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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
Published on: June 21, 2018
Powerful SNP-set analysis for case-control genome-wide association studies.
Michael C Wu1, Peter Kraft, Michael P Epstein
1Department of Biostatistics, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
American Journal of Human Genetics
|June 22, 2010
Summary
This study introduces SNP-set analysis, a novel method for Genome-Wide Association Studies (GWAS). SNP-set analysis improves the detection of genetic variants associated with disease risk by testing joint effects, outperforming traditional single-SNP approaches.
Area of Science:
- Genetics
- Statistical Genetics
- Bioinformatics
Background:
- Genome-Wide Association Studies (GWAS) are widely used to identify genetic variants linked to disease risk.
- Standard single-nucleotide polymorphism (SNP) analysis in GWAS faces challenges with reproducibility and detecting complex genetic interactions.
- Existing multimarker tests have limitations in modeling nonlinear and epistatic effects.
Purpose of the Study:
- To propose and evaluate an alternative analytical strategy for GWAS using SNP-set analysis.
- To develop a method that can effectively test the joint effect of multiple SNPs within genomic regions.
- To compare the performance of SNP-set analysis against standard individual-SNP analysis.
Main Methods:
- Grouping SNPs into sets based on genomic features like genes or haplotype blocks.
- Utilizing a logistic kernel-machine-based test to assess the joint effect of each SNP set.
- Employing simulated data from the International HapMap Project for performance evaluation.
- Applying the method to the Cancer Genetic Markers of Susceptibility (CGEMS) breast cancer GWAS data.
Main Results:
- SNP-set testing demonstrated improved statistical power compared to individual-SNP analysis across various scenarios.
- The proposed method showed higher power when the correlation between disease-susceptibility variants and genotyped SNPs was moderate to high.
- Both methods exhibited low power when the correlation was low.
Conclusions:
- SNP-set analysis offers a powerful and flexible alternative to standard GWAS methods for identifying disease-associated genetic variants.
- This approach effectively models epistatic and nonlinear SNP effects, enhancing the detection of complex genetic architectures.
- The method shows promise for application in large-scale genetic studies, including the analysis of breast cancer GWAS data.
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