报告时间：2020年10月16日上午10:00
报告形式：腾讯会议
报告地点：明德主楼1016会议室
报告嘉宾：王天颖
报告主题：Integrated Quantile Rank Test for gene-level associations in sequencing studies
报告摘要
Integrated Quantile Rank Test for gene-level associations in sequencing studies
Testing gene-based associations is the fundamental approach to identify genetic associations in sequencing studies. The best-known approaches include Burden and Sequence Kernel Association Tests (SKAT). The gene-traits associations are often complex due to population heterogeneity, gene-environmental interactions, and various other reasons. The mean-based tests, including Burden and SKAT, may miss or underestimate some high-order associations that could be scientifically interesting. We propose a new family of gene-level association tests, which integrate quantile rank score processes while combining multiple weighting schemes to accommodate complex associations. The resulting test statistics have multiple advantages. They are as efficient as the mean-based SKAT and Burden test when the associations are homogeneous across quantile levels and have improved efficiency for complex and heterogeneous associations. The test statistics are distribution-free, and could hence accommodate a wide range of distributions. They are also computationally feasible. We established the asymptotic properties of the proposed tests under the null and alternative hypothesis and conducted large scale simulation studies to investigate its finite sample performance.
个人简介
Dr. Tianying Wang is an assistant professor at the Center for Statistical Science in Tsinghua University. She earned her PhD from the Department of Statistics at Texas A&M University in 2018. Methodologically, she works on quantile regression, measurement error analysis, misspecified models, gene-environment interaction analysis, multivariate analysis and high-dimensional data analysis such as variable selection and classification. With respect to specific areas, she is primarily interested in cancer genomics, case-control studies, and epidemiology studies. She has worked on a variety of applied problems such as misspecified model subject to measurement error, high-dimensional binary classification with dimension reduction, semiparametric analysis of complex gene-environment interactions in case-control study.
主持人简介
尹建鑫，中国人民大学统计学院副教授、副院长，中国现场统计研究会监事，计算统计分会副理事长，高维数据统计分会理事，中国人工智能学会不确定性人工智能专委会委员。主要研究方向为图模型与高维统计、生物统计、机器学习和数据科学等，在国际知名统计杂志发表论文多篇，主持过多项国家、省部级和校级课题。