UCLA

Genome-wide association studies have revolutionized our understanding of genetic influences on common diseases and complex traits. However, the majority of discoveries have been limited to individuals of European ancestry, leading to a data collection bias that disproportionately under-samples non-European populations. This bias leads to missed discovery opportunities and differential prediction accuracy across sub-populations defined by genetic ancestry and socioeconomic factors. Although datasets with diverse genetic ancestry backgrounds are increasingly available, existing analytical tools often fail to account for the heterogeneity present in these datasets. Here, I introduce new computational and statistical methods for genetic mapping, inference, and prediction across diverse human populations. First, I investigate the power of genetic mapping approaches in populations with diverse genetic ancestry backgrounds. Second, I explore the inference of genetic architecture, estimating the cross-ancestry sharing of genetic effects. Third, I examine genetic prediction, quantifying differential polygenic scoring accuracy by contexts and developing an approach to account for such differences.