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Updated: May 8, 2026
08:27
Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
Published on: July 27, 2021
ON MODEL SELECTION STRATEGIES TO IDENTIFY GENES UNDERLYING BINARY TRAITS USING GENOME-WIDE ASSOCIATION DATA
1Department of Mathematical Sciences, Worcester Polytechnic Institute, 100 Institute Road, Worecester, MA, 01609, USA, URL: http:users.wpi.edu/zheyangwu/
Statistica Sinica
|August 20, 2013
Summary
Comparing genetic analysis methods, this study shows joint marker analysis is more powerful than single-marker analysis for disease discovery, especially with gene-gene interactions. This framework aids in selecting optimal strategies for genetic association studies.
Area of Science:
- Genetics
- Statistical Genetics
- Bioinformatics
Background:
- Genome-wide association studies (GWAS) commonly use single-marker analysis.
- Identifying genetic variants associated with diseases requires understanding the power of joint vs. single-marker analysis, particularly with gene-gene interactions.
Purpose of the Study:
- To develop a statistical framework for analytical power calculations comparing different model search strategies in genetic association studies.
- To assess the power of joint multi-marker analysis versus single-marker analysis in detecting disease-associated loci.
Main Methods:
- Analytical power calculations for marginal, exhaustive, forward, and two-stage screening search strategies.
- Incorporation of linkage disequilibrium, random genotypes, and correlations among logistic regression score tests.
- Derivation of results under two power definitions and two error control types (discovery number and Bonferroni).
Main Results:
- Demonstrated accuracy of analytical results through simulations.
- Investigated relative performances of different model search strategies across a broad genetic model space.
- Provided insights into the statistical mechanisms of signal capture, including gene-gene interactions.
Conclusions:
- Joint analysis of multiple markers offers greater power for disease variant discovery in GWAS compared to single-marker analysis.
- The developed analytical framework provides rapid computation and generalizable insights into model selection procedures for genetic association and other studies.
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