报告时间：2020年10月29日上午10:00-11:00

报告形式：腾讯会议

报告嘉宾：李子林

报告主题：Scalable Integrative Statistical Inference for Whole-Genome Sequencing Association Studies

Large-scale whole genome sequencing studies have enabled the analysis of rare variants (RVs) associated with complex human traits. Commonly used RV association tests (RVATs) have limited scope to leverage the functions of variants.

We propose STAAR (variant-Set Test for Association using Annotation infoRmation), a scalable and powerful method to increase the power of RVATs by effectively incorporating both variant functional categories and multiple complementary functional annotations using a dynamic weighting scheme. For the latter, we introduce “annotation Principal Components”, multi-dimensional summaries of in silico coding and noncoding variant functional annotations. STAAR accounts for population structure and relatedness and is scalable for analyzing very large cohort and biobank whole-genome sequencing studies of continuous and dichotomous traits. We highlighted STAAR-O, the omnibus test which aggregates multiple annotation-weighted tests including the burden test, SKAT, and ACAT-V in the STAAR framework. We focused on two types of WGS RV association analysis using STAAR-O: gene-centric functional element-based analysis by grouping variants into functional categories for each protein-coding gene and agnostic genetic region analysis using sliding windows.

We applied STAAR-O to identify RV-sets associated with four quantitative lipid traits in 12,316 discovery samples and 17,822 replication samples from the NHLBI Trans-Omics for Precision Medicine (TOPMed) program. We discovered and replicated new RV associations, including disruptive missense RVs of NPC1L1 and an intergenic region near APOC1P1 associated with low-density lipoprotein cholesterol.

李子林，哈佛大学陈曾熙公共卫生学院生物统计系副研究员🩰，本科与博士毕业于清华大学数学科学系，主要研究方向为高维数据中的统计方法理论和遗传统计学。主持美国国家心肺血液研究所的基金一项（Biodata Catalyst Fellowship🍈👋🏿，“A powerful and resource-efficient rare variant meta-analysis workflow for large-scale multi-ethnic sequencing association studies using summary statistics and functional annotations”）🤳🏿。学术论文在Journal of American Statistical Association🙇🏼‍♂️、 The Canadian Journal of Statistics👻、Nature Genetics💵、The American Journal of Human Genetics等国际学术期刊发表。

孙韬，北京AG尊龙凯时平台娱乐登录官方网站统计学院🚴‍♂️，生物统计与流行病学讲师🚛，匹兹堡大学生物统计学博士，获得2019年ENAR和ICSA论文奖👨‍👨‍👧‍👧。主要研究领域为复杂生存数据模型，半参数统计模型，深度学习疾病预测模型🤵🏻，copula模型及其诊断，论文发表在Biostatistics, Statistics in Medicine, Lifetime Data Analysis, R Journal。医学统计方向包括流行病学调查和生物信息学，成果发表于Science, Nature Immunology等。