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2019概率统计及其应用系列报告之四(朱文圣)

  发布日期:2019-04-15  浏览量:210


 

 

报告题目: Covariate-adjusted multiple testing in genome-wide association studies via factorial hidden Markov models

: 朱文圣(东北师范大学数学与统计学院教授、博士生导师、副院长)

报告时间: 2019418(周四)930-1030

报告地点:磬苑校区澳门赌搏网站大全H306

报告摘要:It is more and more important to consider the dependence structure among multiple testings, especially for the genome-wide association studies (GWAS). The existing procedures, such as local index of significance (LIS) and pooled local index of significance (PLIS), were proposed to test hidden Markov model (HMM) dependent hypotheses under the framework of compound decision-theory, which was successfully applied to GWAS. However, the etiology of complex diseases is not only with respect to the genetic effects, but also the environmental factors. Failure to account for the covariates in multiple testing can produce misleading bias of the association of interest, or suffer from loss of testing efficiency. In this paper, we develop a covariate-adjusted multiple testing procedure, called covariate-adjusted local index of significance (CALIS), to account for the effects of environmental factors via a factorial hidden Markov model. The theoretical results show that our procedure can control the false discovery rate (FDR) at the nominal level and has the smallest false non-discovery rate (FNR) among all valid FDR procedures. We further demonstrate the advantage of our novel procedure over the existing procedures by simulation studies and a real data analysis.

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专家概况: 朱文圣,东北师范大学数学与统计学院教授、博士生导师、副院长。200612月博士毕业于东北师范大学,201312月起任东北师范大学数学与统计学院教授。2008-2010年在耶鲁大学做博士后研究,2015-2017年访问北卡大学教堂山分校。现兼任中国现场统计研究会计算统计分会副理事长,中国现场统计研究会数据科学与人工智能分会秘书长,中国概率统计学会副秘书长,吉林省现场统计研究会秘书长等。主要从事统计学的方法与应用研究,研究方向为生物统计学和生物信息学。在统计学国际顶级期刊Journal of the American Statistical Association (JASA)、医学图像著名期刊NeuroImage等发表学术论文多篇,主持并完成国家自然科学基金项目多项。

 

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