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fastMETA: a fast and efficient tool for multivariate meta-analysis of GWAS
Georgios A Manios1, Dionysios Kandylas1, Athanasios Kylonis1
1Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece.
Frontiers in Genetics
|January 5, 2026
Summary
fastMETA offers a computationally efficient framework for multivariate meta-analysis of Genome-Wide Association Studies (GWAS) summary statistics. This novel approach enables researchers to efficiently explore pleiotropy and complex trait relationships in large-scale genetic studies.
Area of Science:
- Genetics and Genomics
- Statistical Bioinformatics
- Computational Biology
Background:
- Genome-Wide Association Studies (GWAS) identify genetic loci for complex traits but often lack statistical power.
- Traditional univariate meta-analysis methods analyze traits individually, risking the discovery of pleiotropy and trait correlations.
- Existing multivariate methods can be computationally intensive, limiting their application in large-scale genetic analyses.
Purpose of the Study:
- To introduce fastMETA, a novel, computationally efficient framework for multivariate meta-analysis of GWAS summary statistics.
- To address the limitations of univariate analyses by enabling the exploration of pleiotropy and complex trait relationships.
- To provide a scalable and robust tool for next-generation genomic meta-analyses.
Main Methods:
- fastMETA implements an adaptation of the marginal method of moments (MmoM) for computational efficiency.
- It offers three estimation strategies: classical MmoM, Pearson correlation-based, and an SNP-aggregated correlation matrix approach.
- The framework was benchmarked against existing multivariate meta-analysis packages using real and synthetic datasets.
Main Results:
- fastMETA demonstrated 15-20x faster runtimes compared to existing methods while maintaining high concordance.
- Applications showed successful replication of pleiotropic effects and near-identical results to published findings in complex diseases.
- The method proved robust, even when within-study correlations were unavailable, highlighting its flexibility for large-scale GWAS.
Conclusions:
- fastMETA provides a practical, scalable, and computationally efficient solution for multivariate meta-analysis of GWAS.
- The framework facilitates the efficient exploration of pleiotropy and complex trait genetic architectures.
- Its open-source Python implementation and web service lower adoption barriers, supporting deeper insights into multifactorial diseases.
