报告简介  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.

嘉宾简介  About the Speaker

Dr. Hongyu Zhao is the Ira V. Hiscock Professor of Biostatistics and Professor of Statistics and Data Science and Genetics, Chair of the Biostatistics Department and the Co-Director of Graduate Studies of the Inter-Departmental Program in Computational Biology and Bioinformatics at Yale University. His research interests are the developments and applications of novel statistical methods to address scientific questions in genetics, molecular biology, drug developments, and precision medicine. He has published over 560 articles in statistics, human genetics, bioinformatics, and proteomics, and edited two books on human genetics analysis and statistical genomics. Dr. Zhao is a Co-Editor of the Journal of the American Statistical Association Theory and Methods, and serves on the editorial boards of several leading statistical and genetics journals.

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