Global Ancestry and Cognitive Ability

Abstract

Using data from the Philadelphia Neurodevelopmental Cohort, we examined whether European ancestry predicted cognitive ability over and above both parental socioeconomic status (SES) and measures of eye, hair, and skin color. First, using multi-group confirmatory factor analysis, we verified that strict factorial invariance held between self-identified African and European-Americans. The differences between these groups, which were equivalent to 14.72 IQ points, were primarily (75.59%) due to difference in general cognitive ability (g), consistent with Spearman's hypothesis. We found a relationship between European admixture and g. This relationship existed in samples of (a) self-identified monoracial African-Americans (B = 0.78, n = 2,179), (b) monoracial African and biracial African-European-Americans, with controls added for self-identified biracial status (B = 0.85, n = 2407), and (c) combined European, African-European, and African-American participants, with controls for self-identified race/ethnicity (B = 0.75, N = 7,273). Controlling for parental SES modestly attenuated these relationships whereas controlling for measures of skin, hair, and eye color did not. Next, we validated four sets of polygenic scores for educational attainment (eduPGS). MTAG, the multi-trait analysis of genome-wide association study (GWAS) eduPGS (based on 8442 overlapping variants) predicted g in both the monoracial African-American (r = 0.111, n = 2179, p < 0.001), and the European-American (r = 0.227, n = 4914, p < 0.001) subsamples. We also found large race differences for the means of eduPGS (d = 1.89). Using the ancestry-adjusted association between MTAG eduPGS and g from the monoracial African-American sample as an estimate of the transracially unbiased validity of eduPGS (B = 0.124), the results suggest that as much as 20%-25% of the race difference in g can be naïvely explained by known cognitive ability-related variants. Moreover, path analysis showed that the eduPGS substantially mediated associations between cognitive ability and European ancestry in the African-American sample. Subtest differences, together with the effects of both ancestry and eduPGS, had near-identity with subtest g-loadings. This finding confirmed a Jensen effect acting on ancestry-related differences. Finally, we confirmed measurement invariance along the full range of European ancestry in the combined sample using local structural equation modeling. Results converge on genetics as a potential partial explanation for group mean differences in intelligence.Psych 2019, 1 433 between ancestry and outcomes or that ancestry is a non-causal correlate of the trait in question that's actually caused by something else if the mediation is not spurious. Ultimately, controls alone cannot establish causality [50].Four studies we know of have related genetically-assessed ancestry to cognitive ability in samples of 25,51,52]. Others have done this for Hispanic Americans [24,25] and various groups in Latin America [53][54][55]. Of these, the earlier ones [51-54] re...

No support for the genetic hypothesis of the Black-white achievement gap using polygenic scores and tests for divergent selection

“…Here, I address this shortcoming using recent methods that leverage genomic data and population genetic theory and that avoid or remedy many of the known biases of polygenic scores. These analyses improve on past attempts by Piffer 21 and Lasker et al 22 by accounting for biases in polygenic scores, using a formal test for divergent selection, and estimating genetic contributions to between group phenotypic differences using evolutionary genetic models . I am 8 15 185 190 195 200 205 therefore able to test both claims made by the genetic hypothesis: 1. that variants associated with educational attainment and cognitive ability are highly differentiated between Black and white populations and such genetic differences cause a substantial portion of the gap in cognitive ability; and 2. that these genetic differences are caused by divergent natural selection.…”

“…Prediction of 5 105 110 115 120 125 130 10 skin color is far from perfect with previous methods only achieving R 2 of 0.48 for their predictions, and the particular method used has been shown to be inaccurate in recently admixed populations 23 Further, the SES variables available were only parental education, even though income (and in particular permanent income) plays a substantial role in attenuating racial achievement gaps 24 . Substantial issues with both confounding variables in the path model weaken the conclusions drawn from Lasker et al 22 . Both analyses also suffer from systematic biases because they apply polygenic scores to people from groups that were not included in the original GWAS.…”

“…A research team conducting a GWAS takes a large group of (mostly) unrelated individuals, usually from the same ethnicity, and correlates hundreds of thousands or millions of variable DNA base pairs (SNPs) with the phenotype. As stockpiles of GWAS data have accumulated, some have attempted to employ polygenic scores, which are linear combinations of alleles identified by a GWAS used to produce an estimated genetic trait value, to test racial genetic hypotheses about achievement and cognitive ability 21,22 .…”

“…These studies have claimed to support a genetic hypothesis with one of two methodologies: calculating correlations between national polygenic scores and national IQ scores 21 ; and path modeling of the relationship between ancestry, polygenic scores and IQ scores 22 . Several problems with these analyses exist.…”

“…For example in Piffer 21 , the correlation of observed national IQ scores and polygenic scores of the 1000 Genomes populations are taken as evidence that differences between populations have been caused by natural selection, even though this is not an accepted test for polygenic selection. In Lasker et al 22 , much of the causal claims turn on path analysis where percentage of European ancestry, skin color, educational attainment polygenic scores, and cognitive test scores are key variables. Notably, the skin color variable is not actually measured skin color, but is imputed from sequence data.…”