An efficient and scalable algorithm for genome-wide association studies on longitudinal outcome

主讲人：袁敏  安徽医科大学校聘教授

时间：2021年10月17日10：30

地点：腾讯会议 790 573 063

举办单位：数理学院

主讲人介绍：袁敏,  安徽医科大学公共卫生管理学院校聘教授、卫生健康大数据分析中心主任。研究兴趣为全基因组易感基因检测、单细胞转录组测序数据分析、脑图像数据分析、公共卫生和环境科学中的定量研究等。承担国家基金委青年基金、教育部高校博士点专项基金、中国博士后基金委特别资助和面上资助、省教育厅自然科学研究重点项目、安徽省自然科学基金面上项目等多项国家级和省级科研项目。在顶级期刊《Briefings  in Bioinformatics》、《Bioinformatics》、《Clinical Pharmacology &  Therapeutics》、《Environment International》等杂志发表三十余篇SCI论文。

内容介绍：Genome-wide association studies (GWAS) using longitudinal phenotypes collected  over time is appealing due to the improvement of power. However, computation  burden has been a challenge because of the complex algorithms for modeling the  longitudinal data. Approximation methods based on empirical Bayesian estimates  (EBEs) from mixed-effects modeling have been developed to expedite the analysis.  However, our analysis demonstrated that bias in both association test and  estimation for the existing EBE-based methods remains an issue. We propose an  incredibly fast and unbiased method simultaneous correction for EBE, SCEBE) that  can correct the bias in the naive EBE approach and provide unbiased P-values and  estimates of effect size. Through application to Alzheimer’s Disease  Neuroimaging Initiative data with 6 414 695 single nucleotide polymorphisms, we  demonstrated that SCEBE can efficiently perform large-scale GWAS with  longitudinal outcomes, providing nearly 10 000 times improvement of  computational efficiency and shortening the computation time from months to  minutes.