TY - JOUR
T1 - Model-based assessment of replicability for genome-wide association meta-analysis
AU - GWAS and Sequencing Consortium of Alcohol and Nicotine Use (GSCAN)
AU - McGuire, Daniel
AU - Jiang, Yu
AU - Liu, Mengzhen
AU - Weissenkampen, J. Dylan
AU - Eckert, Scott
AU - Yang, Lina
AU - Chen, Fang
AU - Wedow, Robbee
AU - Li, Yue
AU - Brazel, David M.
AU - Chen, Fang
AU - Datta, Gargi
AU - Davila-Velderrain, Jose
AU - McGuire, Daniel
AU - Tian, Chao
AU - Zhan, Xiaowei
AU - Choquet, Héléne
AU - Docherty, Anna R.
AU - Faul, Jessica D.
AU - Foerster, Johanna R.
AU - Fritsche, Lars G.
AU - Gabrielsen, Maiken Elvestad
AU - Gordon, Scott D.
AU - Haessler, Jeffrey
AU - Hottenga, Jouke Jan
AU - Huang, Hongyan
AU - Jang, Seon Kyeong
AU - Jansen, Philip R.
AU - Ling, Yueh
AU - Ma ̈gi, Reedik
AU - Matoba, Nana
AU - McMahon, George
AU - Mulas, Antonella
AU - Orru, Valeria
AU - Palviainen, Teemu
AU - Pandit, Anita
AU - Reginsson, Gunnar W.
AU - Skogholt, Anne Heidi
AU - Smith, Jennifer A.
AU - Taylor, Amy E.
AU - Turman, Constance
AU - Willemsen, Gonneke
AU - Young, Hannah
AU - Young, Kendra A.
AU - Zhao, Wei
AU - Kraft, Peter
AU - Polderman, Tinca J.C.
AU - Posthuma, Danielle
AU - Berg, Arthur
AU - Vrieze, Scott
AU - Jiang, Bibo
AU - Li, Qunhua
AU - Liu, Dajiang J.
N1 - Publisher Copyright: © 2021, The Author(s).
PY - 2021/3/30
Y1 - 2021/3/30
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85103743681
U2 - 10.1038/s41467-021-21226-z
DO - 10.1038/s41467-021-21226-z
M3 - Article
C2 - 33785739
AN - SCOPUS:85103743681
SN - 2041-1723
VL - 12
JO - Nature Communications
JF - Nature Communications
IS - 1
M1 - 1964
ER -