Reproduction and In-Depth Evaluation of Genome-Wide Association Studies and Genome-Wide Meta-analyses Using Summary Statistics

文献类型： 外文期刊

第一作者： Niu, Yao-Fang

作者： Niu, Yao-Fang;Guo, Long-Biao;Ye, Chengyin;Zheng, Hou-Feng;He, Ji;Han, Fang;Zheng, Hou-Feng;Chen, Guo-Bo

作者机构：

关键词： GWAS;Arabidopsis;magnesium;transparency;reproducibility;naive summary statistics;meta-analyses;GWAMA;GEAR

期刊名称：G3-GENES GENOMES GENETICS （ 影响因子：3.154； 五年影响因子：3.369 ）

ISSN： 2160-1836

年卷期： 2017 年 7 卷 3 期

页码：

收录情况： SCI

摘要： In line with open-source genetics, we report a novel linear regression technique for genome-wide association studies (GWAS), called Open GWAS algoriTHm (OATH). When individual-level data are not available, OATH can not only completely reproduce reported results from an experimental model, but also recover underreported results from other alternative models with a different combination of nuisance parameters using naive summary statistics (NSS). OATH can also reliably evaluate all reported results in-depth (e.g., p-value variance analysis), as demonstrated for 42 Arabidopsis phenotypes under three magnesium (Mg) conditions. In addition, OATH can be used for consortium-driven genome-wide association meta-analyses (GWAMA), and can greatly improve the flexibility of GWAMA. A prototype of OATH is available in the Genetic Analysis Repository (https://github.com/gc5k/GEAR).

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