This paper derives the exact finite-sample p-value for univariate regression of a quantitative phenotype on individual genome markers, relying on a mixture distribution for the dependent variable. The p-value estimator conventionally used in existing genome-wide association study (GWAS) regressions assumes a normally-distributed dependent variable, or relies on a central limit theorem based approximation. The central limit theorem approximation is unreliable for GWAS regression p-values, and measured phenotypes often have markedly non-normal distributions. A normal mixture distribution better fits observed phenotypic variables, and we provide exact small-sample p-values for univariate GWAS regressions under this flexible distributional assumption. We illustrate the adjustment using a years-of-education phenotypic variable.

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.

New genomic technologies allow the apportionment of individuals’ genotyped DNA into admixture proportions traceable to historically isolated biogeographic ancestry (BGA) groups such as African, European, and Amerindian. These BGA admixture proportions have proven valuable in a wide range of recent epidemiological research. This paper performs a meta-analysis of epidemiological studies and finds that they reveal consistent patterns of correlation between BGA admixture proportions and socio-economic status (SES). Given this finding, the absence of BGA admixture proportions data from almost all extant economic analysis of individuals’ susceptibility to high/low socioeconomic status is indirect evidence for an omitted-variable bias in such analysis. Economic models of SES which do not consider BGA as a possible explanatory variable may be unreliable due to the potential confounding associated with this omitted variable.

I review a diverse collection of research findings on differences in average intelligence test scores across socially-defined racial groups and argue that these test score gaps are partly attributable to genetic variation across biogeographic ancestries such as African, East Asian, and European ancestries. I focus on the U.S. Black-white test score gap, for which the available evidence is most comprehensive. The strictly enforced public dogma that the causes of the Black-white test score gap are entirely environmental is not empirically plausible given this evidence. Other race-related test score gaps may also have a genetic component. I then confront the sensitive public policy question of whether these findings should be acknowledged or suppressed. I argue that society needs to find a democratically legitimate method to decide whether it is best social policy to impose the false but hegemonic doctrine that the sources of racial test score gaps are entirely environmental.