Harnessing the power of diverse populations to empower clinical translation of genome-wide association studies of common human disease

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Genome-wide association studies (GWAS) have been successful in identifying changes in our DNA, referred to as genetic variants, that contribute to our risk of developing many common human diseases, including those that have major public health burden, such as cancers, cardiovascular disease, and diabetes. This success offers exciting opportunities to use genetics for a significant positive impact on human health by: (i) improving our understanding of the biology of disease, thereby informing potential novel treatments; and (ii) predicting the future occurrence of disease in an individual, referred to as polygenic risk scores (PRS). However, despite this success, most GWAS have been performed in white populations of European ancestry, in Europe and North America. This bias in GWAS is in stark contrast to the global and racial/ethnic distribution of many diseases and may therefore exacerbate healthcare disparities because genetic findings from white European ancestry individuals are unlikely to be as impactful in other population groups. For example, the genetic variants that cause disease in white European ancestry individuals might not be observed in other population groups. Consequently, PRS derived from white European ancestry GWAS provide less reliable prediction of disease risk into individuals of African, Asian, or mixed ancestry. The concerns over population bias in genetic studies have prompted a recent expansion of GWAS into individuals from more diverse ancestry groups. The overall vision of this proposal is to develop novel statistical methods for the analysis of multi-ancestry GWAS that allow for the genetic differences observed across diverse population groups. These methods will be implemented into user-friendly software tools that will be shared with the wider research community to provide a better understanding of the impact on disease of genetic differences between population groups to reduce healthcare disparities. Improved knowledge of disease biology that is shared across diverse populations will inform treatment development opportunities that will be relevant to everyone. Furthermore, PRS that take account of the ancestry of an individual will provide more accurate prediction of disease risk, irrespective of their genetic background.