主講人：袁敏  安徽醫(yī)科大學(xué)校聘教授

時(shí)間：2021年10月17日10：30

地點(diǎn)：騰訊會(huì)議 790 573 063

舉辦單位：數(shù)理學(xué)院

主講人介紹：袁敏,  安徽醫(yī)科大學(xué)公共衛(wèi)生管理學(xué)院校聘教授、衛(wèi)生健康大數(shù)據(jù)分析中心主任。研究興趣為全基因組易感基因檢測(cè)、單細(xì)胞轉(zhuǎn)錄組測(cè)序數(shù)據(jù)分析、腦圖像數(shù)據(jù)分析、公共衛(wèi)生和環(huán)境科學(xué)中的定量研究等。承擔(dān)國(guó)家基金委青年基金、教育部高校博士點(diǎn)專(zhuān)項(xiàng)基金、中國(guó)博士后基金委特別資助和面上資助、省教育廳自然科學(xué)研究重點(diǎn)項(xiàng)目、安徽省自然科學(xué)基金面上項(xiàng)目等多項(xiàng)國(guó)家級(jí)和省級(jí)科研項(xiàng)目。在頂級(jí)期刊《Briefings  in Bioinformatics》、《Bioinformatics》、《Clinical Pharmacology &  Therapeutics》、《Environment International》等雜志發(fā)表三十余篇SCI論文。

內(nèi)容介紹：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.