Novel Statistical Methods for Development of Polygenic Scores in Multi-Ancestry Cohorts

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Project Abstract Polygenic scores (PGSs) are measures of an individual’s genetic risk of disease, derived from the results of genome-wide association study (GWAS) summary statistics1. They are a promising tool to identify individuals at high genetic risk and can also be used to assess causal effects of risk factors and examine gene-environment interactions2. However, PGSs are highly ancestry-dependent, and current PGSs do not perform well in underrepresented populations in statistical genetic research3. There is a need to develop new PGS methods that can improve PGS performance in these underrepresented populations. In particular, improved PGS for atrial fibrillation (AF) is vital for ensuring that advances in genetic research of AF are available to all. AF is often asymptomatic, and without treatment can lead to other cardiovascular diseases, including heart failure, stroke, and cardiovascular mortality4. With improved PGS for AF, high risk individuals can be identified and treated5. Previous work has demonstrated that combining GWAS results across ancestries can improve PGS performance, however this work has been limited to two ancestries6, 7. We propose to develop methods for constructing PGSs with multiple ancestry data to improve the performance of PGSs, in particular in underrepresented populations. We will take two distinct approaches combining the results of multiple ancestry- specific GWAS data. Our first aim will develop a method to create improved ancestry-specific scores. Our second aim will develop a method to create one trans-ancestry PGS. We will assess the performance of our new statistical methods using simulation studies, and will validate our methods using AF data from the Million Veterans Program (MVP)8. Additionally, we will make our novel methods available to the greater research community by publishing our methods on GitHub. We will focus our applications of the methods on AF, but our methods can be used for a wide range of diseases. Advancing PGS methods so that they perform well for individuals who are under-represented in genetic studies is imperative for ensuring that advances in genetic research are beneficial to all. My mentoring team has outstanding experience in genetic research of AF, and is committed to supporting me in my training and professional development. We have designed a training plan which includes training in mechanisms and epidemiology of cardiovascular disease, advanced statistical methodologies, and professional development such as scientific writing and responsible conduct of research. Through this fellowship, I will develop the skills to achieve my long-term goal of becoming an independent researcher in statistical genetics with expertise in cardiovascular disease.