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Duke-NUS医学院 刘瑾:Joint Analysis of Individual-level and Summary-level GWAS Data by Leveraging Pleiotropy

([西财新闻] 发布于 :2018-06-12 )

光华讲坛——社会名流与企业家论坛第5002

 

题:Joint Analysis of Individual-level and Summary-level GWAS Data by Leveraging Pleiotropy

主讲人:Duke-NUS医学院 刘瑾

主持人:统计学院 林华珍教授

间:2018614日(星期四)16:00-17:00

点:西南财经大学柳林校区弘远楼408会议室

主办单位:统计研究中心 统计学院 科研处

 

主讲人简介:

刘瑾博士是Duke-NUS医学院计量医学中心的助理教授, 他的研究兴趣包括在整合遗传和基因组数据方面发展新的统计方法。主要工具有变分贝叶斯、MCMCEM算法、凸优化等。

具体详情请见其个人主页: https://www.duke-nus.edu.sg/content/liu-jin.

内容提要:

A large number of recent genome-wide association studies (GWASs) for complex phenotypes confirm the early conjecture for polygenicity, suggesting the presence of large number of variants with only tiny or moderate effects. However, due to the limited sample size of a single GWAS, many associated genetic variants are too weak to achieve the genome-wide significance. These undiscovered variants further limit the prediction capability of GWAS. Restricted access to the individual-level data and the increasing availability of the published GWAS results motivate the development of methods integrating both the individual-level and summary-level data. How to build the connection between the individual-level and summary-level data determines the efficiency of using the existing abundant summary-level resources with limited individual-level data, and this issue inspires more efforts in the existing area. In this study, we propose a novel statistical approach, LEP, which provides a novel way of modeling the connection between the individual-level data and summary-level data. LEP integrates both types of data by LEveraing Pleiotropy to increase the statistical power of risk variants identification and the accuracy of risk prediction. The algorithm for parameter estimation is developed to handle genome-wide-scale data. Through comprehensive simulation studies, we demonstrated the advantages of LEP over the existing methods. We further applied LEP to perform integrative analysis of Crohn’s disease from WTCCC and summary statistics from GWAS of some other diseases, such as Type 1 diabetes, Ulcerative colitis and Primary biliary cirrhosis. LEP was able to significantly increase the statistical power of identifying risk variants and improve the risk prediction accuracy from 63.39% (± 0.58%) to 68.33% (± 0.32%) using about 195,000 variants.

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