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.
Highlights
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
Under the assumption that the genetic effects are similar in different studies, the magnitude of the Z-score statistic should be approximately proportional to the square root of the sample size
We presented a model-based method, MAMBA, for identifying non-zero replicable signals from a GWAMA and refining genetic effect estimates
Summary
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. Genome wide association meta-analysis (GWAMA) is an effective approach to enlarge sample size and empower the discovery of genetic variants associated with complex traits. Large datasets produce more significant p-values compared to smaller studies when the estimated association effect size (either genuine or spurious) is the same, so the significance of association in each cohort is not a reliable measure for replicability Some of these methods (e.g. repfdr18) were developed for a few biological replicates and cannot scale well with meta-analyses with many participating studies. We address the limitations of existing methods by developing a principled approach MAMBA (Meta-Analysis Model-based Assessment of replicability) to assess the replicability of GWAMA association signals. MAMBA further expands the utility of imputation-based genetic studies to robustly study rare variants
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