A survey about methods dedicated to epistasis detection

Abstract

During the past decade, findings of genome-wide association studies (GWAS) improved our knowledge and understanding of disease genetics. To date, thousands of SNPs have been associated with diseases and other complex traits. Statistical analysis typically looks for association between a phenotype and a SNP taken individually via single-locus tests. However, geneticists admit this is an oversimplified approach to tackle the complexity of underlying biological mechanisms. Interaction between SNPs, namely epistasis, must be considered. Unfortunately, epistasis detection gives rise to analytic challenges since analyzing every SNP combination is at present impractical at a genome-wide scale. In this review, we will present the main strategies recently proposed to detect epistatic interactions, along with their operating principle. Some of these methods are exhaustive, such as multifactor dimensionality reduction, likelihood ratio-based tests or receiver operating characteristic curve analysis; some are non-exhaustive, such as machine learning techniques (random forests, Bayesian networks) or combinatorial optimization approaches (ant colony optimization, computational evolution system).

PWAS: Proteome-Wide Association Study

“…Although there are strong indications that the commonly used additive model can capture most of the heritability of complex human traits 40 , non-additive and epistatic effects play key role in many phenotypes 41 . While there have been efforts to address epistatic effects in GWAS 42 , the special case of recessive inheritance in complex traits has been largely neglected. Our results show that recessive inheritance is indeed substantial in a variety of phenotypes.…”

Network-Guided Biomarker Discovery

“…Non-additive epistatic effects are believed to play an important role in a number of human diseases, such as breast cancer [71], ovarian cancer [72], hypertension [73], or type-2 diabetes [74]. A large number of methods, reviewed in [75], have been proposed to perform exhaustive association tests between pairs of SNPs and a phenotype. A first step to address the lack of approaches relying on biological networks for the detection of non-linear interaction effects between SNPs and a phenotype would be to combine them with the approaches outlined above; the penalized relevance framework lends itself particularly well to this.…”

EpiRL: A Reinforcement Learning Agent to Facilitate Epistasis Detection

“…Exhaustive combinatorial search methods like Multifactor Dimensionality Reduction (MDR) (Yang et al, 2018) have been shown successful, but only in small genome-scale due to computational complexity. Later, attempts to reduce search spaces exhibit efficiency, like ReliefF and Spatially Uniform ReliefF (Niel et al, 2015). Besides, machine learning-based algorithms gain popularity.…”