南京财经大学史兴杰教授学术报告 11月15日上午
发布时间: 2019-11-11 访问次数: 197

学术讲座【 A tissue-specific collaborative mixed model for jointly analyzing multiple tissues in transcriptome-wide association studies】

时间:2019年11月15日 10:00-11:00

地点:旗山校区理工北楼601报告厅

主讲:南京财经大学教授,史兴杰

主办:数学与信息学院

参加对象:感兴趣的老师和学生


报告人简介:史兴杰,南京财经大学统计系教授。2014年获得上海财经大学统计学博士学位。主要研究兴趣是复杂数据的建模分析方法,及其在生物统计、生物信息学、风险管理中的应用。目前在Bioinformatics、Statistical Methods in Medical Research、Computational Statistics and Data Analysis以及Genetic Epidemiology等期刊发表多篇学术论文。国际统计学会当选会员、中国现场统计研究会资源与环境统计分会理事,主持国家自然科学基金青年项目一项。


报告摘要:Transcriptome-wide association studies (TWAS) integrate expression quantitative trait loci (eQTLs) studies with genome-wide association studies (GWASs) to prioritize candidate target genes for complex traits. Several statistical methods have been recently proposed to improve the performance of TWAS in gene prioritization by integrating the expression regulatory information imputed from multiple tissues, and made significant achievements in improving the ability to detect gene-trait associations. Unfortunately, most existing multi-tissue methods focus on prioritization of candidate genes, and cannot directly infer the specific functional effects of candidate genes across different tissues. Here, we propose a tissue-specific collaborative mixed model (TisCoMM) for TWAS, leveraging the co-regulation of genetic variations across different tissues explicitly via a unified probabilistic model. TisCoMM not only performs hypothesis testing to prioritize gene-trait associations, but also detects the tissue-specific role of candidate target genes in complex traits. To make use of widely available GWAS summary statistics, we extend TisCoMM to use summary-level data, namely, TisCoMM-S^2. Using extensive simulation studies, we show that type I error is controlled at the nominal level, the statistical power of identifying associated genes is greatly improved, and the false-positive rate (FPR) for non-causal tissues is well controlled at decent levels. We further illustrate the benefits of our methods in applications to summary-level GWAS data of 33 complex traits. Notably, apart from better identifying potential trait-associated genes, we can elucidate the tissue-specific role of candidate target genes. The follow-up pathway analysis from tissue-specific genes for asthma shows that the immune system plays an essential function for asthma development in both thyroid and lung tissues.