Maximal Information Coefficient-Based Testing to Identify Epistasis in Case-Control Association Studies.

Yingjie Guo,Lei Xu,Zhen Liang,Zhian Yuan,Yanpeng Wang,Yang Wang,Leyi Wei

doi:10.1155/2022/7843990

Abstract

Interactions between genetic variants (epistasis) are ubiquitous in the model system and can significantly affect evolutionary adaptation, genetic mapping, and precision medical efforts. In this paper, we proposed a method for epistasis detection, called EpiMIC (epistasis detection through a maximal information coefficient (MIC)). MIC is a promising bivariate dependence measure explicitly designed for rapidly exploring various function types equally and for interpreting and comparing them on the same scale. Most epistasis detection approaches make assumptions about the form of the association between genetic variants, resulting in limited statistical performance. Based on the notion that if two SNPs do not interact, their joint distribution in all samples and in only cases should not be substantially different. We developed a statistic that utilizes the difference of MIC as a signal of epistasis and combined it with a permutation resampling strategy to estimate the empirical distribution of our statistic. Results of simulation and real-world data set showed that EpiMIC outperformed previous approaches for identifying epistasis at varying degrees of heredity.

Highlights

Genome-wide association studies (GWAS) is an emerging research strategy for discovering associations between genetic variation (e.g., single nucleotide polymorphism (SNP)) and traits like human diseases
To test EpiMIC’s capacity to handle true epistasis in a casecontrol data set, we examined the susceptibility of a series of pairings of SNPs in rheumatoid arthritis (RA), an inflammatory disease characterized by pannus development in synovial joints and cartilage and bone loss
We developed EpiMIC, which combined maximal information coefficient (MIC) with permutation strategy for case-control studies in GWAS

Summary

Introduction

Genome-wide association studies (GWAS) is an emerging research strategy for discovering associations between genetic variation (e.g., single nucleotide polymorphism (SNP)) and traits like human diseases. More than 71,000 SNPs have been confirmed to be related significantly to diseases [1–3] since the first GWAS study was published in Science in 2005 [4]. The majority of these markers, are common genetic variants with small effects. Even though the whole genome sequencing enables us to detect several rare variants with large effect, “missing heritability” for the complex disease remains extensive [5–7]. One possible explanation for “missing heritability” is that complex diseases are polygenic, with multiple genes, environmental variables, and interactions involved in their etiology [9, 10]. Genetic interactions are thought to provide a potential answer to the problem of “missing heritability.” The solution may be partial, but it may help develop novel gene pathway topologies [11]

Methods

Results

Conclusion