cryptoGWAS – running GWAS without a deterministic phenotype
报告人: 秦昭晖(Emory University)
时间:2024-11-08 14:00-16:00
地点:BICMR全斋9室
Abstract:
Genome-wide association studies (GWASs) have been widely applied in biomedicine including neuroimaging field to discover genetic variants associated with brain-related traits. So far, almost all GWASs conducted in neuroimaging genetics are performed on univariate quantitative features summarized from brain images. In this study, we proposed and implemented a novel machine learning strategy for systematically identifying genetic variants that lead to detectable differences on full brain MRI data. The classification-based strategy has the potential to be applied to other multivariate phenotypes.
About the Speaker:
秦昭晖于1994年本科毕业于北京大学。2000年在密西根大学获得统计学博士学位。然后在哈佛大学接受博士后训练。并曾在密西根大学生物统计系担任助理教授。现任职于美国埃默里大学生物统计与生物信息学系。秦博士在生物信息学,计算生物学,基因组学及统计遗传学等领域有丰富研究经验。秦博士目前的主要研究兴趣在高通量组学数据分析,以及利用统计建模和机器学习等方法进行组学大数据的数据挖掘和分析。

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