ArticleBMC genomics2015
A Bayesian model for detection of high-order interactions among genetic variants in genome-wide association studies.
Article in BMC genomics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Applying gradient tree boosting to QTL mapping with Shapley additive explanations.Breeding science · 2025Article
- Decoding ancestry-specific genetic risk: interpretable deep feature selection reveals prostate cancer SNP disparities in diverse populations.BioData mining · 2025Article
- Bayesian Approaches in Exploring Gene-environment and Gene-gene Interactions: A Comprehensive Review.Cancer genomics & proteomics · 2023Review
- Mapping Genetic Variation in Arabidopsis in Response to Plant Growth-Promoting BacteriumMicroorganisms · 2023Article
- Using Bayesian networks with Tabu-search algorithm to explore risk factors for hyperhomocysteinemia.Scientific reports · 2023Article
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- Detecting gene-gene interactions from GWAS using diffusion kernel principal components.BMC bioinformatics · 2022Article
- A genome-wide survey of interaction between rice andBioengineered · 2021Article
- Genome-wide association and epistatic interactions of flowering time in soybean cultivar.PloS one · 2020Article
- SoyCSN: Soybean context-specific network analysis and prediction based on tissue-specific transcriptome data.Plant direct · 2019Article
- Phenotype Prediction and Genome-Wide Association Study Using Deep Convolutional Neural Network of Soybean.Frontiers in genetics · 2019Article
- Aggregation of experts: an application in the field of "interactomics" (detection of interactions on the basis of genomic data).BMC bioinformatics · 2018Article
- An Ultrahigh-Dimensional Mapping Model of High-order Epistatic Networks for Complex Traits.Current genomics · 2018Article
- Niche harmony search algorithm for detecting complex disease associated high-order SNP combinations.Scientific reports · 2017Article
- Article
- KNN-MDR: a learning approach for improving interactions mapping performances in genome wide association studies.BMC bioinformatics · 2017Article
- Verification of Three-Phase Dependency Analysis Bayesian Network Learning Method for Maize Carotenoid Gene Mining.BioMed research international · 2017Article
- PGen: large-scale genomic variations analysis workflow and browser in SoyKB.BMC bioinformatics · 2016Article
- Article
- Exploring Human Diseases and Biological Mechanisms by Protein Structure Prediction and Modeling.Advances in experimental medicine and biology · 2016Article
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Authors and funding
8 authors.
Funding
Abstract
backgroundA central question for disease studies and crop improvements is how genetics variants drive phenotypes. Genome Wide Association Study (GWAS) provides a powerful tool for characterizing the genotype-phenotype relationships in complex traits and diseases. Epistasis (gene-gene interaction), including high-order interaction among more than two genes, often plays important roles in complex traits and diseases, but current GWAS analysis usually just focuses on additive effects of single nucleotide polymorphisms (SNPs). The lack of effective computational modelling of high-order functional interactions often leads to significant under-utilization of GWAS data.
resultsWe have developed a novel Bayesian computational method with a Markov Chain Monte Carlo (MCMC) search, and implemented the method as a Bayesian High-order Interaction Toolkit (BHIT) for detecting epistatic interactions among SNPs. BHIT first builds a Bayesian model on both continuous data and discrete data, which is capable of detecting high-order interactions in SNPs related to case--control or quantitative phenotypes. We also developed a pipeline that enables users to apply BHIT on different species in different use cases.
conclusionsUsing both simulation data and soybean nutritional seed composition studies on oil content and protein content, BHIT effectively detected some high-order interactions associated with phenotypes, and it outperformed a number of other available tools. BHIT is freely available for academic users at http://digbio.missouri.edu/BHIT/.
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