Abstract:

    Large-scale whole-genome sequencing (WGS) studies have improved our understanding of the contributions of coding and noncoding rare variants to complex human traits. Leveraging association effect sizes across multiple traits in WGS rare variant association analysis can improve statistical power over single-trait analysis, and also detect pleiotropic genes and regions. Existing multi-trait methods have limited ability to perform rare variant analysis of large-scale WGS data. We propose MultiSTAAR, a statistical framework and computationally-scalable analytical pipeline for functionally-informed multi-trait rare variant analysis in large-scale WGS studies. MultiSTAAR accounts for relatedness, population structure and correlation among phenotypes by jointly analyzing multiple traits, and further empowers rare variant association analysis by incorporating multiple functional annotations. We applied MultiSTAAR to jointly analyze three lipid traits (low-density lipoprotein cholesterol, high-density lipoprotein cholesterol and triglycerides) in 61,861 multi-ethnic samples from the Trans-Omics for Precision Medicine (TOPMed) Program. We discovered new associations with lipid traits missed by single-trait analysis, including rare variants within an enhancer of NIPSNAP3A and an intergenic region on chromosome 1.

Biography:

    李子林，东北师范大学数学与统计学院教授，历任美国印第安纳大学医学院生物统计与健康数据科学系助理教授，哈佛大学生物统计系博士后、副研究员(Research Associate)和研究员(Research Scientist)。本科与博士毕业于清华大学数学科学系，师从美国国家科学院与医学院两院院士林希虹院士。2023年当选为国际统计学会（International Statistical Institute）推选会员（Elected Member）。主要研究方向为高维数据中的统计方法理论和统计遗传学。相关研究成果以第一作者或通讯作者在Nature Methods、Nature Genetics、JASA等国际学术期刊发表。