Performance of epistasis detection methods in semi-simulated GWAS

Abstract

BackgroundPart of the missing heritability in Genome Wide Association Studies (GWAS) is expected to be explained by interactions between genetic variants, also called epistasis. Various statistical methods have been developed to detect epistasis in case-control GWAS. These methods face major statistical challenges due to the number of tests required, the complexity of the Linkage Disequilibrium (LD) structure, and the lack of consensus regarding the definition of epistasis. Their limited impact in terms of uncovering new biological knowledge might be explained in part by the limited amount of experimental data available to validate their statistical performances in a realistic GWAS context. In this paper, we introduce a simulation pipeline for generating real scale GWAS data, including epistasis and realistic LD structure. We evaluate five exhaustive bivariate interaction methods, fastepi, GBOOST, SHEsisEpi, DSS, and IndOR. Two hundred thirty four different disease scenarios are considered in extensive simulations. We report the performances of each method in terms of false positive rate control, power, area under the ROC curve (AUC), and computation time using a GPU. Finally we compare the result of each methods on a real GWAS of type 2 diabetes from the Welcome Trust Case Control Consortium.ResultsGBOOST, SHEsisEpi and DSS allow a satisfactory control of the false positive rate. fastepi and IndOR present an increase in false positive rate in presence of LD between causal SNPs, with our definition of epistasis. DSS performs best in terms of power and AUC in most scenarios with no or weak LD between causal SNPs. All methods can exhaustively analyze a GWAS with 6.105 SNPs and 15,000 samples in a couple of hours using a GPU.ConclusionThis study confirms that computation time is no longer a limiting factor for performing an exhaustive search of epistasis in large GWAS. For this task, using DSS on SNP pairs with limited LD seems to be a good strategy to achieve the best statistical performance. A combination approach using both DSS and GBOOST is supported by the simulation results and the analysis of the WTCCC dataset demonstrated that this approach can detect distinct genes in epistasis. Finally, weak epistasis between common variants will be detectable with existing methods when GWAS of a few tens of thousands cases and controls are available.Electronic supplementary materialThe online version of this article (10.1186/s12859-018-2229-8) contains supplementary material, which is available to authorized users.

A new method for exploring gene-gene and gene-environment interactions in GWAS with tree ensemble methods and SHAP values

“…Another issue is the measure of the expected relative contribution for both marginal effects and interaction effects given in Equations (9) and (12). These measures depend on how many individuals are evaluated to compute the SHAP values in each model given by Gp.…”

“…With the increasing focus on epistasis, many exhaustive search algorithms have been developed such as GBOOST, SHEsisEpi and DSS, and by using graphics processing units (GPUs) [55, 51, 20, 18]. It has been shown that a GWAS investigating pairwise SNP-SNP-interactions with 6 · 10 5 SNPs and 15,000 samples can be computed in a couple of hours using the aforementioned algorithms [9]. However, it is expected that the number of samples will increase by hundreds of thousands and possibly millions of individuals over the next several years.…”

Atlas of epistasis

“…Here we present a genome wide epistasis analysis on 502 diseases from the UK Biobank with more than 500 cases using the likelihood ratio test introduced in BOOST 18 . In a previous study we show that BOOST performs accurate type 1 error rate control even in presence of linkage disequilibrium while reaching satisfying statistical power compared to other tools 26 . In order to control for confounding factors, we prepare case control matched cohorts for each phenotype following the method described by Luca et al 27 .…”

A Bioinformatics Crash Course for Interpreting Genomics Data

“…64 It is likely that epistasis plays an important role in complex diseases, and approaches are also available that search for gene-gene interactions. These methods continue to evolve and have been reviewed by Niel et al 65 and Chatelain et al 66 To obtain additional statistical power, genomics studies are routinely being meta-analyzed; a review on meta-analysis approaches can be found in Evangelou and Ioannidis. 67 As technology continues to advance, analyzing multiple omics platforms in the same individuals (multi-omics) is shedding new light on disease mechanisms.…”