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芝加哥大学 王静姝博士:Estimating Causal Relationship for Complex Traits with Weak and Heterogeneous Genetic Effects

光华讲坛——社会名流与企业家论坛第5653期

主题:Estimating Causal Relationship for Complex Traits with Weak and Heterogeneous Genetic Effects

主讲人:芝加哥大学王静姝博士

主持人:统计学院统计研究中心 林华珍教授

时间:2019年12月12日(星期三)下午4:00-5:00

地点:西南财经大学柳林校区弘远楼408会议室

主办单位:统计研究中心 统计学院 科研处

主讲人简介:

   Jingshu Wang joined The University of Chicago statistics department as an assistant professor in 2019. She received my Ph.D. in statistics from Stanford in 2016 and was a postdoc researcher at Wharton Statistics Department from 2016-2019. Her main research interest is in developing statistical methods for cutting-edge bio-technologies and genetic problems.

王静姝于2019年加入芝加哥大学统计学系,担任助理教授。她于2016年获得斯坦福大学统计学博士学位,并于2016-2019年担任沃顿商学院统计系博士后研究员。她的主要研究兴趣是发展尖端生物技术和遗传问题的统计方法。

主要内容:

Genetic association signals tend to be spread across the whole genome for complex traits. The recently proposed ''omnigenic'' model indicates that, when the risk factor is a complex trait, most genetic variants can weakly affect the risk factor while also easily affecting a common disease not through the risk factor. Existing methods in Mendelian Randomization (MR) are not ideal under such pervasive pleiotropy. We propose a comprehensive framework GRAPPLE (Genome-wide mR Analysis under Pervasive PLEiotropy) for MR, utilizing both strongly and weakly associated genetic variant, and can detect the existence of multiple pleiotropic pathways. We show that GRAPPLE is a comprehensive tool that can detect and adjust for pleiotropy, simultaneously estimate the causal effect of multiple risk factors and can determine the causal relationship direction using three-sample GWAS summary statistics datasets. With GRAPPLE, we conduct a screening for the lipid traits (HDL-C, LDL-C and triglycerides) with around 30 common diseases to understand their roles as risk factors and detect potential pleiotropic pathways.

对于复杂的性状,遗传关联信号往往会在整个基因组中传播。最近提出的“全基因组”模型表明,当风险因子是一种复杂的性状时,大多数遗传变异对风险因子的影响较弱,而对普通疾病的影响很容易不通过危险因子。现有的孟德尔随机化(MR)方法在这种普遍的多效性下并不理想。我们提出了一个适用于MR的综合框架GRAPPLE(普适基因多效型下的全基因组mR分析),它利用了强弱关联的遗传变异,并且可以检测到多种多效性途径的存在。结果表明,GRAPPLE是一个综合工具,可以检测和调整多效性,同时估计多个风险因子的因果关系,并可以使用三样本GWAS汇总统计数据集确定因果关系方向。通过GRAPPLE,我们对大约30种常见疾病的脂质特征(HDL-CLDL-C和甘油三酸酯)进行了筛选,以了解其作为风险因子的作用并检测潜在的多效性途径。


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