主题：Semiparametric efficient estimation of genetic relatedness with machine learning methods
主讲人：郭旭 北京师范大学
主持人：王国长 BETVLCTOR伟德官网下载
时间：2023年5月17日（周三）上午9：30-10：30
地点：BETVLCTOR伟德官网下载石牌校区伟德BETVlCTOR1946大楼（中惠楼）102室
摘要
In this paper, we propose semiparametric efficient estimators of genetic relatedness between two traits in a model-free framework. Most existing methods require specifying certain parametric models involving the traits and genetic variants. However, the bias due to model mis-specification may yield misleading statistical results. Moreover, the semiparametric efficient bounds for estimators of genetic relatedness are still lacking. In this paper, we develop semiparametric efficient estimators with machine learning methods and construct valid confidence intervals for two important measures of genetic relatedness: genetic covariance and genetic correlation, allowing both continuous and discrete responses. Based on the derived efficient influence functions of genetic relatedness, we propose a consistent estimator of the genetic covariance as long as one of genetic values is consistently estimated. The data of two traits may be collected from the same group or different groups of individuals. Various numerical studies are performed to illustrate our introduced procedures. We also apply proposed procedures to analyze Carworth Farms White mice genome-wide association study data.
主讲人简介
郭旭，现为北京师范大学统计学院教授，博士生导师。从事回归分析中复杂假设检验的理论方法及应用研究，近年来皆在对高维数据发展适当有效的检验方法。部分成果发表在JRSSB, JASA，Biometrika和JOE。担任《应用概率统计》杂志第十届编委。先后主持国家自然科学基金青年基金和国家自然科学基金面上项目。曾荣获北师大第十一届“最受本科生欢迎的十佳教师”和北师大第18届青教赛一等奖。
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校对|王国长
责编|麦嘉杰
初审|黄振
审核|郑贤
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