报告题目：Efficient Mendelian randomization analysis with self-adaptive determination of sample structure and multiple pleiotropic effects.

报告人：袁中尚教授 （山东大学）

报告时间：2024年5月8日下午16：00-17：00

报告地点：学院会议室109

报告摘要:

Mendelian randomization (MR) has emerged as a highly valuable tool for inferring the causal effects of exposures on outcomes in observational studies via using genetic variants, typically single-nucleotide polymorphisms (SNPs), as instrumental variables. Here, we developed MAPLE, a novel MR method with self-adaptive determination of sample structure and multiple pleiotropic effects, for effective analysis. MAPLE utilizes a set of correlated SNPs, self-adaptively accounts for the sample structure and the uncertainty that these correlated SNPs may exhibit multiple pleiotropic effects, as well as explicitly models both uncorrelated and correlated horizontal pleiotropy. MAPLE first acquires the accurate estimate of the nuisance error parameter using the genome-wide summary statistics, then places the inference of the causal effect into a likelihood-framework and relies on a scalable sampling-based algorithm to obtain calibrated p-values. We illustrate the advantage of MAPLE through comprehensively realistic simulations, where MAPLE shows calibrated type I error control and reduces false positives while being more powerful than existing approaches. In the real data applications, we focus on three types of 4 lipid traits-centric MR analysis in the UK Biobank, including the positive control analysis to examine the causal effect of each lipid trait on itself, the negative control analysis to investigate the causal effects of the 4 lipid traits on hair color and skin color, as well as the factor screening analysis to delve into the causal relationship among lifestyle factors and lipid profiles.

报告人简介：

袁中尚，教授，博导，山东大学公共卫生学院生物统计学系主任，山东大学健康医疗大数据研究院副院长，山东省泰山学者青年专家，齐鲁青年学者，中国卫生信息与健康医疗大数据学会统计理论与方法专委会委员，中国数学会医学数学专委会委员。长期从事系统流行病学与跨组学数据整合的统计理论方法研究，主持国家自然科学基金5项、山东省重大基础研究1项，统计方法学成果先后发表在Nature Genetics、Nature Communication、Science Advances、The American Journal of Human Genetics、Statistics in Medicine等杂志，跨组学数据分析成果先后发表在JAMA Psychiatry、BMC Medicine等杂志，参编专著2部。