Leveraging the local genetic structure for trans-ancestry association mapping

Abstract

Over the past two decades, genome-wide association studies (GWASs) have successfully advanced our understanding of genetic basis of complex traits. Despite the fruitful discovery of GWASs, most GWAS samples are collected from European populations, and these GWASs are often criticized for their lack of ancestry diversity. Trans-ancestry association mapping (TRAM) offers an exciting opportunity to fill the gap of disparities in genetic studies between non-Europeans and Europeans. Here we propose a statistical method, LOG-TRAM, to leverage the local genetic architecture for TRAM. By using biobank-scale datasets, we showed that LOG-TRAM can greatly improve the statistical power of identifying risk variants in under-represented populations while producing well-calibrated p-values. We applied LOG-TRAM to the GWAS summary statistics of 29 complex traits/diseases from Biobank Japan (BBJ) and UK Biobank (UKBB), and achieved substantial gains in power (the effective sample sizes increased by 49% in average compared to the BBJ GWASs) and effective correction of confounding biases compared to existing methods. Finally, we demonstrated that LOG-TRAM can be successfully applied to identify ancestry-specific loci and the LOG-TRAM output can be further used for construction of more accurate polygenic risk scores (PRSs) in under-represented populations.