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Published on: November 12, 2012
Gene-based interaction analysis by incorporating external linkage disequilibrium information.
Jing He1, Kai Wang, Andrew C Edmondson
1Department of Biostatistics and Epidemiology, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA.
European Journal of Human Genetics : EJHG
|October 7, 2010
Summary
This study introduces a principal component (PC)-based framework for gene-based interaction analysis, improving power over SNP-SNP tests for complex diseases. The method effectively detects gene-gene interactions, even with imputed data, enhancing disease association studies.
Area of Science:
- Genetics
- Bioinformatics
- Computational Biology
Background:
- Gene-gene interactions are crucial in complex human diseases but challenging to detect.
- Standard SNP-SNP interaction tests may lack power due to ignoring linkage disequilibrium (LD) and numerous comparisons.
Purpose of the Study:
- To develop a more powerful gene-based interaction analysis framework.
- To improve the detection of complex gene-gene interactions in human diseases.
Main Methods:
- Proposed a principal component (PC)-based framework for gene-based interaction analysis.
- Derived optimal weights for quantitative and binary traits using pairwise LD.
- Extended the method to incorporate imputation dosage scores (e.g., from MACH) with multilocus LD information.
Main Results:
- Gene-based interaction tests demonstrated higher power than SNP-based tests when multiple variants interact.
- Tests incorporating external LD information generally outperformed those using only genotyped markers.
- The method was successfully applied to a high-density lipoprotein candidate gene study.
Conclusions:
- The PC-based gene-level analysis framework enhances the power to detect gene-gene interactions.
- This approach is suitable for genome-wide association studies and can serve as a screening tool for gene-gene interactions.
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