Multivariate Cluster-Based Multifactor Dimensionality Reduction to Identify Genetic Interactions for Multiple Quantitative Phenotypes.

Hyein Kim,Taesung Park,Hye-Young Jung,Mira Park,Hoe-Bin Jeong

doi:10.1155/2019/4578983

Hyein Kim, Taesung Park + Show 3 more

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https://doi.org/10.1155/2019/4578983

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Abstract

To understand the pathophysiology of complex diseases, including hypertension, diabetes, and autism, deleterious phenotypes are unlikely due to the effects of single genes, but rather, gene-gene interactions (GGIs), which are widely analyzed by multifactor dimensionality reduction (MDR). Early MDR methods mainly focused on binary traits. More recently, several extensions of MDR have been developed for analyzing various traits such as quantitative traits and survival times. Newer technologies, such as genome-wide association studies (GWAS), have now been developed for assessing multiple traits, to simultaneously identify genetic variants associated with various pathological phenotypes. It has also been well demonstrated that analyzing multiple traits has several advantages over single trait analysis. While there remains a need to find GGIs for multiple traits, such studies have become more difficult, due to a lack of novel methods and software. Herein, we propose a novel multi-CMDR method, by combining fuzzy clustering and MDR, to find GGIs for multiple traits. Multi-CMDR showed similar power to existing methods, when phenotypes followed bivariate normal distributions, and showed better power than others for skewed distributions. The validity of multi-CMDR was confirmed by analyzing real-life Korean GWAS data.

Highlights

In genome-wide association studies (GWAS), genotype data from a large number of single nucleotide polymorphisms (SNPs) are collected, to associate SNPs with traits of interest [1]
We conducted simulations to compare the performance of the proposed multiCMDR method, with multi-quantitative MDR (QMDR) and univariate QMDR methods
We compared the performance of the two versions of multi-CMDR

Summary

Introduction

In genome-wide association studies (GWAS), genotype data from a large number of single nucleotide polymorphisms (SNPs) are collected, to associate SNPs with traits of interest [1]. LRMs perform poorly when there is a dimensionality problem Another well-known approach is a multifactor dimensionality reduction (MDR) method [3, 4], which reduces dimensions by converting a high-dimensional to a one-dimensional model. An MDR can avoid the issues of sparse data cells and overparameterization of models [2] and can outperform LRMs, for detecting higher order GGIs [5]. Various approaches such as using multiple contingency table (MODENDR) [6] or particle swarm optimization method (PBMDR) have been developed [7]

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Discussion

Conclusion