Toxo: a library for calculating penetrance tables of high-order epistasis models

Ponte-Fernández, Christian; González-Domínguez, Jorge; Carvajal-Rodríguez, Antonio; Martín, María J.

doi:10.1186/s12859-020-3456-3

Cited by 9 publications

(12 citation statements)

References 22 publications

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“…Penetrance tables with no marginal effects can be obtained natively through GAMETES, which follows a stochastic generation procedure to find epistatic relationships [58] under no model assumption. Model-driven penetrance tables, on the other hand, cannot be calculated within GAMETES and thus were obtained from Toxo [59], a MATLAB library which can compute penetrance tables from epistasis models. In this study, we employed the widely used additive and threshold models proposed by Marchini et al in [60], two models that define epistatic interactions with marginal effects.…”

Section: A Data Simulation Designmentioning

confidence: 99%

“…The criteria were to create penetrance tables of third and fourth-order, with MAF values of 0.10, 0.25 and 0.40 and heritabilities of 0.10, 0.25, 0.50 and 0.80 whose prevalence is above 1E-06. Model-driven tables cannot be obtained for every combination of MAFs, prevalence and heritability due to restrictions in the underlying mathematical model [59], resulting in a different number of tables according to the model. GAMETES, on the other hand, follows a probabilistic approach, which has problems to find modelfree tables when increasing the interaction order, decreasing the MAF and increasing the heritability.…”

Section: A Data Simulation Designmentioning

confidence: 99%

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Evaluation of Existing Methods for High-Order Epistasis Detection

Ponte-Fernández

González-Domínguez

Carvajal-Rodríguez

et al. 2022

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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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.

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Section: A Data Simulation Designmentioning

confidence: 99%

Section: A Data Simulation Designmentioning

confidence: 99%

Evaluation of Existing Methods for High-Order Epistasis Detection

Ponte-Fernández

González-Domínguez

Carvajal-Rodríguez

et al. 2022

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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“…(2) EIME datasets. Four 5th-order additive EIMEs, four 5th-order threshold EIMEs and four 4th-order multiplicative EIMEs [61] were employed to test the performance of detecting epistatic interactions with marginal effects. For each model, 100 datasets with 2000 control samples and 2000 case samples that had 100 SNPs, 1000 SNPs and 10 k SNPs were separately generated using GAMETES software [62].…”

Section: Datasetsmentioning

confidence: 99%

MTHSA-DHEI: multitasking harmony search algorithm for detecting high-order SNP epistatic interactions

Tuo

Liu

et al. 2022

Complex Intell. Syst.

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Genome-wide association studies have succeeded in identifying genetic variants associated with complex diseases, but the findings have not been well interpreted biologically. Although it is widely accepted that epistatic interactions of high-order single nucleotide polymorphisms (SNPs) [(1) Single nucleotide polymorphisms (SNP) are mainly deoxyribonucleic acid (DNA) sequence polymorphisms caused by variants at a single nucleotide at the genome level. They are the most common type of heritable variation in humans.] are important causes of complex diseases, the combinatorial explosion of millions of SNPs and multiple tests impose a large computational burden. Moreover, it is extremely challenging to correctly distinguish high-order SNP epistatic interactions from other high-order SNP combinations due to small sample sizes. In this study, a multitasking harmony search algorithm (MTHSA-DHEI) is proposed for detecting high-order epistatic interactions [(2) In classical genetics, if genes X1 and X2 are mutated and each mutation by itself produces a unique disease status (phenotype) but the mutations together cause the same disease status as the gene X1 mutation, gene X1 is epistatic and gene X2 is hypostatic, and gene X1 has an epistatic effect (main effect) on disease status. In this work, a high-order epistatic interaction occurs when two or more SNP loci have a joint influence on disease status.], with the goal of simultaneously detecting multiple types of high-order (k1-order, k2-order, …, kn-order) SNP epistatic interactions. Unified coding is adopted for multiple tasks, and four complementary association evaluation functions are employed to improve the capability of discriminating the high-order SNP epistatic interactions. We compare the proposed MTHSA-DHEI method with four excellent methods for detecting high-order SNP interactions for 8 high-orderepistatic interaction models with no marginal effect (EINMEs) and 12 epistatic interaction models with marginal effects (EIMEs) (*) and implement the MTHSA-DHEI algorithm with a real dataset: age-related macular degeneration (AMD). The experimental results indicate that MTHSA-DHEI has power and an F1-score exceeding 90% for all EIMEs and five EINMEs and reduces the computational time by more than 90%. It can efficiently perform multiple high-order detection tasks for high-order epistatic interactions and improve the discrimination ability for diverse epistasis models.

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“…Additionally, equations become more and more complex as the interaction order increases, which also makes it difficult to find a solution in a reasonable time. Thus, for example, the EpiSIM simulator can only work, in practice, with second-order models and low prevalence and heritability values [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

“…To overcome these limitations, in a previous work we introduced Toxo [ 6 ], a MATLAB library for calculating penetrance tables of epistasis models with no limitation on the interaction order. To simplify the equation system shown in Eq.…”

Section: Introductionmentioning

confidence: 99%

PyToxo: a Python tool for calculating penetrance tables of high-order epistasis models

et al. 2022

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Background Epistasis is the interaction between different genes when expressing a certain phenotype. If epistasis involves more than two loci it is called high-order epistasis. High-order epistasis is an area under active research because it could be the cause of many complex traits. The most common way to specify an epistasis interaction is through a penetrance table. Results This paper presents PyToxo, a Python tool for generating penetrance tables from any-order epistasis models. Unlike other tools available in the bibliography, PyToxo is able to work with high-order models and realistic penetrance and heritability values, achieving high-precision results in a short time. In addition, PyToxo is distributed as open-source software and includes several interfaces to ease its use. Conclusions PyToxo provides the scientific community with a useful tool to evaluate algorithms and methods that can detect high-order epistasis to continue advancing in the discovery of the causes behind complex diseases.

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Toxo: a library for calculating penetrance tables of high-order epistasis models

Cited by 9 publications

References 22 publications

Evaluation of Existing Methods for High-Order Epistasis Detection

Evaluation of Existing Methods for High-Order Epistasis Detection

MTHSA-DHEI: multitasking harmony search algorithm for detecting high-order SNP epistatic interactions

PyToxo: a Python tool for calculating penetrance tables of high-order epistasis models

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