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Composite kernel machine regression based on likelihood ratio test for joint testing of genetic and gene-environment
Ni Zhao1, Haoyu Zhang1, Jennifer J Clark2
1Department of Biostatistics, Johns Hopkins University, Baltimore, Maryland.
Biometrics
|November 16, 2018
Summary
This study introduces a new kernel machine regression framework to analyze gene-environment (GE) interactions for complex diseases. The method improves statistical power in gene mapping by modeling SNP-sets and GE interactions effectively.
Area of Science:
- Genetics
- Biostatistics
- Computational Biology
Background:
- Common human diseases arise from complex gene-environment (GE) interactions.
- Current SNP-by-SNP analysis methods for GE interactions have limitations, including reduced power and inability to model epistasis.
- Improved statistical methods are needed for robust gene mapping in complex diseases.
Purpose of the Study:
- To develop a novel kernel machine regression framework to model the overall genetic effect of single nucleotide polymorphism (SNP) sets, incorporating gene-environment (GE) interactions.
- To address limitations of traditional SNP-by-SNP approaches in gene mapping studies.
- To enhance statistical power for detecting genetic associations influenced by environmental factors.
Main Methods:
- Developed a kernel machine regression framework utilizing a composite kernel to model both SNP main effects and GE interactions nonparametrically.
- Constructed the composite kernel as a weighted average of kernels for genetic main effects and GE interactions.
- Proposed likelihood ratio test (LRT) and restricted likelihood ratio test (RLRT) for statistical significance, with Monte Carlo methods for finite sample distributions.
Main Results:
- The proposed kernel machine regression framework demonstrated correct type I error rates in simulations and real data analysis.
- The method showed higher statistical power compared to score-based approaches in various scenarios.
- The framework effectively models overall genetic effects of SNP-sets and accounts for GE interactions.
Conclusions:
- The developed kernel machine regression framework offers a powerful and robust approach for gene mapping studies involving gene-environment interactions.
- This method overcomes limitations of traditional SNP-by-SNP analyses, improving the ability to detect complex genetic associations.
- The findings suggest broader applicability in understanding the genetic architecture of common human diseases.
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