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学术预告:Incorporating functional annotation to develop genome-wide association study method

发布日期:2018-03-26 发表者: 浏览次数:880次

报告题目: Incorporating functional annotation to develop  genome-wide association study method

报告人:郝兴杰 博士

报告时间:2018年3月28日(周三)16:30

报告地点:逸夫楼C座314会议室

摘要:

Genome-wide association studies (GWASs) have identified many disease associated loci, the majority of which have unknown biological functions. Understanding the mechanism underlying trait associations requires identifying trait-relevant tissues and investigating associations in a trait-specific fashion. Here, we extend the widely used linear mixed model to incorporate multiple SNP functional annotations from omics studies with GWAS summary statistics to facilitate the identification of trait-relevant tissues, with which to further construct powerful association tests. Specifically, we rely on a generalized estimating equation based algorithm for parameter inference, a mixture modeling framework for trait-tissue relevance classification, and a weighted sequence kernel association test constructed based on the identified trait-relevant tissues for powerful association analysis. We refer to our analytic procedure as the Scalable Multiple Annotation integration for trait-Relevant Tissue identification and usage (SMART). With extensive simulations, we show how our method can make use of multiple complementary annotations to improve the accuracy for identifying trait-relevant tissues. In addition, our procedure allows us to make use of the inferred trait-relevant tissues, for the first time, to construct more powerful SNP set tests. We apply our method for an in-depth analysis of 43 traits from 28 GWASs using tissue-specific annotations in 105 tissues derived from ENCODE and Roadmap. Our results reveal new trait-tissue relevance, pinpoint important annotations that are informative of trait-tissue relationship, and illustrate how we can use the inferred trait-relevant tissues to construct more powerful association tests in the Wellcome trust case control consortium study.

报告人简介:

先后在华中农业大学动科动医学院获得学士(2012),硕士(2014)和博士(2018)学位,研究生期间在加拿大阿尔伯塔大学(1年)和美国密歇根大学(2年)进行合作研究项目。研究兴趣为统计遗传和生物信息学,目前主要从事整合功能注释信息的GWAS/GS方法研究,以及复杂性状和疾病遗传机制的挖掘。分别在Plos Genetics和BMC Genetics上发表一作文章,并有多篇一作文章目前正在BMC Genomics, Animal Genetics和Scientific Reports上审稿,也担任Scientific Reports审稿人。