• 2019概率统计及其应用系列报告之四(朱文圣)

    发布时间:2019-04-15作者:访问量:105

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

    报 告 人: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:磬苑校区数学科学学院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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