Abstract: Rare variants significantly impact complex diseases. This presentation will first introduce SAIGE-GENE and SAIGE-GENE+, methodologies extending SAIGE to gene/region-based rare variant tests. These methods efficiently utilize mixed effects models to adjust for sample relatedness and saddlepoint approximations to account for case-control imbalance. SAIGE-GENE+ additionally incorporates functional annotations and collapsing of ultra-rare variants that can help to improve type I error control and power. In the second part of the talk, I will introduce our recent workto estimate effect sizes of rare variants. The method, RareEffect, uses an empirical Bayesian approach that estimates gene/region-level heritability and then an effect size of each variant. We also show the effect sizes obtained from our model can be leveraged to improve the performance of polygenic scores.
Bio: Seunggeun (Shawn) Lee is a Professor of Data Science at Seoul National University. Before moving to his current position, he was an Associate Professor of Biostatistics at the University of Michigan. He received his PhD in Biostatistics from the University of North Carolina at Chapel and completed postdoctoral training at Harvard School of Public Health. His research focuses on developing statistical and computational methods for the analysis of large biobanks, which is essential to better understand the genetic architecture of complex diseases and traits.