A FAST ALGORITHM FOR DETECTING GENE-GENE INTERACTIONS IN GENOME-WIDE ASSOCIATION STUDIES
Jiahan Li1, Wei Zhong2, Runze Li3
1Department of Applied and Computational Mathematics and Statistics University of Notre Dame Notre Dame, Indiana 46556 USA jli7@nd.edu.
The Annals of Applied Statistics
|October 13, 2015
Summary
This study introduces a new statistical framework to identify gene-gene interactions in genome-wide association studies (GWAS). The two-stage sure independence screening (TS-SIS) method efficiently detects genetic effects and epistasis, crucial for understanding complex traits.
Area of Science:
- Genetics
- Statistical Genetics
- Bioinformatics
Background:
- High-throughput genotyping enables genome-wide association studies (GWAS), but analyzing gene-gene interactions (epistasis) remains challenging due to high dimensionality.
- Epistasis plays a significant role in the genetic variation of complex traits, yet existing methods struggle to detect these interactions effectively.
Purpose of the Study:
- To develop a statistical framework for characterizing main genetic effects and epistatic interactions in GWAS.
- To address the challenge of overwhelming model dimensionality in GWAS by proposing efficient variable selection methods.
Main Methods:
- A two-stage sure independence screening (TS-SIS) procedure to generate candidate SNPs and interactions.
- A rates adjusted thresholding estimation (RATE) approach to determine reduced model size.
- Application of regularization regression methods (LASSO, SCAD) for identifying important genetic effects.
Main Results:
- Simulation studies demonstrate the computational efficiency and strong finite sample performance of TS-SIS in selecting SNPs and gene-gene interactions.
- The framework was applied to Framingham Heart Study GWAS data, identifying 23 active SNPs and 24 active epistatic interactions for body mass index variation.
Conclusions:
- The proposed TS-SIS framework effectively resolves the complexity of genetic control in GWAS.
- The method provides a computationally efficient and powerful tool for detecting both single-nucleotide polymorphism (SNP) associations and epistatic interactions.
Keywords:
GWASGene–gene interactionhigh-dimensional datasure independence screeningvariable selectionMore Related Videos
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