Race, genes, & intelligence, references

References

(1) Barreiro, Laval, Quach, et al., 2008. Natural selection has driven population differentiation in modern humans
(2) Beckman, 2006. The Race for Ancestral Genetics in Clinical Trials
(3) Campbell and Tishkoff, 2009. The Evolution of Human Genetic Review and Phenotypic Variation in Africa
(4) Gottfredson, 2010. The General Intelligence Factor
(5) Gottfredson, 2009. Logical fallacies used to dismiss the evidence on intelligence testing. (appendix here; for the summary and discussion refer here)
(6) Gottfredson, 2007. Flynn, Ceci, and Turkheimer on Race and Intelligence: Opening Moves
(7) Gottfredson, 2005. Suppressing intelligence research: Hurting those we intend to help
(8) Gottfredson, 2005. What if the Hereditarian Hypothesis is True?
(9) Gottfredson, 2004. Social Consequences of Group Differences in Cognitive Ability
(10) Gottfredson and Sakloske, 2009. Intelligence: Foundations and Issues in Assessment
(11) Hardimon, 2009. Wallis Simpson was Wrong
(12) Hunt and Carlson, 2007. Considerations Relating to the Study of Group Differences in Intelligence
(13) Laland, Odling-Smee, and Myles, 2010. How culture shaped the human genome: bringing genetics and the human sciences together
(14) Levin, 1997. Why Race Matters (For a review, refer here: http://mises.org/misesreview_detail.aspx?control=117)
(15) McGrew, 2009. CHC Theory and the human cognitive abilities project: Standing on the shoulders of the giants of psychometric intelligence research
(16) Meisenberg, 2003. IQ Population Genetics: It’s not as Simple as You Think
(17) Mountain, Risch, 2004. Assessing Genomic contributions to phenotypic differences among “racial” and “ethnic” groups
(18) Murray, 2005. The Inequality Taboo
(19) Deary, Penke, and Johnson, 2010. The neuroscience of human intelligence differences
(20) Pickrell, Coop, Novembre, et al., 2009. Signals of recent positive selection in a worldwide sample of human populations

“NRG–ERBB4 pathway Among the top selection candidates shown in Figure 1, we noticed that two—ERBB4 and NRG3—are, in fact, binding partners (Zhang et al. 1997). … Further inspection of genes in the NRG–ERBB4 pathway (Kanehisa et al. 2008) revealed a striking alignment of selection signals (Fig. 5A). ERBB4 shows extreme extreme iHS signals in all non-African populations (Fig. 5B,C), NRG3 shows extreme iHS signals in West Eurasian populations, and two other binding partners of ERBB4—NRG1 and NRG2—fall well into the 1% tail of iHS scores in East Asia (Fig. 5A). Further, ADAM17, the gene encoding the enzyme that converts NRG1 to its active form (Mei and Xiong 2008), falls in a region that contains some of the most extreme XP-EHH scores in East Asia (maximum value of XP-EHHinthe region of 4.2 at rs2709591, empirical P = 2 3 10 5). The NRG–ERBB4 signaling pathway is well-studied and known to be involved in the development of a number of tissues, including heart, neural, and mammary tissue (Gassmann et al. 1995; Tidcombe et al. 2003). Variants in genes in this pathway have been associated with risk of schizophrenia [comment:refer here] and various psychiatric phenotypes (Stefansson et al. 2002; Hall et al. 2006; Mei and Xiong 2008). We suggest that an unidentified phenotype affected by this pathway has experienced strong recent selection in non-African populations”

(21) Pritchard, Pickrell, and Coop, 2009. The Genetics of Human Adaptation Hard Sweeps, Soft Sweeps, and Polygenic Adaptation
(22) Race, Ethnicity, and Genetics Working Group, 2005. The Use of Racial, Ethnic, and Ancestral Categories in Human Genetics Research
(23) Rindermann, 2007. The Big G-Factor of National Cognitive Ability
(24) Schmitt and Quinn, 2009. Reduction in Measured Subgroup Mean Differences: What is possible?
(25) Shermer, 2009. A Noble Conception
(26) Sesardic, 2010. Nature, nurture, and politics
(27) Sesardic, 2000. Philosophy of Science That Ignores Science: Race, IQ and Heritability
(28) Soo-Jin Lee, et al., 2008. The ethics of characterizing difference: guiding principles on using racial categories in human genetics
(29) Sowell, 1995. Race and culture: a world view
(30) Templer and Arikawa, 2006. Temperature, skin color, per capita income, and IQ: An international perspective
(33) Singer, 2007. Should We Talk About Race and Intelligence?
(32) Lahn and Ebenstein, 2009. Let’s Celebrate Human Genetic Diversity
(33) Roth, Bevier, Bobko, et al., 2001. Ethnic Group Differences in Cognitive Ability in Employment and Educational Settings: A Meta-Analysis
(34) Rushton and Jenson, 2005. Thirty years of research on race differences in cognitive ability
(35) Gottfredson, 1987. The practical significance of black–white differences in intelligence
(36) Deary, Johnson, and Houlihan, 2009. Genetic foundations of human intelligence
(37) Gottfredson, 1994. From the ashes of affirmative action
(38) Gottfredson, 1994. The science and politics of race-norming
(39) Gottfredson — Interview by Howard Wainer and Daniel H. Robinson, 2009. Profiles in Research. Page 422.
(40) Mises Institute Media Podcast, 2009. Block Defends Against Charges of Racism and Sexism
(41) Rushton and Jensen, 2005. Wanted: More Race Realism, Less Moralistic Fallacy
(42) Li, Absher, Tang, et al., 2008. Worldwide Human Relationships Inferred from Genome-Wide Patterns of Variation.
(43) Zakharia, Analabha, Basu, et al., 2009. Characterizing the admixed African ancestry of African Americans
(44) Chiao and Blizinsky, 2009. Culture-gene coevolution of individualism-collectivism and the serotonin transporter gene
(45) Green, Krause, Briggs et. al., 2010. A Draft Sequence of the Neandertal Genome
(46) Gottfredson, L. S. (in press). Intelligence and social inequality: Why the biological link?
(47) Refer to Justice Ginsburg’s dissenting opinion in the Ricci v. DeSTEFANO case: http://www.law.cornell.edu/supct/html/07-1428.ZD.html And here for context.

That pretension, essential to the Court’s disposition, ignores substantial eviden of multiple flaws in the tests New Haven used. The Court similarly fails to acknowledge the better tests used in other cities, which have yielded less racially skewed outcomes.

By order of this Court, New Haven, a city in which African-Americans and Hispanics account for nearly 60 percent of the population, must today be served—as it was in the days of undisguised discrimination—by a fire department in which members of racial and ethnic minorities are rarely seen in command positions. In arriving at its order, the Court barely acknowledges the pathmarking decision in Griggs v. Duke Power Co., 401 U. S. 424 (1971) , which explained the centrality of the disparate-impact concept to effective enforcement of Title VII. The Court’s order and opinion, I anticipate, will not have staying power.

(The claim that the tests are flawed and the implication of disguises racism is obviously predicated on the conception that test disparities are a result of the tests itself.)

That pretension, essential to the Court’s disposition, ignores substantial evidence of multiple flaws in the tests New Haven used. The Court similarly fails to acknowledge the better tests used in other cities, which have yielded less racially skewed outcomes.

By order of this Court, New Haven, a city in which African-Americans and Hispanics account for nearly 60 percent of the population, must today be served—as it was in the days of undisguised discrimination—by a fire department in which members of racial and ethnic minorities are rarely seen in command positions. In arriving at its order, the Court barely acknowledges the pathmarking decision in Griggs v. Duke Power Co., 401 U. S. 424 (1971) , which explained the centrality of the disparate-impact concept to effective enforcement of Title VII. The Court’s order and opinion, I anticipate, will not have staying power.

(In principle,Title VII’s disparate-impact provision, concerns unintentional as well as deliberate discrimination, where discrimination is not defined as equal representation, but exclusion on the basis of an individual’s race alone, and for which average group differences are not directly relevant — consitutional justification would be needed to redefine disparate-impact in terms of equal group representation, per se.)

(48) Hawks, Wang, Cochran, Harpending, and Moyzis, 2007. Recent acceleration of human adaptive evolution
(49) Gläscher, Tranel, Paul, and Rudrauf, 2009. Lesion mapping of cognitive abilities
(50) Flynn, 2010. The spectacles through which I see the race and IQ debate
(51) Hirsh, 2010. Race, Genetics, and Scientific Integrity
(52) Roth, Huffcutt, Bobko, 2003. Ethnic Group Differences in Measures of Job Performance:
A New Meta-Analysis
(53) Stevens, 2007. Researching Race/Ethnicity and Educational Inequality in English Secondary Schools: A Critical Review of the Research Literature Between 1980 and 2005
(54) Rindermann, 2007. Relevance of education and intelligence at the national level
for the economic welfare of people
(55) Rindermann, 2007. The g-factor of international cognitive ability comparisons: The homogeneity of results in PISA, TIMSS, PIRLS and IQ-tests across nations
(56) Wicherts, Borsboom, Dolan, 2010. Why national IQs do not support evolutionary theories of intelligence
(57) Wicherts, Dolan, and van der Mass, 2010. A systematic literature review of the average IQ of sub-Saharan Africans
(58) Laland, Odling-Smee, and Myles, 2010. How culture shaped the human genome: bringing genetics and the human sciences together
(59) Enard, et al., 2002. Molecular evolution of FOXP2, a gene involved in speech and language
(60) Enard, et al., 2009. A humanized version of Foxp2 affects cortico-basal ganglia circuits in mice
(61) Nielsen, et al. 2007. Recent and ongoing selection in the human genome.
(62) Lopez, et al., 2009. Genetic variation and recent positive selection in worldwide human populations: evidence from nearly 1 million SNPs.

“We also used a maximum-likelihood method, implemented in the software frappe [24], to assign ancestry components to each individual without any prior assumptions about clustering of individuals into groups. In principle, the sharing of an inferred ancestry component among individuals could reflect recent admixture, ancient shared ancestry, or both. Although additional knowledge concerning the likely history of the populations involved can help distinguish among these possibilities, this is a descriptive rather than a statistical analysis. As the method does not allow inference of the most likely number of clusters (K), we ran the analysis multiple times and observed highly concordant results across multiple runs for values of K up to 6. For K = 7 and beyond different runs of the software gave different results, although continental subdivisions remained largely similar. We therefore present the results obtained with K = 6 (Fig. 4), which are largely concordant with the PC analysis and with previous such analyses of the HGDP-CEPH[13], [21]. The six clusters roughly correspond to the Americas, sub-Saharan Africa, North Africa/Europe/Middle East, Central/South Asia, East Asia, and Oceania.”

(63) Voight, et al., 2006. A map of recent positive selection in the human genome.
(64). Wang, et al., 2006. Global landscape of recent inferred Darwinian selection for Homo sapiens
(65) Way and Lieberman, 2010. Is there a genetic contribution to cultural differences? Collectivism, individualism and genetic markers of social sensitivity
(66) Harpending and Cochran, 2002. In our genes

“These selective forces must not be the same in all populations, because the 7R allele is quite common in some populations (South American Indians), exists at intermediate frequencies in others (Europeans and Africans), and is rare to nonexistent in yet others (East Asia, !Kung Bushmen).”

(67) Settle et al. 2010. Friendships Moderate an Association Between a Dopamine Gene Variant and Political Ideology

“We find that the number of friendships a person has in adolescence is significantly associated with liberal political ideology among thosewith DRD4-7R”

(68) Beaver et al., 2010. Three dopaminergic polymorphisms are associated with academic achievement in middle and high school

“The 7R allele was coded as the risk allele…DRD4 was related to grades in all four subjects at wave 1 and history and science atwave 2. The reasons why these genes had different effects on different subjects at different times are not immediately obvious. The differential genetic effects, however, likely are produced, in part, by the fact that performance in certain academic subjects depends on the use of specific regions of the brain.”

(69) Beckman, 2006. The Race for Ancestral Genetics in Clinical Trials; Race, Ethnicity, and Genetics Working Group, 2005. The Use of Racial, Ethnic, and Ancestral Categories in Human Genetics Research; Rotimi, 2005. Understanding and Using Human Genetic Variation Knowledge in the Design and Conduct of Biomedical Research. (Discusses the relevance of Race in biomedical research)

(70) For a defense of the concept of human subspecies refer to: Woodley, 2009. Is Homo sapiens polytypic? Human taxonomic diversity and its implications; Crow, 2002. Unequal by nature: a geneticist’s perspective on human differences; Mayr, 2002. The biology of race and the concept of equality; Sesardic, 2010. Race: a social destruction of a biological concept

(71) Cluster analysis shows that there are 5-7 main human populations. For example, look at figure 1 in Bastos-Rodriguez, Pinmenta, Penal, 2006. The Genetic Structure of Human Populations Studied Through Short Insertion-Deletion Polymorphisms

Given enough loci to analyze, individuals can be definitively assigned to populations or a set of them. Refer to: Edwards, A.W.F. (2003). Human genetic diversity: Lewontin’s fallacy

(72) Bailey and Geary, 2010. Hominid Brain Evolution: Testing Climatic, Ecological, and Social Competition Models

Abstract: Hypotheses regarding the selective pressures driving the threefold increase in the size of the hominid brain since Homo habilis include climatic conditions, ecological demands, and social competition. We provide a multivariate analysis that enables the simultaneous assessment of variables representing each of these potential selective forces. Data were collated for latitude, prevalence of harmful parasites, mean annual temperature, and variation in annual temperature for the location of 175 hominid crania dating from 1.9 million to 10 thousand years ago. We also included a proxy for population density and two indexes of paleoclimatic variability for the time at which each cranium was discovered. Results revealed independent contributions of population density, variation in paleoclimate, and temperature variation to the prediction of change in hominid cranial capacity (CC). Although the effects of paleoclimatic variability and temperature variation provide support for climatic hypotheses, the proxy for population density predicted more unique variance in CC than all other variables. The pattern suggests multiple pressures drove hominid brain evolution and that the core selective force was social competition.

(73) Odokuma, Igbigbi, Akpuaka, and Esigbenu, 2010. Craniometric patterns of three Nigerian ethnic groups

The findings in this study are similar to previous studies (Morton, 1839) where the mean cranial volume of the skulls of whites was 1,425 cm³, while that of the Blacks was 1,278 cm³. Based on the measurement of 144 skulls of Native Americans, Morton (1839) reported a figure of 1,344 cm³. Gould (1981) and Rushton (1995) have also showed very similar figures. Tribe had a significant effect on cranial volume at 0.05 levels of significance. Intercultural comparisons demonstrated significant variation as reported by Howells (1989), Froment (1992) and Lahr (1996). While the Ibo’s had an average cranial capacity of 1273.39 cm3, that of the Urhobo’s was 1255.89 cm3. The Edo’s was 1310.08 cm3. This may be attributable to a common ancestral origin of the Ibo and Urhobo people or inter marriages which are very common between these cultures with interchange of physical characteristics over the years since these people have been cordial neighbours. Also the cranial volume of male (1334.34 cm3) was significantly different from that of female (1204.54 cm3) in all the studied tribes, male being larger than that of female p< 0.05. This important characteristic which was also previously observed (Rushton, 1995) is very important in sex determination. Cranial volume has demonstrated strong sexual dimorphic patterns and thus individuals from the studied populations can be differentiated from those of other races…

The craniometric patterns of three indigenous Nigerian ethnic groups have been presented highlighting certain features common to Nigerians and perhaps indeed West African populations. It has also been shown that craniometric patterns are significant indices for inter ethnic differentiation of population groups. In spite of these observations, similarities which enabled intracultural differentiation did occur as exhibited by craniometric patterns in this study. Inevitably therefore, craniometric studies are most essential in the study of population dynamics especially with respect to quantitative variables. This study has further demonstrated the well established genealogy that the three studied populations may have evolved from a common ancestral origin.

(74) Rushton and Ankney, 2009. Whole Brain Size and General Mental Ability: A Review

(75) Templer, et al. 2002. Asian-Black differences in aptitude and difficulty of chosen academic discipline

(76) Ash and Gordon. Brain Size, Intelligence, and Paleoclimatic Variation. In Geher and Miller, 2008. Mating intelligence: sex, relationships, and the mind’s reproductive system/

(77) Rushton and Jensen, 2010. Race and IQ: A Theory-Based Review of the Research in Richard Nisbett’s

(78) As Neisser et al., 1996. Intelligence: Knowns and unknowns

Implications. Estimates of h* and c* for IQ (or any other trait) are descriptive statistics for the populations studied. (In this respect they are like means and standard deviations.) They are outcome measures, summarizing the results of a great many diverse, intricate, individually variable events and processes, but they can nevertheless be quite useful. They can tell us how much of the variation in a given trait the genes and family environments explain, and changes in them place some constraints on theories of how this occurs. On the other hand they have little to say about specific mechanisms, i.e., about how genetic and environmental differences get translated into individual physiological and psychological differences. Many psychologists and neuroscientists are actively studying such processes; data on heritabilities may give them ideas about what to look for and where or when to look for it. A common error is to assume that because something is heritable it is necessarily unchangeable. This is wrong. Heritability does not imply immutability. As previously noted, heritable traits can depend on learning, and they may be subject to other environmental effects as well. The value of h* can change if the distribution of environments (or genes) in the population is substantially altered. On the other hand, there can be effective environmental changes that do not change heritability at all. If the environment relevant to a given trait improves in a way that affects all members of the population equally, the mean value of the trait will rise without any change in its heritability (because the differences among individuals in the population will stay the same).

(79) Johnson, 2009. The global bell curve: Race, IQ, and inequality worldwide, Richard Lynn, Washington Summit

(80) Rindermann, Sailer, Thompson, 2009. The impact of smart fractions, cognitive ability of politicians and average competences of peoples on social development

Because average, upper and lower levels are correlated there are at first sight no large differences: The highest values for the smart fractions are found in East Asia …followed by Western and Eastern European and North American countries, by South European countries, Arab or Muslim and Latin American countries and finally by sub-Saharan countries.

(81) Jensen, A. R. (2006). Comments on correlations of IQ with skin color and geographic–demographic variables. Intelligence, 34, 128–131.

A large number of national and geographic population samples were used to test the hypothesis that the variation in mean values of skin color in the diverse populations are consistently correlated with the mean measured or estimated IQs of the various groups, as are some other physical variables, known as an ecological correlation. Straightforward statistical analyses clearly bear out the hypothesis, showing a significant positive ecological correlation between lightness of mean skin color and mean IQ across different populations. The main limitation of such a study design is that correlations obtained from this type of analysis are completely non-informative regarding any causal or functional connection between individual differences in skin pigmentation and individual differences in IQ, nor are they informative regarding the causal basis of the correlation, e.g., simple genetic association due to cross-assortative mating for skin color and IQ versus a pleiotropic correlation in which both of the phenotypically distinct but correlated traits are manifested by one and the same gene.

(82) Gelade, G. A. (2008). IQ, cultural values, and the technological achievement of
nations

(83) Rushton and Jensen, 2010. The rise and fall of the Flynn Effect as a reason to expect a narrowing of the Black–White IQ gap

(84) Jensen, 1998. The G-Factor

(85) Anonymous, 2008. Why Family Income Differences Don’t Explain the Racial Gap in SAT Scores. In: The Journal of Blacks in Higher Education; Winter 2008/2009.

(86) Cross, 1994. Black Africans are the most highly educated group in British society. In: The Journal of Blacks in Higher Education; Spring.

(87) Department of education and skills, 2005. Key stage 1 data.

(88) Leslie, 2005. Why people for the UK’s minority ethnic community achieve weaker degree results than whites.

(89) Burgard, 2002. Does race matter? Children’s Height in Brazil and South Africa.

(90) Rushton, 1998. Secular gains in IQ not related to the g factor and inbreeding depression Ð unlike Black±White differences: A reply to Flynn

(91) Rushton, 2003. Race differences in g and the “Jensen Effect.”

(92) Beals, et al., 1884. Brain Size, Cranial Morphology, Climate, and Time Machines

(93) Rowe and Cleveland, 1996. Academic achievement in Blacks and Whites: Are the developmental processes similar?

(94) Rowe, Vazsonyi, and Flannery, 1994. No more than skin deep: ethnic and racial similarity in developmental process.

(95) Waldman, Weinberg, Scarr, 1994. Racial-group differences in IQ in the Minnesota Transracial Adoption Study: A reply to Levin and Lynn

Second, whatever researchers’ beliefs are regarding the etiology of racial-group differences in IQ, terms such as hereditarianism and environmentalism do not do justice to what is likely to be the true state of affairs in nature regarding the etiology of racial-group differences in IQ. We think that it is exceedingly implausible that these differences are either entirely genetically based or entirely environmentally based. The true causes of racial-group differences in IQ, or in any
other characteristic, are likely to be too complex to be captured by locating them on a single hereditarianism-environmentalism dimension. Furthermore, such terms represent a qualitative shorthand for issues that are explicitly quantitative and should be expressed as such (Loehlin, 1992). We feel that terms such as hereditarianism and environmentalism blur important quantitative differences rather than increase their clarity.

(96) Levin, 1994. A Comment on the Minnesota transracial adoption study

(97) Meisenberg, 2010. The reproduction of intelligence

(98) Vining, 1982. On the possibility of the reemergence of a dysgenic trend with respect to intelligence in American fertility differentials

(99) Hartmann, Kruusea, and Nyborg. Testing the cross-racial generality of Spearman’s hypothesis in two samples

(100) Shockely, 1972. Dysgenics, Geneticity, Raceology: A Chalenge to the Intelectual Responsibility of Educators

(101) Johnson, W., te Nijenhuis, J., & Bouchard, T. J., Jr. (2008). Still just 1 g: Consistent
results from five test batteries. Intelligence, 36, 81-95.

(102) Chabris, 2007. Cognitive and neurobiological mechanisms of the law of general intelligence. In M. J. Roberts (Ed.), Integrating the mind: Domain general versus domain specific processes in higher cognition/

(103) Jung and Haier, 2007. The Parietal-Frontal Integration Theory (P-FIT) of
Intelligence: Converging neuroimaging evidence.

(105) McDaniel, 2007. Big-brained people are smarter: A meta-analysis of the relationship between in vivo brain volume and intelligence.

(106) Montgomery, 2010. Brain Evolution: Microcephaly Genes Weigh In.

(107) Hunt and Carlson, 2007. Research on group differences in intelligence is scientifically valid and socially important

(108) Posthuma et al., 2002. The association between brain volume and intelligence is of genetic origin

(109) Zhang et al., 2010. Meta-analysis of genetic variation in DTNBP1 and general cognitive ability

(110) Hardelid et. al., 2007 The Birth prevalence of PKU in populations of European, South Asian and Sub-Saharan African Ancestry living in South East England

(111) Eisenberg  et. al., 2010 World wide allele frequencies of the human apolipoprotein E gene: climate, local Adaptations, and evolutionary history

(112) Jones, 2008. IQ
in
the
Production
Function:

Evidence
from
Immigrant
Earnings

(113) Brown, et al., 2011. Evolutionary accounts of human behavioural diversity