Fast and accurate exhaustive higher-order epistasis search with BitEpi

Interaction models matter: an efficient, flexible computational framework for model-specific investigation of epistasis

PURPOSE

Epistasis, the interaction between two or more genes, is integral to the study of genetics and is present throughout nature. Yet, it is seldom fully explored as most approaches primarily focus on single-locus effects, partly because analyzing all pairwise and higher-order interactions requires significant computational resources. Furthermore, existing methods for epistasis detection only consider a Cartesian (multiplicative) model for interaction terms. This is likely limiting as epistatic interactions can evolve to produce varied relationships between genetic loci, some complex and not linearly separable.

METHODS

We present new algorithms for the interaction coefficients for standard regression models for epistasis that permit many varied models for the interaction terms for loci and efficient memory usage. The algorithms are given for two-way and three-way epistasis and may be generalized to higher order epistasis. Statistical tests for the interaction coefficients are also provided. We also present an efficient matrix based algorithm for permutation testing for two-way epistasis. We offer a proof and experimental evidence that methods that look for epistasis only at loci that have main effects may not be justified. Given the computational efficiency of the algorithm, we applied the method to a rat data set and mouse data set, with at least 10,000 loci and 1,000 samples each, using the standard Cartesian model and the XOR model to explore body mass index.

RESULTS

This study reveals that although many of the loci found to exhibit significant statistical epistasis overlap between models in rats, the pairs are mostly distinct. Further, the XOR model found greater evidence for statistical epistasis in many more pairs of loci in both data sets with almost all significant epistasis in mice identified using XOR. In the rat data set, loci involved in epistasis under the XOR model are enriched for biologically relevant pathways.

CONCLUSION

Our results in both species show that many biologically relevant epistatic relationships would have been undetected if only one interaction model was applied, providing evidence that varied interaction models should be implemented to explore epistatic interactions that occur in living systems.

Novel Alzheimer's disease genes and epistasis identified using machine learning GWAS platform

Alzheimer's disease (AD) is a complex genetic disease, and variants identified through genome-wide association studies (GWAS) explain only part of its heritability. Epistasis has been proposed as a major contributor to this 'missing heritability', however, many current methods are limited to only modelling additive effects. We use VariantSpark, a machine learning approach to GWAS, and BitEpi, a tool for epistasis detection, to identify AD associated variants and interactions across two independent cohorts, ADNI and UK Biobank. By incorporating significant epistatic interactions, we captured 10.41% more phenotypic variance than logistic regression (LR). We validate the well-established AD loci, APOE, and identify two novel genome-wide significant AD associated loci in both cohorts, SH3BP4 and SASH1, which are also in significant epistatic interactions with APOE. We show that the SH3BP4 SNP has a modulating effect on the known pathogenic APOE SNP, demonstrating a possible protective mechanism against AD. SASH1 is involved in a triplet interaction with pathogenic APOE SNP and ACOT11, where the SASH1 SNP lowered the pathogenic interaction effect between ACOT11 and APOE. Finally, we demonstrate that VariantSpark detects disease associations with 80% fewer controls than LR, unlocking discoveries in well annotated but smaller cohorts.

Leveraging the genetic correlation between traits improves the detection of epistasis in genome-wide association studies

Epistasis, commonly defined as the interaction between genetic loci, is known to play an important role in the phenotypic variation of complex traits. As a result, many statistical methods have been developed to identify genetic variants that are involved in epistasis, and nearly all of these approaches carry out this task by focusing on analyzing one trait at a time. Previous studies have shown that jointly modeling multiple phenotypes can often dramatically increase statistical power for association mapping. In this study, we present the "multivariate MArginal ePIstasis Test" (mvMAPIT)-a multioutcome generalization of a recently proposed epistatic detection method which seeks to detect marginal epistasis or the combined pairwise interaction effects between a given variant and all other variants. By searching for marginal epistatic effects, one can identify genetic variants that are involved in epistasis without the need to identify the exact partners with which the variants interact-thus, potentially alleviating much of the statistical and computational burden associated with conventional explicit search-based methods. Our proposed mvMAPIT builds upon this strategy by taking advantage of correlation structure between traits to improve the identification of variants involved in epistasis. We formulate mvMAPIT as a multivariate linear mixed model and develop a multitrait variance component estimation algorithm for efficient parameter inference and P-value computation. Together with reasonable model approximations, our proposed approach is scalable to moderately sized genome-wide association studies. With simulations, we illustrate the benefits of mvMAPIT over univariate (or single-trait) epistatic mapping strategies. We also apply mvMAPIT framework to protein sequence data from two broadly neutralizing anti-influenza antibodies and approximately 2,000 heterogeneous stock of mice from the Wellcome Trust Centre for Human Genetics. The mvMAPIT R package can be downloaded at https://github.com/lcrawlab/mvMAPIT.

Epi-MEIF: detecting higher order epistatic interactions for complex traits using mixed effect conditional inference forests

Understanding the relationship between genetic variations and variations in complex and quantitative phenotypes remains an ongoing challenge. While Genome-wide association studies (GWAS) have become a vital tool for identifying single-locus associations, we lack methods for identifying epistatic interactions. In this article, we propose a novel method for higher-order epistasis detection using mixed effect conditional inference forest (epiMEIF). The proposed method is fitted on a group of single nucleotide polymorphisms (SNPs) potentially associated with the phenotype and the tree structure in the forest facilitates the identification of n-way interactions between the SNPs. Additional testing strategies further improve the robustness of the method. We demonstrate its ability to detect true n-way interactions via extensive simulations in both cross-sectional and longitudinal synthetic datasets. This is further illustrated in an application to reveal epistatic interactions from natural variations of cardiac traits in flies (Drosophila). Overall, the method provides a generalized way to identify higher-order interactions from any GWAS data, thereby greatly improving the detection of the genetic architecture underlying complex phenotypes.

Data-driven platform for identifying variants of interest in COVID-19 virus

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We provide an automated way to identify emerging variants of concern using viral genome and patient-outcome data.

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We assembled 10,000 sample-strong case-control dataset and identified 117 single nucleotide variants (SNV) associated with adverse patient outcomes.

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We observe co-evolution of protective and pathogenic interactions between the spike, nsp14, and N region with either orf3a or nsp3.

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Structural modelling reveals mutation clusters in the Zn binding domain of nsp14 suggesting ongoing adaptation to the human host.

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Our approach identified Variants Being Monitored (VBM) a week before they were flagged by Health Organizations and offers a clade-independent function-orientated grouping.

New SARS-CoV-2 variants emerge as part of the virus’ adaptation to the human host. The Health Organizations are monitoring newly emerging variants with suspected impact on disease or vaccination efficacy as Variants Being Monitored (VBM), like Delta and Omicron. Genetic changes (SNVs) compared to the Wuhan variant characterize VBMs with current emphasis on the spike protein and lineage markers. However, monitoring VBMs in such a way might miss SNVs with functional effect on disease.

Here we introduce a lineage-agnostic genome-wide approach to identify SNVs associated with disease. We curated a case-control dataset of 10,520 samples and identified 117 SNVs significantly associated with adverse patient outcome. While 40% (47) SNV are already monitored and 36% (43) are in the spike protein, we also identified 70 new SNVs that are associated with disease outcome. 31 of these are disease-worsening and predominantly located in the 3′-5′ exonuclease (NSP14) with structural modelling revealing a concise cluster in the Zn binding domain that has known host-immune modulating function. Furthermore, we generate clade-independent VBM groupings by identifying interacting SNVs (epistasis). We find 37 sets of higher-order epistatic interactions joining 5 genomic regions (nsp3, nsp14, Spike S1, ORF3a, N). Structural modelling of these regions provides insights into potential mechanistic pathways of increased virulence as well as orthogonal methods of validation.

Clade-independent monitoring of functionally interacting (epistasis, co-evolution) SNVs detected emerging VBM a week before they were flagged by Health Organizations and in conjunction with structural modelling provides faster, mechanistic insight into emerging strains to guide public health interventions.