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Multivariate Methods for Meta-Analysis of Genetic Association Studies.
Niki L Dimou1,2, Katerina G Pantavou1, Georgia G Braliou1
1Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece.
Methods in Molecular Biology (Clifton, N.J.)
|June 8, 2018
Summary
This review unifies methods for multivariate meta-analysis in genetic association studies and genome-wide association studies, enhancing analytical precision. It covers various models, applications, and future directions for robust genetic research.
Area of Science:
- Genetics
- Biostatistics
- Bioinformatics
Background:
- Multivariate meta-analysis enhances precision in genetic association studies (GAS) and genome-wide association studies (GWAS).
- Existing methods require a unified framework for comprehensive understanding and application.
Purpose of the Study:
- To review, summarize, and present a unified framework for multivariate meta-analysis methods in GAS and GWAS.
- To discuss statistical approaches, genetic model selection, and various multivariate models applicable to genetic research.
Main Methods:
- Review of univariate and multivariate meta-analysis techniques for genetic association data.
- Discussion of models for single gene-disease associations, haplotype studies, multiple polymorphisms, and multiple outcomes.
- Inclusion of Mendelian randomization and special meta-analysis cases (Hardy-Weinberg Equilibrium, gene-environment interactions).
Main Results:
- Comprehensive overview of existing multivariate meta-analysis methodologies for genetic studies.
- Presentation of practical applications and potential future methodological developments.
- Provision of links to available software for implementing these methods.
Conclusions:
- A unified framework for multivariate meta-analysis in genetic studies improves analytical precision.
- The review provides a valuable resource for researchers in genetics, biostatistics, and bioinformatics.
- Further development of methodologies and software will advance the field of genetic association analysis.
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