psychologist 52 (1997): 613-29. ) {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Castles, Elaine E. (6 June 2012). Inventing Intelligence. ABC-CLIO. p. 145. ISBN 978-1-4408-0338-3. Retrieved 31 August 2013. As psychologist Ulric Neisser, editor of a recent volume on this phenomenon, has pointed out, the pattern of rising IQ test scores [para] either does or does not reflect real increases in g. If it does reflect real increases, g clearly is affected by environmental factors because no genetic process could produce such large changes so quickly. Whatever those environmental factors may be, we can at least reject the hypothesis that intelligence is genetically fixed. But if it does not reflect real increases . . . then the tests are evidently flawed, and all arguments based on tests scores become suspect. Either way, things look bad for g and the arguments of The bell Curve. (citing "Introduction: Rising Test Scores and What They Mean," in The Rising Curve: Long-Term Gains in IQ and Related Measures, ed. Ulric Neisser (Washington, DC: American Psychological Association, 1998), 5. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Crawford, Michael H. (2007). "Chapter 1: Foundations of Anthropological Genetics". In Crawford, Michael (ed.). Anthropological Genetics: Theory, Methods and Applications. Cambridge: Cambridge University Press. p. 1. ISBN 978-0-521-54697-3. Anthropological genetics is a synthetic discipline that applies the methods and theories of genetics to evolutionary questions posed by anthropologists. These anthropological questions concern the processes of human evolution, the human diaspora out of Africa, the resulting patterns of human variation, and bio-cultural involvement in complex diseases. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Crawford, Michael H. (2007). "Chapter 1: Foundations of Anthropological Genetics". In Crawford, Michael (ed.). Anthropological Genetics: Theory, Methods and Applications. Cambridge: Cambridge University Press. p. 1. ISBN 978-0-521-54697-3. What distinguishes anthropological genetics from human genetics is its emphasis on smaller, reproductively isolated, non-Western populations, plus a broader, biocultural perspective on evolution and on complex disease etiology and transmission. Judging from the contents of the American Journal of Human Genetics (premiere journal in the field of human genetics) there is a greater emphasis on the causes and processes associated with disease, and the examination of these processes in affected phenotypes (probands) and their families. Anthropological geneticists tend to focus more on normal variation in non-Western reproductively isolated human populations (Crawford, 2000). Anthropological geneticists also attempt to measure environmental influences through co-variates of quantitative phenotypes, while human geneticists less often attempt to quantify the environment in order to assess the impact of environmental-genetic interactions. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Crawford, Michael H. (2007). "Chapter 1: Foundations of Anthropological Genetics". In Crawford, Michael (ed.). Anthropological Genetics: Theory, Methods and Applications. Cambridge: Cambridge University Press. p. 2. ISBN 978-0-521-54697-3. Anthropological genetics of the late 1960s and early 1970s was preceded by almost a century of discovery and development in evolutionary theory and genetics. Many of the ideas associated with natural selection can be traced to the publication of Charles Darwin's Origin of Species in 1859 (see Table 1.2). Because Darwin was unaware of Gregor Mendel's experiments on the particulate nature of genes (using characteristics of pea plants) Darwin lacked specific mechanisms for generating new variation and had to settle for a blending form of inheritance. Darwin also used Lamarck's concept of the inheritance of acquired characteristics, a concept that persisted well into the twentieth century. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Crawford, Michael H. (2007). "Chapter 1: Foundations of Anthropological Genetics". In Crawford, Michael (ed.). Anthropological Genetics: Theory, Methods and Applications. Cambridge: Cambridge University Press. p. 5. ISBN 978-0-521-54697-3. Most fields of inquiry are fortunate to have one, or maximally two, highly innovative 'founders', such as a Charles Darwin or an Albert Einstein. However, in addition to Charles Darwin, evolutionary theory was developed by three contemporaneous major figures, namely: Sewall Wright, J.B.S. Haldane, and R.A. Fisher (Table 1.2 contains a time-line of the significant genetic breakthroughs). They set the mathematical foundations for the modern synthetic evolutionary theory and provided the formal underpinnings for the measurement of natural selection and statistical methods for estimating the effects of stochastic processes. Other scientists, such as Thomas Hunt Morgan and Ernest Muller, using animal (fruit fly) models provided an understanding of mutation, the source of new genetic variation – which had eluded Charles Darwin. In an essay celebrating his 100th birthdate, the last survivor from the period of the development of the evolutionary synthesis, the eminent German evolutionary biologist, Ernst Mayr, recently reminisced about the era of evolutionary theory development (Mayr, 2004). {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Crawford, Michael H. (2007). "Chapter 1: Foundations of Anthropological Genetics". In Crawford, Michael (ed.). Anthropological Genetics: Theory, Methods and Applications. Cambridge: Cambridge University Press. pp. 5–6. ISBN 978-0-521-54697-3. The next generation of population geneticists included the distinguished Russian émigré geneticist, Theodosius Dobzhansky, the great Chinese agronomist, C. C. Li, and US-born human geneticist, James Crow. They collectively added further refinements and detail to the synthetic theory of evolution. Although Dobzhansky's 'animals of choice' were the beetle and the lowly fruit fly (Drosophila melanogaster), he applied the principles of evolution learned from these models to humans and synthesized the available information on human evolution in a readable form. Similarly, C. C. Li synthesized much of the theory of population genetics in his concisely written primers, which assisted in the training of subsequent generations of evolutionists. James Crow coalesced demographic characteristics with population genetics by developing a method for assessing the opportunity of natural selection in human populations, based on Fertility and mortality components derived from church records and civil documents. Together with his former student, Arthur Mange, Crow also developed methods for estimating levels of inbreeding in human populations using marital records and the likelihood of individuals with the same surname marrying (isonymy). {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Crawford, Michael H. (2007). "Chapter 1: Foundations of Anthropological Genetics". In Crawford, Michael (ed.). Anthropological Genetics: Theory, Methods and Applications. Cambridge: Cambridge University Press. p. 7. ISBN 978-0-521-54697-3. Anthropologists with training in genetics were useful to the medical profession in studies of small, highly isolated, non-Western populations. Unfortunately, until the 1950s, there were few anthropologists with adequate training in human genetics. The reason behind this paucity was that most physical anthropologists were traditionally trained in morphology and racial classification based on typology. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Crawford, Michael H. (2007). "Chapter 1: Foundations of Anthropological Genetics". In Crawford, Michael (ed.). Anthropological Genetics: Theory, Methods and Applications. Cambridge: Cambridge University Press. p. 7. ISBN 978-0-521-54697-3. Frank Livingstone, a former student of Neel and Spuhler at Michigan, conducted a study on the effects of culture (i.e. the introduction of slash-and-burn agriculture into sub-Saharan Africa) on the distribution of falciparum malaria. He demonstrated in his classic dissertation and subsequent publications that the destruction of the tropical rain forest resulted in the creation of standing bodies of water, a prerequisite for the successful breeding conditions of the Anopheles mosquito (Livingstone, 1958). The increased parasitization caused a shift from epidemic to endemic malarial infection and the action of natural selection against various phases of the life cycle of Plasmodium falciparum. Livingstone and Neel also trained a number of anthropological geneticists at Michigan, e.g. Kenneth Weiss, Alan Fix and the late Richard Ward – all went on to distinguished careers in anthropological genetics. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Crawford, Michael H. (2007). "Chapter 1: Foundations of Anthropological Genetics". In Crawford, Michael (ed.). Anthropological Genetics: Theory, Methods and Applications. Cambridge: Cambridge University Press. pp. 7–8. ISBN 978-0-521-54697-3. Richard Lewontin (a population geneticist) statistically partitioned genetic variation within populations and between populations on the basis of 15 protein loci (Lewontin, 1967). He demonstrated that 85% of human genetic diversity is within populations. Thus, a much smaller percentage, 15%, is between populations. This research has been used to discourage genetic comparisons between so-called geographical "races" because most of the variation is contained with the populations. Barbujani (1997) retested Lewontin's findings based on DNA markers and confirmed that 84.4% of the variation was within populations, 4.7% among samples, within groups, and 10.8% among groups (see Chapter 2, Madrigal and Barbujani). However, a controversial analysis of single nucleotide repeat (SNP) diversity (Seielstad et al., 1998) indicated that while autosomal and mtDNA SNPs provide a pattern similar to that observed by Lewontin and Barbujani (within populations 85.5% and 81.4% of the variation is subsumed), Y-chromosomal SNPs apportion almost 53% of the variation between continental populations. ref=harv {{cite book}}: Missing pipe in: |quote= (help); Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Duster, Troy (13 August 2013). "Chapter 5: Ancestry Testing and DNA: Uses, Limits, and Caveat Emptor". In Krimsky, Sheldon; Sloan, Kathleen (eds.). Race and the Genetic Revolution: Science, Myth, and Culture. Columbia University Press. pp. 103–104. ISBN 978-0-231-52769-9. Retrieved 31 August 2013. Since Ancestry Information Markers (AIMs) are overwhelmingly shared across all human groups, it is therefore not their absolute presence or absence, but their rate of incidence, or frequency, that is usually being analyzed, and this is especially true when it comes to claims about continental populations. How did these markers come to represent ancestral populations of Africa, Europe, and Native America? The vast majority of these markers are not 'population specific,' as the inventor of Ancestry Informative Markers (AIMs) originally claimed. Because the companies marketing ancestry tests hold proprietary interests in their techniques, most do not make them available for possible scientific replication, and their modeling constructs are therefore undisclosed. Thus, we are left to speculate about the threshold level of frequency that is used to determine the grounds for inclusion or exclusion, as well as what counts as a 'pure' referent population. (citing Mark D. Shriver, Michael W. Smith, Li Jin et al., 'Ethnic-affiliation Estimation by Use of Population-specific DNA Markers,' American Journal of Human Genetics 60 (1997): 957-64.) {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Duster, Troy (13 August 2013). "Chapter 5: Ancestry Testing and DNA: Uses, Limits, and Caveat Emptor". In Krimsky, Sheldon; Sloan, Kathleen (eds.). Race and the Genetic Revolution: Science, Myth, and Culture. Columbia University Press. p. 105. ISBN 978-0-231-52769-9. Retrieved 31 August 2013. Yet, when taken together, we are told that these markers appear to yield sufficiently distinctive patterns in those continental populations tested. So now we see how a specific pattern of genetic markers on each of a set of chromosomes that have a higher frequency in the 'Native Americans' sampled becomes established as a 'Native American' ancestry reference. (The fact that there are more than 480 different populations of the Tribal Council, of which the vast majority have never been sampled, is no small matter here, but that is not the focus of the critique I am about to make.) The problem is that millions of people around the globe will have a similar pattern. That is, they share similar base-pair changes at the genomic points under scrutiny. This means that someone from Bulgaria whose ancestors go back to the fifteenth century could (and sometimes does) map as partly 'Native American,' although no direct ancestry is responsible for the shared genetic material. There is an overwhelming tendency for those who do AIMs analysis with the purpose of claims about ancestry to arbitrarily reduce all such possiblilities of shared genotypes to 'inherited direct ancestry.' In so doing, the process relies excessively on the idea of 100-percent purity, a condition that could never have existed in human populations. (citing The Native American Tribal Organizations and Councils are part of the federally recognized government-to-government partnership with the US General Services Administration.) {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Duster, Troy (13 August 2013). "Chapter 5: Ancestry Testing and DNA: Uses, Limits, and Caveat Emptor". In Krimsky, Sheldon; Sloan, Kathleen (eds.). Race and the Genetic Revolution: Science, Myth, and Culture. Columbia University Press. p. 106. ISBN 978-0-231-52769-9. Retrieved 31 August 2013. Thus, instead of asserting that someone has no Native American ancestry, the most truthful statement would be: It is possible that while the Native American groups we sampled did not share your pattern of markers, others might since these markers do not exclusively belong to any one group of our existing racial, ethnic, linguistic, or tribal typologies. But computer-generated data provide an appearance of precision that is dangerously seductive and equally misleading. Now we come to one part of the answer as to why different companies come to different results. We cannot conclude that an individual has a close affinity to a particular ethnic or racial group or local geographical population: 'Such a conclusion would require demonstrating that the DNA sequence is not present in other places, it would require demonstrating that the gene pool of that ethnic group or local population had been close and immobile for centuries and millennia.' (citing Kenneth M. Weiss and Jeffrey C. Long, 'Non-Darwinian Estimation: My Ancestors, My Gene's Ancestors,' Genome Research 19 (May 4, 2009): 703-10.) {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Duster, Troy (13 August 2013). "Chapter 5: Ancestry Testing and DNA: Uses, Limits, and Caveat Emptor". In Krimsky, Sheldon; Sloan, Kathleen (eds.). Race and the Genetic Revolution: Science, Myth, and Culture. Columbia University Press. pp. 111–112. ISBN 978-0-231-52769-9. Retrieved 31 August 2013. Much like the industry of assisted reproduction in the United States, there is a complete absence of regulation or quality control with genetic ancestry testing. There is no requirement for transparency in the construction and use of reference populations. Any company can claim that their laboratories can analyze your DNA to provide accurate information about your ancestry. If three different companies provide three different answers (as in the 60 Minutes report noted at the outset), what is a consumer to do? Which company is correct, or, more to the point, which one is more likely to be correct? There is no way of knowing, since we have no 'gold standard' for excellence or professional self-policing. This was pointed out in Science four years ago, and in November 2008, the American Society of Human Genetics (ASHG) issued a statement on ancestry testing that included five recommendations emphasizing the need for greater responsibility, research, explanatory clarity, collaboration, and accountability by these direct-to-consumer companies. The statement also pointedly warned of several important limitations to the scientific approaches used to infer genetic ancestry, including the false assumption that contemporary groups are reliable substitutes for ancestral populations, and most significantly, the lack of transparency regarding the statistical methods that companies use to determine test results. (citing Bolnick, 'The Science and Business.') (citing The American Society of Human Genetics Ancestry Testing Statement, November 13, 2008, 222.ashg.org.) (citing Sandra Soo-Jin Lee, Deborah Bolnick, Troy Duster, et al. 'The Illusive Gold Standard in Genetic Ancestry Testing.' Science 325 (July 3, 2009): 38-39) {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Duster, Troy (13 August 2013). "Chapter 5: Ancestry Testing and DNA: Uses, Limits, and Caveat Emptor". In Krimsky, Sheldon; Sloan, Kathleen (eds.). Race and the Genetic Revolution: Science, Myth, and Culture. Columbia University Press. pp. 99–115. ISBN 978-0-231-52769-9. Retrieved 31 August 2013. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Feldman, Marcus W.; Lewontin, Richard C. (2008). "Chapter 5: Race, Ancestry, and Medicine". In Koenig, Barbara A.; Lee, Sandra Soo-jin; Richardson, Sarah S. (eds.). Revisiting Race in a Genomic Age. New Brunswick (NJ): Rutgers University Press. p. 89. ISBN 978-0-8135-4324-6. Recent analyses of hundreds of microsatellite DNA markers and a few thousand SNPs from human populations have shown that it is possible with a high degree of accuracy to assign the major geographical region (or regions) of origin of individual human beings by using a combination of a number of these polymorphic genes (Rosenberg et al., 2002; Rosenberg et al., 2005; Conrad et al., 2006). In addition, using more markers, it is possible in some cases to narrow down the population of origin to local national populations within major geographic regions. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Feldman, Marcus W.; Lewontin, Richard C. (2008). "Chapter 5: Race, Ancestry, and Medicine". In Koenig, Barbara A.; Lee, Sandra Soo-jin; Richardson, Sarah S. (eds.). Revisiting Race in a Genomic Age. New Brunswick (NJ): Rutgers University Press. p. 90. ISBN 978-0-8135-4324-6. The microsatellite data and data on other DNA polymorphisms (Bamshad et al., 2003) are relevant, among other things, to the problem of the assignment of individuals to lines of geographical ancestry. The question asked is whether it is possible to find genes that are polymorphic in the human species and whose frequencies of alternate alleles are sufficiently different in the different major geographical regions to allow a correct assignment of geographical origin with high probability. The answer to this question is 'yes,' and that answer has been known for 50 years from studies of genetic polymorphisms. This is a problem in biological systematics. [para] The data on general genetic polymorphism for proteins and nucleotide substitutions, also addressed by the study of microsatellites and SNPs, can also be employed to ask a quite different question, which is, What fraction of all human genetic variation, whether based on protein coding genes, microsatellites, or any other polymorphic DNA sequences, lies within geographically separated populations and what fraction lies between these populations? This is not an assignment problem, but a question of the average amount of genetic diversification between and within geographical groups. The two problems can be related to each other by posing the question, Are the genes that are geographically highly differentiated in their allelic frequencies typical of the human genome in general? The answer to that question turns out to be 'no.' While there are indeed genes whose allelic frequencies differ markedly between geographical regions and can be used for taxonomic purposes, these are not typical of the human genome in general. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Feldman, Marcus W.; Lewontin, Richard C. (2008). "Chapter 5: Race, Ancestry, and Medicine". In Koenig, Barbara A.; Lee, Sandra Soo-jin; Richardson, Sarah S. (eds.). Revisiting Race in a Genomic Age. New Brunswick (NJ): Rutgers University Press. pp. 91–92. ISBN 978-0-8135-4324-6. Europe, West Asia, and South/Central Asia are regions of especially mixed ancestry; the separate linguistic groups in these areas are difficult to separate genetically, even with 783 markers. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Feldman, Marcus W.; Lewontin, Richard C. (2008). "Chapter 5: Race, Ancestry, and Medicine". In Koenig, Barbara A.; Lee, Sandra Soo-jin; Richardson, Sarah S. (eds.). Revisiting Race in a Genomic Age. New Brunswick (NJ): Rutgers University Press. p. 92. ISBN 978-0-8135-4324-6. The continental clustering in these large sets of data derives mainly from small differences in allele frequencies at large numbers of markers, not from diagnostic genotypes. This clustering reflects the history of human migrations that began when modern humans left Africa 50,000-100,000 years ago (King & Motulsky, 2002; Excoffier, 2003; Cavalli-Sforza & Feldman, 2003). For those geographical regions such as Europe, West Asia, and South/Central Asia that have a long history of migration and colonization, finer resolution of the clusters is very difficult and will probably require more samples and many more polymorphic markers. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Feldman, Marcus W.; Lewontin, Richard C. (2008). "Chapter 5: Race, Ancestry, and Medicine". In Koenig, Barbara A.; Lee, Sandra Soo-jin; Richardson, Sarah S. (eds.). Revisiting Race in a Genomic Age. New Brunswick (NJ): Rutgers University Press. p. 92. ISBN 978-0-8135-4324-6. We had remarked on the existence of 'continuous gradients across regions or admixture of neighboring groups' (Rosenberg et al., 2002, p. 2382). In fact, geographic clustering and spatial gradients are both features of these large data sets (Rosenberg et al., 2005). For population pairs from the same cluster, as geographic distance increases, genetic distance increases linearly, consistent with a clinal structure. But for pairs of populations from different clusters, genetic distance is generally larger than between pairs of populations from the same clusters that have the same genetic distance. This suggests that the clusters are formed by the small discontinuous jumps in genetic distance caused by major geographic barriers: oceans, mountain ranges, or deserts. Indeed, the history of migration is important (Ramachandran et al., 2005), but migration is not geographically uniform. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Feldman, Marcus W.; Lewontin, Richard C. (2008). "Chapter 5: Race, Ancestry, and Medicine". In Koenig, Barbara A.; Lee, Sandra Soo-jin; Richardson, Sarah S. (eds.). Revisiting Race in a Genomic Age. New Brunswick (NJ): Rutgers University Press. p. 93. ISBN 978-0-8135-4324-6. Finally, it must be borne in mind that the taxonomic problem cannot be inverted. That is, while clustering methods are capable of assigning an individual to a geographic population with a high degree of certainty, given that individual's genotype, it is not possible to predict accurately the genotype of an individual given his or her geographical origin. Thus, knowing an individual's ancestry only slightly improves the ability to predict his or her genotype. The more polymorphic the markers, the more difficult this is. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Feldman, Marcus W.; Lewontin, Richard C. (2008). "Chapter 5: Race, Ancestry, and Medicine". In Koenig, Barbara A.; Lee, Sandra Soo-jin; Richardson, Sarah S. (eds.). Revisiting Race in a Genomic Age. New Brunswick (NJ): Rutgers University Press. p. 93. ISBN 978-0-8135-4324-6. Even when the explicit purpose of studies has been to identify markers that show strong differentiation between groups, none that show a complete difference between major groups has been found. In the microsatellite study mentioned earlier (Rosenberg et al., 2002), the most geographically informative loci in the data set have some striking differences, as shown in figure 5-1, but nowhere near 100%. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Feldman, Marcus W.; Lewontin, Richard C. (2008). "Chapter 5: Race, Ancestry, and Medicine". In Koenig, Barbara A.; Lee, Sandra Soo-jin; Richardson, Sarah S. (eds.). Revisiting Race in a Genomic Age. New Brunswick (NJ): Rutgers University Press. pp. 93–95. ISBN 978-0-8135-4324-6. When we turn from the problem of finding genes that will discriminate ancestry to the problem of the relative amount of human genetic diversity that lies within and between populations, there is no controversy. The first survey, in 1972, of genetic diversity over a very large sample of local human populations from major geographical regions used all the available data for blood groups and enzyme proteins for every local human population that had been studied up to that time (Lewontin, 1972). The result was that 85% of all human genetic diversity, measured by the Shannon-Weaver information measure (or its close equivalent, heterozygosity) is present within local national groups, that is, averaging within Swedes, within Kikuyu, within Japanese, etc. An additional 8% is present between local groups within what were designated classically as races, between Swedes, Italians, and Greeks, or between Kikuyu, Zulu, and Hutu. The remaining 7% lies between the classical major races, between sub-Saharan Africans, East Asians, Australian Aborigines, Europeans, etc. Several similar studies were subsequently carried out on smaller geographical samples with similar results for the within-population variation, but with roughly 5% of the variation between local populations and 10% among the major 'races.' When these studies were repeated using a limited amount of DNA sequence variation (Barbujani, Magagni, Minch, & Cavalli-Sforza, 1997), again 85% of the variation was found within local populations, with about 5% between local populations and 10% among major classical races. The studies of the DNA markers discussed in the previous section (Rosenberg et al., 2002; Rosenberg et al., 2005) in the context of the taxonomic problem also partitioned total variation using a different measure of diversity. With this measure, 86% to 95% of the diversity was assigned within local populations, between 2% and 6% among populations within major geographical regions and between 3% and 10% among major regions (classical races). {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Feldman, Marcus W.; Lewontin, Richard C. (2008). "Chapter 5: Race, Ancestry, and Medicine". In Koenig, Barbara A.; Lee, Sandra Soo-jin; Richardson, Sarah S. (eds.). Revisiting Race in a Genomic Age. New Brunswick (NJ): Rutgers University Press. p. 96. ISBN 978-0-8135-4324-6. The typological and the geographic notions of race are combined in the classical division of human races because it is observed that the native inhabitants of different major regions of the world are characterized by clear phenotypic differences of color, facial features, and hair form. Variation in these phenotypes is also observed among individuals within races to the extent that even categorization according to such traits can be difficult (Brown & Armelagos, 2001). An underlying assumption of human race classification, a classification based on a small number of obvious phenotypic differences, was that these differences in genes influencing cognitive and most physiological traits. Indeed, in the absence of any evidence to the contrary, this is not an absurd assumption, but it turns out to be wrong. The repeated and consistent results on the apportionment of genetic diversity reviewed in the previous section show that the genes underlying the phenotypic differences used to assign race categories are atypical of the genome in general and are not a reliable index to the amount of genetic differentiation between groups. Thus, racial assignment loses any general biological interest. For the human species, race assignment of individuals does not carry with it any general implication about genetic differentiation. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Feldman, Marcus W.; Lewontin, Richard C. (2008). "Chapter 5: Race, Ancestry, and Medicine". In Koenig, Barbara A.; Lee, Sandra Soo-jin; Richardson, Sarah S. (eds.). Revisiting Race in a Genomic Age. New Brunswick (NJ): Rutgers University Press. p. 97. ISBN 978-0-8135-4324-6. What do the clusters constructed, for example, from the data in the microsatellite studies have to do with our common understanding of race? We must remember that the clusters are defined by markers that have no influence on obvious phenotypes. Nevertheless, there are some phenotypes that correlate well with continental origin, usually those that we can see. But even here we can be misled—dark skin is a feature of sub-Saharan Africans but also of southern Indians and Australian aborigines. Thus, if we were to use skin color alone, continental clustering, i.e., common racial classification, fails. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Feldman, Marcus W.; Lewontin, Richard C. (2008). "Chapter 5: Race, Ancestry, and Medicine". In Koenig, Barbara A.; Lee, Sandra Soo-jin; Richardson, Sarah S. (eds.). Revisiting Race in a Genomic Age. New Brunswick (NJ): Rutgers University Press. pp. 97–98. ISBN 978-0-8135-4324-6. The situation is even more complicated when we examine diseases that appear to aggregate in the classically defined races. Sickle cell disease is one that is often thought to be an African trait. But it exists in a number of Mediterranean and Indian populations as well. Sickle cell is not a marker of skin color or race, but more properly a marker of ancestry in a geographic location where malaria is or was prevalent. And, of course, not all Africans or Sardinians carry the gene responsible for sickle cell disease. Thus, classical race is not diagnostic of the disease, and the disease is not diagnostic of race. Rosenberg et al.'s clusters (Rosenberg et al., 2002; Rosenberg et al., 2005) don't tell us very much about traits that are determined by genes that have been under selection. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Feldman, Marcus W.; Lewontin, Richard C. (2008). "Chapter 5: Race, Ancestry, and Medicine". In Koenig, Barbara A.; Lee, Sandra Soo-jin; Richardson, Sarah S. (eds.). Revisiting Race in a Genomic Age. New Brunswick (NJ): Rutgers University Press. p. 98. ISBN 978-0-8135-4324-6. Both the regional heterogeneity within major geographical regions and the widespread mixture of formerly relatively isolated populations result in a confusion between race and ancestry that is critical and must be accounted for in medical practice. The assignment of racial classification to an individual hides the biological information that is needed for intelligent therapeutic and diagnostic decisions (see also Tate & Goldstein, this volume). A person classified as 'black' or 'Hispanic' by social convention may have any mixture of European, African, Native American, and, more rarely, Asian ancestry. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Feldman, Marcus W.; Lewontin, Richard C. (2008). "Chapter 5: Race, Ancestry, and Medicine". In Koenig, Barbara A.; Lee, Sandra Soo-jin; Richardson, Sarah S. (eds.). Revisiting Race in a Genomic Age. New Brunswick (NJ): Rutgers University Press. p. 98. ISBN 978-0-8135-4324-6. We agree entirely with Risch et al. (2002) that conventional socially defined race which, for example, classifies all persons with visually detectable African ancestry as 'black' or 'African American' is of use in a medical context to the extent that it provides information about social circumstances and lifestyle conditions of patients, particularly discrimination. But these socially defined categories should not be confounded with genetically defined races. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Feldman, Marcus W.; Lewontin, Richard C. (2008). "Chapter 5: Race, Ancestry, and Medicine". In Koenig, Barbara A.; Lee, Sandra Soo-jin; Richardson, Sarah S. (eds.). Revisiting Race in a Genomic Age. New Brunswick (NJ): Rutgers University Press. pp. 98–99. ISBN 978-0-8135-4324-6. The actual distribution of human genetic variation, including the distribution of genotypes that are directly relevant to the diagnosis and treatment of disease, is such that race is not a useful biological concept when applied to humans. It is nevertheless true that data about the various lines of ancestry of an individual can provide information on the likelihood that the person carries certain gene alleles. Lines of ancestry, rather than genetically arbitrary racial categories, can provide much accurate, biologically interesting, and potentially medically useful information. For diagnosis and treatment, however, individual genotypes will, in the long run, provide the most useful information. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Fletcher, Richard B.; Hattie, John (11 March 2011). Intelligence and Intelligence Testing. Taylor & Francis. p. 20. ISBN 978-1-136-82321-3. Retrieved 31 August 2013. Ethical issues aside, it is worth dwelling on the notion that Goddard translated the scale from French to English and then retranslated it into various other languages; therefore, the accuracy of these tests was likely compromised, given that French norms were used in the classification. Even today, translating a test into another language is a controversial and difficult process. This combined with the poor reliability and validity of the translated measures, as well as the huge cultural issues involved in testing people from such varied backgrounds and especially in these tense situations of border and customs checks, creates a grossly unfair testing process. The widespread use of Goddard's procedures at Ellis Island saw the numbers of immigrants deported grow exponentially. [para] Although Goddard initially held staunch hereditarian views, he later recanted his opiniion in favour of the nurture over nature argument. In essence, he acknowledged that 'feeble-mindedness' was something that could be treated, and that institutionalizing people on the basis of IQ scores was not necessary. One positive thing in Goddard's favour was that he helped draft one of the first state laws mandating that special provisions be made for special education classes. Obviously Goddard was a complex and contradictory man of his day (see Zenderland, 1998). {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Fletcher, Richard B.; Hattie, John (11 March 2011). Intelligence and Intelligence Testing. Taylor & Francis. p. 26. ISBN 978-1-136-82321-3. Retrieved 31 August 2013. Indeed, this effect, now called the 'Flynn effect', is well established. Nations, almost without exception, have shown gains of about 20 IQ points per generation (30 years). These gains are highest for IQ tests that are most related to reasoning and the capacity to figure out novel problems (this is often called 'fluid intelligence', see Chapter 5); and least related to knowledge, which arises from better educational opportunity, a history of persistence and good motivation for learning (this is often called 'crystallised intelligence', see Chapter 5). It is, therefore, important to note that these gains in IQ across decades and generations is not related to the type of knowledge gained from increased schooling, increased test-taking sophistication, increased nutrition, greater urbanisation, eradication of childhood diseases, upgrading of early childhood or preschool programmes or education in general. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Fletcher, Richard B.; Hattie, John (11 March 2011). Intelligence and Intelligence Testing. Taylor & Francis. p. 26. ISBN 978-1-136-82321-3. Retrieved 31 August 2013. Perhaps that best instance of extreme argument about IQ and race differences is Richard Herrnstein and Charles Murray's now-infamous 1994 book The Bell Curve. The book includes reports of many research studies, but it was the authors' interpretations of the research that caused an outcry. For example, when citing research on the relation between IQ and illegitimacy, they noted that the smarter a woman is, the more likely that she deliberately decides to have a child and calculates the best time to do it. The less intelligent the woman is, the more likely that she does not think ahead from sex to procreation, does not remember to use birth control, does not carefully consider when and under what circumstances she should have a child. How intelligent a woman is may interact with her impulsiveness, and hence her ability to exert self-discipline and restraint on her partner in order to avoid pregnancy. (p. 179) They go on to conclude that 'low intelligence is an important independent cause of illegitimacy' (p. 189). Such (il)logic and the assumption that having a baby is entirely up to one sex, the omission of mentioning the father, and the conclusion of 'welfare being the prime suspect' is more rhetoric than research. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Fletcher, Richard B.; Hattie, John (11 March 2011). Intelligence and Intelligence Testing. Taylor & Francis. p. 27. ISBN 978-1-136-82321-3. Retrieved 31 August 2013. The debates about IQ today are rarely mainstream for psychologists. Instead, research is more related to the notions of 'cognitive functioning', developed during the 1970s and 1980s, whereby there is a search for the various strategies and manners in which we process information. This has led to the identification of many strategies, and some researchers seem to be concerned that some of us have more versatility and expertise in the use of these strategies and that this seems to relate to the notion of general intelligence, or 'g'. In many ways, researchers of today rarely refer to the notion of 'g'. This makes life easier as it avoids the debates about race, heredity and 'designer genes'; but one does not need to scratch too far beyond the surface to note that the issues of IQ are still present. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Fletcher, Richard B.; Hattie, John (11 March 2011). Intelligence and Intelligence Testing. Taylor & Francis. p. 36. ISBN 978-1-136-82321-3. Retrieved 31 August 2013. These results need to be treated with some caution because some of the data sources are questionable. To derive the IQ of a nation on, at times, very small sample sizes is suspect at best. For example, they used 80 students for Tonga and 80 for Western Samoa and both samples were children living in Auckland, New Zealand. It is also very difficult to believe a country's average IQ is 59, as was the case for Equatorial Guinea (with a sample size of 48 adolescents). This would mean that less than 5 per cent of the nation had an IQ above 100, and that if all the people of Equatorial Guinea were living in New Zealand they would all be in special classes. It is absurd to claim a county has an average IQ so low, based on so few, leading to such a ridiculous average. Something is wrong, for example, when it is noted that the overall average IQ score from the 81 countries listed is 88, well below the expected average of 100. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Fletcher, Richard B.; Hattie, John (11 March 2011). Intelligence and Intelligence Testing. Taylor & Francis. p. 36. ISBN 978-1-136-82321-3. Retrieved 31 August 2013. A long-standing debate has been whether schooling can enhance a student's IQ score. Many have claimed it is very difficult to make any changes, but others have disputed this. One claim is that schools can assist students to reach their potential (within the limits of their IQ) and others have claimed that there is no such notion as 'within the limits of their IQ'. Research by Cornell University developmental psychologist Stephen Ceci has exciting implications for schools. Ceci (1991) has demonstrated that schooling increases IQ scores. Teachers who 'believe' that achievement is more a function of effort and teaching rather than of intelligence are more likely to enhance their students' achievement (regardless of the correctness of this belief). However, it is likely that, while schooling may influence IQ, people with higher IQs may also seek more education and derive greater benefits from schooling. There is also a detrimental effect on IQ from dropping out of school early. Ceci (2003) described a study that showed a drop of 2 IQ points for each year of high school not completed beyond compulsory school age. Similarly, missing school (truancy, sickness) can lead to drops in IQ. This suggests that without the opportunity for mental activity provided by schools, intelligence can be significantly limited. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Fletcher, Richard B.; Hattie, John (11 March 2011). Intelligence and Intelligence Testing. Taylor & Francis. p. 83. ISBN 978-1-136-82321-3. Retrieved 31 August 2013. Contrary to an often-cited and popular view, IQ is not immutable or 'fixed' by genetics. Again, James Flynn has noted: Huge g [general intelligence] gains from one generation to another show that IQ is highly sensitive to environmental factors, and some of these may be cultural factors such as learned strategies or problem-solving picked up at school, or at home or elsewhere. (1987:33) Similarly, Stephen Ceci (1991) has demonstrated that schooling increases IQ scores, although it is not easy to change them. Roberts et al. (2008) concluded that educational interventions can increase IQ by about 8 points - which could make a major difference in how we then apply these enhanced cognitive-thinking attributes to learning, to being selected into more elite programmes and to gaining the other wealth, health and happiness dividends that come from increased intelligence. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Fletcher, Richard B.; Hattie, John (11 March 2011). Intelligence and Intelligence Testing. Taylor & Francis. p. 89. ISBN 978-1-136-82321-3. Retrieved 31 August 2013. Although the largest contributor to intelligence may be heredity, the effects of environment have been greatly underestimated - particularly how environment leads to opportunities, encouragement and basic living quality. It is, however, the interaction of the effects of genetics and environment that is most powerful. It is likely that there are as many people genetically well endowed with IQ who live in poverty, have little access to schooling and have been given few opportunities to learn as there are people genetically less endowed with IQ-ruling nations and living in excess. Dickens, W.T. and Flynn, J. R. (2001). Heritability estimates versus large environmental effects: The IQ paradox resolved. Psychological Review, 108(2), 346-69. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Fletcher, Richard B.; Hattie, John (11 March 2011). Intelligence and Intelligence Testing. Taylor & Francis. p. 90. ISBN 978-1-136-82321-3. Retrieved 31 August 2013. Stephen Ceci (1991) has demonstrated that schooling increases IQ scores, although it is difficult to find many specific or short-term programmes that make a difference to IQ scores. Teachers who believe that achievement is more a function of effort and teaching than of intelligence are more likely to enhance their student's achievement (regardless of the correctness of this belief). It is likely that, while schooling may influence IQ, people with higher IQs may also seek more education and derive greater benefits from schooling. There is a detrimental effect on IQ from dropping out of school early. Ceci (2003) described a study that showed a drop of 2 IQ points for each year of high school not completed beyond compulsory school age. Similarly, missing school (truancy, sickness) can lead to drops in IQ. This suggests that without the opportunity for mental activity provided by schools, intelligence can be significantly limited. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. pp. 2–3. ISBN 978-0-19-955990-9. Structural equation modeling is not for the faint-hearted, so that many of its insights remain obscure and easy to misinterpret: the concept of heritability, for example, still gets a lot of flak (it was called—with a hint of the improper if not the obscene—'the H word' at one scientific meeting). {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. p. 3. ISBN 978-0-19-955990-9. In some instances, several of which have been very high profile, the findings of these loudly trumpeted studies failed to stand up to further scrutiny. Notable in this category are an early study of bipolar disorder (manic depression) (Egeland et al., 1987) and the claim of a gene for homosexual orientation in men (Hamer et al., 1993). {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. p. 12. ISBN 978-0-19-955990-9. We found that if you are in the immediate family of someone with schizophrenia, the chances are 13 times higher that you too will develop schizophrenia compared with being related to a randomly chosen individual from the electoral role. The odds are not actually all that high in absolute terms, 65 out of 1,000 compared with five out of 1,000, but given the nature of this disease for those affected by it and their families, any number of cases is a tragedy. Either way, a family history is a better predictor of future disease than any other factor. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. p. 20. ISBN 978-0-19-955990-9. In the ideal adoption study, there should be no correlation between the characteristics of the biological parents who are giving the child up and the adoptive family into which the child is going. This is critical in ordere to separate the effects of genes and family environment. In reality, however, adoption agencies are sometimes asked to match characteristics of the adoptive family with the biological parents. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. p. 20. ISBN 978-0-19-955990-9. Another concern in adoption studies is the possible impact of the intrauterine environment and early (pre-adoption) childhood experiences. Some resemblance between the biological mother and her child might be due to what happens before birth, during the 9 months of pregnancy, when in effect child and mother inhabit the same body. Furthermore, many children are not adopted until after they have spent the earliest years of life with their biological parents. If being raised as an infant and toddler by an individual with a severe psychiatic illness contributes to future risk of illness (although the evidence to date does not suggest that this is the case), it could confound adoption studies. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. pp. 20–21. ISBN 978-0-19-955990-9. Finally, biological or adoptive parents of adoptees are not generally representative of the population at large. Adoption agencies see it as their task to try to find ideal homes for their adoptees. Because of this, adoptive parents have higher socio-economic status and lower rates of drug, alcohol, and psychiatric problems than are found in the general population. The biological relatives of adoptees are also not really a random sampling either, but the nature of the bias varies with social circumstances and historical period. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. p. 21. ISBN 978-0-19-955990-9. None of the completed adoption studies of schizophrenia was free of some methodological limitations (as indeed are all human genetic studies—which can never approach laboratory sciences in the degree of the control of the experimental variables). To increase our confidence in the validity of these findings, it would be best to try to replicate these adoption results in a completely different kind of human study that could also separate out the effects of genetic and environmental influences. Fourtunately, there is one such other method—twin studies. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. p. 22. ISBN 978-0-19-955990-9. By comparing the degree of correlation for a trait in MZ and DZ pairs, it is possible to infer the role of genetic versus shared environmental factors. Take religious affiliation, for example—whether people self-identify as Catholic, Jewish, Methodist, Baptist, Muslem, etc. Twins strongly resemble one another in their religion, but the degree of resemblance is virtually the same in MZ and DZ twins. These results suggest, consistent with common sense, that genes have nothing to do with one's religious identity. Resemblance in twins for religious affiliation appears to result from shared environmental experiences. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. p. 25. ISBN 978-0-19-955990-9. Because heritability is an often misunderstood and sometimes maligned statistic, we want to spend a bit of time explaining what it is and what it is not. [para] Most importantly, heritability is based on the concept of variability. Assume that we are studying height in a population of 5,000 individuals and imagine that we could line up all these individuals from the tallest to the shortest. [para] We would see a great deal of variation, with the largest number of people of middling height and a diminution in numbers at the extremes of the very tall and the very short. We also know that, just as individuals differ in height, they also have differences in their genomes. The extent of DNA variation among humands is still not fully known, but it's definitely there. Heritability is nothing more than the proportion of variation in height (or whatever phenotype we study) that is due to the genetic differences between individuals in the population. This is important enough to express in a different way as the following ratio: Heritability = genetic variance/total variance [para] In this formula, total variance in a trait is in turn broken down into genetic and environmental variance. If height had a heritability of 100%, it would mean that all of its variability could be explained by genetic differences between individuals. In fact, the heritability of height is about 90%. A heritability of 0% would indicate that genes contribute nothing at all to the observed differences between individuals. This is close to what is seen when we study religious affiliation in human populations. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. p. 29. ISBN 978-0-19-955990-9. The first methodological concern, and one that Kamin raises, is whether the assumption that the environmental exposures of MZ and DZ twins are equally similar is valid. Standard twin analyses assume that the greater resemblance in MZ twins compared with DZ twins results entirely from sharing greater genetic similarity (the 'equal environment assumption' or EEA for short). {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. pp. 29–30. ISBN 978-0-19-955990-9. Sometimes, twins and their parents are misinformed about their true twin status; that is, some twins who are really MZ think they are DZ and some who are really DZ think they are MZ. This provides an ideal natural experiment to test whether the self-perception of twins about their zygosity, or the expectations of them by their family and social environment, or both, might actually influence the degree of resemblance. This test of the EEA—whether twins who are really MZ but think they are DZ are any less similar than twins who are truly MZ and also think they are MZ—haas been applied in several different studies to personality measures, IQ, and risk for psychiatric disorders. In nearly all cases, the results have been consistent: in misidentified twins, self-perception of twin status is not a good predictor of resemblance. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. p. 31. ISBN 978-0-19-955990-9. So, what can we say about the validity of the EEA assumption for twin studies of psychological and psychiatric traits? Tests have with substantial but not perfect consistency supported its validity. The studies are not faultless and may not, for example, have identified the right kind of environment that truly impacts on twin resemblance (for example, the impact of intrauterine environment). In sum, however, it seems unlikely that most twin studies are seriouly biased by violations of the EEA. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. pp. 31–32. ISBN 978-0-19-955990-9. There are a few important conclusions we can now draw from the foregoing discussion. Firstly, research studies using adoptees and twins are far from flawless. Like most other kinds of science, these studies can be done well or poorly, and the confidence that we should have in the results relates directly to the quality of the methodology employed. Secondly, the main worry about the validity of the twin method is the equal environment assumption. Based on the available empirical studies, this assumption is probably not seriously violated in most twin studies. Other concerns with the twin method—for example that twins are highly atypical—have little evidence in their favor. Like other epidemiologic methods, however, twin and adoption studies are susceptible to unrepresentative sampling. Thirdly, the evidence to date suggests no large differences in results across various countries, although far more studies are needed representing additional human populations. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. p. 32. ISBN 978-0-19-955990-9. Both twin and adoption studies have many possible methodological problems. Fortunately, the problems in the two kinds of studies are rather different. Therefore, if twin and adoption methods give broadly similar answers, the probability that the results are spurious is low. As one colleague put it, Nature would have to be particularly perverse to provide us with a set of different biases that would each produce quite similar findings in twin and adoption studies. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. p. 32. ISBN 978-0-19-955990-9. One way to divide sciences is into those that observe the world and those that go out and manipulate it—typically in laboratory situations. The first kind of science includes astronomy, geology and human genetics. Laboratory genetics, by contrast, belongs in the second group. We cannot do traditional genetic studies in humans. Human genetics researchers always have to be on their toes, looking for the unsuspected bias—because we cannot control anything in our studies. We can just look at the experiments that nature provides us with. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. p. 67. ISBN 978-0-19-955990-9. Up until now, we have rather ignored the effects of the environment. This is not so surprising because we, the authors, are geneticists. However, in this section, we aim to convince you that the impact of genetic risk factors on psychiatric disorders is often so closely intertwined with environmental factors that studying genes in isolation is often asking for trouble. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. p. 67. ISBN 978-0-19-955990-9. We need to describe two quite different but sometimes confused ways in which genes and the environment can inter-relate in contributing to the risk of illness. The first mechanism is termed gene-environment interaction (or, as we prefer to call it, genetic control of sensitivity to the environment) and the second is gene-environment correlation (or, as we prefer to say, genetic control of exposure to the environment). {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. p. 68. ISBN 978-0-19-955990-9. Our second example involves MD. In addition to the clear evidence of genetic risk factors for MD reviewed above, adverse stressful life events have been shown to consistently and strongly increase the risk for MD. In the Virginia twin study, we asked whether these two risk factors added together or interacted (Kendler et al., 1995). We focused only on the most severe kinds of stressful life events, which in this study included death of a close relative, assault, serious marital problems, and divorce or romantic break-up. The results are shown in Figure 4.9. [para] Genetic risk was assessed as a function of the history of major depression in their co-twin and zygosity (mono-veresus dizygotic). As predicted from the interactive model, the impact of a severe stressful life event on the risk for depression was more striking in those at highest genetic risk than in thoses at lowest genetic risk. In this example, genetic risk factors altered an individual's sensitivity to the depressogenic effects of these very stressful life events. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. p. 71. ISBN 978-0-19-955990-9. One important consequence of theses results (as well as the prior findings about gene-environment interaction) is a blurring of the boundaries between genes and environment (or nature and nurture). It won't be possible to understand the action of genes on human behavioral disorders and traits without a consideration of the environment. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. p. 73. ISBN 978-0-19-955990-9. 9. Genes and environment often do not simply add together in their impact on risk for psychiatric disorders. Sometimes genes can moderate the sensitivity of individuals to the effects of environmental adversity. This is called gene-environment interaction. Genes can also influence an individual's selection of low- or high-risk environments. This is called gene-environment correlation. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. p. 76. ISBN 978-0-19-955990-9. Academic life contains many torments. One is public speaking when you have to cram months of work that you still only partly understand into 15 minutes with an audience half asleep from the combined effects of lunch and three prior grueling hours of such talks by others. Then there is the agony of receiving the funding decision on a grant application, a grant that took weeks of anxiety-filled effort to complete and a decision that will impact on whether your post-docs and lab technicians will have a pay check or not. We should not forget the agony of having to attend strategy review meetings organized by funding agencies whose pointlessness you cannot comment on as the agency pays for your science. However, as bad as all these torments are, a thing you do not wish even on your competitors (well, at least not all of them) is to have to review psychiatric genetic association studies. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. p. 77. ISBN 978-0-19-955990-9. However, linkage analysis has two important drawbacks. Firstly, it has low power. It is only good at detecting relatively large genetic signals—gene regions that contain variants that quite substantially alter the risk for a disorder. Secondly, even when you find positive results, linkage signals are very broad, typically smeared over tens of millions of base pairs, a region large enough to contain bundreds, if not thousands, of genes. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. p. 77. ISBN 978-0-19-955990-9. Association analysis has the opposite combination of strengths and weaknesses. Firstly, signals detected by association are much more focused, typically stretching over tens of thousands rather than tens of millions of DNA base pairs. Secondly, association analysis can detect genes with modest effects on disease risk. The main drawback to association is that, until recently, it could not screen the genome, as linkage can. Instead, you could use a few (typically less than 20) markers in what is called a 'candidate' gene. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. p. 78. ISBN 978-0-19-955990-9. The candidate gene approach itself comes in two flavors. In the more common approach, the gene is picked because you think it might have something to do with the physiology of the illness. Thus, these genes are called 'physiological candidate' genes. In the second approach, genes are picked because of where they lie in the genome; in particular because they are under linkage peaks. Such genes are called 'positional candidates'. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. pp. 82–83. ISBN 978-0-19-955990-9. The result of the genetic association test, in a rather counter-intuitive fashion, is defined by the likelihood that it is wrong. The P value that is conventionally used is 0.05, 1 in 20, or 5%, a figure that has a sacrosanct place in medical research, not always well deserved. It is particularly inappropriate for association studies of complex diseases and we can explain why. Let's assume, as a first approximation, that we have 20,000 human genes of which 20 are involved in the etiology of schizophrenia. If I pick a gene at random (which is almost what we are doing when we study physioilogical candidate genes because we have so little idea of which genes are actually involved), that gene has 20/20,000 or a 1/1,000 chances of being a real schizophrenia susceptibility gene. Imagine I am a successful scientist with a grant to study, by genetic association, 1,000 of these genes using the case-control design. Applying the 5% P value, we would expect that around 50 out of our 1,000 genes will be 'significant' by chance alone—random results that we call by the inappropriately benign phrase 'false positives.' (These are anything but benign because other research groups will often spend months of time and thousands of dollars trying to replicate such results.) Of the 1,000 genes, one is likely to be a true positive. So, to a rough first approximation, our well-funded project would be expected to produce 51 significant results from our 1,000 genes tested of which 50 (roughly 98%) are false. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. p. 83. ISBN 978-0-19-955990-9. Clearly, we need to use much more stringent P values (that is, much lower than 5%) to give us a more reasonable proportion of true positive findings. In one of the deeper ironies of the field of psychiatric genetics, this problem was much better worked out for linkage studies. Due to the influence of one statistical geneticist—Newton Morton—it was very early imprinted on the field that an LOD (log₁₀ of odds) score of 3.0 (which depending on some technical issues equals a P value of between 0.0001 and 0.0001) was needed to declare significant linkage. This is about right in that most linkage studies would involve something like 300 different tests. With stunning inconsistency, association studies, when they began to appear in the literature, utilized a P value of 0.05, even though the multiple testing problem was far greater than in linkage studies. This fateful decision allowed many scientists to publish 'significant' association results and was therefore very beneficial to their Curriculum Vitae. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. p. 86. ISBN 978-0-19-955990-9. This problem of genetic admixture is often blamed for the reporting of spurious associations, although, to be fair, it's not that easy to find confirmed examples. In fact, a joint analysis of DRD2 studies in different ethnic populations gives the same answer as analyzing the studies separately (again the large number of DRD2 papers makes this possible) (Munafò et al., 2007), indicating that ethnic heterogeneity is probably not an issue in this case, nor indeed in the vast majority of other disorders that have been studied by genetic association. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. p. 88. ISBN 978-0-19-955990-9. There are, naturally, attempts to replicate the result—14 independent studies (by 2009). But only one reported a statistically significant interaction apparently identical to that observed in the original report. [para] Three studies reported no evidence of a statistically significant interaction, one interpreted their results as offering 'modest support' based on subgroup analyses, and six reported a significant interaction, which was different from that observed in the original report. Overall, when we reviewed the literature, the positive results for the serotonin-transporter-linked polymorphic region (5-HTTLPR) interactions are still compatible with chance findings (Munafò et al., 2009), Moreover, as the main effect of 5-HTTLPR genotype and the interaction effect between 5-HTTLPR and environment on risk of depression are negligible, given reasonable assumptions regarding likely genetic and environmental effect sizes, the published studies are underpowered. And a significant finding from an underpowered study is a false positive. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. pp. 88–89. ISBN 978-0-19-955990-9. 'The great tragedy of science—the slaying of a beautiful hypothesis by an ugly fact' (Thomas Huxley, 1825-1895) has not stopped the seemingly endless production of genetic association studies, nor dampened enthusiastic endorsement of claims to have identified genes contributing to psychiatric disease, as well as to personality, sexual orientation, intelligence, even empathy. It is true that there are cases where things has simply gone wrong (as the DUP25 example shows). And there are statistical problems: the significance threshold (the P value to use to decide whether a result is significant or not) has been set too high so that the result is likely to be a false positive. But these factors are not enough to hold back the tide of publications. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. pp. 91–92. ISBN 978-0-19-955990-9. By 2007, 14 studies had been published that looked at the relationship between 5-HTT and amygdala activation, far fewer than the hundreds that analyzed the relationship between 5-HTT and personality, but enough to carry out a meta-analysis of the results (Munafò et al., 2008). This showed a significant result, but with a greatly reduced effect size: down to 10%. In fact, Marcus Munafò at Bristol University who carried out the meta-analysis plotted the downward trend in the estimated effect and predicted that, in 2008, the first study showing an effect in the opposite direction would be published. That prediction was fulfilled, raising the possibility that there may after all be no true effect attributable to the 5-HTT locus, or indicating that the effect is small, just as small as in the classical psychiatric genetic association studies. Similar conclusions have been reached in meta-analyses of other intermediate phenotypes (Flint and Munafò, 2007). {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. p. 94. ISBN 978-0-19-955990-9. 2. Results of association studies for behavioral and psychiatric phenotypes have often been inconsistent, difficult to replicate, and influenced by the inevitable preconceptions inherent in choosing candidate genes. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. p. 96. ISBN 978-0-19-955990-9. But we have one important insight into the cause of the disease, confirmed by numerous studies, using different, complementary designs: we know the illness has a genetic basis, that genetic variation contributes, in part, to disease susceptibility, or that the heritability has some non-trivial value (let's say 40%—this is generally true for most traits). {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. p. 98. ISBN 978-0-19-955990-9. One simple and unavoidable interpretation of the relative failure of linkage mapping was that the method was underpowered, a point made, among others, by Neil Risch and Kathleen Merikangas in 1996 as follows: 'The modest nature of the gene effects for these disorders likely explains the contradictory and inconclusive claims about their identification' (Risch and Merikangas, 1996). {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. p. 100. ISBN 978-0-19-955990-9. One hypothesis of how humans came to populate the rest of world is that there was a migration out of Africa, between 70,000 and 50,000 years ago. There are various versions of how this might have happened, the dates and the number of migrations varying considerably, but one piece of evidence could not be gainsaid: genetic diversity, from sequence and genotyping data, is greater in Africans than in other populations. This suggested that a small number of people (initial estimates even suggested a handful) had left Africa to populate the rest of the world. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. p. 101. ISBN 978-0-19-955990-9. Soon after Risch and Merikangas' argument in favor of genetic association, Eric Lander laid down ten goals for genomics ('with the aim of stimulating ferment'). One goal was the discovery of all common sequence variants within genes (coding variants), because, Lander argued, these variants would be a major contributor to the causes of common disease. His argument ran as follows: [block quote] Human diversity is quite limited in that most genes have only a handful of common variants in their coding regions, with the vast majority of alleles being exceedingly rare. The effective number of alleles...is rather small, often two or three. This limited diversity reflects the fact that modern humans are descended from a relatively small population that underwent exponential explosion in evolutionarily recent time. ...The catalog of common variants will transform the search for susceptibility genes through the use of association studies. Lander (1996). {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. p. 102. ISBN 978-0-19-955990-9. Francis Collins, Mark Guyer, and Aravinda Chakravarti argued for the dense SNP map approach in 1997, again based on theoretical speculation about how disease alleles might have arisen and be distributed through the population: [block quote] This strategy is based on the hypothesis that each sequence variant that causes disease must have arisen in a particular individual at some time in the past, so the specific array of polymorphisms (haplotype) in the neighborhood of the altered gene in that individual must be inherited in all of his or her descendants. The presence of a recognizable ancestral haplotype therefore becomes an indicator of the disease-associated polymorphism. The size of this region (in which the genetic markers are said to be in 'linkage disequilibrium') will vary with the age of the variant. Collins et al. (1997). {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. p. 108. ISBN 978-0-19-955990-9. Up to the end of 2006, a dozen or so loci were widely accepted as susceptibility loci for complex diseases. By the end of 2008, that number was approaching 500. Not all GWAS findings are backed by the seal of Bayesian statistics, but they have all exceeded impressive thresholds and, generally, have been replicated, at least a couple of times. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. pp. 108–109. ISBN 978-0-19-955990-9. One big problem for the GWAS results is tying results like this back into biology. How could these variants be contributing to disease? Mostly, we do not yet have a clue. That is not the same as saying that these GWAS studies were not successful, or that they have not taught us important lessons about the genetic basis of human disease. They have certainly done that, and perhaps the most important lesson is that each individual effect is so small. Remember the estimates put forward by Neil Risch? Small as they seemed at the time, they turned out to be overly optimistic. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. p. 109. ISBN 978-0-19-955990-9. Here is the remarkable thing. Twin and family studies have shown with great consistency that height is highly heritable: at least 80% of the variation has a genetic basis. But although 54 SNPs were found, a great advance on what we knew before the advent of GWAS, the total heritability accounted for by all these SNPs is only about 5%! Each variant contributes less than half a percent. Another way of putting this is that if you inherit an allele that increases your height, you'll be about 0.4cm taller as a consequence. And all of the loci are this size; there are no big effects, no exponential distribution with a few large ones and a tail of smaller effects. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. p. 110. ISBN 978-0-19-955990-9. Peter Visscher a statistical geneticist in Brisbane, working with Shaun Purcell at Harvard Medical School came up with the following idea for investigating the genetic architecture of schizophrenia; that is, to find out how many loci might be contributing to disease susceptibility. Firstly, screen through all of the genetic markers (SNPs) from a GWAS (call this the discovery sample) and pick those that are statistically independent of one another. Secondly, compare the frequency of the alleles at those SNPs in cases and controls and rank them with regard to the significance of the differences. Thirdly, pick a threshold for a level of significance and examine all of the SNPs below that threshold in one discovery sample. Fourthly, tabulate all of the alleles that are in excess in the cases versus the controls, and from this tabulation develop a scoring algorithm. Finally, take that scoring algorithm and apply it to a second case-control GWAS sample of the same disorder (call this the test sample). {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. p. 110. ISBN 978-0-19-955990-9. If true, then the risk to schizophrenia is influenced by quite a large number of common variants, each with a very small effect on risk. How large is not yet clear, but Purcell's analyses suggest that hundreds and more likely thousands of individual genes contribute to the liability to schizophrenia. How small would their effect be? Given that we know from twin studies the total genetic contribution to schizophrenia (recall heritability estimates of ~0.80), then the effects of these individual genes would be very small indeed—so small that robustly detecting each allele individually might be impossible, requiring samples of hundreds of thousands of cases and controls. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. p. 111. ISBN 978-0-19-955990-9. GWAS are premised on the contribution of common variants to disease: the common disease-common variant hypothesis might not be true. GWAS cannot detect, or rather has a lower power to detect, contributions from rare variants. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. p. 114. ISBN 978-0-19-955990-9. 2. About 60,000 years ago, humans migrated out of Africa, creating a population bottleneck that reduced our genetic diversity. Consequently, about half a million genetic markers are sufficient to capture a large fraction of the common sequence variants in the human genome. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. p. 118. ISBN 978-0-19-955990-9. As a general rule, training works best when it manipulates pre-existing innate behavioral patterns. Despite their Skinnerian beliefs, the Brelands had come across what appeared to be biologically determined behaviors that they could not condition. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. p. 118. ISBN 978-0-19-955990-9. Determining whether there is a genetic effect in mice is much simpler, and quicker, once you have the resource. The resource, in this case as in so much else in mouse genetics, is the inbred animal. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. p. 119. ISBN 978-0-19-955990-9. If you are a worm geneticist, then your favorite organism actually prefers to have sex with itself, so it's really no problem to get rid of all of that genetic heterogeneity that so confuses human geneticists. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. p. 120. ISBN 978-0-19-955990-9. Animal and plant breeders have been applying selection for millennia. The size of our wheat plants and tomatoes, the large amount of milk we get from our cows, and the extraordinary diversity of shapes, sizes, and temperaments in domestic dogs all result from selection over many generations. DeFries and Hegmann did the same for anxiety-related behavior in mice. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. pp. 120–121. ISBN 978-0-19-955990-9. Firstly, it is possible to estimate the heritability: provided there is no non-genetic cause of resemblance between offspring and parents, and no natural selection operating (for instance, the more active animals should be equally fertile and as viable as the inactive animals), then the ratio of response to the selection is equal to the heritability. Heritability estimated in this way is called a realized heritability, and for the experiment described above, it turns out to be about 20%. (For technical reasons, realized heritability will nearly always be lower than the kind of heritability we explored in our twin studies—differences in genetic control of a phenotype in natural populations.) {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. p. 121. ISBN 978-0-19-955990-9. Heritability estimates for 143 different behavioral traits from a wide variety of organisms, including the fruit fly Drosophila and 75 other invertebrate and vertebrate species, show remarkable congruence (Mousseau and Roff, 1987). While there is considerable variation in the heritability of individual traits, the median heritability (a median is the value in a distribution where half the values are smaller and half are greater) was 25%. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. pp. 121–122. ISBN 978-0-19-955990-9. Apply the same argument to the mouse studies and you see the problem: even under the controlled conditions of laboratory testing, heritability is nowhere near 100%. It's true that there are other sources of variation that we need to take into account, but it is also true that we are not as good as we might like at providing a controlled, uniform, stable environment for testing. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. p. 123. ISBN 978-0-19-955990-9. Yeast has never been used for behavioral analysis, but it is great for studying recombination. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. p. 123. ISBN 978-0-19-955990-9. Mice have 20 pairs of chromosomes (19 pairs and the two sex chromosomes, the X and Y). One way to find out which of these 20 has gene-influencing behavior is to create mice that have one chromosome from one inbred strain, but all of the others from a second inbred strain (the mice with the appropriate chromosomes are called chromosome substitution strains) (Singer et al., 2004). {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. p. 128. ISBN 978-0-19-955990-9. Unlike human studies, where results were just about significant, mapping behavior in crosses between inbred strains yields highly significant findings that can, and have, been replicated. Does this mean the genetic basis of behavior is somehow different between the two species? [para] The short answer is no. The relatively simple genetic architecture that emerges from the studies of inbred strains reflects the simplicity of the experiment. In human studies, every family, every subject, is unique. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. p. 132. ISBN 978-0-19-955990-9. However, we have a second problem: the effect of the single locus depends critically on its context. As the other regions of the genome are homogenized, turned into a uniform genetic background (light blue in the figure), the effect of the single locus we are chasing begins to waver, as if in sympathy with the loss of its partners, and may even disappear altogether. There are even cases where the effect has reversed: what appears to be a genetic effect that increases susceptibility changes into one that decreases susceptibility. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. p. 133. ISBN 978-0-19-955990-9. Figure 7.11 is remarkable in showing so little variation among phenotypes. Every trait looks much the same. While there are a few exceptions (the tail of the distribution of effect sizes spreads out on the horizontal axis), the majority of QTLs explain less than 3% of the variation in a phenotype (this is much larger than the average effects found in human outbred populations; the larger figure occurs because of the reduced genetic diversity in the outbred mice: all of the chromosomes derive from just eight progenitors). So the genetic basis of behavior is no different from that of other phenotypes. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. p. 134. ISBN 978-0-19-955990-9. The genetic data also tells us something about how the environment affects behavior. The picture is similar to that described in Chapter 4 where we discussed human studies: there are important interactions between genes and the environment. In fact, on aggregate, the total effect of interactions is at least as important as that of the straight, immediate, unadulterated heritability. We can also see this working at a gene level. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. p. 136. ISBN 978-0-19-955990-9. The genetic architecture of behavior in rodents is in many ways very similar to what we have seen emerge from human studies. There are many loci contributing to individual variation in behavior and their effect sizes are consistently small. Exactly how many loci and how small is not clear. The genetic effects operate in a complex fashion, often in interaction with the environment. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. p. 137. ISBN 978-0-19-955990-9. Mouse geneticists, working with animals selected for laboratory life, simplify things so that they work only with common alleles; human geneticists have to work with everything that a combination of environmental constraint, historical accident, and biological necessity can engineer in the genome. Perhaps the remarkable thing is that the picture of genetic architecture emerging from both research fields is so similar (Flint and Mackay, 2009). {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. p. 139. ISBN 978-0-19-955990-9. A direct test of the heritability of behavior calls for genetic experiments, and for this purpose nothing beats the fruit fly D. melanogaster. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. p. 172. ISBN 978-0-19-955990-9. No gene is an island, as amply shown by our many examples in earlier chapters of the interactions among many genes to produce a phenotype. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. p. 175. ISBN 978-0-19-955990-9. The remarkable finding is that the mouse's clock is so similar to the fly's (Figure 9.13). There are some differences: cryptochrome substitutes for timeless in the transcriptional feedback loop, and the mouse has multiple forms for many of the genes. Maybe this can be explained by the fact that the mammalian skull blocks all light (this would make it unnecessary for the clock neurons to have a light receptor protein such as cryptochrome, thus allowing them to re-adapt to a different function). But whatever the reason, the similarities are remarkable and more than justify Benzer's intuition that the little fruit fly could teach us about the genes' underlying behavior. Much more has been conserved through evolution than anyone had suspected. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. p. 181. ISBN 978-0-19-955990-9. When Benzer began his explorations of genes and behavior in the fruit fly, traditional neuro-biologists told him he was crazy to think that genetics would have anything to contribute to understanding the brain. The circadian rhythm story is the most dramatic, but by no means the only, refutation of the naysayers. The added, completely unexpected, bonus is that not only do we understand more about the fruit fly's brain but also about our own. The extent to which all animals share so many of their genes has been one of the major revelations of genome sequencing, and the circadian clock is one of the many molecular mechanisms that is highly conserved throughout evolution. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. p. 181. ISBN 978-0-19-955990-9. 1. The induction of new genetic variants by mutagenesis, originally with radiation and subsequently with chemicals, opened up a new avenue to analyze biological mechanisms, vastly expanding the range and severity of variants that could be obtained. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. p. 182. ISBN 978-0-19-955990-9. 6. An almost identical mechanism is found in mammals, except that there are multiple versions of each gene and cryptochrome takes the place of timeless. Similar mechanisms also exist in fungi and bacteria, suggesting that the mechanism is exceedingly ancient. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. p. 183. ISBN 978-0-19-955990-9. Heritability testing, gene-environment correlations and interactions, genetic mapping by linkage and association, then genome sequences, catalogues of every gene, every genetic variant, genomic technology that consumes a budget large enough to run a small country, the weight of 21st-century molecular genetics is enough to win an argument that genes produce behavior. But of course they don't. Genes, in fact, merely serve as a library that each cell draws upon when it needs to make proteins. Behavior comes out of the ensemble of cellular activities, and the cells that usually matter most are those in the nervous system. Genetic variations that affect behavior often do so by modifying the activity of neurons, and such modifications result from altering the timing, placement, amount, or effectiveness of a gene's product. The relevant neurons, in turn, take part in the brain's complex circuitry. So we need to look at circuits to understand how behavior is produced out of genetic variation. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. p. 185. ISBN 978-0-19-955990-9. The pathway turned out to be invariant: in each animal, the same neurons made the same connections. This turned out to be true for other behaviors as well (e.g. locomotion, feeding, and the other exciting activities of sea snails). Precise wiring raises a problem for learning—how can a flexible behavior be hard-wired? [para] The answer lies in understanding what happens in the connections between neurons, in the way neurons pass information between each other. Possibly the most influential theory in neurobiology, the ionic hypothesis (proposed by Alan Hodgkin, Andrew Huxley, and Bernhard Katz), explains information flow through neurons as a change in electrical potential. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. p. 208. ISBN 978-0-19-955990-9. Two mouse geneticists, Joe Nadeau and Wayne Frankel, argued strongly against QTL approaches to finding genes in complex phenotypes (Nadeau and Frankel, 2000). They started by pointing out the disappointing record of QTL methods for identifying genes and argued that alternatives needed to be considered. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. p. 209. ISBN 978-0-19-955990-9. Finding genes with a specific role in behavior turns out to be very hard. Genes for behavior, genes that carry a label saying 'my job is to make sure you don't become autistic/murder people/eat too much food' are thin on the ground. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. p. 210. ISBN 978-0-19-955990-9. In flies evidence for pervasive pleiotropy comes from the substantial overlap of the transcripts for which there are correlated responses in expression to selection from the same base population for copulation latency, aggressive behavior, locomotor startle response, and ethanol resistance (Jordan et al., 2007). {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. p. 210. ISBN 978-0-19-955990-9. Pleiotropic action, on the scale we've just described, makes the idea of 'a gene for' behavior impossible to maintain. Single genes do not specify behavior, but in combination with each other and with the environment, they may. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. p. 210. ISBN 978-0-19-955990-9. Each allele of a gene can potentially contribute in several ways to a phenotype. These contributions, in turn, depend on the partners with which that gene interacts. Variation can thus occur in a restricted portion of a gene's range of activities, if its interacting partners are more sensitive to perturbation in one place than in another. If its interacting partners also come in allelic variants, a further dimension is added. [para] Phenotypic variation in a population, which is what one measures, is thus not a monotonic function of allelic variation. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. p. 211. ISBN 978-0-19-955990-9. The two perspectives can be distilled into one: many genes for each behavior (e pluribus unum) and many behaviors from each gene (ex uno plura) (Greenspan, 2004). {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. p. 211. ISBN 978-0-19-955990-9. One of us (K.S.K.) has proposed criteria that can be used to judge the validity of a claim that 'X is a gene for Y,' where X is a scientist's favorite gene and Y a particular behavior or psychiatric disorder (Kendler, 2005). Four criteria are appropriate in this context: the strength of the association, the specificity of the relationship, the non-contingency of the effect, and the causal proximity of X to Y. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. p. 211. ISBN 978-0-19-955990-9. How often might these criteria apply to genes and human behavioral or psychiatric syndromes? The short answer is 'hardly ever.' As we have documented, the strength of the association between the discovered risk genes (and the even fewer that have been replicated) and behavioral phenotypes are quite weak. They account, at most, for a few percentage points of the total liability, and often even less than that. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. p. 211. ISBN 978-0-19-955990-9. Do genes have a specific effect on behavior? Almost certainly not. Variation in gene X hardly ever influences trait Y only, because variants in X affect other phenotypes. Almost never are the genetic influences of trait Y restricted to only gene X. Thus, the association between genes and behaviors are not typically one to one, or one to many. Evolution is to blame for this complexity; it is a tinkerer and promiscuous in the use of genes, taking whatever material is to hand to work on. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. p. 212. ISBN 978-0-19-955990-9. So, in aggregate, this thought experiment indicates that we are almost never justified in using the language or concept of a 'gene for' a behavior or psychiatric trait. The relationship between genes and behavior is too contingent and indirect for such language to be appropriate. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. p. 212. ISBN 978-0-19-955990-9. Gene-environment interaction, while still much under-researched, may be widespread in its effects. It is equally likely that genes, through 'outside the skin pathways,' play critical roles in influencing important aspects of the social or physical environment to which the organism is exposed. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. p. 214. ISBN 978-0-19-955990-9. The world of genetics in general and behavioral and psychiatric genetics in particular is, in our view, too 'gene-centric.' If you wanted to be less polite, you could call it 'gene-crazy.' While important, the key issue is how genes actually influence behavior. One important step in this process is identifying the genes that can make a difference, but this is only a start. The real action begins when we try to put these genes back into their gene networks, into nerve cells and nerve networks, and finally into organisms who sit in and interact with their environment. It will not work, we believe, to assume that the little 'bits' of this puzzle all just add together. We know already that they do not. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. pp. 214–215. ISBN 978-0-19-955990-9. Many people associate strongly the idea of 'genes' and 'determinism.' If something is 'in your genes' then 'God help you,' because there is nothing you can do about it. As shown by the science we have reviewed in this book, that is not how the world works for most of the behaviors we care about. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Flint, Jonathan; Greenspan, Ralph J.; Kendler, Kenneth S. (28 January 2010). How Genes Influence Behavior. Oxford University Press. p. 216. ISBN 978-0-19-955990-9. 6. Saying that genes influence behavior is not equivalent to saying that genes determine behavior. Thus, the study of genes and behavior will not make obsolete the idea of morality. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Fullwiley, Duana (13 August 2013). "Chapter 6: Can DNA "Witness" Race?". In Krimsky, Sheldon; Sloan, Kathleen (eds.). Race and the Genetic Revolution: Science, Myth, and Culture. Columbia University Press. pp. 117–118. ISBN 978-0-231-52769-9. Retrieved 31 August 2013. Whether the investigator uses external information or makes estimates from the samples at hand, the parental populations are abstractions that conform to only the simplest kind of genetic structure. This structure places heavy emphasis on the idea that the world once harbored distinct and independently evolved populations that have now undergone admixture of an unstated type (often seeming to connote admixture due to colonial era migrations). Regardless of the intent, this idea of population structure is unfortunately more in line with race concepts held by European explorers and traders than with the recent genetic evidence supporting the serial sampling of human evolutionary history. (citing K. M Weiss and J. C Long, 'Non-Darwinian Estimation: My Ancestors, My Genes' Ancestors,' Genome Research 19 (2009): 703-710, at 705. The technology in question, which was once produced by DNA Print Genomics, is also packaged as Ancestry by DNA for recreational genealogical ancestry testing. Variations of it are also used in biomedical research settings for purposes of admixture mapping for disease traits and to prevent confounding in 'mixed' populations in case-control studies for complex disease traits. See www.dnaprint.com/welcome/productsandservices/index2.php (accessed March 28, 2008). Since DNAPrint ceased operations in 2009 DNA Diagnostic Center has began marketing AncestrybyDNA. See www.ancestrybydna.com (accessed April 19, 2010).) {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Fullwiley, Duana (13 August 2013). "Chapter 6: Can DNA "Witness" Race?". In Krimsky, Sheldon; Sloan, Kathleen (eds.). Race and the Genetic Revolution: Science, Myth, and Culture. Columbia University Press. p. 121. ISBN 978-0-231-52769-9. Retrieved 31 August 2013. Critical evaluation of estimates of M [Caucasian mixture in 'American Negroes'] requires complete specification of the needed criteria and judgment on the degree to which these criteria are met. These criteria are simple and obvious, but the demands they make have not always been appreciated. They are as follows: 1) The exact ethnic compositions of the two ancestral populations, African Negro and Caucasian, are known; 2) No change in gene frequency (for the gene in question) between ancestral and modern populations either of African Negroes or of American Caucasians has occurred; 3) Interbreeding of the two ancestral populations is the only factor affecting gene frequency in U.S. Negroes—that is, there has been no selection, mutation, or genetic drift. (citing T. Edward Reed, Science 165 (1969): 762-68 (emphasis mine).) {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Fullwiley, Duana (13 August 2013). "Chapter 6: Can DNA "Witness" Race?". In Krimsky, Sheldon; Sloan, Kathleen (eds.). Race and the Genetic Revolution: Science, Myth, and Culture. Columbia University Press. pp. 123–124. ISBN 978-0-231-52769-9. Retrieved 31 August 2013. To complicate matters more, Mexian Americans often have more Amerindian heritage than the referent groups posited as their Native American ancestors. For political and historical reasons, individuals need only possess one-eighth (12.5 percent) demonstrable Native American ancestry to be considered Native American, whereas Mexicans may have considerably more. Finally, in addition to these quandaries, when alleles that have a high frequency in the specific reference groups tested (those labeled 'African,' 'European,' 'Native American,' etc.) appear in a 'client' taking the test, the AIMs test reads that the client has inherited the specific referent ancestry rather than, say, ancestry (or SNPs) from other still unsampled parts of the globe. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Fullwiley, Duana (13 August 2013). "Chapter 6: Can DNA "Witness" Race?". In Krimsky, Sheldon; Sloan, Kathleen (eds.). Race and the Genetic Revolution: Science, Myth, and Culture. Columbia University Press. pp. 116–126. ISBN 978-0-231-52769-9. Retrieved 31 August 2013. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Goldhaber, Dale (9 July 2012). The Nature-Nurture Debates: Bridging the Gap. Cambridge University Press. p. 60. ISBN 978-0-521-14879-5. Retrieved 24 November 2013. An interest in course and variability is not unique to behavior geneticists; it is the stuff of all theory and research in human development. What is particular about the behavior genetic perspective is that its argument that attempts to explain course and variability exclusively through environmental variables, what Pinker (2004) refers to as the 'blank slate' approach, is simply wrong. Instead, Pinker and others (Scarr 1996; Plomin and Asbury 2005; Plomin 2009) argue that much of this pattern and variability reflects genetics and, because genes and environment covary, much of the presumed environmental contribution to development is also to a large degree a reflection of genetics. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Goldhaber, Dale (9 July 2012). The Nature-Nurture Debates: Bridging the Gap. Cambridge University Press. p. 61–62. ISBN 978-0-521-14879-5. Retrieved 24 November 2013. On the one hand, for example, if adopted siblings are more like the biological children of their adopted parents than a comparable group of children chosen at random, this would argue for an environmental effect. But if, on the other hand, adopted children were more like their biologocal parents than their adopted parents—even when separated from their biological parents at birth—then this would be evidence for a strong genetic effect. Even here the partition is not perfect. Even if separated from her child at birth, the bioilogical mother served as the prenatal environment for the then adopted child. One would really have to find a sample of children who had surrogate mothers prenatally and then were adopted at birth by still another family, fortunately a very unlikely circumstance. And then there is the issue of what adoptive parents are typically like, that is, they are not a very heterogeneous group, quite the opposite in fact. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Goldhaber, Dale (9 July 2012). The Nature-Nurture Debates: Bridging the Gap. Cambridge University Press. p. 62. ISBN 978-0-521-14879-5. Retrieved 24 November 2013. Behavior geneticists are interested in differential influence, not influence per se. No one is suggesting that parents do not have an influence on their children with respect to what these children are like. Rather, the issue for the behavior geneticist is not what the children are like but rather how alike they are. Statistically, what the children are like is typically measured by mean differences: Do children reared one way do better in school, on average, than those reared a different way? But for the behavior geneticists, the issue is not average differences but rather is a comparison of differences in the degree of variability between groups of children. It might sound like the two are really the same but they are not, and, in fact, statistically, they are actually independent of each other. For example, there are data documenting the fact that adopted children's IQ scores correlate higher with those of their biological parents than with those of their adopted parents (i.e., the adopted child with the highest IQ score had a biological mother with the highest IQ score, etc.) but, at the same time, the adopted children's actual IQ scores are more similar to those of their adopted parents than to those of their biological parents, that is, the adopted children's mean IQ scores are closer to those of the adopted parents than to those of the bioilogical parents. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Goldhaber, Dale (9 July 2012). The Nature-Nurture Debates: Bridging the Gap. Cambridge University Press. p. 62. ISBN 978-0-521-14879-5. Retrieved 24 November 2013. Good research designs allow for the collection of data in the most scientifically rigorous way as possible. However, because data are almost always collected from a sample of a population rather than from the entire population, there still needs to be a way of determining the likelihood of the data from the sample being representative of the population from which they were drawn. Here is where the statistics come in and, in the case of behavior genetics, much of the controversy. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Goldhaber, Dale (9 July 2012). The Nature-Nurture Debates: Bridging the Gap. Cambridge University Press. p. 65–66. ISBN 978-0-521-14879-5. Retrieved 24 November 2013. Although there are many people pursuing behavior genetic research, the work of two of these scholars is perhaps most significant, both in terms of the volume of their efforts and in terms of its visibility. These two individuals are Robert Plomin and Sandra Scarr. It is worth looking closely at the work of each because doing so provides an excellent illustration of what behavior genetic research is all about. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Goldhaber, Dale (9 July 2012). The Nature-Nurture Debates: Bridging the Gap. Cambridge University Press. p. 70. ISBN 978-0-521-14879-5. Retrieved 24 November 2013. Plomin also believes that the generalist gene hypothesis leads to the conclusion that children with either learning disorders or exceptional learning abilities do not have unique genotypes compared with more typical children but rather are simply children at either end of the generalist continuum. As he puts it, the abnormal is normal. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Goldhaber, Dale (9 July 2012). The Nature-Nurture Debates: Bridging the Gap. Cambridge University Press. p. 70. ISBN 978-0-521-14879-5. Retrieved 24 November 2013. As is true of virtually all human behavior genetic data, the TEDS data are correlational; they show a pattern of relationship between variables. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Goldhaber, Dale (9 July 2012). The Nature-Nurture Debates: Bridging the Gap. Cambridge University Press. p. 70–71. ISBN 978-0-521-14879-5. Retrieved 24 November 2013. The Work of Sandra Scarr: Unlike Plomin, Sandra Scarr has not been reluctant to talk about the social implications of a behavior genetic perspective. Rather, she has loudly and frequently critiqued (Scarr 2009) what she sees as the 'intellectual bankruptcy of socialization research.' Over a long and active academic career, using the same kinship and adoption research models as other behavior geneticists, she has advanced a number of arguments concerning the impact of parents on children's development, on matters of race, on socioeconomic influences on development, and on the changing influence of genetics on development across the life span (Scarr and McCartney 1983; Scarr and Weinberg 1983; Scarr 1991; Scarr and Ricciuti 1991; Scarr 1992, 1993; Scarr and Weinberg 1994; Scarr 1996, 1997). {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Gottfredson, Linda S. (2003). "Chapter 3: The Science and Politics of Intelligence in Gifted Education". In Colangelo, Nicholas; Davis, Gary A. (eds.). Handbook of Gifted Education. Julian C. Stanley (Guest Foreword). Boston: Allyn & Bacon. p. 32. ISBN 978-0-205-34063-7. When behavioral geneticists speak of the heritability of a trait, they are actually using a short-hand phrase that can be easily misunderstood. Degree of heritability—say, 40 percent or 80 percent—is not a physical constant, free of time and place, like absolute zero in temperature. Heritability is simply the proportion of (a) phenotypic (observed) variation in an attribute that can be attributed to (b) genotypic variation in the group studied. Heritability estimates therefore apply only to environments and populations like the ones studied, not to all possible ones. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Gottfredson, Linda S. (2003). "Chapter 3: The Science and Politics of Intelligence in Gifted Education". In Colangelo, Nicholas; Davis, Gary A. (eds.). Handbook of Gifted Education. Julian C. Stanley (Guest Foreword). Boston: Allyn & Bacon. p. 32. ISBN 978-0-205-34063-7. Current estimates of heritability have been derived from populations in rich and poor, Western and non-Western populations, but not often from the extremes of environmental privilege or deprivation. The emerging pattern of estimates, therefore, may not apply to all human groups. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Gottfredson, Linda S. (2003). "Chapter 3: The Science and Politics of Intelligence in Gifted Education". In Colangelo, Nicholas; Davis, Gary A. (eds.). Handbook of Gifted Education. Julian C. Stanley (Guest Foreword). Boston: Allyn & Bacon. p. 33. ISBN 978-0-205-34063-7. How could it be that intelligence becomes more genetic with age while the influences of family advantage and disadvantage vanish? Currently, the major theory is that people to some extent seek out and create their own environments based on their genetic proclivities. Scarr's 'niche-seeking' theory (Scarr & McCartney, 1983), which is similar to Bouchard and colleagues' 'genes-drive-experience' theory (Bouchard, Lykken, Tellegen, & McGue, 1994), is that children increasingly choose and change their own environments as they become more independent of their families. They bring their environments more in line with their latent tastes and abilities, which further enhances the development of those tastes and abilities. Early shared family influences cease to operate about the age when children leave home. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Gottfredson, Linda S. (2003). "Chapter 3: The Science and Politics of Intelligence in Gifted Education". In Colangelo, Nicholas; Davis, Gary A. (eds.). Handbook of Gifted Education. Julian C. Stanley (Guest Foreword). Boston: Allyn & Bacon. p. 33. ISBN 978-0-205-34063-7. The genes-drive-experience-and -niche-seeking theory supports the notion that individuals have a hand in creating themselves and their own destiny. It tells us that we are not the hapless putty of either nature or nurture. It also seems consistent with observations of gifted children. Many of them are relentless in reshaping their environments. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Gottfredson, Linda S. (2003). "Chapter 3: The Science and Politics of Intelligence in Gifted Education". In Colangelo, Nicholas; Davis, Gary A. (eds.). Handbook of Gifted Education. Julian C. Stanley (Guest Foreword). Boston: Allyn & Bacon. p. 34. ISBN 978-0-205-34063-7. The real question, then, is not whether nature or nurture dominates, but how the two work together. The two forces are not independent and parallel, but the venue for each other's operation. Two phenomena that illustrate this are highly relevant to understanding giftedness: First, our genotypes influence our sensitivity to environments and, second, they influence our exposure to them. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Gottfredson, Linda S. (2003). "Chapter 3: The Science and Politics of Intelligence in Gifted Education". In Colangelo, Nicholas; Davis, Gary A. (eds.). Handbook of Gifted Education. Julian C. Stanley (Guest Foreword). Boston: Allyn & Bacon. p. 34. ISBN 978-0-205-34063-7. The second phenomenon, which is genetically driven exposure to environments, refers to gene-environment correlation. This is simply the fact that genetically distinct individuals (different genotypes) are not randomly distributed across environments. Rather, they tend to be clustered in different environments. This happens partly because the same parental genes that produce the child's genotype also influence the environment the parents create for the child. This is called passive gene-environment correlation. But the most interesting reasons for gene-environment correlations are that people with different geno-types (shyness, aggressiveness, high intelligence, and so on) evoke different responses from their environments, and they also actively create different environments for themselves. These are labeled, respectively, 'evocative' (or reactive) and 'active' gene-environment correlations. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Gottfredson, Linda; Saklofske, Donald H. (5 March 2012). "1: Intelligence: Foundations and Issues in Assessment (reprint of Gottfredson, L., Saklofske, D. H. (2009) Canadian Psychology (Canadian Journal of Psychology), 50(3): 183–195)". In Boyle, Gregory J; Saklofske, Donald H; Matthews, Gerald (eds.). Psychological Assessment. Vol. 1: Intelligence Assessment. SAGE Publications. pp. 1–24, 4. ISBN 978-0-85702-270-7. Retrieved 4 September 2013. The study of intelligence involves almost all areas of psychology and other sciences such as neurobiology and behaviour genetics. This has expanded our view of what intelligence is, how it develops, and the many factors that influence it, such as aging (see Deary, Whalley, & Starr, 2009; Kaufman, 2008; Lee, Gorsuch, Saklofske, & Patterson, 2008). It is well established that intelligence is a product of both hereditary and environmental factors and their interaction. But the complexity of intelligence and how we measure it and interpret these measures go much further. Intelligence does not exist or affect human behaviour in isolation. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Gottfredson, Linda; Saklofske, Donald H. (5 March 2012). "1: Intelligence: Foundations and Issues in Assessment (reprint of Gottfredson, L., Saklofske, D. H. (2009) Canadian Psychology (Canadian Journal of Psychology), 50(3): 183–195)". In Boyle, Gregory J; Saklofske, Donald H; Matthews, Gerald (eds.). Psychological Assessment. Vol. 1: Intelligence Assessment. SAGE Publications. pp. 1–24, 20. ISBN 978-0-85702-270-7. Retrieved 4 September 2013. For example, the average gap between White and both African American and Hispanic FSIQ scores on the WISC-IV FSIQ is 10 points (Weiss et al., 2006). Gaps may wax and wane somewhat, but are vexingly large and persisting (Jencks & Phillips, 1998; Jensen, 1998) This is one reason why some people argue that unbiased tests are not necessarily fair tests. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Gottfredson, Linda; Saklofske, Donald H. (5 March 2012). "1: Intelligence: Foundations and Issues in Assessment (reprint of Gottfredson, L., Saklofske, D. H. (2009) Canadian Psychology (Canadian Journal of Psychology), 50(3): 183–195)". In Boyle, Gregory J; Saklofske, Donald H; Matthews, Gerald (eds.). Psychological Assessment. Vol. 1: Intelligence Assessment. SAGE Publications. pp. 1–24, 22. ISBN 978-0-85702-270-7. Retrieved 4 September 2013. The shift toward framing social utility as if it were just another technical matter in adjudicating test validity is both embodied by and hidden in the noting of consequential validity, introduced in the 1980s. The wise psychologist using intelligence tests today will know that key factors such as affluence and education are highly correlated with FSIQ (Georgas et al., 2008), and that factors such as parent education, income, and expectations have reduced the WISC-IV FSIQ discrepancies to 6 points for Whites and African Americans and essentially 0 for White compared with Hispanic groups. So again, it is not the test but how we use it that is the issue here. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Gottfredson, Linda; Saklofske, Donald H. (5 March 2012). "1: Intelligence: Foundations and Issues in Assessment (reprint of Gottfredson, L., Saklofske, D. H. (2009) Canadian Psychology (Canadian Journal of Psychology), 50(3): 183–195)". In Boyle, Gregory J; Saklofske, Donald H; Matthews, Gerald (eds.). Psychological Assessment. Vol. 1: Intelligence Assessment. SAGE Publications. pp. 1–24, 23. ISBN 978-0-85702-270-7. Retrieved 4 September 2013. Language and spatial visualisation are more localised in the brain, which allows their associated psychometric factors to vary somewhat independently. g is most certainly not unitary at the genetic level. Approximately a third of our genes are expressed in the brain, and current thinking amongst behaviour geneticists is that no single gene will account for more than a minuscule amount of the variance in normal intelligence. [Para] Psychophysical measures of intellectual strength seem feasible in principle but unlikely in practise. But they do have one tremendous advantage that psychometric tests lack - ratio measurement. Speed (distance per time) is like height, weight, and many other physiological measures in that it has a zero point and is counted in equal units from there. No psychological scale can currently do that, either count from zero (total absence) or in equal units of quantity. Ratio measures of brain function might be exploited in some manner to provide more criterion-related or developmentally informative interpretations of psychometric tests. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Graves, Joseph L. (13 August 2013). "Chapter 8: Evolutionary Versus Racial Medicine". In Krimsky, Sheldon; Sloan, Kathleen (eds.). Race and the Genetic Revolution: Science, Myth, and Culture. Columbia University Press. p. 142. ISBN 978-0-231-52769-9. Retrieved 31 August 2013. In the classic paper 'Nothing in Biology Makes Sense Except in the Light of Evolution,' Theodosius Dobzhansky explained why and how evolutionary biology was the core unifying theme in biology. (citing Theodosius Dobzhansky, 'Nothing in Biology Makes Sense Except in the Light of Evolution,' The American Biology Teacher 35 (1973): 125-29.) {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Graves, Joseph L. (13 August 2013). "Chapter 8: Evolutionary Versus Racial Medicine". In Krimsky, Sheldon; Sloan, Kathleen (eds.). Race and the Genetic Revolution: Science, Myth, and Culture. Columbia University Press. p. 143. ISBN 978-0-231-52769-9. Retrieved 31 August 2013. Thus far, health disparity research and literature has not incorporated the full evolutionary medical approach. Generally, when biology is addressed as a cause of disparity and the focus has been on genetic differences that exist between reputed racial/ethnic groups, the evidence supporting the connection has been tenuous. The logical errors concerning genetic causality result from either ignoring or misunderstanding evolutionary genetics. (citing T. LaVeist, Minority Populations and Health: An Introduction to Health Disparities in the United States (San Francisco, CA: Josey Bass, 2005), 24; J. H. Fujimura, T. Duster, and R. Rajagopalan, 'Race, Genetics, and Disease: Questions of Evidence, Matters of Consequence,' Social Studies of Science 38, no. 5 (2008): 643-56; J. L. Graves, The Race Myth: Why We Pretend Race Exists in America (New York: Dutton, 2005); J. L. Graves, 'Biological V. Social Definitions of Race: Implications for Modern Biomedical Research,' Review of Black Political Economy 37, no. 1 (2009): 43-60; DOI: 10.1007/s12114-009-9053-3.) {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Graves, Joseph L. (13 August 2013). "Chapter 8: Evolutionary Versus Racial Medicine". In Krimsky, Sheldon; Sloan, Kathleen (eds.). Race and the Genetic Revolution: Science, Myth, and Culture. Columbia University Press. p. 150. ISBN 978-0-231-52769-9. Retrieved 31 August 2013. Woodley disputes the notion that population subdivision is a sufficient reason to invalidate the existence of race in anatomically modern humans. This is first attempted by reference to 'Lewontin's fallacy.' Lewontin's fallacy was first coined by the statistician A. W. F. Edwards in an essay that appeared in the journal Bioessays in 2003. This piece claimed that population geneticist Richard Lewontin was in error when he argued that the amount of genetic variation within populations was far greater than that between populations, which therefore invalidated the ability to assign individuals to racial groups. Edwards argued that if enough genetic loci are used, individuals can be distinguished into clusters, and that such clusters could represent biological races. Graves points out the many problems with this analysis. The largest problem is how population genetic data have been collected thus far. Generally, samples are taken from groups who are descended from populations whose geographic range is very far apart. Thus, we expect clustering of allele frequencies if one compares sub-Saharan Africans to Northern Europeans to East Asians. However, if the full continuum of human populations were examined, when a specific cluster began and when another ended would not be so clear. For example, in 2009 54,794 SNPs were examined in 1,928 individuals from 73 Asian populations. These data were compared with data from sub-Saharan Africans (Yorubans) and European Americans. Running Structure with K = 14 showed that linguistic groups tended to cluster together; however, there were populations that fell in clusters that did not belong to their linguistic or geographic affinity. (citing see file) {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Graves, Joseph L. (13 August 2013). "Chapter 8: Evolutionary Versus Racial Medicine". In Krimsky, Sheldon; Sloan, Kathleen (eds.). Race and the Genetic Revolution: Science, Myth, and Culture. Columbia University Press. p. 152. ISBN 978-0-231-52769-9. Retrieved 31 August 2013. Furthermore, for racial medicine to have utility, one would have to first be able to identify human races and, second, there would have to be a high certainty that individuals within these races would share specific disease-related genes in common that are different from those of other races. This scenario fails at even the simplest levels, especially within the United States. One study found that the genetic ancestry of African Americans varied widely (averaging 0.69-0.74 alleles most common in Western African and 0.11-0.15 alleles most common in Europe and the Middle East). A later study found that the median reputed European ancestry of self-reported African Americans was 0.185 but that the subjects varied in frequency from 0.99 to 0.01 European American. Thus, for this population the notion of racial medicine is absurd from the outset. (citing Tishkoff et al., 'The Genetic Structure.') (citing K. Bryc, A. Auton, M. R. Nelson et al., 'Genome-wide Patterns of Population Structure.') {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Graves, Joseph L. (13 August 2013). "Chapter 8: Evolutionary Versus Racial Medicine". In Krimsky, Sheldon; Sloan, Kathleen (eds.). Race and the Genetic Revolution: Science, Myth, and Culture. Columbia University Press. pp. 160–161. ISBN 978-0-231-52769-9. Retrieved 31 August 2013. For example, chimpanzees from three different regions of Africa (Eastern, Western, and Southern) have 43.33 times more genetic variability between them than the most genetically different human populations do. [para] The small amount of genetic variation within our species explains why we don't show biological races under Ernst Mayr's notion of incipient species. Mayr thought that in some cases natural selection and genetic drift produce incipient species that are akin to subspecies or geographical races. However, the small amount of genetic variation that exists in humans is inconsistent with this stage of speciation. On the other hand, it is clear that human populations do have geographically based genetic variation. What is not clear is why any professional scientist should believe that these genetic variants explain the differentials for complex disease we see in American society. Graves and Rose argue that while human populations do differ in the frequency of genetic variants, the evidence associating this variation with the differentials we observe in the prevalence of complex disease is extremely weak. How can it be that, while the body of evolutionary theory rails against the notion of genetically based differentials in complex disease among American racial/ethnic groups, this proposition is still so popular? To explain this contradiction, we must look to ideology and the social-political practices of the United States, not biology. [para] Few would argue against that African Americans have higher frequencies of the sicle-cell anemia variant than Europeans of Western European ancestry. It has been shown that persons who are heterozygous at this locus have elevated protection against the malaria parasite. Thus, if a group's health status was always about having the correct genes, we should never have expected African Americans to have higher mortality rates from malaria than European Americans. Yet, between the years 1921 and 1923, ten Southern states reported that the malaria death rate for African Americans was three times greater than for European Americans (25 per 100,000 versus only 7 per 100,000). Indeed, in the same time period, African Americans suffered disproportionate death rates due to pellagra (a vitamin-deficiency disease), while Charles Davenport and the Eugenics Record office railed against the victims of this disease and their many genetic deficiencies. [para] These sad episodes illustrate a simple fact of public health. Mortality and morbidity have always been strongly influenced by social conditions. (citing R. Boyd and J. B. Silk, How Humans Evolved, 3rd ed. (New York: Norton, 2003).) (citing Graves and Rose, 'Against Racial Medicine,' 481-493.) (citing M. Gover, 'Trends in Mortality Among Southern Negroes Since 1920,' Journal of Negro Education 6 (1937): 276-88.) (citing Graves, The Myth of Race.) {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Harpending, Henry (2007). "Chapter 16: Anthropological Genetics: Present and Future". In Crawford, Michael (ed.). Anthropological Genetics: Theory, Methods and Applications. Cambridge: Cambridge University Press. p. 457. ISBN 978-0-521-54697-3. The debate about modern human origins reinvigorated the study of human neutral diversity and changed the time and area focus from that of small regions over centuries to the whole species over tens of millennia. We are now not so concerned with marker frequency differences among valleys; we are concerned with marker frequency differences over the globe. We have gone from using a handful of markers to hundreds, even thousands, in these comparisons but nothing has really changed. Among major human groups the fraction of diversity, computed as some variant of Wright's F_ST statistic, that is among (rather than within) populations is approximately 1/8. This value has been known for 35 years or so and it is the answer to a vast corpus of pointless unnecessary literature about whether or not there are human races. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Harpending, Henry (2007). "Chapter 16: Anthropological Genetics: Present and Future". In Crawford, Michael (ed.). Anthropological Genetics: Theory, Methods and Applications. Cambridge: Cambridge University Press. p. 458. ISBN 978-0-521-54697-3. On the other hand, information about the race of patients will be useless as soon as we discover and can type cheaply the underlying genes that are responsible for the associations. Can races be enumerated in any unambiguous way? Of course not, and this is well known not only to scientists but also to anyone on the street. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Harpending, Henry (2007). "Chapter 16: Anthropological Genetics: Present and Future". In Crawford, Michael (ed.). Anthropological Genetics: Theory, Methods and Applications. Cambridge: Cambridge University Press. p. 458. ISBN 978-0-521-54697-3. The important patterns in human mtDNA were the relatively recent coalescence of human mtDNA, the topology of the gene tree in which populations outside Africa were represented by a subtree of the global tree, and later the apparent star-like structure of the tree, indicating a major population expansion 40 to 80 thousand years ago, just when the earliest modern-looking fossils appear in the record. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Harpending, Henry (2007). "Chapter 16: Anthropological Genetics: Present and Future". In Crawford, Michael (ed.). Anthropological Genetics: Theory, Methods and Applications. Cambridge: Cambridge University Press. p. 459. ISBN 978-0-521-54697-3. A wave of advance destroys diversity as it progresses since the small wavefront population accounts for most of the growth. Such a process accounts handily for the overall low diversity and small effective size of our species. We do not need to postulate any bottlenecks or episodes of size reduction in our species at all, such dramatic events probably never happened. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Kahn, Jonathan (13 August 2013). "Chapter 7: Bidil and Racialized Medicine". In Krimsky, Sheldon; Sloan, Kathleen (eds.). Race and the Genetic Revolution: Science, Myth, and Culture. Columbia University Press. p. 132. ISBN 978-0-231-52769-9. Retrieved 31 August 2013. In medical practice what matters is our shifting understanding of the correlations between such evolving social identities and the evolving economic, political, and environmental conditions to which they may be related. For example, what are we to make of the fact that African Americans suffer from disproportionately high rates of hypertension, but Africans in Nigeria have among the world's lowest rates of hypertension, far lower than the overwhelmingly white population of Germany? Genetics certainly plays a role in hypertension. But any role it plays in explaining such differences must surely be vanishingly small. (citing Richard Cooper et al., 'An International Comparative Study of Blood Pressure in Populations of European vs. African Descent,' BMC Medicine 3 (January 5, 2005): 2, www.biomedcentral.com/1741-7015/3/2 (accessed March 9, 2010).) {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Kahn, Jonathan (13 August 2013). "Chapter 7: Bidil and Racialized Medicine". In Krimsky, Sheldon; Sloan, Kathleen (eds.). Race and the Genetic Revolution: Science, Myth, and Culture. Columbia University Press. p. 133. ISBN 978-0-231-52769-9. Retrieved 31 August 2013. As a 2001 editorial in the journal Nature Genetics put it, 'Scientists have long been saying that at the genetic level there is more variation between two individuals in the same population than between populations and that there is no biological basis for race."' More recently, an editorial in Nature Biotechnology asserted, 'Race is simply a poor proxy for the environmental and genetic causes of disease or drug response. ... Pooling people in race silos is akin to zoologists grouping raccoons, tigers and okapis on the basis that they are all stripey.' (citing Editorial, 'Illuminating BiDil,' Nature Biotechnology 23 (2005):903.) {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Keller, Evelyn Fox (21 May 2010). The Mirage of a Space between Nature and Nurture. Duke University Press. p. 2. ISBN 978-0-8223-4731-6. By far the most common assumption—at least in the popular and semipopular literature—is that what is at issue is a comparison of the contributions of nature and nurture to the formation of individual traits. For example, this is the assumption that underlies much of the argument of Matt Ridley's widely read book, Nature via Nurture (2003). Ridley's central thesis is that modern genomics has shown us that the nature-nurture debate, as traditionally framed, is premised on a meaningless opposition. He writes: The discovery of how genes actually influence human behavior, and how human behavior influences genes, is about to recast the debate entirely. No longer is it nature versus nurture, but nature via nurture. Genes are designed to take their cues from nurture. (2003, 5) {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Keller, Evelyn Fox (21 May 2010). The Mirage of a Space between Nature and Nurture. Duke University Press. p. 3. ISBN 978-0-8223-4731-6. But in a review of Ridley's book, the evolutionary geneticist H. Allen Orr argues that Ridley misses the main point of the nature-nurture debate. Orr's chief complaint is that Ridley 'seems to have the right answer to the wrong question' (2003). What Orr refers to as the 'traditional question' of this debate is altogether different from Ridley's concern with how genes respond to experience: The first question is statistical. It asks about the percentage of variation in, say, IQ, that arises from inherited differences among individuals (do some parents pass on smart genes to their kids?) versus the percentage that arises from environmental differences (do some parents pass on books to their children?). The second question is mechanistic. It asks about how genes behave within individuals...The fact that genes respond to experience is certainly interesting and important...But it's the wrong kind of fact to settle the nature-nurture debate. (ibid.) {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Keller, Evelyn Fox (21 May 2010). The Mirage of a Space between Nature and Nurture. Duke University Press. pp. 4–5. ISBN 978-0-8223-4731-6. Epigenetics is a term that Conrad H. Waddington (1942) coined to refer quite generally to developmental processes (i.e., how we get from genotype to phenotype), and we have known for a long time that such processes involve far more than DNA. In this sense of the term, epigenetics is not a new field. Also not new is the recognition that the various factors involved in development—nucleic acids (DNA and RNA), metabolites, and proteins; nuclear and cytoplasmic factors; genetics and environment—are so deeply intertwined, so profoundly interdependent, as to make any attempt to partition their causal influence simply meaningless. Long before the discovery of DNA, the geneticist Lancelot Hogben was obliged to caution his readers that 'genetical science has outgrown the false antithesis between heredity and environment productive of so much futile controversy in the past' (1933, 201). {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Keller, Evelyn Fox (21 May 2010). The Mirage of a Space between Nature and Nurture. Duke University Press. p. 6. ISBN 978-0-8223-4731-6. By themselves, the entities we call genes do not act; they do not have agency. Strictly speaking, the very notion of a gene as an autonomous element, as an entity that exists in its own right, is a fiction. In order for a sequence of nucleotides to become what is conventionally called a gene requires that the sequence be embedded in a cellular complex that not only reads, translates, and interprets that sequence, but also defines it, giving it its very meaning. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Keller, Evelyn Fox (21 May 2010). The Mirage of a Space between Nature and Nurture. Duke University Press. p. 10. ISBN 978-0-8223-4731-6. The persistence of the nature-nurture debate has been a source of considerable puzzlement to many scholars. Many years ago, the developmental psychologist Daniel S. Lehrman had this to say: When opposing groups of intelligent, highly educated, competent scientists continue over many years to disagree, and even to wrangle bitterly, about an issue which they regard as important, it must sooner or later become obvious that the disagreement is not a factual one, and that it cannot be resolved by calling to the attention of the members of one group...the existence of new data which will make them see the light...If this is, as I believe, the case, we ought to consider the roles played in this disagreement by semantic difficulties arising from concealed differences in the way different people use the same words, or in the way the same people use the same words at different times; [and] by differences in the concepts used by different workers...(Lehrman 1970, 18-19) {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Keller, Evelyn Fox (21 May 2010). The Mirage of a Space between Nature and Nurture. Duke University Press. pp. 11–12. ISBN 978-0-8223-4731-6. Contemporary biology of development has clearly exposed the assumption in Galton's formulation as meaningless, as not making sense. Indeed, the problem was already evident early in the twentieth century, and in an effort to salvage the questions that had interested Galton, a reformulation of his project was soon provided. The English statistician R. A. Fisher was one of the first who taught us the necessity of making two fundamental distinctions if we wished to address Galton's concerns: we must distinguish first between traits and trait differences, and second between individual and population. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Keller, Evelyn Fox (21 May 2010). The Mirage of a Space between Nature and Nurture. Duke University Press. p. 12. ISBN 978-0-8223-4731-6. In the technical literature of population genetics, heritability was defined as referring to a statistical measure that has meaning only in relation to populations. Unfortunately, however, the word was already in use, but with another, simpler meaning—namely, transmissibility from parents to offspring. The double meaning of heritability has been frequently noted, but, in my view, its role in the continuing confounding of the two meanings, and accordingly of individual and population dynamics—both in the technical and popular literature—has not been adequately pursued. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Keller, Evelyn Fox (21 May 2010). The Mirage of a Space between Nature and Nurture. Duke University Press. p. 16–17. ISBN 978-0-8223-4731-6. It used to be claimed that the nature-nurture debate began with Francis Galton, and it certainly was Galton who, with English Men of Science: Their Nature and Nurture (1874), put the conjuction into wide circulation. Galton refers to his phrase 'nature and nurture' as 'a convenient jingle of words' (1974, 12), and indeed it is. But as I've already said, it is also more than that: it is a catchphrase that conjoins two domains on the tacit assumption that they are initially disjoint; it sneaks into our language—as if it had always been there, and as if it were self-evident—the presupposition of disjunction on which conjunction rests. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Keller, Evelyn Fox (21 May 2010). The Mirage of a Space between Nature and Nurture. Duke University Press. p. 17. ISBN 978-0-8223-4731-6. But in fact, although both Mulcaster and Shakespeare juxtapose the workings of nature and nurture, neither invokes the terms as an explicit conjunction; they do not write of 'nature and nurture' as such. Indeed, when we look more closely at what they do write, we can see that their use of the two terms does not invite such a conjuction, for there is no presumption of an a priori disjuction. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Keller, Evelyn Fox (21 May 2010). The Mirage of a Space between Nature and Nurture. Duke University Press. p. 23. ISBN 978-0-8223-4731-6. To be sure, this reformulation does not give us the means to make such a causal partition in practice; it only claims that partition's feasibility. Furthermore, Galton goes on to disavow any implication that either of the terms' implies any theory; natural gifts may or may not be hereditary; nurture does not especially consist of food, clothing, education or tradition, but it includes all these and similar influences whether known or unknown' (1874, 12). He even acknowledges that neither is self-sufficient. Nevertheless, he also claims that 'the distinction is clear: the one produces the infant such as it actually is, including its latent faculties of growth of body and mind; the other affords the environment amid which the growth takes place,' and he insists that 'when nature and nurture compete for supremacy..., the former proves the stronger.' Moreover, his professed agnosticism about the character of the components of nature is scarcely evident in his actual practice; in virtually all his investigations, the elements belonging to nature—what he refers to in his preface as 'pre-efficients,' a term that he defines as 'all that has gone into the making of eminent men' (vi)—are assumed to be elements of heredity. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Keller, Evelyn Fox (21 May 2010). The Mirage of a Space between Nature and Nurture. Duke University Press. p. 24. ISBN 978-0-8223-4731-6. In Galton's mind, such a distinction was not only clear; it was also necessary to make explicit. He was well aware of the criticism mounted against his emphasis on the importance of heredity, especially in the develdopment of genius, but he believed that separating what belongs to nature from what belongs to nurture would make a conclusive argument possible; indeed, for him, the argument depended on 'drawing the necessary distinction' (1876a, 391). The difficulty was how to do so in practice. Seeking a 'method by which it would be possible to weigh in just scales the respective effects of nature and nurture,' he seized on the idea that his program could be implemented through the study of twins. The history of twins, he writes, 'affords the means of distinguishing between the effects of tendencies received at birth, and of those that were imposed by the circumstances of their after lives; in other words, between the effects of nature and nurture.' {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Keller, Evelyn Fox (21 May 2010). The Mirage of a Space between Nature and Nurture. Duke University Press. p. 27–28. ISBN 978-0-8223-4731-6. Galton may have coined the term eugenics, but, as a number of historians have clearly shown, he was hardly the first to put forth hereditarian arguments for a program of race improvement. Yet his commitment to such a program seems to have been particularly strong. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Keller, Evelyn Fox (21 May 2010). The Mirage of a Space between Nature and Nurture. Duke University Press. p. 30. ISBN 978-0-8223-4731-6. Today, it is widely accepted by contemporary biologists and lay readers alike—as Daniel Dennett puts it, 'everyone knows'—that genes and environment must interact to produce any biological trait, that nature (understood as heredity) and nurture (understood as environment) are not alternatives. And yet. And yet, the image of separable ingredients continues to exert a surprisingly strong hold on our imagination, even long after we have learned better. Although 'everyone knows' it not to be true, many authors continue to argue as if nature and nurture were (or, at the very least, could be regarded as) separable and clearly distinguishable causes of development. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Keller, Evelyn Fox (21 May 2010). The Mirage of a Space between Nature and Nurture. Duke University Press. p. 31–32. ISBN 978-0-8223-4731-6. But it was probably the English mathematician R. A. Fisher (1890-1962) who contributed most to reformulating Galton's question. Fisher devoted his life to trying to distinguish genetic from environmental influences, and he was clearly aware of the difficulties involved. Like Pearson, he shared many of Galton's concerns; perhaps especially, he too felt that a science of eugenics was much needed, both socially and scientifically. In 1911, while still a student, he helped form the Cambridge University Eugenics Society (together with John Maynard Keynes, R. C. Punnett, and Charles Darwin's son Horace). And a few years later, in an effort to save what was meaningful in Galton's quest, Fisher (1918) published a reformulation of that quest in a paper that was to be enormously important in shaping the future of population genetics. Here he clearly recognized—the point that if it were to be realizable, Galton's hope of sorting genetic from environmental influences would need to be recast in two important ways. First, it was necessary to reformulate the question of causation in terms of trait differences rather than in terms of traits per se, and second, it was necessary to turn from the analysis of heredity in individual lineages to the analysis of heredity in populations. Only if we ask a statistical question about the relative contributions of variations in genetics and in environment to our differences from each other—rather than their relative contributions to the processes that make us what we are—would we have a question that makes sense, and furthermore, one that we might be able to answer. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Keller, Evelyn Fox (21 May 2010). The Mirage of a Space between Nature and Nurture. Duke University Press. p. 39–40. ISBN 978-0-8223-4731-6. This is a lesson classical geneticists knew well. At least when pressed, they readily acknowledged that the study of phenotypic differences was of limited use in trying to tease out the influence of genotype on phenotype. But as I discuss below, phenotypic differences were all they had access to, and the habit of conflating the two questions—of attempting to infer a causal relation between genes and traits from analyses of trait differences—may well have begun with the limits of their methodology. But this habit has obvious dangers: just consider, e.g., the folly of attempting to understand the causal dynamics of vision by studying all the ways in which blindness (an extreme example of phenotypic difference) can be induced. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Keller, Evelyn Fox (21 May 2010). The Mirage of a Space between Nature and Nurture. Duke University Press. p. 40. ISBN 978-0-8223-4731-6. But despite such direct access to at least part of the causal substrate of development, anyone expecting to be able to infer the causal dynamics of DNA from studying the effect of differences in that molecule on phenotype is subject to a few surprises. For example, the effect of changing a variable that is itself known to be causally important to the production of the phenotypic end product may be (in fact, often is) reduced or erased by a system of buffering that is built into the dynamical networks mediating between genotype and phenotype. Indeed, when such effects (or rather, the absence of effects) were first observed, geneticists greeted these results with surprise and consternation. More recently, however, such insensitivity to changes in contributing variables has come to be recognized as the hallmark of systems designed to be robust in the face of common fluctuations. In such systems, the difference effected by a change in variable is no guide at all to the causal importance of that variable. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Keller, Evelyn Fox (21 May 2010). The Mirage of a Space between Nature and Nurture. Duke University Press. p. 41. ISBN 978-0-8223-4731-6. Sesardic's own formulations unfortunately escape comparable scrutiny. Although his arguments center on the concept of heritability that I discuss only in chapter 3, I include them here because of their illustrative conflations between traits and trait differences on the one hand and between the causal impact of genes and gene differences on the other. For example, he writes: 'The idea that heritability reflects the causal strength of genetic influences on phenotypic differences has been consistently opposed by a number of authors. It has been said, e.g., . . . that it is dubious whether a clear meaning can be given to "'genetic determination of traits'" (Burian); that inferences about genetic determination of traits should be disavowed once and for all (Kitcher); . . . and so forth' (1993, 399, italics added). Notice that the difference between 'the causal strength of genetic influences on phenotypic differences' and the 'genetic determination of traits' is completely elided here. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Keller, Evelyn Fox (21 May 2010). The Mirage of a Space between Nature and Nurture. Duke University Press. pp. 41–42. ISBN 978-0-8223-4731-6. Sesardic's recent book (2005) on the subject of genetic causation is permeated by the same slippage. Throughout his writing, Sesardic sets himself apart from virtually all other philosophers of science in insisting that measures of the causal factors giving rise to trait difference can and do inform our understanding of the causal dynamics of individual trait development. Unfortunately, however, his argument rests on a routine confounding of the two questions. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Keller, Evelyn Fox (21 May 2010). The Mirage of a Space between Nature and Nurture. Duke University Press. p. 42. ISBN 978-0-8223-4731-6. And in response to what he refers to the 'mistaken conclusion' by the Nuffield Council on Bioethics—namely, that 'it is vital to understand that neither [the broad or narrow] concept of heritability allows us to conclude anything about the role of heredity in the development of a characteristic in an individual'—he writes: 'On the contrary, if the broad heritability of a trait is high, this does tell us that any individual's phenotypic divergence from the mean is probably more caused by a non-standard genetic influence than by a non-typical environment.' {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Keller, Evelyn Fox (21 May 2010). The Mirage of a Space between Nature and Nurture. Duke University Press. p. 44. ISBN 978-0-8223-4731-6. Wilhelm Johannsen, the man to whom we owe the word gene, was himself clearly worried about this problem when he asked: 'Is the whole of Mendelism perhaps nothing but an establishment of very many chromosomal irregularities, disturbances or diseases of enormously practical and theoretical importance but without deeper value for an understanding of the "normal" constitution of natural biotypes?' (1923, quoted in Moss 2003, 62). {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Keller, Evelyn Fox (21 May 2010). The Mirage of a Space between Nature and Nurture. Duke University Press. p. 48. ISBN 978-0-8223-4731-6. PKU is a disorder (now recognized as genetic) associated with a range of disabling symptoms, including mental retardation, and it is caused by the inability of the body to properly metabolize the essential amino acid phenylalanine. A major breakthrough in the treatment of this disease came with the recognition that its symptoms can be significantly alleviated if the affected individual adheres to a carefully monitored low-phenylalanine diet for his or her entire life. However, the development of a strategy to treat PKU had nothing to do with either the identification or the mapping of the gene(s) or genetic sequence(s) involved. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Keller, Evelyn Fox (21 May 2010). The Mirage of a Space between Nature and Nurture. Duke University Press. p. 53. ISBN 978-0-8223-4731-6. I want to return to my canonical example of PKU and consider two individuals with a measurable difference in IQ, a clear genetic difference (one has a mutation in the relevant sequence, and the other does not), and a clear difference in environment (one is raised on a normal diet, the other on a controlled, low-phenylalanine diet). Now let us ask, how much of the observed difference in IQ is due to the genetic difference, and how much to the difference in diet? This question is well posed, but alas, it cannot be answered. The reason is straightforward: individuals with the mutation react to the difference in diet very differently from those who do not. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Keller, Evelyn Fox (21 May 2010). The Mirage of a Space between Nature and Nurture. Duke University Press. p. 54. ISBN 978-0-8223-4731-6. Yet even where a difference between two individuals can, at least in principle, be clearly analyzed, in practice, it is far from easy to do so. Few studies in human biology can be so carefully controlled (ensuring no other genetic differences and no other environmental differences obtain), and those that can (be they on humans or on other organisms) are almost certain to be studies not of individuals but of populations. Indeed, it is population differences that seem to be of the greatest interest to many people, including sponsors of research, researchers, and the general public. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Keller, Evelyn Fox (21 May 2010). The Mirage of a Space between Nature and Nurture. Duke University Press. p. 54. ISBN 978-0-8223-4731-6. Questions about differences between groups require a different kind of analysis than do questions about differences between individuals. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Keller, Evelyn Fox (21 May 2010). The Mirage of a Space between Nature and Nurture. Duke University Press. p. 55. ISBN 978-0-8223-4731-6. Introducing his very first paper on the subject, Fisher warned that while 'it is desirable on the one hand that the elementary ideas...should be clearly understood; and easily expressed in ordinary language,' nonetheless 'loose phrases about the percentage of causation which obscure the essential distinction between the individual and the population, should be carefully avoided' (1918, 300-400). {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Keller, Evelyn Fox (21 May 2010). The Mirage of a Space between Nature and Nurture. Duke University Press. p. 55. ISBN 978-0-8223-4731-6. When authors write about sorting genetic from environmental contributions to the development of traits, it is not only the distinction between trait and trait difference that has been tacitly erased, but also the distinction between individual and population. In this chapter, I will argue that, like the first erasure, the second also is rampant—among behavioral geneticists, evolutionary psychologists, and journalists alike, in popular and semipopular literature as well as in technical journals. Its persistence long after Fisher's warning is puzzling, and my aim is to suggest that once again, at least part of the reason for that persistence lies in our language. That is, our difficulty in maintaining this conceptual distinction is sustained, if not caused, by the words we use. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Keller, Evelyn Fox (21 May 2010). The Mirage of a Space between Nature and Nurture. Duke University Press. p. 55. ISBN 978-0-8223-4731-6. The culprit here is not the word gene, but the closely related words heritable and heritability. The latter is a term that has become widely used in behavioral genetics in the effort to get at questions about the relative importance of nature and nurture, and it is used in this literature with a very specific technical meaning that was first introduced in 1936. But unfortunately, as has already been noted numerous times, the word itself was not new. Even then, it was in common use, but with a quite ordinary meaning: it referred simply to the quality of being passed on from parent to offspring—i.e., the quality of being inheritable, or just heritable. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Keller, Evelyn Fox (21 May 2010). The Mirage of a Space between Nature and Nurture. Duke University Press. pp. 55–56. ISBN 978-0-8223-4731-6. Although a number of others have noted the double meaning of the term 'heritability,' in my view, the implications of that ambiguity have not been adequately pursued. I will argue, first, that slippage between the two meanings of the term is chronic in both the technical and the popular literature, and second, that this slippage is a primary source of our continuing difficulty in keeping claims about populations apart from claims about inheritance per se. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Keller, Evelyn Fox (21 May 2010). The Mirage of a Space between Nature and Nurture. Duke University Press. p. 57. ISBN 978-0-8223-4731-6. By most accounts, the technical meaning of heritability was introduced in 1936 by Jay Laurence Lush, an animal breeder. Lush used the word to refer not to the quality of being inherited from parent to offspring, but to a statistical quantity associated with the ratio of genetic variation to phenotypic variation within a specified population of organisms. Interestingly, however, Lush felt no need to give an explicit definition, and as A. Earl Bell pointed out years ago (1977, 297), he showed no awareness that he was coining a new term. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Keller, Evelyn Fox (21 May 2010). The Mirage of a Space between Nature and Nurture. Duke University Press. p. 58. ISBN 978-0-8223-4731-6. Lush's technical measure itself had at least two variants, and about this he was explicit; he was also careful to distinguish between the two forms of the term. He called the first measure narrow heritability and the second broad heritability, and here too his terminology has prevailed. Narrow heritability—typically designated as h—is the measure most commonly used in agriculture: it is the proportion of total phenotypic variation that is due to the additive variation in genes (i.e., what is left when one leaves out any variation due to genetic interactions, either between genes or between alleles), and it is a good indicator of the responsiveness of the population to selection; furthermore, it is a quantity readily obtained from the correlation between parent and offspring phenotype. Broad heritability—typically designated as H—is the proportion of total phenotypic variation that is due to the total genetic variation, including that coming from interactions, and this is the measure more commonly used in behavioral genetics. This quantity may be more intuitively accessible, but unfortunately, it is far more difficult to measure. But whether the reference is to narrow or to broad heritability—indeed, in all technical discussions of the relation between genetic and phenotypic variation—a crucial distinction divides both variants of the term (h and H) from the colloquial meaning: the technical definition (or definitions) is a statistical rather than a causal measure. In other words, it has meaning only in relation to the properties of a population, not to properties either of an individual or of an individual lineage. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Keller, Evelyn Fox (21 May 2010). The Mirage of a Space between Nature and Nurture. Duke University Press. p. 59. ISBN 978-0-8223-4731-6. Although it is enormously important, many people find the distinction between the ordinary and technical meanings of heritability almost impossible to keep in sight, particularly when discussing human behavioral traits. Authors and readers alike routinely slide from one meaning to the other, wreaking havoc on the ways in which legitimate scientific measurements are interpreted. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Keller, Evelyn Fox (21 May 2010). The Mirage of a Space between Nature and Nurture. Duke University Press. pp. 59–60. ISBN 978-0-8223-4731-6. In any case, the ban on human experimentation precludes measurements of narrow heritability of the kind typically performed by animal breeders, and for that reason alone human behavioral geneticists have been obliged to turn to broad heritability. Furthermore, many harbor the hope that measurements of broad heritability—i.e., of the proportion of phenotypic variation that can be attributed to the total genetic variation in the population—will provide some causal understanding of how a trait acquired its particular value or form. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Keller, Evelyn Fox (21 May 2010). The Mirage of a Space between Nature and Nurture. Duke University Press. p. 60. ISBN 978-0-8223-4731-6. The difficulties of both making and interpreting such measurements—and the misuses to which they have so frequently been put—are the subject of a voluminous literature that began even before the introduction of heritability as a technical term. Lancelot Hogben's Nature and Nurture (1933) may have been the first contribution to this literature; in our own time, we have Richard Lewontin (1974 and 2000), Ned Block (1996), Patrick Bateson (2004), and many others to thank. One point of these critiques is the fact (already mentioned in chapter 2) that calculated ratios of genetic variance to overall variance in phenotype are meaningless in the presence of either statistical interaction (when genetic and environmental variations are correlated) or constitutive interaction (when genetic and environmental effects are intertwined), for under such circumstances, phenotypic variance cannot be partitioned into a genetic component plus an environmental component. This criticism is of particular relevance to human behavioral genetics for the simple reason that such interactions are ubiquitous in the development of human behavior. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Keller, Evelyn Fox (21 May 2010). The Mirage of a Space between Nature and Nurture. Duke University Press. pp. 60–61. ISBN 978-0-8223-4731-6. Another common theme is the reminder that the significance of such measurements (when they can be meaningfully performed) is entirely dependent on the particular choice of population and the particular range of environments that the members of the population inhabit. For example, a finding of high heritability may allow one to conclude that most of the variation observed in the particular population under study is due to genetic variation, but that conclusion cannot be generalized to other populations with different ranges of genetic properties, or having available different ranges of environmental contexts. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Keller, Evelyn Fox (21 May 2010). The Mirage of a Space between Nature and Nurture. Duke University Press. p. 61. ISBN 978-0-8223-4731-6. Above all, such a finding does not provide an argument for genetic determination. Bluntly put, technical heritability neither depends on, nor implies anything about, the mechanisms of transmission (inheritance) from parent to offspring. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Keller, Evelyn Fox (21 May 2010). The Mirage of a Space between Nature and Nurture. Duke University Press. pp. 62–63. ISBN 978-0-8223-4731-6. What is most surprising to me about this history of criticism—and what was the original motivation for this book—is its relative ineffectiveness. Notwithstanding the soundness of their logic, these critiques have scarcely made a dent in the general enthusiasm (both popular and professional) for measuring the heritability of human behavior. Furthermore, reports of high heritability continue almost invariably to be read by lay readers as implying that the trait in question is genetic, and in case anyone misses this implication, it is almost always made explicit by the reporter. Indeed, he or she can hardly be expected to grasp the distinction I am making here, when scientific authors and readers so often make the same mistaken inference. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Keller, Evelyn Fox (21 May 2010). The Mirage of a Space between Nature and Nurture. Duke University Press. p. 64. ISBN 978-0-8223-4731-6. For example, in his widely read The Blank Slate, Steven Pinker—following an earlier suggestion by Eric Turkheimer and Irving Gottesman (1991)—claims that the 'first law' of behavioral genetics is that 'all human behavioral traits are heritable' (2002, 373). What does he mean by this? Here is a slightly fuller statement from the same book that might help us understand: All five of the major personality dimensions are heritable, with perhaps 40–50 percent of the variation in a typical population tied to differences in their genes. The unfortunate wretch who is introverted, neurotic, narrow, selfish, and undependable is probably that way in part because of his genes, and so, most likely, are the rest of us who have tendencies in any of those directions as compared with our fellows. It's not just unpleasant temperaments that are heritable, but actual behavior with real consequences. Study after study has shown that a willingness to commit antisocial acts...is partly heritable (though like all heritable traits it is exercised more in some environments than in others). (ibid., 50) {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Keller, Evelyn Fox (21 May 2010). The Mirage of a Space between Nature and Nurture. Duke University Press. p. 69–70. ISBN 978-0-8223-4731-6. But, for many of the same reasons as I have advanced here, Gayon concludes that heritability, 'the only magnitude that modern genetics may appear ... to offer as a measurement of heredity is not a good candidate for this status. True, heritability is a measurable magnitude, but it is incorrect to say that it would measure heredity' (85) {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Keller, Evelyn Fox (21 May 2010). The Mirage of a Space between Nature and Nurture. Duke University Press. p. 71. ISBN 978-0-8223-4731-6. Moreover, I suggest more generally that much of our interest in heritability—as well as many of the arguments put forth on behalf of the importance of measuring such a quantity—rests deeply and inextricably on the unspoken ambiguity of the terms, and on the slippage that ambiguity invites. It rests, in short, on the informal fallacy that formal logic calls equivocation. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Krimsky, Sheldon (13 August 2013). "Introduction: How Science Embraced the Racialization of Human Populations". In Krimsky, Sheldon; Sloan, Kathleen (eds.). Race and the Genetic Revolution: Science, Myth, and Culture. Columbia University Press. p. 2. ISBN 978-0-231-52769-9. Retrieved 31 August 2013. Historically, the concept of 'race' has been steeped in paradox, embraced by ideology, adopted and rejected by science, but nevertheless remains an indisputable part of public discourse. The term 'race' is merely a shadow of what it once represented in science. Simply put, race is a scientific myth and a social reality. (citing In answering the question of why 'race' retains such force in society, Adolph Reed writes, 'The short answer is that race is a social reality.' Adolph Reed, 'Making Sense of race, I: The Ideology of race, the Biology or Human Variation, and the Problem of Medical and Public Health Research, Journal of Race and Policy 1 (2005): 11-42, at 13.) {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Krimsky, Sheldon (13 August 2013). "Introduction: How Science Embraced the Racialization of Human Populations". In Krimsky, Sheldon; Sloan, Kathleen (eds.). Race and the Genetic Revolution: Science, Myth, and Culture. Columbia University Press. p. 3. ISBN 978-0-231-52769-9. Retrieved 31 August 2013. The idea that there were fixed, unalterable human morphological or genetic qualities of certain population groups, transmitted from generation to generation, was in disfavor by most scientists in the late nineteenth century. Even the German physical anthropologist Johann Friedrich Blumenbach, who is often credited because of his book On the Natural Variety of Mankind as one of the progenitors of racializing the human population, acknowledged that no sharp distinction can be made between people. He wrote, 'No variety of mankind exists, whether of colour, countenance, or stature, etc., so singular as not to be connected with others, of the same kind by such an imperceptible transition that it is very clear that they are all related, or only differ from each other in degree.' (citing Ashley Montagu, Man's Most Dangerous Myth: The Fallacy of Race (New York: World Publishing Co., 1964), 41. Originally published in 1942 by Columbia University Press.) {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Krimsky, Sheldon (13 August 2013). "Introduction: How Science Embraced the Racialization of Human Populations". In Krimsky, Sheldon; Sloan, Kathleen (eds.). Race and the Genetic Revolution: Science, Myth, and Culture. Columbia University Press. p. 3. ISBN 978-0-231-52769-9. Retrieved 31 August 2013. Nearly sixty years later, Michael Omi reaffirmed the consensus: 'Biologists, geneticists, and physical anthropologists among others, long ago reached a common understanding that race is not a "scientific" concept rooted in discernable biological differences.' By the end of the twentieth century, race had been defined in the Unabridged Random House Dictionary as 'an arbitrary classification of modern humans, sometimes, esp. formerly, based on any or a combination of various physical characteristics, as skin color, facial form, or eye shape.' (citing Random House Unabridged Dictionary, 1993.) {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Ossorio, Pilar N. (13 August 2013). "Chapter 9: Myth and Mystification: The Science of Race and IQ". In Krimsky, Sheldon; Sloan, Kathleen (eds.). Race and the Genetic Revolution: Science, Myth, and Culture. Columbia University Press. p. 176. ISBN 978-0-231-52769-9. Retrieved 31 August 2013. Because human biological variation is complex and continuous, allocating people to categories requires us to "draw lines" where none exist in nature. Differences in features and, as it turns out, in DNA sequences are greatest between groups of people who are geographically distant from each other. The pattern in which some measurable feature varies gradually, and that variation correlates with geographic distance, is called "clinal" variation. Many human traits and many human genetic differences exhibit a clinal pattern. (citing "Going the Distance: Human Population Genetics in a Clinal World," Trends in Genetics 23 (2008): 432-39.) (citing "Evidence for Gradients of Human Genetic Diversity Within and Among Continents," Genome Research 14 (2004): 1679-85. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Ossorio, Pilar N. (13 August 2013). "Chapter 9: Myth and Mystification: The Science of Race and IQ". In Krimsky, Sheldon; Sloan, Kathleen (eds.). Race and the Genetic Revolution: Science, Myth, and Culture. Columbia University Press. p. 177. ISBN 978-0-231-52769-9. Retrieved 31 August 2013. If one assesses the genetic variation in a group of Yoruba people from Nigeria, and in a group of Swedish people from the city of Malmo, somewhere between 85 percent and 95 percent of the genetic variants will be found in both groups, although some variants will be found at a higher frequency in one group than the other. Furthermore, if one examines a single gene or region of the genome, then an individual whose recent ancestors are from Lagos, Nigeria, may be more similar to somebody from Malmo, Sweden, than to most other people from Nigeria. Depending on how one measures, the component of genetic variation that occurs between human groups from different continents could be as low as 2.8 percent, whereas the component of genetic variation between human groups from the same continent could be 2.5 percent. If one associates races with particular continents, then all but 2.8 to 5 percent of the human genetic variation is found within any race. (citing "The Application of Molecular Genetic Approaches to the Study of Human Evolution," Nature Genetics Supplement 33 (2003): 266-75 ) (citing Jorde and Wooding, "Genetic Variation.") (citing "Response to Comment on 'Genetic Structure of Human Populations,'" Science 300 (2003):1877.) {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Ossorio, Pilar N. (13 August 2013). "Chapter 9: Myth and Mystification: The Science of Race and IQ". In Krimsky, Sheldon; Sloan, Kathleen (eds.). Race and the Genetic Revolution: Science, Myth, and Culture. Columbia University Press. p. 179. ISBN 978-0-231-52769-9. Retrieved 31 August 2013. Researchers can group people of Iceland according to the county or counties in which their ancestors (five generations back) were born. This does not mean that the people of Finland and Sweden are of different races, or that Iceland's counties are populated by different races. Also, keep in mind that only about 0.1 to 0.5 percent of the human genome differs from one person to another, and only 5 to 15 percent of that variation can be used to distinguish between human groups, so statistical grouping of people by patterns of genetic variation is based on a minute fraction of our genomes. Finally, it is crucial to reemphasize that the amount of genetic variation between groups is very small compared to the 85 to 95 percent of variation found between different individuals within human groups. (citing "An Icelandic Example of the Impact of Population Structure on Association Studies," Nature Genetics 37 (2005): 90-95. ) {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Ossorio, Pilar N. (13 August 2013). "Chapter 9: Myth and Mystification: The Science of Race and IQ". In Krimsky, Sheldon; Sloan, Kathleen (eds.). Race and the Genetic Revolution: Science, Myth, and Culture. Columbia University Press. p. 180. ISBN 978-0-231-52769-9. Retrieved 31 August 2013. Many nongenetic features of our world and ourselves are real because we make them so, we bring them into existence through beliefs, customs, laws, physical arrangements of our environment, and numerous everyday acts. Marriages, schools, and subways are not encoded in any person's genes, but they are all real. Likewise, race is real because people believe in it and act on those beliefs. Race is deeply rooted in the consciousness of individuals and groups, and it structures our lives and our physical world in myriad ways. It is a strong predictor of where people live, what schools they attend, where and how their spirituality is practiced, what jobs they have, and the amount of income they will earn. Race is real because human beings continually create and recreate it through the process of racialization. There is no unitary definition of race, no definition that applies in all places, at all times, and for all purposes. Scholars who include race as a variable in their studies must operationalize the concept of race in a manner that meets the needs of their study, while acknowledging that such "working definitions" merely "fulfill the need for an analytical strategy, they do not reflect a fixed social or biological reality." (citing Why Segregation Matters: Poverty and Educational Inequality (Cambridge, MA: Harvard Civil Rights Project, 2005). ) ( citing "An Overview of Trends in Social and Economic Well-being by Race," in America Becoming. ) ( citing Racial Formation in the United States, 2nd Edition (New York: Routledge, 1994). ) ( citing America Becoming, 4. ) {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Ossorio, Pilar N. (13 August 2013). "Chapter 9: Myth and Mystification: The Science of Race and IQ". In Krimsky, Sheldon; Sloan, Kathleen (eds.). Race and the Genetic Revolution: Science, Myth, and Culture. Columbia University Press. p. 181. ISBN 978-0-231-52769-9. Retrieved 31 August 2013. Scholars who study race generally agree on a few themes regarding how notions of race operate in society. One point of agreement is that race is a second-order construct—a belief about beliefs, behaviors, and traits. A person's racial self-identification, and her ascription of race to other people, is based on her beliefs about skin color, head shape, hair texture, religion, ancestry, language spoken, nationality, dress, political philosophy, and many other factors. Genes encode some of the traits on which people base racial attributions and identity, but genes do not encode all of them. Because a person's race is not reducible to her genetically encoded traits, one could know everything about a person's genome and still not know her race. Another point of agreement among race scholars is that race is a very malleable concept. This claim developed from numerous observations of how the meaning of racial categories, and the categories themselves, change over time. One well-known example of this malleability is that every decennial census since the early twentieth century has defined race differently than the previous census. Thus, a person who was white in one census might have been Mexican or black in another. Another example is that some people have a different race on their birth certificate and their death certificate. (citing "About Face: Forensic Genetic Testing for Race and Visible Traits," Journal of Law, Medicine & Ethics 34 (2006): 277-87 ) (Omin and Winant, Racial Formation; I. H. Lopez, White by Law (New York: New York University Press, 1996); M.A. Omi, "The Changing Meaning of Race," in America Becoming. " ( citing "Race on the 2010 Census: Hispanics and the Shrinking White Majority," Daedalus 134 (2005): 42-52; K. Prewitt, "Racial Classification in America: Where Do We Go from Here?" Daedalus 134 (2005): 5-17 ) {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Ossorio, Pilar N. (13 August 2013). "Chapter 9: Myth and Mystification: The Science of Race and IQ". In Krimsky, Sheldon; Sloan, Kathleen (eds.). Race and the Genetic Revolution: Science, Myth, and Culture. Columbia University Press. p. 184. ISBN 978-0-231-52769-9. Retrieved 31 August 2013. Hereditarian claims are based on the alleged heritability of IQ. Heritability assesses the way a trait varies in a population and purports to measure how much of that variation is explained by genetic differences within the population. The remaining variation is attributed to all other factors (the environment and nongenetic aspects of biology). If children in a classroom score between 90 and 180 on an IQ test, a hereditarian might claim that 65 percent of the 40 point difference in IQ is due to genetic differences between the students, and 35 percent is due to other factors. Strong proponents of hereditarian theories tend to believe that genetic differences explain as much as 85 percent of the variation in adult IQ in a population, but other scholars believe that genes explain much less than 50 percent of the variation in IQ. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Ossorio, Pilar N. (13 August 2013). "Chapter 9: Myth and Mystification: The Science of Race and IQ". In Krimsky, Sheldon; Sloan, Kathleen (eds.). Race and the Genetic Revolution: Science, Myth, and Culture. Columbia University Press. p. 186. ISBN 978-0-231-52769-9. Retrieved 31 August 2013. Research also undermines the hereditarian claim that IQ is the primary determinant of achievement. Many environmental variables predict achievement as well as or better than IQ, except for people whose IQ scores are at the abnormally low end of the scale. For instance, a person's social environment may be an important determinant of her achievement, yet variables that capture a person's social environment are often, literally, left out of the equation in work done by hereditarians. The social environment includes the expectations of one's peers, encouragement by one's parents and teachers, enrichment opportunities available in the neighborhood, etc. (citing Why Segregation Matters; Fisher et al., Inequality By Design; S.R. Sirin, "Socioeconomic Status and Academic Achievement: A Meta-analytic Review of Research," Review of Educational Research 75 (2005): 417-53. ) {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Ossorio, Pilar N. (13 August 2013). "Chapter 9: Myth and Mystification: The Science of Race and IQ". In Krimsky, Sheldon; Sloan, Kathleen (eds.). Race and the Genetic Revolution: Science, Myth, and Culture. Columbia University Press. p. 187. ISBN 978-0-231-52769-9. Retrieved 31 August 2013. The most likely explanation for the rise in IQ is that some relevant environmental factors have changed, causing people to develop in ways that are reflected in higher average IQ scores. Another piece of evidence concerning widespread environmental influences on IQ is that the mean difference between black Americans' and white Americans' test scores has narrowed since the 1970s. Using data from several different IQ tests that were administered in a standard manner to black and non-Hispanic white people, Dickens and Flynn showed that blacks have narrowed the IQ gap by one-third to one-half of what it was in the 1970s. If IQ were a fixed, intrinsic quality of races, then the IQ gap should be stable over time, but it is not. (citing "Black Americans Reduce the Racial IQ Gap," Psychological Science 17 (2006) :913-20. ) {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Ossorio, Pilar N. (13 August 2013). "Chapter 9: Myth and Mystification: The Science of Race and IQ". In Krimsky, Sheldon; Sloan, Kathleen (eds.). Race and the Genetic Revolution: Science, Myth, and Culture. Columbia University Press. p. 188. ISBN 978-0-231-52769-9. Retrieved 31 August 2013. Some researchers have attempted to correlate genetic markers with people's scores on tests of cognitive ability. Thus far, these studies have yielded some claims about gene variants that are correlated with variation in cognitive ability, but no research has demonstrated a causal connection between a particular gene variant and a particular degree of cognitive skill (within the normal ability ranges). Even proponents of such research note that "there are an unknown number of genetic influences on different abilities; [and] some ...proportion of these may be too small to feasibly be detected." When variants of particular genes have been associated with IQ, the effects reported are relatively small—in the range of a couple of IQ points—and few of the observed correlations have been replicated. In contrast, altering environmental factors, such as the quality of education and day care, has been associated with IQ variation of 30 points or more. (citing "Intelligence Research and Assessment in the United Kingdom," in International Handbook of Intelligence, ed. R.J. Sternberg, (Cambridge, Cambridge University Press, 2004). ) (citing Nisbett, Intelligence and How to Get It. ) {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Ossorio, Pilar N. (13 August 2013). "Chapter 9: Myth and Mystification: The Science of Race and IQ". In Krimsky, Sheldon; Sloan, Kathleen (eds.). Race and the Genetic Revolution: Science, Myth, and Culture. Columbia University Press. p. 189. ISBN 978-0-231-52769-9. Retrieved 31 August 2013. Since 2005, other researchers have evaluated the same data on MCHPI and ASPM, plus some additional data, and concluded that there is no evidence that these genes have been under natural selection in modern humans. These reanalyses undercut the idea that the particular variants found at high frequency among people of European descent somehow made Europeans better adapted for modern civilization. Additional studies have discovered that the MCHPI and ASPM variants reported in the 2005 papers do not correlate with larger (or smaller) than average head size. The genes were originally described as having to do with head size because some variants of these genes can cause microcephaly (extremely small heads that lack major portions of the brain). However, those microcephaly-causing variants were not included in the studies published in 2005. Finally, several research groups have tried and failed to show any correlation between the variants described in the 2005 papers and IQ reading abilities, or verbal abilities. (citing "Comment on 'Ongoing Adaptive Evolution of ASPM, a Brain Size Determinant in Homo Sapiens' and 'Microcephalin, a Gene Regulating Brain Size Continues to Evolve Adaptively in Humans," Science 313 (2006): 172 (a); F.Yu, S.R.Hill, S.F.Schaffner, et al., "Comment on 'Ongoing Adaptive Evolution of ASPM, a Brain Size Determinant in Homo Sapiens,'" Science 316 (2007): 370 (b). ) {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Plomin, Robert; DeFries, John C.; Knopik, Valerie S. (24 September 2012). Behavioral Genetics. Shaun Purcell (Appendix: Statistical Methods in Behavioral Genetics). Worth Publishers. p. 81. ISBN 978-1-4292-4215-8. Retrieved 4 September 2013. If a trait is influenced genetically, identical twins must be more similar than fraternal twins. However, it is also possible that the greater similarity of MZ twins is caused environmentally rather than genetically because MZ twins are the same sex and age and they look alike. The 'equal environments assumption' of the twin method assumes that environmentally caused similarity is roughly the same for both types of twins reared in the same family. If the assumption were violated because identical twins experience more similar environments than fraternal twins, this violation would inflate estimates of genetic influence. The equal environments assumption has been tested in several ways and appears reasonable for most traits (Bouchard & Propping, 1993; Derks, Dolan, & Boomsma, 2006). {{cite book}}: Unknown parameter |coauthors= ignored (|author= suggested) (help); Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Plomin, Robert; DeFries, John C.; Knopik, Valerie S. (24 September 2012). Behavioral Genetics. Shaun Purcell (Appendix: Statistical Methods in Behavioral Genetics). Worth Publishers. p. 81–82. ISBN 978-1-4292-4215-8. Retrieved 4 September 2013. Another way in which the equal environments assumption has been tested takes advantage of the fact that differences within pairs of identical twins can only be due to environmental influences. The equal environments assumption is supported if identical twins who are treated more individually than others do not behave more differently. This is what has been found for most tests of the assumption in research on behavioral disorders and dimensions (e.g., Cronk et al., 2002; Kendler, Neale, Kessler, Heath, & Eaves, 1994, Loehlin & Nichols, 1976; Mazzeo et al., 2010; Morris-Yates, Andrews, Howie, & Henderson, 1990). {{cite book}}: Unknown parameter |coauthors= ignored (|author= suggested) (help); Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Plomin, Robert; DeFries, John C.; Knopik, Valerie S. (24 September 2012). Behavioral Genetics. Shaun Purcell (Appendix: Statistical Methods in Behavioral Genetics). Worth Publishers. p. 82–83. ISBN 978-1-4292-4215-8. Retrieved 4 September 2013. As in any experiment, generalizability is an issue for the twin method. Are twins representative of the general population? Two ways in which twins are different are that twins are often born three to four weeks prematurely and intrauterine environments can be adverese when twins share a womb (Phillips, 1993). Newborn twins are also about 30 percent lighter at birth than the average singleton newborn, a difference that disappears by middle childhood (MacGillivray, Campbell, & Thompson, 1988). There is also the suggestion that brain development differs in twins vs. singleton children during early infancy (Knickmeyer et al., 2011). In childhood, language develops more slowly in twins, and twins also perform less well on tests of verbal ability and IQ (Deary, Pattie, Wilson, & Whalley, 2005; Ronalds, De Stavola, & Leon, 2005; Voracek & Haubner, 2008). These delays are similar for MZ and DZ twins and appear to be due to the postnatal environment rather than prematurity (Rutter & Redshaw, 1991). Most of this cognitive deficit is recovered in the early school years (Christensen, Petersen, et al., 2006). {{cite book}}: Unknown parameter |coauthors= ignored (|author= suggested) (help); Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Plomin, Robert; DeFries, John C.; Knopik, Valerie S. (24 September 2012). Behavioral Genetics. Shaun Purcell (Appendix: Statistical Methods in Behavioral Genetics). Worth Publishers. p. 87. ISBN 978-1-4292-4215-8. Retrieved 4 September 2013. The statistic that estimates the genetic effect size is called 'heritability'. Heritability is the proportion of phenotypic variance that can be accounting for by genetic differences among individuals. {{cite book}}: Unknown parameter |coauthors= ignored (|author= suggested) (help); Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Plomin, Robert; DeFries, John C.; Knopik, Valerie S. (24 September 2012). Behavioral Genetics. Shaun Purcell (Appendix: Statistical Methods in Behavioral Genetics). Worth Publishers. p. 87. ISBN 978-1-4292-4215-8. Retrieved 4 September 2013. Heritability estimates, like all statistics, include error of estimation, which is a function of the effect size and the sample size. . . . For this reason, heritability estimates based on a single study need to be taken as very rough estimates surrounded by a large confidence interval unless the study is very large. For example, if the correlation of 0.24 were based on a sample of 2000 instead of 200, there would be a 95 percent chance that the true heritability is between 40 and 56 percent. {{cite book}}: Unknown parameter |coauthors= ignored (|author= suggested) (help); Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Plomin, Robert; DeFries, John C.; Knopik, Valerie S. (24 September 2012). Behavioral Genetics. Shaun Purcell (Appendix: Statistical Methods in Behavioral Genetics). Worth Publishers. p. 88. ISBN 978-1-4292-4215-8. Retrieved 4 September 2013. For example, in Figure 3.7, IQ correlations for identical and fraternal twins are 0.85 and 0.60, respectively. Doubling the difference between these correlations results in a heritability estimate of 50 percent, which also suggests that about half of the variance of IQ scores can be accounted for by genetic factors. Because these studies include more than 10,000 pairs of twins, the error of estimation is small. There is a 95 percent chance that the true heritability is between 0.48 and 0.52. {{cite book}}: Unknown parameter |coauthors= ignored (|author= suggested) (help); Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Plomin, Robert; DeFries, John C.; Knopik, Valerie S. (24 September 2012). Behavioral Genetics. Shaun Purcell (Appendix: Statistical Methods in Behavioral Genetics). Worth Publishers. p. 89. ISBN 978-1-4292-4215-8. Retrieved 4 September 2013. For combination designs that compare several groups, and even for simple adoption and twin designs, modern genetic studies are typically analyzed by using an approach called 'model fitting'. Model fitting tests the significance of the fit between a model of genetic and environmental relatedness against the observed data. Different models can be compared, and the best-fitting model is used to estimate the effect size of genetic and environmental effects. {{cite book}}: Unknown parameter |coauthors= ignored (|author= suggested) (help); Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Plomin, Robert; DeFries, John C.; Knopik, Valerie S. (24 September 2012). Behavioral Genetics. Shaun Purcell (Appendix: Statistical Methods in Behavioral Genetics). Worth Publishers. p. 89. ISBN 978-1-4292-4215-8. Retrieved 4 September 2013. Heritability refers to the genetic contribution to individual differences (variance), not to the phenotype of a single individual. For a single individual, both genotype and environment are indispensable—a person would not exist without both genes and environment. {{cite book}}: Unknown parameter |coauthors= ignored (|author= suggested) (help); Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Plomin, Robert; DeFries, John C.; Knopik, Valerie S. (24 September 2012). Behavioral Genetics. Shaun Purcell (Appendix: Statistical Methods in Behavioral Genetics). Worth Publishers. p. 92. ISBN 978-1-4292-4215-8. Retrieved 4 September 2013. For example, the heritability of height is about 90 percent, but this does not mean that you grew to 90 percent of your height for reasons of heredity and that the other inches were added by the environment. What it means is that most of the height differences among individuals are due to the genetic differences among them. Heritability is a statistic that describes the contribution of genetic differences to observed differences among individuals in a particular population at a particular time. In different populations or at different times, environmental or genetic influences might differ, and heritability estimates in such populations could differ. {{cite book}}: Unknown parameter |coauthors= ignored (|author= suggested) (help); Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Plomin, Robert; DeFries, John C.; Knopik, Valerie S. (24 September 2012). Behavioral Genetics. Shaun Purcell (Appendix: Statistical Methods in Behavioral Genetics). Worth Publishers. p. 93. ISBN 978-1-4292-4215-8. Retrieved 4 September 2013. A related issue concerns average differences between groups, such as average differences between males and females, between social classes, or between ethnic groups. It should be emphasized that the causes of individual differences within groups have no implications for the causes of average differences between groups. Specifically, heritability refers to the genetic contribution to differences among individuals within a group. High heritability within a group does not necessarily imply that average differences between groups are due to genetic differences between groups. The average differences between groups could be due solely to environmental differences even when heritability within the groups is very high. {{cite book}}: Unknown parameter |coauthors= ignored (|author= suggested) (help); Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Plomin, Robert; DeFries, John C.; Knopik, Valerie S. (24 September 2012). Behavioral Genetics. Shaun Purcell (Appendix: Statistical Methods in Behavioral Genetics). Worth Publishers. p. 93. ISBN 978-1-4292-4215-8. Retrieved 4 September 2013. Finding heritability for individual differences within the normal range of variation does not necessarily imply that the average difference between an extreme group and the rest of the population is also due to genetic factors. For example, if individual differences in depressive symptoms for an unselected sample are heritable, this finding does not necessarily imply that severe depression is also due to genetic factors. This point is worth repeating: The causes of average differences between groups are not necessarily related to the causes of individual differences within groups. {{cite book}}: Unknown parameter |coauthors= ignored (|author= suggested) (help); Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Plomin, Robert; DeFries, John C.; Knopik, Valerie S. (24 September 2012). Behavioral Genetics. Shaun Purcell (Appendix: Statistical Methods in Behavioral Genetics). Worth Publishers. p. 93. ISBN 978-1-4292-4215-8. Retrieved 4 September 2013. A related point is that heritability describes what is in a particular population at a particular time rather than what could be. That is, if either genetic influences change (e.g., changes due to migration) or environmental influences change (e.g., changes in educational opportunity), then the relative impact of genes and environment will change. Even for a highly heritable trait such as height, changes in the environment could make a big difference, for example, if an epidemic struck or if children's diets were altered. Indeed, the huge increase in children's heights during the past century is likely to be a consequence of improved diet. {{cite book}}: Unknown parameter |coauthors= ignored (|author= suggested) (help); Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Plomin, Robert; DeFries, John C.; Knopik, Valerie S. (24 September 2012). Behavioral Genetics. Shaun Purcell (Appendix: Statistical Methods in Behavioral Genetics). Worth Publishers. p. 93–94. ISBN 978-1-4292-4215-8. Retrieved 4 September 2013. Most important, heritability has nothing to say about what should be. Evidence of genetic influence for a behavior is compatible with a wide range of social and political views, most of which depend on values, not facts. For example, no policies necessarily follow from finding genetic influences or even specific genes for cognitive abilities. It does not mean, for example, that we ought to put all our resources into educating the brightest children. Depending on our values, we might worry more about children falling off the low end of the bell curve in an increasingly technological society and decide to devote more public resources to those who are in danger of being left behind. {{cite book}}: Unknown parameter |coauthors= ignored (|author= suggested) (help); Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Plomin, Robert; DeFries, John C.; Knopik, Valerie S. (24 September 2012). Behavioral Genetics. Shaun Purcell (Appendix: Statistical Methods in Behavioral Genetics). Worth Publishers. p. 94. ISBN 978-1-4292-4215-8. Retrieved 4 September 2013. A related point is that heritability does not imply genetic determinism. Just because a trait show genetic influences does not mean that nothing can be done to change it. Environmental change is possible even for single-gene disorders. {{cite book}}: Unknown parameter |coauthors= ignored (|author= suggested) (help); Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Plomin, Robert; DeFries, John C.; Knopik, Valerie S. (24 September 2012). Behavioral Genetics. Shaun Purcell (Appendix: Statistical Methods in Behavioral Genetics). Worth Publishers. p. 94. ISBN 978-1-4292-4215-8. Retrieved 4 September 2013. For behavioral disorders and dimensions, the links between specific genes and behavior are weaker because behavioral traits are generally influenced by multiple genes and environmental factors. For this reason, genetic influences on behavior involve probabilistic propensities rather than predetermined programming. In other words, the complexity of most behavioral systems means that genes are not destiny. {{cite book}}: Unknown parameter |coauthors= ignored (|author= suggested) (help); Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Plomin, Robert; DeFries, John C.; Knopik, Valerie S. (24 September 2012). Behavioral Genetics. Shaun Purcell (Appendix: Statistical Methods in Behavioral Genetics). Worth Publishers. pp. 94–95. ISBN 978-1-4292-4215-8. Retrieved 4 September 2013. Finally, finding genetic influences on complex traits does not mean that the environment is unimportant. For simple single-gene disorders, environmental factors may have little effect. In contrast, for complex traits, environmental influences are usually as important as, or in some cases more important than, genetic influences. When one member of an identical twin pair is schizophrenic, for example, the other twin is not schizophrenic in about half the cases, even though members of identical twin pairs are identical genetically. Such differences within pairs of identical twins can only be caused by nongenetic factors. {{cite book}}: Unknown parameter |coauthors= ignored (|author= suggested) (help); Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Plomin, Robert; DeFries, John C.; Knopik, Valerie S. (24 September 2012). Behavioral Genetics. Shaun Purcell (Appendix: Statistical Methods in Behavioral Genetics). Worth Publishers. p. 105–106. ISBN 978-1-4292-4215-8. Retrieved 4 September 2013. One thing we know for sure about the environment is that it is important. Quantitative genetic research, reviewed in Chapters 11 to 19, provides the best available evidence that the environment is an important source of individual differences throughout the domain of behavior. Moreover, quantitative genetic research is changing the way we think about the environment. Three of the most important discoveries from genetic research in the behavioral sciences are about nurture rather than nature. The first discovery is that nonshared environmental influences are surprisingly large and important in explaining individual differences. The second discovery is equally surprising: Many environmental measures widely used in the behavioral sciences show genetic influence. This research suggests that people create their own experiences, in part for genetic reasons. This topic has been called the nature of nurture, although in genetics it is known as genotype-environment correlation because it refers to experiences that are correlated with genetic propensities. The third discovery at the interface between nature and nurture is that the effects of the environment can depend on genetics and that the effects of genetics can depend on the environment. This topic is called genotype-environment interaction, genetic sensitivity to environments. {{cite book}}: Unknown parameter |coauthors= ignored (|author= suggested) (help); Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Plomin, Robert; DeFries, John C.; Knopik, Valerie S. (24 September 2012). Behavioral Genetics. Shaun Purcell (Appendix: Statistical Methods in Behavioral Genetics). Worth Publishers. p. 106–107. ISBN 978-1-4292-4215-8. Retrieved 4 September 2013. Behavioral genetic research has found genetic influence nearly everywhere it has looked. Indeed, it is difficult to find any behavioral dimension or disorder that reliably shows no genetic influence. On the other hand, behavioral genetic research also provides some of the strongest available evidence for the importance of environmental influences for the simple reason that heritabilities are seldom greater than 50 percent. This means that environmental factors are also important. {{cite book}}: Unknown parameter |coauthors= ignored (|author= suggested) (help); Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Plomin, Robert; DeFries, John C.; Knopik, Valerie S. (24 September 2012). Behavioral Genetics. Shaun Purcell (Appendix: Statistical Methods in Behavioral Genetics). Worth Publishers. p. 122. ISBN 978-1-4292-4215-8. Retrieved 4 September 2013. Another analysis of this type showed that heritability of general cognitive ability is significantly greater in families with more highly educated parents (74 percent) than in families with less well educated parents (26 percent) (Rowe, Jacobson, & van den Oord, 1999), a finding replicated in four other studies for parental education and socioeconomic status (Harden, Turkheimer, & Loehlin, 2007; Kremen et al., 2005; Tucker-Drob, Rhemtulla, Harden, Turkheimer, & Fask, 2011; Turkheimer, Haley, Waldron, D'Onofrio, & Gottesman, 2003), although opposite results were found in a fifth study (Asbury, Wachs, & Plomin, 2005). A recent report took a longitudinal approach to examining the potential moderating effects of socioeconomic status on children's intelligence assessed eight times from ages 2 to 14 and found no evidence that socioeconomic status moderated heritability (Hanscombe et al., 2012). Life events were found to moderate heritability of cognitive ability in adults, with more life events reducing heritability (Vinkhuyzen, van der Sluis, & Posthuma, 2011). {{cite book}}: Unknown parameter |coauthors= ignored (|author= suggested) (help); Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Plomin, Robert; DeFries, John C.; Knopik, Valerie S. (24 September 2012). Behavioral Genetics. Shaun Purcell (Appendix: Statistical Methods in Behavioral Genetics). Worth Publishers. p. 125. ISBN 978-1-4292-4215-8. Retrieved 4 September 2013. A recent report examined all published studies of candidate gene-by-environment interactions—103 studies published from 2000 to 2009—and found that 96 percent of novel reports were significant, while only 27 percent of replication attempts were significant (Duncan & Keller, 2011). In addition, there appeared to be a publication bias among replication attempts; power analyses suggested that most candidate gene-by-environment interaction studies are underpowered. This report and those described above by Munafo and colleagues (2009) and Risch and colleagues (2009) highlight the critical role that replication has in helping to clarify how genes and environments work together. {{cite book}}: Unknown parameter |coauthors= ignored (|author= suggested) (help); Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Plomin, Robert; DeFries, John C.; Knopik, Valerie S. (24 September 2012). Behavioral Genetics. Shaun Purcell (Appendix: Statistical Methods in Behavioral Genetics). Worth Publishers. p. 156. ISBN 978-1-4292-4215-8. Retrieved 4 September 2013. Levels of analysis lower than behavior itself are sometimes called endophenotypes, where endo means 'inside.' The term intermediate phenotype has also been used as a synonym for endophenotype. It has been suggested that these lower levels of analysis, such as the brain level, might be more amenable to genetic analysis than behavior (Bearden & Freimer, 2006; Gottesman & Gould, 2003). {{cite book}}: Unknown parameter |coauthors= ignored (|author= suggested) (help); Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Plomin, Robert; DeFries, John C.; Knopik, Valerie S. (24 September 2012). Behavioral Genetics. Shaun Purcell (Appendix: Statistical Methods in Behavioral Genetics). Worth Publishers. p. 163. ISBN 978-1-4292-4215-8. Retrieved 4 September 2013. However, the term mental retardation is now considered pejorative, {{cite book}}: Unknown parameter |coauthors= ignored (|author= suggested) (help); Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Plomin, Robert; DeFries, John C.; Knopik, Valerie S. (24 September 2012). Behavioral Genetics. Shaun Purcell (Appendix: Statistical Methods in Behavioral Genetics). Worth Publishers. p. 164. ISBN 978-1-4292-4215-8. Retrieved 4 September 2013. A similar result was found in the largest family study of mild cognitive disability, which considered 80,000 relatives of 289 mentally disabled individuals (Reed & Reed, 1965). This family study showed that mild mental disability is very strongly familial. If one parent is mildly disabled, the risk for cognitive disability in the children is about 20 percent. If both parents are mildly disabled, the risk is nearly 50 percent. {{cite book}}: Unknown parameter |coauthors= ignored (|author= suggested) (help); Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Plomin, Robert; DeFries, John C.; Knopik, Valerie S. (24 September 2012). Behavioral Genetics. Shaun Purcell (Appendix: Statistical Methods in Behavioral Genetics). Worth Publishers. p. 187. ISBN 978-1-4292-4215-8. Retrieved 4 September 2013. The phrase general cognitive ability is a better choice to describe g than the word intelligence because the latter has so many different meanings in psychology and in the general language (Jensen, 1998). General texts on g are available (Hunt, 2011; see Deary, 2012, for an overview of other books). {{cite book}}: Unknown parameter |coauthors= ignored (|author= suggested) (help); Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Plomin, Robert; DeFries, John C.; Knopik, Valerie S. (24 September 2012). Behavioral Genetics. Shaun Purcell (Appendix: Statistical Methods in Behavioral Genetics). Worth Publishers. p. 187. ISBN 978-1-4292-4215-8. Retrieved 4 September 2013. In addition, just as there is more to cognition than g, there is clearly much more to achievement than cognition. Personality, motivation, and creativity all play a part in how well someone does in life. {{cite book}}: Unknown parameter |coauthors= ignored (|author= suggested) (help); Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Plomin, Robert; DeFries, John C.; Knopik, Valerie S. (24 September 2012). Behavioral Genetics. Shaun Purcell (Appendix: Statistical Methods in Behavioral Genetics). Worth Publishers. p. 190. ISBN 978-1-4292-4215-8. Retrieved 4 September 2013. A clear genotype-environment interaction emerged for the enriched and restricted environments. The enriched condition had no effect on the maze-bright rats, but it greatly improved the performance of the maze-dull rats. On the other hand, the restricted environment was very detrimental to the maze-bright rats but had little effect on the maze-dull ones. {{cite book}}: Unknown parameter |coauthors= ignored (|author= suggested) (help); Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Plomin, Robert; DeFries, John C.; Knopik, Valerie S. (24 September 2012). Behavioral Genetics. Shaun Purcell (Appendix: Statistical Methods in Behavioral Genetics). Worth Publishers. p. 193. ISBN 978-1-4292-4215-8. Retrieved 4 September 2013. After his death in 1973, Burt's work was attacked, with allegations that some of his data were fraudulent (Hearnshaw, 1979). Two subsequent books reopened the case (Fletcher, 1990; Joynson, 1989). Although the jury is still out on some of the charges (Mackintosh, 1995; Rushton, 2002), it appears that at least some of Burt's data are dubious. {{cite book}}: Unknown parameter |coauthors= ignored (|author= suggested) (help); Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Plomin, Robert; DeFries, John C.; Knopik, Valerie S. (24 September 2012). Behavioral Genetics. Shaun Purcell (Appendix: Statistical Methods in Behavioral Genetics). Worth Publishers. pp. 193–194. ISBN 978-1-4292-4215-8. Retrieved 4 September 2013. Then, in 1969, a monograph on the genetics of intelligence by Arthur Jensen almost brought the field to a halt because a few pages in this lengthy monograph suggested that ethnic differences in IQ might involve genetic differences. Twenty-five years later, this issue was resurrected in The Bell Curve (Herrnstein & Murray, 1994) and caused a similar uproar. As we emphasized in Chapter 7, the causes of average differences between groups need not be related to the causes of individual differences within groups. The former question is much more difficult to investigate than the latter, which is the focus of the vast majority of genetic research on IQ. {{cite book}}: Unknown parameter |coauthors= ignored (|author= suggested) (help); Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Plomin, Robert; DeFries, John C.; Knopik, Valerie S. (24 September 2012). Behavioral Genetics. Shaun Purcell (Appendix: Statistical Methods in Behavioral Genetics). Worth Publishers. p. 194. ISBN 978-1-4292-4215-8. Retrieved 4 September 2013. For g, the correlation between adopted children and their genetic parents is 0.24. Because first-degree relatives are only 50 percent similar genetically, doubling these correlations gives a rough estimate of heritability of 48 perecent. As discussed in Chapter 7, this outcome means that about half of the variance in IQ scores in the populations sampled in these studies can be accounted for by genetic differences among individuals. {{cite book}}: Unknown parameter |coauthors= ignored (|author= suggested) (help); Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Plomin, Robert; DeFries, John C.; Knopik, Valerie S. (24 September 2012). Behavioral Genetics. Shaun Purcell (Appendix: Statistical Methods in Behavioral Genetics). Worth Publishers. pp. 194–195. ISBN 978-1-4292-4215-8. Retrieved 4 September 2013. The twin method supports this conclusion. Identical twins are nearly as similar as the same person tested twice. (Test-retest correlations for g are generally between 0.80 and 0.90.) The average twin correlations are 0.86 for identical twins and 0.60 for fraternal twins. Doubling the difference between MZ and DZ correlations estimates heritability as 52 percent. {{cite book}}: Unknown parameter |coauthors= ignored (|author= suggested) (help); Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Plomin, Robert; DeFries, John C.; Knopik, Valerie S. (24 September 2012). Behavioral Genetics. Shaun Purcell (Appendix: Statistical Methods in Behavioral Genetics). Worth Publishers. pp. 195–196. ISBN 978-1-4292-4215-8. Retrieved 4 September 2013. Model-fitting analyses that simultaneously analyze all the family, adoption, and twin data summarized in Figure 12.6 yield heritability estimates of about 50 percent (Chipuer, Rovine, & Plomin, 1990; Loehlin, 1989). {{cite book}}: Unknown parameter |coauthors= ignored (|author= suggested) (help); Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Plomin, Robert; DeFries, John C.; Knopik, Valerie S. (24 September 2012). Behavioral Genetics. Shaun Purcell (Appendix: Statistical Methods in Behavioral Genetics). Worth Publishers. p. 196. ISBN 978-1-4292-4215-8. Retrieved 4 September 2013. What about high g? In Chapter 11, we saw that most cognitive disability appears to be the low end of the same genetic and environmental factors that affect individual differences throughout the g distribution. The same story appears to apply to high g, as indicated by the first large-scale twin study of high g (Haworth, Wright, et al., 2009). {{cite book}}: Unknown parameter |coauthors= ignored (|author= suggested) (help); Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Plomin, Robert; DeFries, John C.; Knopik, Valerie S. (24 September 2012). Behavioral Genetics. Shaun Purcell (Appendix: Statistical Methods in Behavioral Genetics). Worth Publishers. pp. 196–197. ISBN 978-1-4292-4215-8. Retrieved 4 September 2013. Direct estimates of the importance of shared environmental influence come from correlations for adoptive parents and children and for adoptive siblings. Particularly impressive is the correlation of 0.32 for adoptive siblings (see Figure 12.6). Because they are unrelated genetically, what makes adoptive siblings similar is shared rearing—having the same parents, the same diet, attending the same schools, and so on. The adoptive sibling correlation of 0.32 suggests that about a third of the total variance can be explained by shared environmental influences. The correlation for adoptive parents and their adopted children is lower (r = 0.19) than that for adoptive siblings, a result suggesting that shared environment accounts for less resemblance between parents and offspring than between siblings. {{cite book}}: Unknown parameter |coauthors= ignored (|author= suggested) (help); Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Plomin, Robert; DeFries, John C.; Knopik, Valerie S. (24 September 2012). Behavioral Genetics. Shaun Purcell (Appendix: Statistical Methods in Behavioral Genetics). Worth Publishers. p. 197. ISBN 978-1-4292-4215-8. Retrieved 4 September 2013. Twin studies also suggest shared environmental influence. In addition, shared environmental effects appear to contribute more to the resemblance of twins than to that of non-twin siblings because the correlation of 0.60 for DZ twins exceeds the correlation of 0.47 for nontwin siblings. Twins may be more similar than other siblings because they shared the same womb and are exactly the same age. Because they are the same age, twins also tend to be in the same school, even if not the same class, and share many of the same peers (Koeppen-Schomerus, et al., 2003). {{cite book}}: Unknown parameter |coauthors= ignored (|author= suggested) (help); Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Plomin, Robert; DeFries, John C.; Knopik, Valerie S. (24 September 2012). Behavioral Genetics. Shaun Purcell (Appendix: Statistical Methods in Behavioral Genetics). Worth Publishers. p. 197. ISBN 978-1-4292-4215-8. Retrieved 4 September 2013. Model-fitting estimates of the role of shared environment for g based on the data in Figure 12.6 are about 20 percent for parents and offspring, about 25 percent for siblings, and about 40 percent for twins (Chipuer et al., 1990). The rest of the environmental variance is attributed to nonshared environment and errors of measurement. However, when these data are examined developmentally, a different picture emerges, as discussed later in this chapter. {{cite book}}: Unknown parameter |coauthors= ignored (|author= suggested) (help); Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Plomin, Robert; DeFries, John C.; Knopik, Valerie S. (24 September 2012). Behavioral Genetics. Shaun Purcell (Appendix: Statistical Methods in Behavioral Genetics). Worth Publishers. p. 197. ISBN 978-1-4292-4215-8. Retrieved 4 September 2013. For example, although there is some positive assortative mating for physical characteristics, the correlations between spouses are relatively low—about 0.25 for height and about 0.20 for weight (Spuhler, 1968). Spousal correlations for personality are even lower, in the 0.10 to 0.20 range (Vandenberg, 1972). Assortative mating for g is substantial, with average spousal correlations of about 0.40 (Jensen, 1978). In part, spouses select each other for g on the basis of education. Spouses correlate about 0.60 for education, which correlates about 0.60 with g. {{cite book}}: Unknown parameter |coauthors= ignored (|author= suggested) (help); Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Plomin, Robert; DeFries, John C.; Knopik, Valerie S. (24 September 2012). Behavioral Genetics. Shaun Purcell (Appendix: Statistical Methods in Behavioral Genetics). Worth Publishers. p. 198. ISBN 978-1-4292-4215-8. Retrieved 4 September 2013. Assortative mating is also important because it affects estimates of heritability. For example, it increases correlations for first-degree relatives. {{cite book}}: Unknown parameter |coauthors= ignored (|author= suggested) (help); Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Plomin, Robert; DeFries, John C.; Knopik, Valerie S. (24 September 2012). Behavioral Genetics. Shaun Purcell (Appendix: Statistical Methods in Behavioral Genetics). Worth Publishers. p. 200. ISBN 978-1-4292-4215-8. Retrieved 4 September 2013. Despite the complications caused by assortative mating and nonadditive genetic variance, the general summary of behavioral genetic results for g is surprisingly simple (Figure 12.7). About half of the variance is due to genetic factors. Some, but not much, of this genetic variance might be nonadditive. Of the half of the variance that is due to nongenetic factors, about half of that is accounted for by shared environmental factors. The other half is due to nonshared environment and errors of measurement. However, during the past decade, it has been discovered that these average results are largely based on children; results change dramatically during development, as described in the following section. {{cite book}}: Unknown parameter |coauthors= ignored (|author= suggested) (help); Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Pollack, Robert (13 August 2013). "Chapter 2: Natural Selection, the Human Genome, and the Idea of Race". In Krimsky, Sheldon; Sloan, Kathleen (eds.). Race and the Genetic Revolution: Science, Myth, and Culture. Columbia University Press. pp. 41–42. ISBN 978-0-231-52769-9. Retrieved 31 August 2013. As a social construct, 'race' may appear as a useful idea when it is couched in contexts of service, in particular when it is used as a way to identify an American population that might specifically benefit from a medical or other social intervention. But that apparent utility will always be reduced to close to nil by the complexity of our history and the diversity of the human genome. We have seen a drug marketed to 'African American' Americans on the argument that the drug requires the presence of a single DNA sequence found in many Africans and not many Europeans (see chapters 7 and 8). We know already that this allocation scheme has assured that the drug has been wasted on some number of Americans with both European and African ancestors who are identified as 'African American' but who did not inherit that version of that DNA, while a number of 'white' Americans of similar parentage who did inherit it and therefore could benefit from the drug do not get to use it. (citing Charles Darwin, 'On the Races of Man,' in The Descent of Man (New York: Appleton, 1880), 172.) {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Simonton, Dean Keith (2003). "Chapter 28: When Does Giftedness Become Genius? And When Not?". In Colangelo, Nicholas; Davis, Gary A. (eds.). Handbook of Gifted Education. Julian C. Stanley (Guest Foreword). Boston: Allyn & Bacon. p. 363. ISBN 978-0-205-34063-7. Genius is not just born; it is also made—by the environment in which the talented youth emerges. Genetic endowment merely offers the raw materials on which the events and circumstances of childhood and adolescence must operate. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Sternberg, Robert J.; Grigorenko, Elena L.; Kidd, Kenneth K.; Stemler, Steven E. (13 August 2013). "Chapter 10: Intelligence, Race, and Genetics". In Krimsky, Sheldon; Sloan, Kathleen (eds.). Race and the Genetic Revolution: Science, Myth, and Culture. Columbia University Press. p. 211. ISBN 978-0-231-52769-9. Retrieved 31 August 2013. The more geographically distant individuals are from each other, the fewer genes they seem to have in common, on average. Third, the data presented by Templer and Arikawa and by Lynn showing correlations between 'national skin color' and 'national average IQ' suffer from many conceptual and methodological problems that have been addressed in detail by others in the literature. One of the more blatant problems with these data is that the samples used are not random selections from the population, but rather samples of convenience. (citing D.I. Templer and H. Arikawa, 'Temperature, Skin Color, Per Capita Income, and IQ: An International Perspective,' Intelligence 34 (2006): 21-139.) (citing Lynn, The Global Bell Curve.) (citing N. J. Mackintosh, Book review of Race Differences in Intelligence: An Evolutionary Hypothesis, Intelligence 35 (2007): 94-96; R. J. Sternberg and E. Hunt, 'Sorry, Wrong Numbers: An Analysis of a Study of a Correlation Between Skin Color and IQ,' Intelligence 34 (2006): 131-37.) {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Sternberg, Robert J.; Grigorenko, Elena L.; Kidd, Kenneth K.; Stemler, Steven E. (13 August 2013). "Chapter 10: Intelligence, Race, and Genetics". In Krimsky, Sheldon; Sloan, Kathleen (eds.). Race and the Genetic Revolution: Science, Myth, and Culture. Columbia University Press. p. 213. ISBN 978-0-231-52769-9. Retrieved 31 August 2013. It is worth noting that in February 2001 the editors of the medical journal Archives of Pediatrics and Adolescent Medicine asked authors no longer to use race as an explanatory variable and not to use obsolescent terms. Some other high-impact peer-reviewed medical journals, such as the New England Journal of Medicine and the American Journal of Public Health, have made similar appeals. (citing F.P. Rivara and L. Finberg, 'Use of the Terms Race and Ethnicity,' Archives of Pediatrics & Adolescent Medicine 155 (2001):119.) (citing see file) {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Sternberg, Robert J.; Grigorenko, Elena L.; Kidd, Kenneth K.; Stemler, Steven E. (13 August 2013). "Chapter 10: Intelligence, Race, and Genetics". In Krimsky, Sheldon; Sloan, Kathleen (eds.). Race and the Genetic Revolution: Science, Myth, and Culture. Columbia University Press. pp. 217–218. ISBN 978-0-231-52769-9. Retrieved 31 August 2013. Heritability (also referred to as h²) is the ratio of genetic variation to total variation in an attribute within a population. Thus, the coefficient of heritability tells us nothing about sources of between-population variation. Moreover, the coefficient of heritability does not tell us the proportion of a trait that is genetic in absolute terms, but rather, the proportion of variation in a trait that is attributable to genetic variation within a specific population. [para] Trait variation in a population is referred to as phenotypic variation, whereas genetic variation in a population is referred to as genotypic variation. Thus, heritability is a ratio of genotypic variation to phenotypic variation. Heritability has a complementary concept, that of environmentality. Environmentality is a ratio of environmental variation to phenotypical variation. Note that both heritability and environmentality apply to populations, not to individuals. There is no way of estimating heritability for an individual, nor is the concept meaningful for individuals. Consider a trait that has a heritability statistic equaling 70 percent; it is nonsense to say that the development of the trait in an individual is 70 percent genetic. [para] Heritability is typically expressed on a 0 to 1 scale, with a value of 0 indicating no heritability whatsoever (i.e., no genetic variation in the trait) and a value of 1 indicating complete heritability (i.e., only genetic variation in the trait). Heritability and environmentality add to unity (assuming that the error variance related to measurement of the trait is blended into the environmental component). Heritability tells us the proportion of individual-difference (variation) within a population. Thus, if IQ has a heritability of .50 within a certain population, then 50 percent of the variation in scores on the attribute within that population is due (in theory) to genetic influences. This statement is completely different from the statement that 50 percent of the attribute is inherited. [para] An important implication of these facts is that heritability is not tantamount to genetic influence. An attribute could be highly genetically influenced and have little or no heritability. The reason is that heritability depends on the existence of individual differences. If there are no individual differences, there is no heritability (because there is a 0 in the denominator of the ratio of genetic to total trait variation in a given population). For example, being born with two eyes is 100 percent under genetic control (except in the exceedingly rare case of severe dismorphologies, with which we will not deal here). Regardless of the environment into which one is born, a human being will have two eyes. But it is not meaningful to speak of the heritability of having two eyes, because there are no individual differences. Heritability is not 1: it is meaningless (because there is a 0 in the denominator of the ratio) and cannot be sensibly calculated. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Sternberg, Robert J.; Grigorenko, Elena L.; Kidd, Kenneth K.; Stemler, Steven E. (13 August 2013). "Chapter 10: Intelligence, Race, and Genetics". In Krimsky, Sheldon; Sloan, Kathleen (eds.). Race and the Genetic Revolution: Science, Myth, and Culture. Columbia University Press. p. 218. ISBN 978-0-231-52769-9. Retrieved 31 August 2013. Heritability is not a fixed value for a given attribute. Although we may read about 'the heritability of IQ,' there really is no single fixed value that represents any true, constant value for the heritability of IQ or anything else, as Herrnstein and Murray and most others in the field recognize. Heritability depends on many factors, but the most important one is the range of environments. Because heritability represents a proportion of variation, its value will depend on the amount of variation. As Herrnstein pointed out, if there were no variation in environments, heritability would be perfect, because there would be no other source of variation. If there is wide variation in environments, however, heritability is likely to decrease. (citing See Herrnstein and Murray, The Bell Curve.) (citing T.J. Bouchard, Jr., 'IQ Similarity in Twins Reared Apart: Findings and Responses to critics,' in Intelligence, Heredity, and Environment, ed. R.J. Sternberg and E.L. Grigorenko (New York: Cambridge University Press, 1997), 126-160.) (citing R.J. Herrnstein, IQ in the Meritocracy (Boston: Alantic Monthly Press, 1973).) {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Sternberg, Robert J.; Grigorenko, Elena L.; Kidd, Kenneth K.; Stemler, Steven E. (13 August 2013). "Chapter 10: Intelligence, Race, and Genetics". In Krimsky, Sheldon; Sloan, Kathleen (eds.). Race and the Genetic Revolution: Science, Myth, and Culture. Columbia University Press. p. 219. ISBN 978-0-231-52769-9. Retrieved 31 August 2013. Because the value of the heritability statistic is relevant only to existing circumstances, it does not and cannot address a trait's modifiability. A trait could have zero, moderate, or even total heritability and, in any of these conditions, be not at all, partially, or fully modifiable. The heritability statistic deals with correlations, whereas modifiability deals with mean effects. Correlations, however, are independent of score levels. For example, adding a constant to a set of scores will not affect the correlation of that set with another set of scores. Consider height as an example of the limitation of the heritability statistic in addressing modifiability. Height is highly heritable, with a heritability of over .90. Yet height also is highly modifiable, as shown by the fact that average heights have risen dramatically throughout the past several generations. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Sternberg, Robert J.; Grigorenko, Elena L.; Kidd, Kenneth K.; Stemler, Steven E. (13 August 2013). "Chapter 10: Intelligence, Race, and Genetics". In Krimsky, Sheldon; Sloan, Kathleen (eds.). Race and the Genetic Revolution: Science, Myth, and Culture. Columbia University Press. p. 226. ISBN 978-0-231-52769-9. Retrieved 31 August 2013. The important message here is that the division lines between racial and ethnic groups 'are highly fluid and that most genetic variation exists within all social groups—not between them'. Studies based on hundreds of genetic polymorphisms confirm earlier studies such as that by Lewontin cited above and show that only 11 to 23 percent of observed genetic variation is due to differences among populations and that is mostly attributable to differences in allele frequencies, not all-or-nothing genetic differences. In fact, most common genetic variants exist in almost all populations. The overwhelming majority of the variation occurs among individuals with different genotypes within each population. (citing M. W. Foster and R. R. Sharp, 'Race, Ethnicity, and Genomics: Social Classifications as Proxies of Biological Heterogeneity,' Genome Research 12 (2002): 844-50, at 848.) (citing Lewontin, 'Annotation'; Lewontin, Human Diversity.) (citing Reviewed in Tishkoff and Kidd, 'Biogeography of Human Populations.') {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Tattersall, Ian; DeSalle, Rob (1 September 2011). Race?: Debunking a Scientific Myth. Texas A&M University Press. p. xi. ISBN 978-1-60344-425-5. Retrieved 17 November 2013. Races are very evidently not categories, on a par with chairs or trees. What's more, it is becoming ever clearer that those differences among human populations that we intuitively view as racial are not only superficial in terms of import but also of astonishingly recent origin. Among biologists, physical anthropologists are more aware of this fact than most, as indeed they are of the appalling historical record. Painfully mindful of the horrors to which members of their own profession have contributed in the not so distant past, many contemporary physical anthropologists thus prefer to point to the impossibility of classification and to deny that races exist at all. Still, while we will see that there is ample justification for this conclusion, it is hardly a helpful one when anybody walking down the street in a major city can see that 'race' does indeed exist in some intuitive sense. The human variety we see around us isn't random, and its order must, at some level, have a biological dimension, since those features that catch the eye are, after all, heritable. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Tattersall, Ian; DeSalle, Rob (1 September 2011). Race?: Debunking a Scientific Myth. Texas A&M University Press. p. xiii. ISBN 978-1-60344-425-5. Retrieved 17 November 2013. In another irony, random innovations may in fact be more accurately descriptive of particular geographical populations ('races') than adaptive ones are, since fewer forces favor them and they are thus less likely to be independently acquired. To illustrate this we might cite skin color—which is one of the few varying human traits that we can truly describe as adaptive, since dark pigmentation is vitally protective in the tropics, yet may in some respects be a liability at high latitudes. And in this case, adaptation turns out to be a very poor guide indeed to history. Bantus, native Australians, and Tamils and all typically very dark-skinned but have entirely different geographical and temporal histories—as do light-skinned Europeans and northern Asians. The result is that, whatever those entities may actually be that we habitually describe in the United States as 'blacks' and 'whites,' they lie in the realm of sociocultural constructs rather than in that of biologically coherent groups. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Tattersall, Ian; DeSalle, Rob (1 September 2011). Race?: Debunking a Scientific Myth. Texas A&M University Press. p. xii. ISBN 978-1-60344-425-5. Retrieved 17 November 2013. Through divergence, distinctive 'racial' features are acquired in far-flung and isolated populations. The alternative process is reintegration, which occurs when differentiated populations that are nonetheless reproductively compatible (i.e., are members of the same species) come back into contact and interbreed. When this happens the boundaries between differentiated local populations become blurred and, if reintegration is given enough time, ultimately disappear. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Tattersall, Ian; DeSalle, Rob (1 September 2011). Race?: Debunking a Scientific Myth. Texas A&M University Press. p. xiv. ISBN 978-1-60344-425-5. Retrieved 17 November 2013. To members of an egotistic species, 'human biology' is inevitably one of the more fascinating areas of science, and this is why we will explore it in later pages. But the issues of 'race' goes far beyond strict biology, to encompass many of the more murky and unfathomable aspects of the human psyche. The problem in Darfur, as elsewhere, is at heart one of clashing historical, cultural, political, economic, and social identities—and not one of biology at all. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Tattersall, Ian; DeSalle, Rob (1 September 2011). Race?: Debunking a Scientific Myth. Texas A&M University Press. p. xv. ISBN 978-1-60344-425-5. Retrieved 17 November 2013. At some point (we're not there yet) we will have enough information to see how this history of spread maps on to the history of physical differentiation in Homo sapiens. Yet population studies already also bear eloquent witness to the extraordinary levels of population intermingling that have occurred in subsequent phases of this eventful history. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Tattersall, Ian; DeSalle, Rob (1 September 2011). Race?: Debunking a Scientific Myth. Texas A&M University Press. p. xv. ISBN 978-1-60344-425-5. Retrieved 17 November 2013. New techniques and new approaches can and will tell us an enormous amount about the biological history of our species, but they also teach us that this history was a very complex one that is very inaccurately—indeed, distortingly—summed up by any attempt to classify human variety on the basis of discrete races. While we can acknowledge that our ideas of race do in some sense reflect a historical reality, and that human variety does indeed have biological underpinnings, it is important to realize that those biological foundations are both transitory and epiphenomenal. Despite cultural barriers that uniquely help slow the process down in our species, the reintegration of Homo sapiens is proceeding apace. And this places the notion of 'races' as anything other than sociocultural constructs ever more at odds with reality. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Tattersall, Ian; DeSalle, Rob (1 September 2011). Race?: Debunking a Scientific Myth. Texas A&M University Press. p. 1. ISBN 978-1-60344-425-5. Retrieved 17 November 2013. Sadly, though, with a few notable exceptions, scientists over the years have done little to counter the notion that race in the larger sense and biology are inextricably entangled, and some have actually fanned the flames. As a result, there is still a huge amount of misunderstanding about what 'race' really is in biological terms, and its intrinsic importance and functional significance to larger society have consequently been enormously overestimated. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Tattersall, Ian; DeSalle, Rob (1 September 2011). Race?: Debunking a Scientific Myth. Texas A&M University Press. p. 2. ISBN 978-1-60344-425-5. Retrieved 17 November 2013. Human populations undeniably differ in various visible characteristics. Biology has shown such variation to be typically trivial (in biological terms, selectively neutral), and always epiphenomenal; yet it is equally true that hideous historical excesses have been committed in the name of the differences perceived. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Tattersall, Ian; DeSalle, Rob (1 September 2011). Race?: Debunking a Scientific Myth. Texas A&M University Press. p. 17. ISBN 978-1-60344-425-5. Retrieved 17 November 2013. Yet while he was prepared to give names to these 'principal' varieties of humankind, Blumenbach also perceived numerous 'insensible transitions' among them. Physical boundaries between adjacent populations were not sharp, and to Blumenbach this was further evidence that all known humans belonged to a single species. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Tattersall, Ian; DeSalle, Rob (1 September 2011). Race?: Debunking a Scientific Myth. Texas A&M University Press. p. 20. ISBN 978-1-60344-425-5. Retrieved 17 November 2013. Morton himself had been wary of polygenist ideas, because he had difficulty in reconciling them with the evident monogenism of Genesis. His professed disciples George Gliddon and Josiah Nott had no such scruples. In 1854 they published Types of Mankind, in which they made a specific argument for the separate creation of the races and for the inferiority of blacks to whites. This provided a tailor-made argument in defense of the southern proponents of slavery, a practice that was otherwise morally indefensible in a country of which the founding document asserted that 'all men are created equal.' {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Tattersall, Ian; DeSalle, Rob (1 September 2011). Race?: Debunking a Scientific Myth. Texas A&M University Press. p. 29. ISBN 978-1-60344-425-5. Retrieved 17 November 2013. Both Haeckel and Virchow were first-rate scientific minds, and each made fundamental contributions to the progress of nineteenth-century empirical biology, Haeckel in the field of embryology and Virchow to an even greater extent in cellular pathology and the theory of disease. But when it came to race and evolution, each co-opted their science in the service of broader political, philosophical, and social agendas, and thereby distorted both. By the time they were through, science and politics had become inextricably intermixed, and for a long time there was no going back. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Tattersall, Ian; DeSalle, Rob (1 September 2011). Race?: Debunking a Scientific Myth. Texas A&M University Press. pp. 31–32. ISBN 978-1-60344-425-5. Retrieved 17 November 2013. Nowhere was anxiety about such perceived deterioration of the stock more widely shared than in the 'melting pot' of the United States, where peoples of hugely diverse geographical origins were mixing as never before and raising deep fears in the minds of some over the dilution of the founding 'white race.' A leading spokesman for worries of this kind was Madison Grant, author of The Passing of the Great Race, published in 1916. An energetic amalgam of racism, stereotyping, paranoia, and pseudoscience, The Passing sold like hotcakes. Delving for distinctions within distinctions, Grant divided the traditional 'Caucasian' race into three: Nordics, Alpines, and Mediterraneans. And he identified the Nordics (a cultural and biological group centered in Scandinavia but with ultimately Teutonic origins) as the engine of all civilized society. The Nordic race had acquired its admirable features in response to the bracing and challenging environmental conditions in which it had evolved, and Grant deplored its adulteration by admixture with lesser races, including Alpines and Mediterraneans. His solution? The use of eugenic (or rather, dysgenic) methods to eliminate 'undesirable' characteristics and 'worthless race types' from the population. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Tattersall, Ian; DeSalle, Rob (1 September 2011). Race?: Debunking a Scientific Myth. Texas A&M University Press. p. 39. ISBN 978-1-60344-425-5. Retrieved 17 November 2013. Washburn wanted a bigger picture, and one reason he wanted it was that, while a junior faculty member at Columbia University, he had fallen under the influence of the geneticist Theodosius Dobzhansky. In turn, Dobzhansky was one of the primary architects of the intellectual edifice known as the Evolutionary Synthesis and author of perhaps its most influential single document, Genetics and the Origin of Species, initially published in 1937. In this work Dobzhansky was much more interested in the distribution of human variables such as blood group frequencies than in the populations into which they were packaged, and in general he championed the view that a race was no more than a 'group of individuals which inhabit a certain territory and which is genetically different from other geographically limited groups.' {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Tattersall, Ian; DeSalle, Rob (1 September 2011). Race?: Debunking a Scientific Myth. Texas A&M University Press. p. 41. ISBN 978-1-60344-425-5. Retrieved 17 November 2013. Even Dobzhansky was by then prepared to pursue this logic. In his 1962 book Mankind Evolving, he made no bones about the fact that human races exist: 'Race differences are facts of nature.' The human species is 'polytypic'; it consists, like most successful and widespread mammal species, of several physically and genetically distinguishable local populations that you could call either 'subspecies' or 'races.' Biologically speaking, Dobzhansky said, there was no difference. Some of the between population differences were probably due to natural selection, others to genetic drift. Critically, though, since the races were fully interfertile, 'pure' races did not exist. Nothing here for anyone to take exception to, but it did bring things to something of an impasse, because Dobzhansky never quite finished his thought, something we will do later. Still, Dobzhansky made his key point crystal clear: 'nobody can discover the cultural capacities of human individuals, populations or races until they have been given...equality of opportunity to demonstrate those capacities.' This statement was both incontrovertible and strictly scientific, but it was also one that carried a strong social message. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Tattersall, Ian; DeSalle, Rob (1 September 2011). Race?: Debunking a Scientific Myth. Texas A&M University Press. p. 42. ISBN 978-1-60344-425-5. Retrieved 17 November 2013. Even such new wheezes as using blood-group frequencies in place of traditional morphological characters had only served to complicate things further. By its very nature, human variation across the globe defied classifications that recognized discrete boundaries at any level. And it thus defied any classification at all. Representative of a powerful trend toward the recognition of this reality in the post-synthesis United States was the genetic anthropologist Frank B. Livingstone, of the University of Michigan. Livingstone's hugely influential 1962 article On the Non-existence of Human Races urged not only an abandonment of the race concept but its replacement in physical anthropology by the study of the ways in which physical traits and their underlying genes were distributed in different environments around the world. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Tattersall, Ian; DeSalle, Rob (1 September 2011). Race?: Debunking a Scientific Myth. Texas A&M University Press. p. 42. ISBN 978-1-60344-425-5. Retrieved 17 November 2013. Still, Coon's connection to the blatantly racist Putnam did nothing to ease his relations with opponents when he published his volume The Origin of Races, also in 1962. Neither did its contents. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Tattersall, Ian; DeSalle, Rob (1 September 2011). Race?: Debunking a Scientific Myth. Texas A&M University Press. ISBN 978-1-60344-425-5. Retrieved 17 November 2013. Predictably enough, Dobzhansky, Montagu, Washburn, and many others vilified Coon as a racist. Whether or not he was a conscious or an unconscious racist was later disputed among those who knew him, though the historian John P. Jackson has recently argued cogently that, at the very least, Coon 'actively aided the segregationist cause in violation of his own standards for scientific objectivity.' {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Tattersall, Ian; DeSalle, Rob (1 September 2011). Race?: Debunking a Scientific Myth. Texas A&M University Press. p. 44. ISBN 978-1-60344-425-5. Retrieved 17 November 2013. Adopting Dobzhansky's idea that culture broadened the ecological niche of hominids to such an extent that only one kind of hominid could ever have existed at a time (albeit evidently in diverse geographical varieties), Loring Brace of the University of Michigan, one of Coon's leading critics, energetically preached the 'single-species hypothesis' from the mid-1960s on. In Brace's view, which closely followed Ernst Mayr's declaration of 1950, all known hominid fossils could be attributed to one single evolving lineage. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Tattersall, Ian; DeSalle, Rob (1 September 2011). Race?: Debunking a Scientific Myth. Texas A&M University Press. pp. 45–46. ISBN 978-1-60344-425-5. Retrieved 17 November 2013. VARIATION AND RACE No sooner had the brouhaha among physical anthropologists over Coon's ideas died down than Richard Lewontin, an evolutionary biologist then at the University of Chicago, entered the lists. In a very influential paper in 1972 he examined the frequencies of seventeen different genes, measured in populations from around the world. His analysis focused on how the genetic variation was apportioned among and within 'populations.' His estimate of within-population variation is usually cited as 85 percent, meaning that most of the variation in human genes could be explained by variation within populations. Because 'population' is one of those nebulous biological entities that can be defined hierarchically, he conducted his analyses at two different levels—first at the level of local populations within regions, and second at the level of regions. The former revealed that 8.5 percent of the variation could be attributed to among-population variation, and the latter approach revealed that 6.3 percent of the variation could be attributed to among-region variation. Later studies upped the percentage of among-region variation to 10 to 15 percent; but in the long run the pattern of more within-population variation than among-population variation has held firm. The notion of greater within- than between-population variation among humans was not actually new with Lewontin: the second UNESCO statement had actually made much the same point back in 1951. But Lewontin had quantified the matter, and his conclusion from the resulting figures was much more emphatic: 'our perception of relatively large differences between human races and subgroups, is ... a biased perception.' What's more, 'human racial classification is of no social value and is positively destructive of social and human relations. ...[N]o justification can be offered for its continuance.' [para] Lewontin's analysis was followed during the 1970s by several contributions that looked at other gene loci or other methods of analyzing the data. Most of these studies were in broad agreement with Lewontin, although in 1982 the mathematical geneticist Ranajit Chakraborty argued that average differences between populations and their classification on the basis of genetic markers were different questions, and that 'the classification of human ethnic or racial groups remains a viable, important feature in understanding the nature and mechanism of human evolution.' [para] The most concerted attack on Lewontin's position came much later, from the geneticist A. W. F. Edwards, who contended in 2003 that, as more genetic loci were considered, the higher became the probability that an individual could be 'correctly' classified into his or her population. He thus christened the Harvard biologist's position 'Lewontin's fallacy.' Actually, the plant geneticist Jeffry Mitton had made the same observation in 1970, without finding that Lewontin's conclusion was fallacious. And Lewontin himself not long ago pointed out that the 85 percent within-group genetic variability figure has remained remarkably stable as studies and genetic markers have multiplied, whether you define populations on linguistic or on physical grounds. What's more, with a hugely larger and more refined database to deal with, D. J. Witherspoon and colleagues concluded in 2007 that although, armed with enough genetic information, you could assign most individuals to 'their' population quite reliably, 'individuals are frequently more similar to members of other populations than to members of their own.' {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Urbina, Susana (2004). Essentials of Psychological Testing. John Wiley & Sons. p. 66. ISBN 978-0-471-41978-5. In spite of the significance of his discovery, Galton's conclusions about the phenomenon of regression were not quite accurate (see Cowles, 2001). This was partly a result of restrictions in the data he used in his analyses and partly due to his misinterpretation of the causes of correlations between variables. Given that the genetic bases of heredity were unclear at the time when Galton was working on these problems, his misinterpretation of regression is understandable. Nevertheless, the procedures he developed to portray the relationship between variables have proved to be extremely useful in assessing the amount of variance shared by variables. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Urbina, Susana (2004). Essentials of Psychological Testing. John Wiley & Sons. p. 103. ISBN 978-0-471-41978-5. A puzzling longitudinal trend in the opposite direction, known as the 'Flynn effect,' has been well documented in successive revisions of major intelligence tests (like the S-B and the Wechsler scales) that invariably involve the administration of both the old and new versions to a segment of the newer standardization sample, for comparative purposes. Data from revisions of various intelligence tests in the United States as well as in other countries—extensively analyzed by J.R. Flynn (1984, 1987)—show a pronounced, longterm upward trend in the level of performance required to obtain any given IQ score. The Flynn effect presumably reflects population gains over time in the kinds of cognitive performance that intelligence tests sample. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Worrell, Frank C. (21 August 2012). "Chapter 38: Gifted African Americans". In Callahan, Carolyn M.; Hertberg-Davis, Holly L. (eds.). Fundamentals of Gifted Education: Considering Multiple Perspectives. Routledge. p. 391–392. ISBN 978-1-136-94643-1. Despite criticisms of the acting White hypothesis, there is growing empirical evidence in support of this claim, particularly among males. Taylor and Graham (2007) found that African American girls at all grade levels reported admiring and respecting high achievers, whereas African American boys' admiration and respect for high achievers plummeted in the seventh grade. This finding is also reflected in the huge gender achievement gap in favor of African American females (Aud et al., 2010), a gap that widens across grades K–12 and is ultimately reflected in major gender discrepancies in post-secondary education settings. Indeed, there is a growing literature on the education crisis for African American males (e.g., Garibaldi, 2007). [Para] Ford, Grantham, & Whiting (2008) asked 166 gifted African American students (classified as gifted on the basis of either an IQ score of at least 127 or an achievement test percentile rank of at least 98) if they had heard of the phrases, 'acting White' and 'acting Black.' If they answered yes, they were asked to explain what the phrases meant. About 80 percent of the participants reported they had heard of these two phrases and described 'acting White' as completing one's homework, doing well in school, taking advanced classes, and preferring to study instead of hanging out with one's friends. 'Acting Black' involved underachieving, being uneducated, and pretending not to be smart. Thus, gifted African American students may have a difficult choice: Do they choose to be Black or to be good students; to fit in with their friends or to face accusations of betraying their ethnic heritage; or to belong to a group with whom they share an ethnic heritage or to belong to a group of high achievers that includes many individuals who are not like them and who may also not embrace them?

Yudell, Michael (13 August 2013). "1: A Short History of the Race Concept". In Krimsky, Sheldon; Sloan, Kathleen (eds.). Race and the Genetic Revolution: Science, Myth, and Culture. Columbia University Press. p. 13. ISBN 978-0-231-52769-9. Retrieved 31 August 2013. At the dawn of the twenty-first century, the idea of race—the belief that the peoples of the world can be organized into biologically distinctive groups, each with their own physical, social, and intellectual characteristics—is understood by most natural and social scientists to be an unsound concept. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Yudell, Michael (13 August 2013). "1: A Short History of the Race Concept". In Krimsky, Sheldon; Sloan, Kathleen (eds.). Race and the Genetic Revolution: Science, Myth, and Culture. Columbia University Press. p. 15. ISBN 978-0-231-52769-9. Retrieved 31 August 2013. Most scholars now accept the viewpoint that in the ancient world 'no concept truly equivalent to that of "race" can be detected in the thought of the Greeks, Romans, and early Christians.' (citing George M. Fredrickson, Racism: A Short History (Princeton, NJ: Princeton University Press, 2002), 17) {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Yudell, Michael (13 August 2013). "1: A Short History of the Race Concept". In Krimsky, Sheldon; Sloan, Kathleen (eds.). Race and the Genetic Revolution: Science, Myth, and Culture. Columbia University Press. pp. 13–30. ISBN 978-0-231-52769-9. Retrieved 31 August 2013. {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Yudell, Michael (13 August 2013). "1: A Short History of the Race Concept". In Krimsky, Sheldon; Sloan, Kathleen (eds.). Race and the Genetic Revolution: Science, Myth, and Culture. Columbia University Press. p. 23. ISBN 978-0-231-52769-9. Retrieved 31 August 2013. In 1972, the geneticist Richard Lewontin, who had been a student of Dobzhansky's at Columbia in the 1950s and was considered a leader in his field, published a study showing that human populations were even more genetically diverse than once thought. Lewontin, using molecular genetic techniques in gel electrophoresis he himself had pioneered in the mid-1960s, found that most genetic variation (85.4 percent) was 'contained within' racial groups or 'between populations within a race' (8.3 percent), whereas only 6.3 percent of 'human variation was accounted for by racial classification.' From these findings, Lewontin concluded that race had 'virtually no genetic . . . significance.' After all, if more genetic diversity occurred within so-called racial groups than between them, then what exactly would race be measuring if it were meant to organize populations by genetic difference? Lewontin concluded that the 'use of racial categories must take its justifications from some other source than biology. The remarkable feature of human evolution and history has been the very small degree of divergence between geographical populations as compared with the genetic variation among individuals.' (citing Richard Lewontin, 'The apportionment of Human Diversity,' Evolutionary Biology 6 (1972): 381-98; Richard Lewontin, Human Diversity (New York: W.H. Freeman, 1982).) {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)

Yudell, Michael (13 August 2013). "1: A Short History of the Race Concept". In Krimsky, Sheldon; Sloan, Kathleen (eds.). Race and the Genetic Revolution: Science, Myth, and Culture. Columbia University Press. p. 25. ISBN 978-0-231-52769-9. Retrieved 31 August 2013. On that day Venter and Collins emphasized that their work confirmed that human genetic diversity cannot be captured by the concept of race, and also showed that all humans have genome sequences that are 99.9 percent identical. At the White House celebration Venter said, 'the concept of race has no genetic or scientific basis.' A year later, Collins wrote, 'Those who wish to draw precise racial boundaries around certain groups will not be able to use science as a legitimate justification.' (citing Rick Weiss and Justin Gillis, 'Teams Finish Mapping Human DNA,' Washington Post, June 27, 2000) (citing F. S. Collins and M. K. Mansoura, 'The Human Genome Project: Revealing the Shared Inheritance of All Humankind,' Cancer 92 (2001): S221-S225.) {{cite book}}: Unknown parameter |lay-date= ignored (help); Unknown parameter |lay-url= ignored (help)