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 hundreds of thousands of disease-associated genetic variants for thousands of traits through genome-wide association studies in the past two decades, 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 and populations. 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. This is joint work with Geyu Zhou, Wei Jiang, Yixuan Ye, and others.