TrioMDR: Detecting SNP interactions in trio families with model-based multifactor dimensionality reduction

Abstract

Single nucleotide polymorphism (SNP) interactions can explain the missing heritability of common complex diseases. Many interaction detection methods have been proposed in genome-wide association studies, and they can be divided into two types: population-based and family-based. Compared with population-based methods, family-based methods are robust vs. population stratification. Several family-based methods have been proposed, among which Multifactor Dimensionality Reduction (MDR)-based methods are popular and powerful. However, current MDR-based methods suffer from heavy computational burden. Furthermore, they do not allow for main effect adjustment. In this work we develop a two-stage model-based MDR approach (TrioMDR) to detect multi-locus interaction in trio families (i.e., two parents and one affected child). TrioMDR combines the MDR framework with logistic regression models to check interactions, so TrioMDR can adjust main effects. In addition, unlike consuming permutation procedures used in traditional MDR-based methods, TrioMDR utilizes a simple semi-parameter P-values correction procedure to control type I error rate, this procedure only uses a few permutations to achieve the significance of a multi-locus model and significantly speeds up TrioMDR. We performed extensive experiments on simulated data to compare the type I error and power of TrioMDR under different scenarios. The results demonstrate that TrioMDR is fast and more powerful in general than some recently proposed methods for interaction detection in trios. The R codes of TrioMDR are available at: https://github.com/TrioMDR/TrioMDR.

<p><em>CHDH-PNPLA3</em> Gene–Gene Interactions Predict Insulin Resistance in Children with Obesity</p>

“…MDR is increasingly popular and has been recognized as a robust methodology for the evaluation of gene-gene interaction effects. [42][43][44] Different studies have chosen different SNPs to evaluate the association with insulin resistance, using a hypothesis-driven approach, as presented in the 2017 ISNN consensus regarding nutrigenetic, nutrigenomic and nutriepigenetic approaches for precision nutrition involving the prevention and management of chronic diseases associated with obesity. 39 Most of this research has used results from Genome Wide Association Studies (GWAS) to identify the effects of genetic variants on the disease risk.…”