Pesta, B. J. | Search bioRxiv

bioRxiv2021

Linear and partially linear models of behavioural trait variation using admixture regression

Admixture regression methodology exploits the natural experiment of random mating between individuals with different ancestral backgrounds to infer the environmental and genetic components to trait variation across racial and ethnic groups. This paper provides a statistical framework for admixture regression based on the linear polygenic index model and applies it to neuropsychological performance data from the Adolescent Brain Cognitive Development (ABCD) database. We develop and apply a new test of the differential impact of multi-racial identities on trait variation, an orthogonalization procedure for added explanatory variables, and a partially linear semiparametric functional form. We find a statistically significant genetic component to neuropsychological performance differences across racial identities, and find some possible evidence of nonlinearity in the link between admixture and neuropsychological performance scores in the ABCD data.

genomics↗

bioRxiv2020

More Research Needed: There is a Robust Causal vs. Confounding Problem for Intelligence-associated Polygenic Scores in Context to Admixed American Populations.

Polygenic scores for educational attainment and intelligence (eduPGS), genetic ancestry, and cognitive ability have been found to be inter-correlated in some admixed American populations. We argue that this could either be due to causally-relevant genetic differences between ancestral groups or be due to population stratification-related confounding. Moreover, we argue that it is important to determine which scenario is the case so to better assess the validity of eduPGS. We investigate the confounding vs. causal concern by examining, in detail, the relation between eduPGS, ancestry, and general cognitive ability in East Coast Hispanic and non-Hispanic samples. European ancestry was correlated with g in the admixed Hispanic (r = .30, N = 506), European-African (r = .26, N = 228), and African (r = .084, N = 2,179) American samples. Among Hispanics and the combined sample, these associations were robust to controls for racial / ethnic self-identification, genetically predicted color, and parental education. Additionally, eduPGS predicted g among Hispanics (B = 0.175, N = 506) and all other groups (European: B = 0.230, N = 4914; European-African: B = 0.215, N = 228; African: B = 0.126, N = 2179) with controls for ancestry. Path analyses revealed that eduPGS, but not color, partially statistically explained the association between g and European ancestry among both Hispanics and the combined sample. Of additional note, we were unable to account for eduPGS differences between ancestral populations using common tests for ascertainment bias and confounding related to population stratification. Overall, our results suggest that eduPGS derived from European samples can be used to predict g in American populations. However, owing to the uncertain cause of the differences in eduPGS, it is not yet clear how the effect of ancestry should be handled. We argue that more research is needed to determine the source of the relation between eduPGS, genetic ancestry, and cognitive ability.

genomics↗