Until recently, most GWAS were conducted in relatively homogeneous samples.16 For many samples collected in the US, the analyses were limited to individuals of European and African ancestry and excluded groups with too few individuals for statistical analysis. However, as samples become increasingly large and diverse, there is a need to establish practices for cosmopolitan samples. Here we describe procedures for empirically assigning individuals to genetically informed ancestry groups for cross-population meta-analysis with the goal of maximizing power for discovering etiologically relevant genetic variants while minimizing false positive associations due to population stratification. Our approach is applied to data from Spit for Science (S4S): the University Student Survey, an ongoing study following college students longitudinally that consists of both genetic and environmental data collected on substance use and mental health. Our framework for empirically assigning genetic ancestry groups can be applied to other cross-population studies, including those of admixed ancestry such as African- and Asian-Americans.
