主 讲 人：侯琳 清华大学统计学研究中心

报告时间：2021年11月5日9：00

报告地点：腾讯ID：936 259 261

主办单位：数学与统计学院

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  Genetic correlation analysis has quickly gained popularity in the past few years and provided insights into the genetic etiology of numerous complex diseases. However, existing approaches oversimplify the shared genetic architecture between different phenotypes and cannot effectively identify precise genetic regions contributing to the genetic correlation. In this work, we introduce LOGODetect, a powerful and efficient statistical method to identify small genome segments harboring local genetic correlation signals. LOGODetect automatically identifies genetic regions showing consistent associations with multiple phenotypes through a scan statistic approach. It uses summary association statistics from genome-wide association studies (GWAS) as input and is robust to sample overlap between studies. Applied to seven phenotypically distinct but genetically correlated neuropsychiatric traits, we identify 227 non-overlapping genome regions associated with multiple traits, including multiple hub regions showing concordant effects on five or more traits. Our method addresses critical limitations in existing analytic strategies and may have wide applications in post-GWAS analysis.

  侯琳，清华大学统计学研究中心副教授、博士生导师，主要从事生物统计、生物信息、统计遗传学等方向的研究。担任中国现场统计研究会计算统计分会常务理事、秘书长；Statistics in Biosciences编委。研究成果发表在Nature Communications， PNAS，Bioinformatics， PLOS Computational Biology，Human Molecular Genetics等期刊。