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Note from Razib: I haven’t watched BSG since the first few episodes. Please be careful about your first few words in your comments as I have to moderate and will also see them on the right side under recent comments. I plan to watch the whole series on DVD over a weekend at some point in the future when I have time. Thanks.

(Republished from GNXP.com by permission of author or representative)
 
• Category: Science 
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S knows that P, iff:
1. P is true,
2. S believes that P is true, and
3. S is justified in believing that P.

Winner of the thread for the most clear exposition of what’s wrong with this and how to improve it.

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• Category: Science 
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• Category: Science • Tags: Academia, Larry Summers 
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Heiner Rindermann, Relevance of education and intelligence at the national level for the economic welfare of people, Intelligence, In Press

Cognitive abilities are important for the economic and non-economic success of individuals and societies. For international analyses, the collection of IQ-measures from Lynn and Vanhanen was supplemented and meliorated by data from international student assessment studies (IEA-Reading, TIMSS, PISA, PIRLS). The cognitive level of a nation is highly correlated with its educational level (r = .78, N = 173). In international comparisons, it also shows a high correlation with gross national product (GNP, r = .63, N = 185). However, in cross-sectional studies, the causal relationship between intelligence and national wealth is difficult to determine. In longitudinal analyses with various samples of nations, education and cognitive abilities appear to be more important as developmental factors for GNP than economic freedom. Education and intelligence are also more relevant to economic welfare than vice versa, but at the national level the influence of economic wealth on cognitive development is still substantial.

Combining IQ scores with a variety of other assessments of average cognitive ability at the national level has a lot to recommend it, and I’m glad others have caught on. The conclusions are quite interesting:

The results reported here show that during the last third of the 20th century, education and cognitive abilities were more important for economic wealth than economic wealth was for education and cognitive abilities. This result is stable across the different national samples of education and ability and remains after adding additional factors like economic freedom. Intelligence is even more important for wealth than economic freedom (see also Weede, 2006)! Whereas the importance of intelligence for many personal life outcomes has been recognized for some time (Gottfredson, 2003 and Herrnstein and Murray, 1994), we should realize that intelligence is also an important determinant for the economic and social development of nations (for example the functioning of institutions in the systems of law, economics and politics). The present study shows that a high level of cognitive development can be an antecedent and likely cause for economic growth, but other macro-social outcomes (e.g., democracy, rule of law, national power or health) are likely to be influenced by education and intelligence as well (Rindermann, submitted for publication and Rindermann, submitted for publication). Certainly the positive influence of young people’s schooling and intelligence on the level of economic freedom 30 years later (Fig. 4 and Fig. 5) deserves further investigation. Future theoretical and empirical research has to analyze the causal mechanism underlying the effects of ability on development of societies in a more detailed manner. For example, there is a positive relationship with low government spending ratio (r = .47 and rp = .24). Abilities seem to enable a more liberal economic constitution and thriftiness of state interventions. Conversely, a population with low education and intelligence seems to necessitate more state intervention, which tends to widen the influence of powerful special-interest groups.

So higher IQ populations tend to be more libertarian?

A re-colored version of Figure 1 — a world map — is below the fold.

(Republished from GNXP.com by permission of author or representative)
 
• Category: Science • Tags: General Intelligence 
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“Evolution and religion: In the beginning” from The Economist

One time could be an accident:

In the second camp are those, including some high up in the Vatican bureaucracy, who feel that Catholic scientists like Father Coyne have gone too far in accepting the world-view of their secular colleagues. This camp stresses that Darwinian science should not seduce people into believing that man evolved purely as the result of a process of random selection. While rejecting American-style intelligent design, some authoritative Catholic thinkers claim to see God’s hand in “convergence”: the apparent fact that, as they put it, similar processes and structures are present in organisms that have evolved separately.

Twice is a serious error:

But Benedict XVI apparently wants to lay down an even stronger line on the status of man as a species produced by divine ordinance, not just random selection. “Man is the only creature on earth that God willed for his own sake,” says a document issued under Pope John Paul II and approved by the then Cardinal Ratzinger.

Let’s be clear, “random selection” is not a short-hand for “random mutation and natural selection”. If anything, “random selection” is a description of neutral evolution.

Thus, as written, I have to join the camp that believes “that Darwinian science should not seduce people into believing that man evolved purely as the result of a process of random selection” and that “the status of man as a species [is] produced by … not just random selection”. Amen!

So WTF is wrong with the editorial staff at The Economist? They don’t seem to actually understand evolution. You can send them an email and explain it to them.

(Republished from GNXP.com by permission of author or representative)
 
• Category: Science • Tags: Creationism, Evolution, Molecular Evolution 
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You’ll find one professional’s answer below the fold. What’s missing is a discussion of genes as replicators.

from the SEP – Molecular genetics:

In official and public contexts, scientists appeal to the fundamental theory associated with molecular genetics to justify centering research on genes and DNA (e.g., see the websites of funding agencies such as National Center for Biotechnology Information). Genes are typically referred to as “the fundamental units” that are responsible for guiding all basic life processes. Usually a combination of causal and information metaphors are invoked to explain the role of genes. Genes are said to produce RNA and polypeptides, to provide instructions, or direct processes. But philosophical investigation has shown that these kind of sweeping claims cannot withstand careful scrutiny. Why, then, is so much research centered on genes and DNA? One answer to this question is that biologists are blinded by an ideology of genetic determinism. But Wagner’s defense of gene centrism suggests another answer, an answer that resonates with Keller’s explanation (2000) of why gene talk is useful.

It has been proposed that the real reason biologists center attention on genes and DNA is that genes are difference makers that can be used to trace and manipulate a broad range of biological processes (Waters 2004a and 2006). This scientific practice makes sense independently of any fundamental theory associated with molecular genetics. In the case of molecular genetics, it is investigative pragmatics, not fundamental theorizing, that drives scientific research. The basic theory suffices to explain the investigative utility and results of gene-centered approaches. The fundamental theory is, in an important sense, epiphenomenal with respect to the design and implementation of gene-centered research. On this view, the role of the fundamental theory should be understood in Latourian terms (1987, 1988), as a platform for rallying the troops and bringing resources to research endeavors. The design of the laboratory experiments and the reason why the experiments work, can be explained in terms of broad investigative strategies, the basic causal theory of molecular genetics, and the details of the experimental contexts.

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• Category: Science • Tags: Evolution, Genetics 
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Miura, Yoshiura, Miura, Shimada, Yamasaki, Yoshida, et al. A strong association between human earwax-type and apocrine colostrum secretion from the mammary gland. Human Genetics.

Here we provided the first genetic evidence for an association between the degree of apocrine colostrum secretion and human earwax type. Genotyping at the earwax-type locus, rs17822931 within the ABCC11 gene, revealed that 155 of 225 Japanese women were dry-type and 70 wet-type. Frequency of women without colostrum among dry-type women was significantly higher than that among wet-type women (P < 0.0002), and the measurable colostrum volume in dry-type women was significantly smaller than in wet-type women (P = 0.0341).

Related from Razib: Here are two posts from me on earwax distributions worldwide.

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• Category: Science • Tags: Genetics, Race 
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We previously reported that a measure of school achievement built from national test scores has a nearly perfect correlation with national IQ (at least in the range of scores tested). Subsequently, Lynn et al. (in press) published a very similar analysis:

This paper examines the relationship of the national IQs reported by Lynn & Vanhanen (2002, 2006) to national achievement in mathematics and science among 8th graders in 67 countries. The correlation between the two is 0.92 and is interpreted as establishing the validity of the national IQs. The correlation is so high that national IQs and educational achievement appear to be measures of the same construct. National differences in educational achievement are greater than differences in IQ, suggesting an amplifier effect such that national differences in IQs amplify differences in educational achievement. Controlling for national differences in IQ, slight inverse relationships of educational achievement are observed with political freedom, subjective well-being, income inequality, and GDP. However, public expenditure on education (as % of GDP) was not a significant predictor of differences in educational achievement.

The IQ’s Corner blog has an interesting note about forthcoming commentary.

On a related note, recall that Templer & Arikawa (2006) reported a near perfect environmental correlation between national skin color and national IQ for old-world countries. An unfortunately confused commentary by Hunt & Sternberg accompanied the publication. They wrote: “We argue that the report by Templer and Arikawa contains misleading conclusions and is based upon faulty collection and analysis of data. The report fails to hold up for quality of data, statistical analysis, and the logic of science.” The criticisms by Hunt & Sternberg are based largely on a misreading of Templer & Arikawa’s methods, particularly the method for deriving national skin color values.

A paper published in 2000 by Jablonski & Chaplin (“The evolution of human skin coloration”) can more directly address these criticisms. Jablonski & Chaplin published a table of skin color reflectance values from many old world populations (Table 6, also see the appendix). I very crudely averaged values from the same country to make a new measure of national skin color. This measure of national skin color correlates with the skin color index of Templer & Arikawa at r=-.91 (the negative is not important here). The reflectance measure of skin color correlates with national IQ at r=.87. The school achievement measure of Lynn et al. correlates r=-.79 with the skin color index of Templer & Arikawa and r=.75 with the skin color reflectance values crudely averaged from Jablonski & Chaplin. Thus, the skin color values derived by Templer & Arikawa are well validated by an external data source and the national IQ-skin color relationship is found to be robust across two measures of national IQ and two measures of national skin color.

Note that there are substantially more missing values in the school achievement and skin reflectance data sets (no imputation of missing values) with missing values skewed towards lower values of national IQ/school achievement and darker skin colors. Also note that the blind averaging use on the skin reflectance data most likely attenuates the correlations.

Templer & Arikawa had two abstracts at the 2006 ISIR conference, which provide additional support for the validity of the measures and their relationships:

source

Correlations of Skin Color and Continent with IQ
Donald I. Templer & Hiroko Arikawa

The present study determined (1) the correlations between skin color and IQ across the countries of three different continents; and (2) the correlations of both skin color and continent in the three pair combinations with the three continents. The product-moment correlations between IQ and skin color were -.86 across the 48 African countries, -.55 across the 48 Asian countries, and -.63 across the European countries. When the 96 countries of Africa and Asia were combined skin color correlated -.86 and continent correlated .75 with IQ. The respective correlations were -.97 and .89 across the 81 countries of Asia and Europe, and -.71 and .54 across the 81 countries of Europe and Asia. In multiple regression continent yielded minimal increment to skin color in predicting IQ. In an earlier study (Templer & Arikawa, 2006a) skin color correlated more highly with IQ than racial category, but racial category yielded greater increments in multiple regression than did continent in the present study. The present findings, combined with previous research relating skin color and IQ (Templer &amp;amp;amp;amp;amp;amp; Arikawa, 2006a; 2006b), indicate that skin color is a robust correlate of IQ in an international perspective.

Empirical Support for Rushton’s K Differential Theory
Donald I. Templer & Hiroko Arikawa

The purpose of the present study was to empirically substantiate Rushton’s Differential K Theory that purports that groups of persons with K (in contrast to r) characteristics have a life history and reproductive strategy that includes higher intelligence, less reproduction, less sexual activity, better care of offspring, lower birth rates, greater life expectancy, better impulse control, and greater social organization. The present research intercorrelated national mean IQ, infant mortality, HIV/AIDS rates, birth rates, prevalence rates, and life expectancy in 129 countries in Africa, Asia and Europe. All of the correlations were substantial and in the expected direction. Also supportive of Rushton’s theory is that there was only one factor which accounted for 75% of the variance and was labeled “K-r continuum.” All five variables were correlated with an economic variable (per capita income) and a biological variable (skin color, which correlated highly with intelligence in previous research). Skin color correlated more highly with all five variables than per capita income so as to support the contention of Rushton that this continuum is biologically based. Factor analysis with all seven variables yielded one factor that accounted for 73% of the variance.

Jason Malloy adds: Templer & Arikawa’s research follows Lynn and Rushton in arguing that cold temperatures were a significant force in the evolution of human race differences in intelligence. I have stated some problems I find with this hypothesis here, although it is also largely consistent with the geographic distribution of global populations by IQ. A recent analysis by blogger Audacious Epigone adds yet another revealing data point to this association.

Latitude (and hence colder climate) is associated with IQ not only cross-nationally (.67) but within the US as well. AE found a correlation of .70 between his measure of state IQ and the latitude of the most populous city in each of the 50 states. Furthermore intelligence is associated with latitude equally for both US whites and blacks (.52 and .51).

It’s not immediately apparent if and how this association is genetic or environmental. Either way it seems fair to seriously consider that global warming will provide yet another detrimental negative pressure on the intelligence of human populations in the coming decades.

(Republished from GNXP.com by permission of author or representative)
 
• Category: Science • Tags: General Intelligence, Pigmentation, Skin Color 
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Follow up to IQ, height & Crooked Timber: John Quiggin @ Crooked Timber wrote “I’d be interested to read a GNXP view of the main developments in recent decades, taking account of the Flynn effect.” I don’t know that a “GNXP view” exists on this subject aside from what appears to be the scholarly consensus where such a consensus exists. However, as a down payment on a response, I’ve gathered several sources which should help to inform the interested reader about modern views on the genetics of IQ and the Flynn effect.

For a general background on IQ and intelligence, two publications in response to The Bell Curve:
* “Intelligence: Knowns and Unknowns”, the APA task force report (1995)
* “Mainstream Science on Intelligence”, signed by 52 professors (1994)

For a quick technical review of the genetics of g, see the review by Plomin (2003), which I pasted below the fold. (Lest you think there’s nothing new, note the distribution of publication dates among the references.)

For a bleeding-edge discussion of the Flynn effect, I can recommend two sources. A draft of a new book by Flynn and a book review by Lynn (pasted below the fold).

Regulars may want to begin by reading below the fold.

Guest Editorial
Genetics, genes, genomics and g
Robert Plomin1

1Social, Genetic and Developmental Psychiatry Research Centre, Institute of Psychiatry, London SE5 8AF, UK. Email: r.plomin@iop.kcl.ac.uk
Abstract

Molecular Psychiatry (2003) 8, 1-5. doi:10.1038/sj.mp.4001249

This issue includes three papers1,2,3 on a topic of increasing interest to molecular psychiatrists: the genetics of intelligence. There was also a related article in a previous issue of Molecular Psychiatry.4 These four papers represent the range of research on genetics (quantitative genetic twin studies), genes (molecular genetic attempts to identify genes) and genomics (understanding the function of genes). The goal of this editorial is to put these papers in perspective.

Intelligence is the most complex¾and most controversial¾of all complex traits. So why study the genetics of such a complex and controversial trait? The word ‘intelligence’ has so many connotations that the symbol ‘g’ was proposed nearly a century ago to denote the operational definition of intelligence as a ‘general cognitive ability’ representing the substantial covariance among diverse tests of cognitive abilities such as abstract reasoning, spatial, verbal and memory abilities.5 In a meta-analysis of 322 studies, the average correlation among such diverse tests is about 0.306 and a general factor (first unrotated principal component) typically accounts for about 40% of the tests’ total variance.7 As discussed below, multivariate genetic analysis shows that the genetic overlap among cognitive tests is twice as great as the phenotypic overlap, suggesting that g is where the genetic action is. Although g is not the whole story, trying to tell the story of cognitive abilities without g loses the plot entirely.

This strong genetic g factor running through diverse cognitive processes has important implications for genetic research in neuroscience since g is molar and flies in the face of the widespread assumption in cognitive neuroscience that the brain functions in a modular manner.8 In addition, the long-term stability of g after childhood is greater than for any other behavioral trait,9 it predicts important social outcomes such as educational and occupational levels far better than any other trait,10 and it is a key factor in cognitive aging.11 g is specifically relevant to molecular psychiatry because, as discussed below, mild mental retardation appears to be the low extreme of the normal distribution of g. Moreover, at least 200 single-gene disorders include mental retardation among their symptoms.12

Quantitative genetics

Quantitative genetic research¾twin and adoption studies¾estimates the net effect of genetic variation on phenotypic variation regardless of the number of genes involved or the complexity of their interactions. Such research charts the course for molecular genetic studies by identifying the most heritable components and constellations of phenotypes. The first twin and adoption studies were conducted in the 1920s on g and suggested substantial genetic influence.13,14,15 Since then, with the exception of personality assessed by self-report questionnaires, more research has addressed the genetics of g than any other human characteristic. Dozens of studies including more than 10 000 twin pairs and hundreds of adoptive families as well as more than 8000 parent-offspring pairs and 25 000 sibling pairs consistently indicate substantial heritability.16 Heritability estimates vary from 40 to 80% but meta-analyses based on the entire body of data yield estimates of about 50%,17,18 with increasing heritability from infancy (20%) to childhood (40%) to adulthood (60%).19 Most of the genetic variance for g is additive, which facilitates attempts to identify genes responsible for its heritability.20

Since the substantial heritability of g is better documented than for any other biological or behavioral dimension or disorder, quantitative genetic research has moved beyond heritability to ask more refined questions about development, about the interface between nature and nurture, and about multivariate issues.21 A finding of great significance for molecular psychiatry and neuroscience has emerged from multivariate genetic research that analyzes the covariance among cognitive tests rather than the variance of each test considered separately.20 As noted earlier, the average phenotypic correlation among diverse cognitive tests is about 0.30. In contrast, multivariate genetic research indicates that genetic correlations among such tests are at least 0.80 on average.22 (A genetic correlation indexes the extent to which genetic effects on one trait correlate with genetic effects on another trait independent of the heritability of the two traits.) The extremely high genetic correlation among diverse cognitive tests means that genes associated with one cognitive ability are highly likely to be associated with all other cognitive abilities. This evidence for ‘genetic g’ means that g is an excellent target for molecular genetic research in the cognitive domain.

It should be noted that genetic g does not necessarily imply that there is a single fundamental brain process that permeates all other brain processing, such as a ‘speedy brain’,8 neural plasticity,23 or the quality and quantity of neurons.24 It has been proposed that g exists in the brain in the sense that diverse brain processes are genetically correlated.25 For example, gray and white matter densities in diverse brain regions are highly heritable, substantially intercorrelated across brain regions, and correlated genetically with g.26,27

One of the papers in this issue provides a good example and description of multivariate genetic analysis.3 Rather than analyzing the covariance between cognitive tests, the study investigated the genetic and environmental origins of the covariance between normal variation in behavior problems and g in children. For 376 pairs of twins from 6 to 17 years of age, nearly all of the modest phenotypic correlation (-0.19) between behavior problems and g could be accounted for by genetic covariation. Similar results were obtained in another study of 4000 pairs of young twins assessed at 2, 3 and 4 years; the large sample made it possible to show that phenotypic and gene
tic links may be stronger at the extremes of behavior problems and cognitive problems.28

Another multivariate genetic finding of great importance concerns genetic links between common disorders and dimensions of normal variation. This research suggests that common disorders (but not rare disorders) are merely the quantitative extreme of the same genetic and environmental influences that operate throughout the normal distribution. For example, a sibling study of mental retardation found that the average IQ of siblings of severely retarded probands was normal, 103, which implies that severe mental retardation shows no familial links with normal variation in g.29 This finding makes sense in relation to the rare single-gene12 and chromosomal causes30 of severe retardation that are not usually inherited because they occur spontaneously. In contrast, siblings of mildly retarded probands showed a substantially lower mean IQ score of 85.29 In other words, mild mental retardation but not severe retardation shows familial (presumably genetic) links with normal g variation. The first twin study of mild mental retardation confirms that mild mental retardation is strongly linked genetically to normal variation in g.31 This evidence for strong genetic links between disorders and dimensions¾evidence that is typical of common disorders such as hyperactivity, depression and alcoholism¾provides support for the quantitative trait locus approach to molecular genetics, discussed later.

Identifying genes

There is a lot of life left in the old workhorse of quantitative genetics, especially in investigating developmental, multivariate and environmental issues that go beyond merely estimating heritability. However, the most exciting direction for research on intelligence and cognition is to move beyond genetics to genes, that is, to identify some of the genes responsible for the substantial heritability of g and other cognitive abilities and disabilities. In contrast to the slow progress in identifying genes for schizophrenia and manic-depression, greater progress has been made in the cognitive domain, most notably the well-documented association between apolipoprotein E gene and dementia32 and a solid 6p21 linkage with reading disability that is beginning to be narrowed down in association studies.33

The quantitative trait locus (QTL) perspective has come to dominate molecular genetic research on complex quantitative traits such as g as well as common disorders such as dementia and reading disability. The QTL perspective is the molecular genetic extension of quantitative genetics whereby multiple genes are assumed to be responsible for heritability, implying that genetic variation is distributed quantitatively.34 For this reason, a QTL perspective on g naturally leads to molecular genetic research on normal variation, as is also the case for personality research.35 Two papers on molecular genetics in this issue are distinctive in that they focus on normal variation in g using large unselected samples.1,4 They report positive associations between normal variation in g and two candidate genes: Cathepsin D (CTSD; 4) and cholinergic muscarinic 2 receptor (CHRM2; 1). The effect sizes are small (heritabilities of 3 and 1%, respectively) as expected for QTLs, but are easily detected as significant with the large sample sizes of these studies (767 and 828, respectively). Research on complex traits should be aiming to break the 1% QTL barrier, that is, 80% power to detect QTLs when they account for as little as 1% of the total variance (1% heritability), which requires an unselected sample of about 800 individuals when a single marker is studied (P = 0.05, two-tailed; 36).

The CTSD paper4 is especially interesting in relation to the extensive molecular genetic research on dementia, which will be the source of much more molecular genetic research on g. Beginning with individuals at least 50 years old, g was assessed during a 15-year period in order to investigate the cognitive decline indicative of dementia. As in other studies, initial g scores are correlated negatively with decline across the 15 years, supporting the brain reserve capacity theory of dementia, as explained in the paper. However, CTSD is not associated with cognitive decline, which confirms the results of several other studies that found no association between CTSD and dementia. The exciting finding is that CTSD is associated with g at the first test session. Longitudinal quantitative genetic research on g indicates that age-to-age stability is largely mediated genetically whereas change is largely environmental in origin.21 This suggests that the heritability of dementia defined as decline might be modest in contrast to the heritability of g. We do not yet know how heritable dementia is because only a few small twin studies have been reported and their results are mixed.37 What is needed is a multivariate genetic analysis of g and dementia in order to investigate the extent of their genetic overlap.

Other reports are beginning to emerge of candidate gene associations with g. Most notably, a functional polymorphism (VAL158MET) in the enzyme catechol O-methyltransferase (COMT) has been reported to be associated with g-related cognitive functioning in two studies.38,39 An association with g has also been reported for a gene involved in controlling homocystein/folate metabolism.40 Because research on dementia will be the immediate source of more molecular genetic research on g as in the CTSD study in this issue,4 it is worth noting that the apolipoprotein gene, which shows a strong association with dementia, shows no association with g in childhood41,42 or in adults.43

Despite the power of the two studies in this issue to detect QTL associations, replication will be crucial because the track record for replicating candidate gene associations is not good.44 This is of particular concern with studies using unselected samples because it is tempting to study many measures as well as many candidate genes thus increasing vulnerability to false positives. As a chastening confession to underline the need for replication, both papers cite our report of an association between IGF2R and g in two samples,45 but our new independent sample as large as the previous two samples combined has not replicated the association.46

Other molecular genetic issues relevant to these CTSD and CHRM2 reports are generic issues involved in any attempt to find QTLs for complex traits whether assessed as disorders or dimensions. One such issue is the use of functional polymorphisms. In the CTSD study,4 the candidate gene polymorphism is functional (C>T, Ala>Val); in the CHRM2 study,1 the single nucleotide polymorphism (SNP) is in the 3′ untranslated region of the gene. The use of functional polymorphisms involves direct association that greatly increases power because it tests the hypothesis that the polymorphism is the QTL rather than relying on the marker being in linkage disequilibrium with the QTL associated with the trait (indirect association). Another advantage of using functional polymorphisms is that when associations are found, the usual house-to-house search for the culprit gene is circumvented, although it is always difficult to identify beyond reasonable doubt the QTL suspect from a line-up of genes in the neighborhood.

Another generic issue is that more systematic approaches to candidate genes are needed because any of the tens of thousands of genes expressed in the brain could be proposed as candidate genes for g.47 One early association study of g examined 100 candidate genes (not including CTSD or CHRM2) but found no more replicated associations than expected by chance, although the design only provided power to detect QTLs of about 2% heritability.48 A more systematic strategy is to investigate all polymorphisms in particular gene systems.49

Another strategy is to conduct genome-wide scans for association analogous to genome scans for linkage except that many thousands of markers are needed in the case of
association. The first genome-wide search for association with g has been reported using 1842 simple sequence repeat (SSR) markers using DNA pooling and groups selected for high g and controls.50 Despite a highly conservative replication procedure designed to avoid false positives, two SSRs replicated cleanly in two independent case-control samples but neither SSR association was replicated in a transmission disequilibrium test using parent-offspring trios. Genomic control analyses showed that the failure to replicate using the parent-offspring trios was not due to population stratification. Since SSR markers are unlikely to be functional, they rely on indirect association for which power falls off quickly as a function of the linkage disequilibrium distance between the marker and the QTL.51,52 Using indirect association, tens or hundreds of thousands of markers are needed for genome scans in order to exclude QTLs of 1% heritability, although haplotype maps can reduce the required number of markers.53,54

Ultimately what is needed for genome-wide association scans is to genotype every functional polymorphism in the genome. As a step in this direction, we are currently using DNA pooling to conduct a genome-wide g scan of all brain-expressed nonsynonymous SNPs in coding regions that are currently available in public databases with allele frequencies greater than 10% in Caucasian samples.55 Polymorphisms in promoters and other gene regulatory coding regions seem even better candidates for QTLs but they are much more difficult to identify and to demonstrate their functionality. Moreover, coding DNA does not have a monopoly on QTLs¾noncoding RNA is likely to be a source of QTLs too,56 although determining functionality of polymorphisms in noncoding RNA will be even more difficult.

It remains to be seen whether increasing power using large samples and direct association will yield replicable QTLs. DNA pooling will be useful in this context because it costs no more to genotype 1000 individuals than 100 individuals.57 Pessimists can reasonably worry about the gloomy prospect that the culprit genes will never be caught because the heritability of g might be caused by many genes with miniscule heritabilities. Some might hope that such research is never successful because of the ethical issues that would be raised if genes for g were found.21 Interesting discussions of these issues are available specifically in relation to genes and g58 and more generally in relation to behavioral genetic research.59

Behavioral genomics

Quantitative genetics assesses the net effect of genes on behavior without knowing anything about which genes are involved. Molecular genetics identifies genes associated with behavior without knowing anything about the mechanisms responsible for the association. As we approach the postgenomic era in which the complete human genome sequence and all functional variations in the genome sequence are identified, the future of behavioral genetics is functional genomics, that is, understanding how genes affect behavior.60

Functional genomics usually refers to the bottom-up agenda of molecular biology such as gene expression profiling and proteomics. However, there are higher levels of analysis for understanding how genes function which need not wait until the bottom-up approach reaches them. At the other end of the continuum is the top-down approach that investigates the function of genes in relation to behavior of the whole organism. For example, the issues about multivariate relationships of heterogeneity and comorbidity, developmental change and continuity, and the interface between genes and environment can be addressed with much greater precision once genes are identified. The term behavioral genomics has been proposed to emphasize the value of this top-down level of analysis.61

Rodent models will be valuable for functional genomic research because of their ability to manipulate both genes and environment and the power they offer for investigating brain processes such as single cell recordings, micro-stimulation, targeted gene mutations, antisense DNA that disrupts gene transcription, and DNA expression. The value of rodent models rests with understanding genetically driven brain processes, not with phenotypic validity. For example, mouse models have made the greatest progress in understanding the psychopharmacogenetics of alcohol-related processes even though mice do not become drunk of their own volition.62 In this sense, although it sounds absurd, mouse models of reading disability will be valuable for understanding the brain processes underlying the genetics of reading disability. The ultimate test is whether the same genes affect the same brain processes in mouse and man.

In terms of rodent models of g, clearly there are major differences in brain and mind between the human species and other animals, most notably in the use of language and the highly developed prefrontal cortex in the human species. However, g in man does not depend on the use of language¾a strong g factor emerges from a battery of completely nonverbal tests.7 Moreover, low-level tasks¾for example, information-processing tasks assessed by reaction time¾contribute to g.63 Indeed, g can be used as a criterion to identify animal models of individual differences in cognitive processes. If g represents the way in which genetically driven components of the brain work together to solve problems, it would not be unreasonable to hypothesize that g exists in all animals.64 Although much less well documented than g in humans, increasing evidence exists for a g factor in mice across diverse tasks of learning, memory and problem solving.65 A large-scale integrative program of research called genes-to-cognition is under way that uses mouse models for functional genomic research in the cognitive domain.66

One of the papers in this issue serves as an example of the value of rodent models for functional research.2 The research brings together neurotransmitter assays, brain anatomy, a broad battery of behavioral measures, a development approach from infancy to adolescence to adulthood, and pharmacology in an experimental study in which epidermal growth factor (EGF) was administered to neonatal rats. Although a test of learning ability did not appear to be affected by the neonatal treatment, other abnormalities were observed in adults but not in adolescents such as sensorimotor gating, motor activity and social interaction in a pattern reminiscent of schizophrenic symptoms and which were ameliorated by clozapine. This research covers a wide range of functional approaches, but the missing link from a functional genomics perspective is genetics. Although transgenic studies indicate the important role of the EGF gene family on brain structures and monoamine pharmacology, there is as yet no evidence that polymorphisms in genes related to EGF are involved in schizophrenia or other cognitive disabilities or abilities. This program of research showing the importance of EGF is likely to stimulate genetic research using EGF candidate genes.

In our age of increasing specialization, the most exciting prospect for functional genomic research in the postgenomic era is that DNA will integrate research in the life sciences from cells to societies and that bottom-up approaches will meet top-down approaches in the brain. g is an excellent target for such integrative research because an exciting synergy will quickly emerge simply by connecting the dots of knowledge already available, for example, in gene targeting studies of learning and memory in mice, brain imaging studies of cognitive processes in the human species, and extensive quantitative genetic research.
References

1 Comings DE et al. Mol Psychiatry 2002; 7.

2 Futamura T et al. Mol Psychiatry 2002; 7.

4 Payton A et al. Mol Psychiatry 7, in press.

3 Jacobs N et al. Mol Psychiatry 2002; 7: 368-374. Article

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——————–

Book review
J.R. Flynn, What is intelligence? Beyond the Flynn Effect, Cambridge University Press (2007).
doi:10.1016/j.intell.2007.03.003 How to Cite or Link Using DOI (Opens New Window)
Copyright © 2007 Elsevier B.V. All rights reserved.

Richard Lynna, E-mail The Corresponding Author
a4 Longwood House, Bristol BS8 3TL, UK
Received 2 March 2007; accepted 13 March 2007. Available online 17 April 2007.

A warm welcome must be extended to this book in which the author discusses the issues raised by the Flynn Effect. There are two major problems. First, what are the factors responsible for the increase of intelligence that has been observed in a number of countries during the last 80 years or so? Second, why has this increase been so much greater in reasoning ability/fluid intelligence, as measured by the Wechsler similarities and non-verbal tests where it has averaged around 3.6 IQ points a decade, and the Progressive Matrices, where in some samples it has averaged around 7 IQ points a decade, than in tests that measure acquired knowledge/crystallized intelligence (vocabulary, information and arithmetic), where it has averaged only around 0.5 IQ points a decade.

Flynn’s answer to the problem of the cause of the Flynn Effect is that increases in education have led the people thinking more scientifically and logically (“science has engendered a sea change … formal education played a proximate role”). He uses Piaget’s concepts of concrete and formal thought processes to explicate this. Previous generations we
re as good as later generations at concrete thinking, but more recent generations have advanced to the formal stage where they analyse problems in terms of abstract concepts. But he does not mention that this theory has been disconfirmed by Fleiller, Jautz, and Kop (1989) who demonstrated that concrete thinking has improved at the same rate as formal thinking.

Flynn is by no means the first to attribute the Flynn Effect to improvements in education. Many others have done the same, including several of the early observers of the Flynn Effect such as Cattell (1973, p. 275): “the inter-generational changes … probably represent the unquestionably marked improvement in schooling”.

The theory that improvements in education can explain the Flynn Effect encounters two problems. The first is that the cognitive abilities that are learned in schools (arithmetic, information, vocabulary, and math, science and reading tested in the American NAEP) have shown very little increase; it is the cognitive skills that are not learned in schools that have shown the large increases. This is the opposite of what would be expected if better or more education has enhanced cognitive abilities. A second problem is that the Flynn Effect has been found in 4–6 year olds who have had very little education, and even in infants (e.g. Hanson, Smith, & Hume, 1985). This suggests that an important contributor to the Effect lies in improvements in pre-natal and early post-natal nutrition, as argued in detail in Lynn, 1990 and Lynn, 1998. It may be, however, that some of the large gains in fluid intelligence found in military conscripts are attributable to later cohorts having had more education than earlier.

Flynn attempts to refute the nutrition theory of the Flynn Effect by asserting that there is no evidence that nutrition has improved in the second half of the twentieth century. He asserts that there have been no increases in height (improvements in nutrition are indexed by increases in height) in the United States in children born after about 1952, although intelligence has continued to increase. Contrary to this contention (1) the data compiled by Komlos and Lauderdale (in press) show that height in the United States increased in those born from 1955 to 1975 (white men from 177.8 to 179.5; white women from 164.1 to 164.9); (2) height stabilised after 1975 and Flynn’s own data show that intelligence gains decelerated after 1985 and turned negative in children from 1989 to 1995. In Europe also heights increased from 1960 to 1990 (Larnkjaer, Schroder et al., 2006); from around 1990 heights and intelligence have both stabilized in Denmark and Norway. The case for improvements in height running parallel with increases in intelligence, as predicted by the nutrition theory, is much stronger that Flynn allows.

Furthermore, the nutrition theory of the Flynn Effect explains why fluid intelligence has increased so much more than crystallized intelligence. Several studies have shown that sub-optimal nutrition impairs fluid intelligence more than crystallized intelligence. Hence as nutrition has improved over time, fluid intelligence has increased more. It has even been shown that the Wechsler subtests that are most impaired by sub-optimal nutrition and improve most with nutritional supplements are those for which the Flynn Effects have been the greatest (e.g. arithmetic, similarities and block design) (Botez, Botez, & Maag, 1984).

Flynn proposes that the effect of better education on the increase in intelligence is enhanced by the “individual multiplier” and the “social multiplier”. The concept of the “individual multiplier” is that the intelligent have a thirst for cognitive stimulation and this increases their intelligence. This again encounters the problem that the Flynn Effect is present in infants. The “social multiplier” posits “that other people are the most important feature of our cognitive development and that the mean IQ of our social environs is a potent influence on our own IQ”. If this were so, the IQs of adopted children should be associated with the IQs of their adoptive parents, and there should also be a strong correlation between the IQs of unrelated children reared in the same adoptive families. Both these predictions have been disconfirmed. Scarr and Weinberg’s (1978) study found that the correlation between the IQs of adopted children aged 18 and the IQs of their adoptive parents was .14 (i.e. zero), while the correlation between the IQs of unrelated children reared in the same adoptive families was − .03. The effectively zero correlation between the IQs of unrelated children reared in the same adoptive families has been confirmed in a study of 52 pairs aged 13 (r = − .16) (Plomin, 1986, p. 237).

Although I have not been persuaded by Flynn’s arguments on the causes of the Flynn Effect, and I could not find an answer to the question “What is Intelligence?” beyond what is already widely accepted, I found his book to contain many interesting ideas and observations and I recommend it in the confident expectation that many potential readers will find the same.

References
Botez, M. I., Botez, T., & Maag, U. (1984). The Wechsler subtests in
mind organic brain damage associated with folate deficiency.
Psychological Medicine, 14, 431−437.
Cattell, R. B. (1973). Abilities: Their structure, growth and action.
Boston: Houghton Mifflin.
Fleiller, A., Jautz, M., & Kop, J. -L. (1989). Les reponses au test
mosaique a quarante ans d’intervalle. Enfance, 42, 7−22.
Hanson, R., Smith, J. A., & Hume,W. (1985). Achievements of infants
on items of the Griffiths scales: 1980 compared with 1950. Child:
Care, Health and Development, 11, 91−104.
Komlos, J. and Lauderdale, B. E. (in press). The mysterious trend in
American heights in the 20 century. Annals of Human Biology.
Larnkjaer,A., Schroder, S.A., et al. (2006). Secular change in adult stature
has come to a halt in northern Europe and Italy. Acta Paediatrica, 95,
754−755.
Lynn, R. (1990). The role of nutrition in secular increases of
intelligence. Personality and Individual Differences, 11, 273−285.
Lynn, R. (1998). In support of the nutrition theory. In U. Neisser (Ed.),
The rising curve: Long term gains in IQ and related matters
Washington, D.C.: American Psychological Association.
Plomin, R. (1986). Development, Genetics and Psychology. Hillsdale,
New Jersey: Lawrence Erlbaum.
Scarr, S., & Weinberg, R. A. (1978). The influence of family
background on intellectual attainment. American Sociological
Review, 43, 674−692.

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Everybody okay?

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In the spirit of Gapminder, I’ve noticed three data visualization sites which present user-generated content:
* Many Eyes (does maps!!!)
* Swivel
* Data360

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AP and others have the story.

Benedict added that the immense time span that evolution covers made it impossible to conduct experiments in a controlled environment to finally verify or disprove the theory.

“We cannot haul 10,000 generations into the laboratory,” he said.

Setting aside the inappropriately narrow view of how science is done, this is factually incorrect. 10k E. coli generations take ~1 year. 10k yeast generations is

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That seems to be the conclusion of a recent review of Nick Wade’s Before the Dawn published in Nature Genetics:

My reluctance to recommend the book stems also from Wade’s discussions of ‘race’ and biology. I agree with Wade that there is something biological about racial categories. In my opinion, although racial identity is socially negotiated, people use physical traits as cues when ‘assigning’ a racial identity to themselves or another individual. Racial categorization isn’t blind to biology. … Although one might detect biological differences between races, any highlighting of the racial categories (just a subset of groups with biological correlates) has social costs, according to recent social science research. On the other hand, Neil Risch, cited often in the book, has argued that there are significant (medical) costs of ignoring the relationship between racial categories and biology. I suggest that these different costs be weighed in each circumstance where one might link ‘race’ and genetics. Wade’s broad description of races as clearly delineated biological entities is unjustified in the context of a book about human history intended for a general audience. Why use the term ‘race’, when ‘geographic ancestry’ or ‘continental origin’ are more accurate and less costly in social terms, especially since Wade’s definition of ‘race’ is “continent of origin”? I suggest acknowledging the correlation between racial labels and continents of origin, and saving the term ‘race’ for contexts in which social costs are outweighed by other costs.

This is not the argument I expected to follow the sentence “My reluctance to recommend the book stems also from Wade’s discussions of ‘race’ and biology.” In this case, the author isn’t being snide by putting race in quotes, as she really means the word race rather than its referent. How often do scholars write that consternation over race is largely related to extra-scientific concerns?

However, I have to criticize this argument, at least to the extent that I’m able to examine the evidence presented. A footnote to the “recent social science research” showing that using the word ‘race’ is harmful (but that cryptic synonyms are OK) would be appreciated, as this forms the basis of the argument against discussing ‘race’. Is it only harmful to discuss ‘race’ and ‘genetics’ or ‘biology’? Is the attribution of racial differences to environmental/cultural causes not similarly harmful? Is it really true, as is implied, that Wade is morally obliged to substitute most instances of “race” in his text with “continent of origin”?

There’s a lot to commend in this review, largely stemming from the reviewers’ honesty and directness, and especially in contrast with this hatchet job published in the sister journal Nature.

Update – full text:

Given the rich content of Nicholas Wade’s latest book, Before the Dawn, I wish I could simply recommend the book, describe its highlights and stop there. Wade provides a valuable overview of the last ten years of scientific literature on genetic insights into the history of our species. He is an excellent storyteller, weaving the scientific results into a thrilling tale of human migration and settlement, competition and warfare, cultural and linguistic evolution and environmental challenges. The history of our species is a fascinating one, and Wade brings it to life.

I congratulate Wade for taking great pains to qualify many of his statements with terms such as “seems” and “appears to.” In an important, related vein, early in the book he notes that any “intent” suggested in biologists’ statements about evolution reflects shorthand communication and is not meant to imply that evolution has any particular goal “in mind.” Evolutionary biologists will certainly appreciate that note. Furthermore, given that few readers will be specialists in all the fields represented in the book (paleoanthropology, archaeology, linguistics, genetics and more), many will appreciate Wade’s practice of defining terms.

Despite the book’s many strengths, I am reluctant to recommend the book unconditionally. I found some sections of the book challenging to read, as I looked for supporting evidence for various claims. For example, Wade suggests that the San, peoples in southern Africa who subsist via foraging, are the “closest living approximation to the ancestral human population.” Behaviorally, this might be true. However, Wade goes on to suggest that the San may not have evolved genetically, as “foragers have presumably had much the same environment for the last 50,000 years.” Wade appears to be unaware of the diverse environments even today within sub-Saharan Africa; furthermore, the changing global climate over the past 50,000 years has often had dramatic impacts on humans living in Africa.

Although at many points in the book Wade notes the speculative nature of conclusions from genetic, archaeological or geographic data, he occasionally treats those conclusions as fact elsewhere. For example, he writes, “There is no way to know for certain the nature of the interaction between the two human species [anatomically modern humans and Neanderthals].” Yet elsewhere he writes, “…[the Neanderthals] crushed the attempt by anatomically modern humans to penetrate the Levant.” The reader is at risk of being lulled by numerous “maybes,” “seems” and “appears” into trusting unsupported but confidently stated comments elsewhere in the book.

My reluctance to recommend the book stems also from Wade’s discussions of ‘race’ and biology. I agree with Wade that there is something biological about racial categories. In my opinion, although racial identity is socially negotiated, people use physical traits as cues when ‘assigning’ a racial identity to themselves or another individual. Racial categorization isn’t blind to biology. Yet Wade puts words in the mouths of the American Anthropological Association (AAA) when he states that the AAA “dismisses the idea that biological differences can be recognized between races.” He backs up his statement with an AAA quote that makes a different point: “any attempt to establish lines of division among biological populations [is] both arbitrary and subjective.” Although one might detect biological differences between races, any highlighting of the racial categories (just a subset of groups with biological correlates) has social costs, according to recent social science research. On the other hand, Neil Risch, cited often in the book, has argued that there are significant (medical) costs of ignoring the relationship between racial categories and biology. I suggest that these different costs be weighed in each circumstance where one might link ‘race’ and genetics. Wade’s broad description of races as clearly delineated biological entities is unjustified in the context of a book about human history intended for a general audience. Why use the term ‘race’, when ‘geographic ancestry’ or ‘continental origin’ are more accurate and less costly in social terms, especially since Wade’s definition of ‘race’ is “continent of origin”? I suggest acknowledging the correlation between racial labels and continents of origin, and saving the term ‘race’ for contexts in which social costs are outweighed by other costs.

Wade’s chapter on language is replete with details of relationships among languages, methodology for reconstructing those relationships and arguments in support of methods that are purported to give ages of languages. Although much of this discussion will undoubtedly provoke many linguists, the most provocative element in this chapter is a more general statement: “The mutability of language reflects the dark truth that humans evolved in a savage and dangerous world in which the deadliest threat cam
e from other human groups.” I see little support for this conjecture. Language, at least a language rich in elements, cannot come into being without being mutable. And as Wade notes earlier in the book, “Language would have made small groups more cohesive, enabled long-range planning and fostered the transmission of local knowledge and learned skills.” Mutability may reflect these advantages rather than a “savage and dangerous world.”

Where I am familiar with the relevant scientific literature, I see the details that Wade includes in this, his latest book, as accurately representing scientific findings. Wade often wraps these scientific details in dramatic stories, thereby creating a book both informative and entertaining. However, some of Wade’s general themes, such as his claim of a very high level of aggressiveness of prehistoric hunter-gatherers, are just that—dramatic stories. Readers will benefit most by considering each such claim as one among several plausible interpretations of the data.

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I’ve previously argued for the expansion of DNA databases to universal coverage. The reasoning being in part that all-or-nothing coverage is in many ways preferable to the patch-work system now in place. I’m not alone in making this argument, and I find it comforting that most authors commenting on this subject agree that some kind of changes are needed. The consensus of most commentary is that greater legislative regulation and oversight is needed regardless of what direction we take.

Making action on this issue more urgent, several developments have occurred which bring us to a situation where de facto universal coverage seems likely to occur merely as an extension of current policy (without further legislative action). The first development is the finding that the STR profiles currently used in law-enforcement DNA databases are good enough to allow identification first-degree (and even second-degree) relatives in a substantial percentage of cases. While there are technical limitations to this approach, this development has the net effect of significantly expanding the number of individuals who are identifiable. The second development is one of law-enforcement technique — the surreptitious collection of discarded DNA from targeted individuals. Regardless of the legality of individual methods used, it seems inevitable that certain forms of surreptitious collection will be legally permissible. This has the net effect of making any targeted individual’s DNA open to law enforcement without a court order. Lastly, the pace of development of genotyping technologies is quickly bringing us to a point of virtually-limitless genotyping capacity. The possibilities of surreptitious DNA collection that this will open up are limited only by imagination (and the cost of human labor).

All of these developments point towards a situation where a de facto universal DNA database (or a functional near-equivalent) will develop even in the absence of any changes in legislation. This appears to be a largely-unexamined issue, but it seems to call for debate and legislative action.

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Everything I’ve read about DNA profiling and DNA databases suggests the following:

  1. A universal national DNA database should be constructed
  2. DNA profiling for this database should switch to SNP genotyping

DNA database – The U.S. national and state governments operate a number of DNA databases. Most law enforcement databases are integrated under the FBI’s CODIS system, which now contains several million DNA profiles in its “offender” index. Privacy advocates raise a number of concerns about these databases, but most political action concerns the criteria for getting profiles into and out of these databases. As far as I can tell, all of the concerns about inclusion/exclusion criteria would be circumvented by the existence of a universal database. For example, issues of contention include:

  • What crimes warrant the collection of DNA?
  • Should DNA be automatically collected at arrest or not until conviction?
  • If DNA is collected at arrest, should DNA profiles be expunged if no conviction is made?
  • Does the mass collection of DNA raise the risk of false positives and subsequent false convictions? [Note, ostensibly it does - especially when imperfect forensic profiles are used to search for a match.]

The retention of DNA samples is a second concern for privacy advocates. This is a real issue which should be addressed by maximizing protections of stored samples or by choosing to discard samples. Other concerns are aimed at the application of DNA databases in criminal prosecution. These criticisms exist regardless of the databases’ size/scope, but there is reason to believe that the increased attention to the caveats of DNA evidence that a universal database provides would improve these conditions. Along those lines, there are a number of benefits which come from universal coverage:

  • Universal coverage is perhaps the best way to ensure proper privacy protection and oversight of the database.
  • False positives would be more easily detected and corrected.
  • The advantages to law enforcement would be obvious.
  • Paternity would be known for all children.
  • It would have beneficial uses for identification outside of law-enforcement.

DNA profiling – The most common form of DNA profiling used for DNA databases (and other DNA-identification applications) is STR genotyping. Even with the best foreseeable technological advancements, STR genotyping has many disadvantages to SNP genotyping. If we were to implement a universal DNA database, it would be prudent to make the switch to SNP genotyping.

  • While STR genotyping is currently performed on ~13-16 highly polymorphic loci, it would be technically trivial to genotype hundreds (or thousands or more!) of biallelic SNPs.
  • High-throughput SNP genotyping platforms are advanced, and the pace of development (i.e. reduction in costs) is enormous.
  • SNP genotyping is technically simpler than STR genotyping, and it would be easier to miniaturize.
  • Huge databases of SNPs are already known, making it possible to select a panel of SNPs to meet almost any reasonable requirements. For example, SNPs could be chosen to minimize the chance that they are actually markers for socially-important phenotypic differences between individuals or groups.
  • Multiple correlated SNPs can be chosen for redundancy against genotyping error.
  • Poor quality forensic samples can be more accurately assigned to database profiles when there are hundreds (or thousands) or points of comparison, in contrast to the 13 STRs used in CODIS.
  • You can imagine the on-demand genotyping of a select subset of SNPs as an identity-verification scheme.

Previous posts: [1],[2],[3]

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PLoS One has been out for a couple months now. They currently list 70 papers in the category of genetics and genomics. They rely in part of community feedback for peer review, so give feedback where you have comments.

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In IQ and the Wealth of Nations (2002; IQatWoN) and IQ and Global Inequality (2006; IQGI), Richard Lynn and Tatu Vanhanen (L&V) present measurements and estimates of average national IQ (national IQ). In IQatWoN, L&V argue that national IQ predicts per-capita GDP (sup Fig 1). In IQGI, L&V argue that national IQ predicts quality of life measures (sup Fig 2). A common criticism of both works is to question the validity of national IQ. This criticism is motivated in part by the very low scores reported for countries in sub-Saharan African. A look at the distribution of national IQ is instructive (Fig 1).

Figure 1. The distribution of national IQ values (192 countries from IQGI).

L&V address the issue of validity by comparison of national IQ values with international test scores in math and science such as TIMSS and PISA. IQGI presents data from 10 different tests, with different scoring scales, in the form of 3 tables. To get a better grasp on the question of the validity of national IQ, I reanalyzed the test score data from IQGI. For better comparison, I renormalized each set of test scores relative to the maximum test score for each assessment. This is an imperfect but sufficient technique. An unweighted average of the available test score data was used to calculate a composite national test score for the set of 62 countries for which at least 1 test score was available (Fig 2).

Figure 2. The association between national test scores and national IQ for 62 nations.

National test scores are available for a limited range of national IQ scores, with few test scores available for countries with national IQs below the mid 80s. I interpret this to mean that for countries with national IQs below ~85, the test score data is insufficient to inform the question of validity. However, for the available scores (i.e., mostly above ~85), the relationship between national IQ and national test scores is very strong (see Sup Table 1).

The validity of sub-80 national IQs is addressed in part by the finding that IQ correlates with GDP and QHC (Sup Figs 1,2) throughout the observed range of IQ.

Update: Although there are only four values, the sub-80 national IQs are outliers, all with positive residuals. While this is hardly informative, it trends in the direction of casting doubt on the validity of sub-80 national IQ values.

Supplemental Figure 1. National IQ correlates with GDP per-capita (192 countries from IQGI).

Supplemental Figure 2. National IQ correlates with a L&V’s quality-of-life index (QHC; 192 countries from IQGI).

Supplemental Table 1. Correlation matrix for national IQ (IQ), national test score (Test), L&V’s quality of life index (QHC) and log per-capita GDP (logGPD) for 62 countries.

r QHC logGDP IQ Test
QHC 1 0.898936 0.7933265 0.7803476
logGDP 0.898936 1 0.760138 0.7565582
IQ 0.7933265 0.760138 1 0.9008035
Test 0.7803476 0.7565582 0.9008035 1

Related papers:
* Earl Hunt and Werner Wittmann, National intelligence and national prosperity, Intelligence, In Press –examines PISA scores
* Richard Lynn and Jaan Mikk, National differences in intelligence and educational attainment, Intelligence, Volume 35, Issue 2, March-April 2007, Pages 115-121. –examines TIMSS scores

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Hobolth A, Christensen OF, Mailund T, Schierup MH (2007) Genomic Relationships and Speciation Times of Human, Chimpanzee, and Gorilla Inferred from a Coalescent Hidden Markov Model. PLoS Genet 3(2): e7

They estimate 4.1 million years ago. It’s open access so no excerpts needed.

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Jensen (1998) makes a point that is worth repeating:

The pernicious notion that IQ discriminates mainly along racial lines, however, is utterly false.

Jensen presents what should be a predictable pattern for a highly heritable trait:

Source % of Variance Average IQ Difference
Between races (within social classes) 14 30 12
Between social classes (within races) 8 6
Interaction of race and social class 8
Between families (within race and social class) 26 65 9
Within families (siblings) 39 11
Measurement error 5 4
Total 100 17

This can be demonstrated most clearly in terms of a statistical method known as the analysis of variance. Table 11.1 shows this kind of analysis for IQ data obtained from equal-sized random samples of black and white children in California schools. Their parents’ social class (based on education and occupation) was rated on a ten-point scale. In the first column in Table 11.1 the total variance of the entire data set is of course 100 percent and the percentage of total variance attributable to each of the sources6 is then listed in the first column. We see that only 30 percent of the total variance is associated with differences between race and social class, whereas 65 percent of the true-score variance is completely unrelated to IQ differences between the races and social classes, and exists entirely within each racial and social class group. The single largest source of IQ variance in the whole population exists within families, that is, between full siblings reared together in the same family. The second largest source of variance exists between families of the same race and the same social class. The last column of Table 11.1 shows what happens when each of the variances in the first column is transformed into the average IQ difference among members of the given classification. For example, the average difference between blacks and whites of the same social class is 12 IQ points. The average difference between full siblings (reared together) is 11 IQ points. Measurement error (i.e., the average difference between the same person tested on two occasions) is 4 IQ points. (By comparison, the average difference between persons picked at random from the total population is 17 IQ points.) Persons of different social class but of the same race differ, on average, only 6 points, more or less, depending on how far apart they are on the scale of socioeconomic status (SES). What is termed the interaction of race and social class (8 percent of the variance) results from the unequal IQ differences between blacks and whites across the Spectrum of SES, as shown in Figure 11.2. This interaction is a general finding in other studies as well. Typically, IQ in the black population is not as differentiated by SES as in the white population, and the size of the mean W-B difference increases with the level of SES.

(Republished from GNXP.com by permission of author or representative)
 
• Category: Science • Tags: General Intelligence, IQ, Race 
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Sam Harris and Andrew Sullivan have been debating religion. Here’s an interesting excerpt from Sullivan’s Feb 14 entry:

…That is because I have never met a human being or a human mind that is “contingency-free”, and never will. No child grows up without the contingent facts of their family, place, genes, and any number of details that make us who we are. You and I would be very different people if we had different contingent genetics and different contingent histories. This is the experience of being human, an experience eternally different from the dream of your new, unfettered, purely rational “education,” where the young are severed from the toxins of contingent culture and faith and family….

(Republished from GNXP.com by permission of author or representative)
 
• Category: Science • Tags: Genetics, Religion 
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