Evaluation of Existing Methods for High-Order Epistasis Detection

Abstract

Finding epistatic interactions among loci when expressing a phenotype is a widely employed strategy to understand the genetic architecture of complex traits in GWAS. The abundance of methods dedicated to the same purpose, however, makes it increasingly difficult for scientists to decide which method is more suitable for their studies. This work compares the different epistasis detection methods published during the last decade in terms of runtime, detection power and type I error rate, with a special emphasis on high-order interactions. Results show that in terms of detection power, the only methods that perform well across all experiments are the exhaustive methods, although their computational cost may be prohibitive in large-scale studies. Regarding non-exhaustive methods, not one could consistently find epistasis interactions when marginal effects are absent. If marginal effects are present, there are methods that perform well for high-order interactions, such as BADTrees, FDHE-IW, SingleMI or SNPHarvester. As for false-positive control, only SNPHarvester, FDHE-IW and DCHE show good results. The study concludes that there is no single epistasis detection method to recommend in all scenarios. Authors should prioritize exhaustive methods when sufficient computational resources are available considering the data set size, and resort to nonexhaustive methods when the analysis time is prohibitive.

A SIMD algorithm for the detection of epistatic interactions of any order

“…MI quantifies the degree of association between the genotype distribution of cases and controls and the phenotype distribution obtained from the previously calculated contingency table. MI has shown a very good detection power in our previous comprehensive study [12], and the presence of low frequencies in the data, which become more prevalent as we move towards larger combination sizes, do not seem to be a problem.…”

“…For this reason, a multitude of methods have been proposed in order to solve this problem, many of which refrain from exploring every combination and, instead, implement non-exhaustive alternatives ranging from greedy algorithms to machine learning techniques (see, for instance, [6,8,9,10,11]). In a previous study [12], we compared the performance of all epistasis detection methods published during the last decade that offer an implementation available to the scientific community, when locating epistatic interactions of different orders. The study concludes that, despite the rich variety of methods, only the exhaustive approaches (those which explore every combination of loci up to a certain combination size) can reliably identify interactions with no marginal effects.…”

“…Starting from an existing method for third-order epistasis detection (MPI3SNP [16]), Section 2 describes how this method can be expanded to support loci combinations of any size. We selected MPI3SNP due to its good performance shown when locating epistasis [12] and the simplicity of the algorithm implemented. Section 3 explains how each of the operations can be vectorized through AVX Intrinsics using the AVX2 and AVX512BW extensions.…”

Fiuncho: a program for any-order epistasis detection in CPU clusters

“…Fiuncho, as MPI3SNP [4], uses a Mutual Information (MI) based association test. As shown in [2], MI obtains a very good detection power.…”

“…Consequently, they allow for larger GWAS analysis at the cost of the possibility of not finding the target variant combination. Prior to this work, the performance of exhaustive and non-exhaustive methods has been studied thoroughly in [2]. The paper concluded that exhaustive methods are the only ones capable of identifying epistasis interactions in the absence of marginal effects.…”