rqt: an R package for gene-level meta-analysis.
Ilya Y Zhbannikov1, Konstantin G Arbeev1, Anatoliy I Yashin1
1Biodemography of Aging Research Unit (BARU), Social Science Research Institute, Duke University, Durham, NC 27708, USA.
Bioinformatics (Oxford, England)
|June 22, 2017
Summary
This study introduces an R package, rqt, for gene-level Genome-Wide Association Studies (GWAS) meta-analysis. It effectively addresses the challenge of calculating effect sizes and p-values for entire genes, demonstrating applicability in Alzheimer's disease research.
Area of Science:
- Genetics
- Bioinformatics
- Statistical Genetics
Background:
- Calculating gene-level effect sizes and p-values remains a challenge in Genome-Wide Association Studies (GWAS).
- Existing GWAS methods often focus on single variants, limiting comprehensive gene-level analysis.
Purpose of the Study:
- To develop and present an R package, rqt, for performing gene-level GWAS meta-analysis.
- To provide a tool that can be integrated into bioinformatics pipelines or used independently.
Main Methods:
- Development of the R package 'rqt'.
- Application of the 'rqt' package to Alzheimer's disease datasets (CHS, FHS, LOADFS).
- Gene-level meta-analysis approach for association testing.
Main Results:
- The 'rqt' package facilitates gene-level GWAS meta-analysis.
- Demonstrated applicability of the tool through meta-analysis of three Alzheimer's disease studies.
- Successful association testing at the gene level was achieved.
Conclusions:
- The 'rqt' package offers a valuable solution for gene-level GWAS meta-analysis.
- The tool is user-friendly and applicable to real-world genetic association studies.
- This approach enhances the ability to identify genes associated with complex diseases like Alzheimer's.
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