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Integrative analysis of genome-wide association studies (GWASs) and gene expression studies in the form of a transcriptome-wide association study (TWAS) has the potential to better elucidate the molecular mechanisms underlying disease etiology. Here we present a method, METRO, that can leverage gene expression data collected from multiple genetic ancestries to enhance TWASs. METRO incorporates expression prediction models constructed in different genetic ancestries through a likelihood-based inference framework, producing calibrated p values with substantially improved TWAS power. We illustrate the benefits of METRO in both simulations and applications to seven complex traits and diseases obtained from four GWASs. These GWASs include two of primarily European ancestry (n = 188,577 and 339,226) and two of primarily African ancestry (n = 42,752 and 23,827). In the real data applications, we leverage gene expression data measured on 1,032 African Americans and 801 European Americans from the Genetic Epidemiology Network of Arteriopathy (GENOA) study to identify a substantially larger number of gene-trait associations as compared to existing TWAS approaches. The benefits of METRO are most prominent in applications to GWASs of African ancestry where the sample size is much smaller than GWASs of European ancestry and where a more powerful TWAS method is crucial. Among the identified associations are high-density lipoprotein-associated genes including PLTP and PPARG that are critical for maintaining lipid homeostasis and the type II diabetes-associated gene MAPT that supports microtubule-associated protein tau as a key component underlying impaired insulin secretion.

Most existing expression quantitative trait locus (eQTL) mapping studies have been focused on individuals of European ancestry and are underrepresented in other populations including populations with African ancestry. Lack of large-scale well-powered eQTL mapping studies in populations with African ancestry can both impede the dissemination of eQTL mapping results that would otherwise benefit individuals with African ancestry and hinder the comparable analysis for understanding how gene regulation is shaped through evolution. We fill this critical knowledge gap by performing a large-scale in-depth eQTL mapping study on 1,032 African Americans (AA) and 801 European Americans (EA) in the GENOA cohort. We identified a total of 354,931 eSNPs in AA and 371,309 eSNPs in EA, with 112,316 eSNPs overlapped between the two. We found that eQTL harboring genes (eGenes) are enriched in metabolic pathways and tend to have higher SNP heritability compared to non-eGenes. We found that eGenes that are common in the two populations tend to be less conserved than eGenes that are unique to one population, which are less conserved than non-eGenes. Through conditional analysis, we found that eGenes in AA tend to harbor more independent eQTLs than eGenes in EA, suggesting potentially diverse genetic architecture underlying expression variation in the two populations. Finally, the large sample sizes in GENOA allow us to construct accurate expression prediction models in both AA and EA, facilitating powerful transcriptome-wide association studies. Overall, our results represent an important step toward revealing the genetic architecture underlying expression variation in African Americans.