Evaluating the detection ability of a range of epistasis detection methods on simulated data for pure and impure epistatic models

Russ, Dominic; Williams, John A.; Cardoso, Victor Roth; Bravo-Merodio, Laura; Pendleton, Samantha C.; Aziz, Furqan; Acharjee, Animesh; Gkoutos, Georgios V.

doi:10.1371/journal.pone.0263390

Cited by 7 publications

(7 citation statements)

References 57 publications

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“…As expected, a larger MAF at SNP1 or SNP2 also increased the power. Moreover, the vQTL method is also capable of detecting “pure epistasis” (without individual SNP main effects) ( Russ et al, 2022 ), as demonstrated by Figures 1 , 2C .…”

Section: Resultsmentioning

confidence: 95%

“…I considered two situations, as follows, (I) Without SNP main effects: By specifying

(scenario 1), I evaluated the type I error rates given the significance level of 3.6E-10. To demonstrate that this method is also capable of detecting “pure epistasis” (no individual SNP main effects) ( Russ et al, 2022 ), power was assessed at

and

. Because the four SNPs were, in turn, regarded as SNP2, the power was evaluated under scenarios 2–5.…”

Section: Methodsmentioning

confidence: 99%

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Searching for gene-gene interactions through variance quantitative trait loci of 29 continuous Taiwan Biobank phenotypes

Lin

2024

Front. Genet.

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Introduction: After the era of genome-wide association studies (GWAS), thousands of genetic variants have been identified to exhibit main effects on human phenotypes. The next critical issue would be to explore the interplay between genes, the so-called “gene-gene interactions” (GxG) or epistasis. An exhaustive search for all single-nucleotide polymorphism (SNP) pairs is not recommended because this will induce a harsh penalty of multiple testing. Limiting the search of epistasis on SNPs reported by previous GWAS may miss essential interactions between SNPs without significant marginal effects. Moreover, most methods are computationally intensive and can be challenging to implement genome-wide.Methods: I here searched for GxG through variance quantitative trait loci (vQTLs) of 29 continuous Taiwan Biobank (TWB) phenotypes. A discovery cohort of 86,536 and a replication cohort of 25,460 TWB individuals were analyzed, respectively.Results: A total of 18 nearly independent vQTLs with linkage disequilibrium measure r2 < 0.01 were identified and replicated from nine phenotypes. 15 significant GxG were found with p-values <1.1E-5 (in the discovery cohort) and false discovery rates <2% (in the replication cohort). Among these 15 GxG, 11 were detected for blood traits including red blood cells, hemoglobin, and hematocrit; 2 for total bilirubin; 1 for fasting glucose; and 1 for total cholesterol (TCHO). All GxG were observed for gene pairs on the same chromosome, except for the APOA5 (chromosome 11)—TOMM40 (chromosome 19) interaction for TCHO.Discussion: This study provided a computationally feasible way to search for GxG genome-wide and applied this approach to 29 phenotypes.

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Section: Resultsmentioning

confidence: 95%

“…I considered two situations, as follows, (I) Without SNP main effects: By specifying

and

. Because the four SNPs were, in turn, regarded as SNP2, the power was evaluated under scenarios 2–5.…”

Section: Methodsmentioning

confidence: 99%

Searching for gene-gene interactions through variance quantitative trait loci of 29 continuous Taiwan Biobank phenotypes

Lin

2024

Front. Genet.

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“…Ren et al (2018) established a predictive model for the scope of caved rock zones (CRZs) concerning the varying laws of lateral pressure and forecasted the CRZ scope using a caving mining method. Based on modified parameters in a traditional probability integral model, (Russ et al, 2022) established a subsidence prediction model.…”

Section: Open Accessmentioning

confidence: 99%

Multiple regression method for working face mining pressure prediction based on hydraulic support monitoring dataset

Dong

Xie²,

Jiang³

et al. 2023

Front. Earth Sci.

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Introduction: In the coal mining process, the intense mining pressure is an important factor hindering the safe and efficient production of the working face. In severe cases, it causes deformations in roadways such as roof breakages and rockbursts, and leads to instability. This can result in the roof falling over a large area and the coal wall, thereby inducing dynamic disasters. These aspects have restricted the economic benefits of coal.Methods: In this study, we set four model limitations based on the limited scope of action of the mining pressure itself and the quantitative relationships between mining pressures in different regions. A multiple linear regression model with these limitations is proposed for predicting the mining pressure for preventing roof breakages and rockbursts. Based on a hydraulic support monitoring dataset from a fully mechanized caving face of coal mining, the mining pressure prediction model is trained by using the first 70% of the dataset. And the linear regression coefficient of the model and the predicted value of the mining pressure are obtained. Then, the last 30% of the dataset was used for the validation of the model.Results: The research results show that the constrained multiple linear regression model can achieve remarkable prediction results. According to predictions of tens of thousands of on-site mining pressure datasets, the predicted data and actual pressure data have the same change trend and maintain a low relative error.Discussion: Therefore, after real-time mining pressure monitoring, the system obtains the roof pressure of the fully mechanized mining face. According to the dataset, the proposed prediction model algorithm quickly predicts the roof pressure value of the next mining section and effectively forewarns roof breakages and other accidents.

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“…MAPIT can be used as a way to screen for all loci that have significant marginal epistastic effects for subsequent tests to see which pairs of loci may be involved in epistasis. More comprehensive recent surveys on epistasis are offered by Ogbunugafor and Scarpino on higher order epistasis [33], Niel et al on statistical and computational challenges of varied approaches [34], and Russ et al for performance comparison of June 2, 2023 3/39 many varied epistasis detection methods [35].…”

Section: Computational Challenges In Detecting Epistasismentioning

confidence: 99%

Computational framework for statistical epistasis supports XOR penetrance function in a living system

Batista

Madar²,

Freda

et al. 2023

Preprint

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Statistical epistasis has been studied extensively because of its potential to provide evidence for genetic interactions for phenotypes, but there have been methodological limitations to its exhaustive, widespread application. We present new algorithms for the interaction coefficients for standard regression models for epistasis that permit many varied encodings 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 a new 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 10000 loci and 1000 samples each, using the standard Cartesian encoding and the XOR penetrance function for the interactions, to test for evidence of statistical epistasis for the phenotype of body mass index. This study revealed that the XOR penetrance function found greater evidence for statistical epistasis in many more pairs of loci in both data sets and in the rat data set, those pairs of loci found using the XOR penetrance function are enriched for biologically relevant pathways.Author summaryEpistasis, the interaction between two or more genes, is likely integral to the study of genetics and present throughout nature. Yet, it is seldom fully explored as most approaches primarily focus on single-locus effects (such as GWAS), partly because analyzing all pairwise and higher-order interactions requires significant computational resources. Many current methods for epistasis detection only consider a Cartesian encoding for interaction terms. This is likely limiting as epistatic interactions can evolve to produce varied relationships between genes, some non-linear. In this work we describe computationally efficient algorithms for the detection of statistical epistasis that allow for varied interaction encodings for modeling epistasis. Our methodology efficiently detects pairwise and three-way epistatic interactions in two closely related species (rat and mouse) under both Cartesian and XOR interaction encodings. Our results in both species show that many biologically relevant epistatic relationships would have been undetected if only one interaction encoding was applied providing evidence that more varied models for interaction may need to be applied to describe epistasis that occurs in living systems.

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Evaluating the detection ability of a range of epistasis detection methods on simulated data for pure and impure epistatic models

Cited by 7 publications

References 57 publications

Searching for gene-gene interactions through variance quantitative trait loci of 29 continuous Taiwan Biobank phenotypes

Searching for gene-gene interactions through variance quantitative trait loci of 29 continuous Taiwan Biobank phenotypes

Multiple regression method for working face mining pressure prediction based on hydraulic support monitoring dataset

Computational framework for statistical epistasis supports XOR penetrance function in a living system

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