Abstract
Genome-wide association studies (GWAS) have successfully discovered hundreds of associations between genetic variants and complex traits. Most GWAS have focused on the identification of single variants. It has been shown that most of the variants that were discovered by GWAS could only partially explain disease heritability. The explanation for this missing heritability is generally believed to be gene-gene (GG) or gene-environment (GE) interactions and other structural variants. Generalized multifactor dimensionality reduction (GMDR) has been proven to be reasonably powerful in detecting GG and GE interactions; however, its performance has been found to decline when outlying quantitative traits are present. This paper proposes a robust GMDR estimation method (based on the L-estimator and M-estimator estimation methods) in an attempt to reduce the effects caused by outlying traits. A comparison of robust GMDR with the original MDR based on simulation studies showed the former method to outperform the latter. The performance of robust GMDR is illustrated through a real GWA example consisting of 8,577 samples from the Korean population using the Homeostasis Model Assessment of Insulin Resistance (HOMA-IR) level as a phenotype. Robust GMDR identified the KCNH1 gene to have strong interaction effects with other genes on the function of insulin secretion.
Highlights
A genome-wide association (GWA) study has become a common approach for testing the association between a single nucleotide polymorphism (SNP) and a complex trait of interest [1]
Simple example, it is demonstrated that the power of the Generalized Multifactor Dimensionality Reduction (GMDR) method may decline in the presence of outlying observations
This problem was addressed by proposing the robust GMDR: L-estimator GMDR and M-estimator GMDR
Summary
A genome-wide association (GWA) study has become a common approach for testing the association between a single nucleotide polymorphism (SNP) and a complex trait of interest [1]. SNPs that were identified by GWAS have been shown to explain only a small fraction of disease etiology, because the relatedness between complex diseases and multiple genes and/or their interactions are ignored. For this reason, the analysis of gene-gene (GG) and geneenvironment (GE) interactions have been emphasized as a new alternative for understanding the etiology of common complex traits. The issue of data sparseness can be addressed by using exponentially large sample sizes when parametric statistical methods
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
