Reproduction and In-Depth Evaluation of Genome-Wide Association Studies and Genome-Wide Meta-analyses Using Summary Statistics. Issue 3 (1st March 2017)

• Record Type: Journal Article
• Authors: Niu, Yao-Fang; Ye, Chengyin; He, Ji; Han, Fang; Guo, Long-Biao; Zheng, Hou-Feng; Chen, Guo-Bo
• Is Part Of: G3. Volume 7:Issue 3(2017)
• Page Start: 943
• Page End: 952
• Publication Date: 2017-03-01
• Subjects: GWAS -- Arabidopsis -- magnesium -- transparency -- reproducibility -- naïve summary statistics -- meta-analyses -- GWAMA -- GEAR; Genetics -- Research -- Periodicals; Genomics -- Periodicals; Genetics; Genomics; Genes; Genetics -- Research; Genomics; Electronic journals; Periodical; Periodicals; Fulltext; Internet Resources; Periodicals; 572.8
• DOI: 10.1534/g3.116.038877
• Languages: English
• ISSNs: 2160-1836

Abstract

Abstract: 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 naïve 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 ).