1. Model-based assessment of replicability for genome-wide association meta-analysis
- Author
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McGuire, Daniel, Jiang, Yu, Liu, Mengzhen, Weissenkampen, J. Dylan, Eckert, Scott, Yang, Lina, Chen, Fang, Liu, MengZhen, Wedow, Robbee, Li, Yue, Brazel, David M., Datta, Gargi, Davila-Velderrain, Jose, Tian, Chao, Zhan, Xiaowei, Choquet, H. éléne, Docherty, Anna R., Faul, Jessica D., Foerster, Johanna R., Fritsche, Lars, 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, Tõnu, 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, JP, 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 M, Whitfield, John B., Winsvold, Bendik K S, 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, Jiang, Bibo, Li, Qunhua, Technology Centre, Institute for Molecular Medicine Finland, Genetic Epidemiology, HUSLAB, Centre of Excellence in Complex Disease Genetics, Jaakko Kaprio / Principal Investigator, Department of Public Health, Human genetics, Amsterdam Neuroscience - Complex Trait Genetics, Amsterdam Neuroscience - Compulsivity, Impulsivity & Attention, APH - Aging & Later Life, APH - Mental Health, Child and Adolescent Psychiatry / Psychology, Biological Psychology, APH - Health Behaviors & Chronic Diseases, APH - Personalized Medicine, Complex Trait Genetics, APH - Methodology, and Clinical Developmental Psychology
- Subjects
0301 basic medicine ,Genotype ,Computer science ,Science ,General Physics and Astronomy ,Genome-wide association study ,Genomics ,Computational biology ,Genome-wide association studies ,Polymorphism, Single Nucleotide ,General Biochemistry, Genetics and Molecular Biology ,Article ,03 medical and health sciences ,Consistency (database systems) ,0302 clinical medicine ,Meta-Analysis as Topic ,Replication (statistics) ,Genetic Association Studies ,Multidisciplinary ,biology ,Models, Genetic ,Statistics ,Mamba ,Computational Biology ,Reproducibility of Results ,General Chemistry ,Replicate ,biology.organism_classification ,030104 developmental biology ,Phenotype ,Sample size determination ,Sample Size ,1182 Biochemistry, cell and molecular biology ,3111 Biomedicine ,030217 neurology & neurosurgery ,Imputation (genetics) ,Software ,Algorithms ,Genome-Wide Association Study - Abstract
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., In genome-wide association meta-analysis, it is often difficult to find an independent dataset of sufficient size to replicate associations. Here, the authors have developed MAMBA to calculate the probability of replicability based on consistency between datasets within the meta-analysis.
- Published
- 2021