Empowering gene discovery and accelerating clinical translation for diverse admixed populations

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PROJECT SUMMARY/ABSTRACT Gene discovery has revolutionized medical genetics, but gaps remain in the understanding of complex disorders in minority populations, in particular “admixed” groups of mixed ancestry. Admixed populations are systematically excluded from genomic studies due largely to the lack of analytic approaches that can account for their genomic diversity. Admixed populations, including African American and Latinx individuals, make up more than a third of the U.S. populace and have higher rates of some common complex disorders including cardiovascular disease, diabetes, some cancers, and PTSD – disorders which rank amongst the top global contributors to years lived with disability. Yet, these groups face severe disparities in health research and treatment due to being so sorely underrepresented in genomics. To reap full and equitable benefits from existing and ongoing efforts including All of Us, there is an unmet need for the development of tools permitting the study of complex traits in admixed peoples. Dr. Atkinson proposes to address this pressing issue by developing a suite of innovative statistical methods, software packages, and analytical resources that will make genomics more inclusive. In this 5-year R01 award, she will: 1a) build a novel statistical method to allow for the integration of admixed individuals into rare variant association studies; 1b) test this new method in a scalable cloud-based software implementation across phenotypes of varying genetic architectures in the All of Us Research Program; and 1c) leverage the linkage disequilibrium in admixed individuals to improve fine-mapping of top loci. We will also develop novel software tools to 2a) realistically simulate diverse cohorts; which we will use to 2b) define best practice recommendations for multi-ancestry phasing, imputation, and local ancestry inference; and 2c) assess the impact of common analytic strategies for diverse collections on gene discovery outcomes. Finally, we will leverage admixture to 3a) extend our ancestry-informed frameworks to quantify gene-gene interactions both locally and distally; and 3b) characterize trends in allelic effect size differences across ancestry components with control over the environment. These efforts fill a major gap in existing resources and remove barriers to the inclusion of underrepresented populations in medical genomics. This work is in direct line with the strategic missions of the NIH/NHGRI to focus on inclusion and diversity, highlighting the crucial and timely nature of the proposed project.