Multi-ancestry gene-disease association testing via cross-population modeling of eQTLs

One approach to infer causal genes in disease is a transcriptome-wide association study (TWAS). However, TWAS is not powerful in non-European populations due to poor trans-ancestry portability of gene expression prediction models and smaller genome-wide association study (GWAS) sample sizes. The purpose of this project is to develop a novel machine learning approach to mitigate the issue of trans-ancestry portability. This approach will allow for powerful TWAS in non-European populations by simultaneously modeling genetic and genomic data from different populations, which has previously been challenging due to population-specific differences in genetic architecture such as linkage disequilibrium.