Meta-analysis of quantitative pleiotropic traits for next-generation sequencing with multivariate functional linear
Chi-Yang Chiu1, Jeesun Jung2, Wei Chen3
1Biostatistics and Bioinformatics Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD USA.
European Journal of Human Genetics : EJHG
|December 22, 2016
Summary
New multivariate models enhance genetic association analysis by combining multiple studies and traits. This approach improves statistical power for complex disorders, outperforming traditional univariate methods.
Area of Science:
- Genetics
- Biostatistics
- Bioinformatics
Background:
- Analyzing complex genetic disorders requires robust methods to link genetic variants to multiple quantitative traits.
- Existing methods often analyze studies or traits individually, limiting statistical power and comprehensive insights.
Purpose of the Study:
- To develop multivariate functional linear models for meta-analysis of genetic variant data across multiple studies and traits.
- To enhance statistical power and enable unified association analysis for complex disorders.
Main Methods:
- Development of multivariate functional linear models incorporating meta-analysis and pleiotropic analysis.
- Introduction of approximate F-distributions (Pillai-Bartlett, Hotelling-Lawley, Wilks's Lambda) for association testing.
- Simulation studies to evaluate false-positive rates and power; application to lipid traits in European cohorts.
Main Results:
- Multivariate analysis demonstrated superior power compared to univariate analysis for genetic association studies.
- Meta-analysis of multiple studies provided greater advantages than analyzing individual studies separately.
- The proposed methods require individual-level genotype data.
Conclusions:
- The developed multivariate functional linear models offer a powerful framework for genetic association studies.
- These methods are valuable for studies with individual genotype data and can inform future work using summary statistics.
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