Webinar Series: Mathematical Foundations of Data Science

Predicting Disease Risk from Genomics Data

Speaker

Hongyu Zhao, (Yale University)

Abstract

Accurate disease risk prediction based on genetic and other factors can lead to more effective disease screening, prevention, and treatment strategies. Despite the identifications of thousands of disease-associated genetic variants through genome-wide association studies in the past 15 years, performance of genetic risk prediction remains moderate or poor for most diseases, which is largely due to the challenges in both identifying all the functionally relevant variants and accurately estimating their effect sizes. Moreover, as most genetic studies have been conducted in individuals of European ancestry, it is even more challenging to develop accurate prediction models in other populations. Furthermore, many studies only provide summary statistics instead of individual level genotype and phenotype data. In this presentation, we will discuss a number of statistical methods that have been developed to address these issues through jointly estimating effect sizes (both across genetic markers and across populations), modeling marker dependency, incorporating functional annotations, and leveraging genetic correlations among different diseases. We will demonstrate the utilities of these methods through their applications to a number of complex diseases/traits in large population cohorts, e.g. the UK Biobank data. This is joint work with Yiming Hu, Yixuan Ye, Wei Jiang, Geyu Zhou, Qiongshi Lu, and others.