SMMB: a stochastic Markov blanket framework strategy for epistasis detection in GWAS

Niel, Clément; Sinoquet, Christine; Dina, Christian; Rocheleau, Ghislain

doi:10.1093/bioinformatics/bty154

Cited by 18 publications

(7 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Symmetrical uncertainty (SU ): Mutual information has been widely adopted for data mining from high-dimensional data. However, it tends to favor features with more values while ignoring interactive features [ 31 ]. In this study, SU was utilized to compensate for the bias of mutual information toward features with more values, as previously described [ 27 , 30 ], and this was defined as Equation (7):

…”

Section: Methodsmentioning

confidence: 99%

“…True positives (TPs) are defined as the discovery of a k-way SNP combination that is associated with disease status, and FNs (false negatives) are defined as a non-discovery of a SNP combination that is associated with disease. TNs (true negatives) indicate no discovery, and FPs (false positives) are defined as a k-way SNP combination that is falsely associated with a disease status [ 31 ].…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

FDHE-IW: A Fast Approach for Detecting High-Order Epistasis in Genome-Wide Case-Control Studies

Tuo

2018

Genes

View full text Add to dashboard Cite

Detecting high-order epistasis in genome-wide association studies (GWASs) is of importance when characterizing complex human diseases. However, the enormous numbers of possible single-nucleotide polymorphism (SNP) combinations and the diversity among diseases presents a significant computational challenge. Herein, a fast method for detecting high-order epistasis based on an interaction weight (FDHE-IW) method is evaluated in the detection of SNP combinations associated with disease. First, the symmetrical uncertainty (SU) value for each SNP is calculated. Then, the top-k SNPs are isolated as guiders to identify 2-way SNP combinations with significant interaction weight values. Next, a forward search is employed to detect high-order SNP combinations with significant interaction weight values as candidates. Finally, the findings were statistically evaluated using a G-test to isolate true positives. The developed algorithm was used to evaluate 12 simulated datasets and an age-related macular degeneration (AMD) dataset and was shown to perform robustly in the detection of some high-order disease-causing models.

show abstract

…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

FDHE-IW: A Fast Approach for Detecting High-Order Epistasis in Genome-Wide Case-Control Studies

Tuo

2018

Genes

View full text Add to dashboard Cite

show abstract

“…Furthermore, the expressions of ∈ (1, ∞). Recent studies that include simulations based on epistasis models to generate their evaluation data [20][21][22] settle on low-order models whose heritability values are worryingly moderate. However, real-world diseases are usually determined by a higher number of genes [1] and a higher heritability [23,24].…”

Section: Model Restrictions and Existing Epistasis Modelsmentioning

confidence: 99%

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

et al. 2020

View full text Add to dashboard Cite

Background: Epistasis is defined as the interaction between different genes when expressing a specific phenotype. The most common way to characterize an epistatic relationship is using a penetrance table, which contains the probability of expressing the phenotype under study given a particular allele combination. Available simulators can only create penetrance tables for well-known epistasis models involving a small number of genes and under a large number of limitations. Results: Toxo is a MATLAB library designed to calculate penetrance tables of epistasis models of any interaction order which resemble real data more closely. The user specifies the desired heritability (or prevalence) and the program maximizes the table's prevalence (or heritability) according to the input epistatic model boundaries. Conclusions: Toxo extends the capabilities of existing simulators that define epistasis using penetrance tables. These tables can be directly used as input for software simulators such as GAMETES so that they are able to generate data samples with larger interactions and more realistic prevalences/heritabilities.

show abstract

“…However, these methods focus only on qualitative traits and tend to lead to complex models. Machine learning algorithms such as support vector machine, ant colony algorithm, and random forest attempt to make nonparametric models to detect epistasis ( Chen et al 2008 ; Li et al 2016 ; Yuan et al 2017 ; Niel et al 2018 ). Machine learning approaches are useful in detecting higher-order epistatic relationships thanks to their low computational costs.…”

Section: Introductionmentioning

confidence: 99%

RIL-StEp: epistasis analysis of rice recombinant inbred lines reveals candidate interacting genes that control seed hull color and leaf chlorophyll content

Sakai

Abe

Shimizu

et al. 2021

G3 Genes|Genomes|Genetics

View full text Add to dashboard Cite

Characterizing epistatic gene interactions is fundamental for understanding the genetic architecture of complex traits. However, due to the large number of potential gene combinations, detecting epistatic gene interactions is computationally demanding. A simple, easy-to-perform method for sensitive detection of epistasis is required. Due to their homozygous nature, use of recombinant inbred lines (RILs) excludes the dominance effect of alleles and interactions involving heterozygous genotypes, thereby allowing detection of epistasis in a simple and interpretable model. Here, we present an approach called RIL-StEp (recombinant inbred lines stepwise epistasis detection) to detect epistasis using single nucleotide polymorphisms in the genome. We applied the method to reveal epistasis affecting rice (Oryza sativa) seed hull color and leaf chlorophyll content and successfully identified pairs of genomic regions that presumably control these phenotypes. This method has the potential to improve our understanding of the genetic architecture of various traits of crops and other organisms.

show abstract

SMMB: a stochastic Markov blanket framework strategy for epistasis detection in GWAS

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 18 publications

References 32 publications

FDHE-IW: A Fast Approach for Detecting High-Order Epistasis in Genome-Wide Case-Control Studies

FDHE-IW: A Fast Approach for Detecting High-Order Epistasis in Genome-Wide Case-Control Studies

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

RIL-StEp: epistasis analysis of rice recombinant inbred lines reveals candidate interacting genes that control seed hull color and leaf chlorophyll content

Contact Info

Product

Resources

About