Model-based assessment of replicability for genome-wide association meta-analysis
Authors: McGuire Daniel , Jiang Yu , Liu Mengzhen , Weissenkampen J. Dylan , Eckert Scott , Yang Lina , Chen Fang ,   GWAS and Sequencing Consortium of Alcohol and Nicotine Use (GSCAN), Liu Mengzhen , Jiang Yu , Wedow Robbee , Li Yue , Brazel David M. , Chen Fang , Datta Gargi , Davila-Velderrain Jose , McGuire Daniel , Tian Chao , Zhan Xiaowei , Choquet H. éléne , Docherty Anna R. , Faul Jessica D. , Foerster Johanna R. , Fritsche Lars G. , Gabrielsen Maiken Elvestad , Gordon Scott D. , Haessler Jeffrey , Hottenga Jouke-Jan , Huang Hongyan , Jang Seon-Kyeong , Jansen Philip R. , Ling Yueh , Ma ̈gi Reedik , Matoba Nana , McMahon George , Mulas Antonella , Orru Valeria , Palviainen Teemu , Pandit Anita , Reginsson Gunnar W. , Skogholt Anne Heidi , Smith Jennifer A. , Taylor Amy E. , Turman Constance , Willemsen Gonneke , Young Hannah , Young Kendra A. , Zajac Gregory J. M. , Zhao Wei , Zhou Wei , Bjornsdottir Gyda , Boardman Jason D. , Boehnke Michael , Boomsma Dorret I. , Chen Chu , Cucca Francesco , Davies Gareth E. , Eaton Charles B. , Ehringer Marissa A. , Esko To ̃nu , Fiorillo Edoardo , Gillespie Nathan A. , Gudbjartsson Daniel F. , Haller Toomas , Harris Kathleen Mullan , Heath Andrew C. , Hewitt John K. , Hickie Ian B. , Hokanson John E. , Hopfer Christian J. , Hunter David J. , Iacono William G. , Johnson Eric O. , Kamatani Yoichiro , Kardia Sharon L. R. , Keller Matthew C. , Kellis Manolis , Kooperberg Charles , Kraft Peter , Krauter Kenneth S. , Laakso Markku , Lind Penelope A. , Loukola Anu , Lutz Sharon M. , Madden Pamela A. F. , Martin Nicholas G. , McGue Matt , McQueen Matthew B. , Medland Sarah E. , Metspalu Andres , Mohlke Karen L. , Nielsen Jonas B. , Okada Yukinori , Peters Ulrike , Polderman Tinca J. C. , Posthuma Danielle , Reiner Alexander P. , Rice John P. , Rimm Eric , Rose Richard J. , Runarsdottir Valgerdur , Stallings Michael C. , Stanˇca ́kova Alena , Stefansson Hreinn , Thai Khanh K. , Tindle Hilary A. , Tyrfingsson Thorarinn , Wall Tamara L. , Weir David R. , Weisner Constance , Whitfield John B. , Winsvold Bendik Slagsvold , Yin Jie , Zuccolo Luisa , Bierut Laura J. , Hveem Kristian , Lee James J. , Munafo Marcus R. , Saccone Nancy L. , Willer Cristen J. , Cornelis Marilyn C. , David Sean P. , Hinds David , Jorgenson Eric , Kaprio Jaakko , Stitzel Jerry A. , Stefansson Kari , Thorgeirsson Thorgeir E. , Abecasis Goncalo , Liu Dajiang J. , Vrieze Scott , Berg Arthur , Vrieze Scott , Jiang Bibo , Li Qunhua , Liu Dajiang J. 
Published: March 2021

Genome-wide association meta-analysis (GWAMA) is an effective approach to enlarge sample sizes and empower the discovery of novel associations between genotype and phenotype. Independent replication has been used as a gold-standard for validating genetic associations. However, as current GWAMA often seeks to aggregate all available datasets, it becomes impossible to find a large enough independent dataset to replicate new discoveries. Here we introduce a method, MAMBA (Meta-Analysis Model-based Assessment of replicability), for assessing the “posterior-probability-of-replicability” for identified associations by leveraging the strength and consistency of association signals between contributing studies. We demonstrate using simulations that MAMBA is more powerful and robust than existing methods, and produces more accurate genetic effects estimates. We apply MAMBA to a large-scale meta-analysis of addiction phenotypes with 1.2 million individuals. In addition to accurately identifying replicable common variant associations, MAMBA also pinpoints novel replicable rare variant associations from imputation-based GWAMA and hence greatly expands the set of analyzable variants.