ArticleFrontiers in genetics2025
fastMETA: a fast and efficient tool for multivariate meta-analysis of GWAS.
Article in Frontiers in genetics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Genome-Wide Association Studies (GWAS) have transformed human genetics by identifying thousands of loci associated with complex traits and diseases. Yet, individual GWAS are often underpowered, and traditional meta-analysis methods - though widely used in tools such as METAL, GWAMA, and PLINK- typically analyze one trait at a time. This univariate focus risks overlooking pleiotropy and the correlations among traits that underlie complex genetic architectures. To address this gap, we introduce fastMETA, a novel and computationally efficient framework for multivariate meta-analysis of GWAS summary statistics. fastMETA implements an adaptation of the marginal method of moments (MmoM), avoiding the computational burden of hierarchical multivariate models while retaining statistical rigor. Three estimation strategies are provided: (i) a direct replication of the classical MmoM, (ii) a Pearson correlation-based approach, and (iii) a new method that aggregates correlations across all SNPs to estimate a stable trait correlation matrix. This last approach is particularly suited to large-scale GWAS, where within-study correlations are rarely available. We benchmarked fastMETA against existing multivariate meta-analysis packages (mvmeta in R and Stata, xmeta in R) using both real and synthetic datasets. Across scenarios, fastMETA consistently achieved 15-20× faster runtimes while maintaining high concordance with established methods. Applications included: (a) a bivariate meta-analysis of pediatric musculoskeletal traits, replicating pleiotropic effects at the TOM1L2/SREBF1 locus; (b) a multivariate meta-analysis of inflammatory bowel disease gene-expression data, showing near-identical results to published findings; and (c) a large set of genetic association meta-analyses, demonstrating robustness even when within-study correlations were ignored. By combining speed, robustness, and flexibility, fastMETA enables researchers to efficiently explore pleiotropy and complex trait relationships in modern GWAS. Its open-source Python implementation is available both as a standalone tool and as a web service (https://github.com/pbagos/fastMETA), lowering barriers to adoption. Importantly, fastMETA provides a practical and scalable solution for the next-generation of genomic meta-analyses, supporting deeper insights into the genetic basis of multifactorial diseases.
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