FEPI-MB: identifying SNPs-disease association using a Markov Blanket-based approach

<p>Abstract</p> <p>Background</p> <p>The interactions among genetic factors related to diseases are called epistasis. With the availability of genotyped data from genome-wide association studies, it is now possible to computationally unravel epistasis related to the sus...

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Main Authors: Han Bing, Chen Xue-wen, Talebizadeh Zohreh
Format: Article
Language:English
Published: BMC 2011-11-01
Series:BMC Bioinformatics
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spelling doaj-5007d68b9ae04219bd596608ee03236b2020-11-25T02:16:01ZengBMCBMC Bioinformatics1471-21052011-11-0112Suppl 12S310.1186/1471-2105-12-S12-S3FEPI-MB: identifying SNPs-disease association using a Markov Blanket-based approachHan BingChen Xue-wenTalebizadeh Zohreh<p>Abstract</p> <p>Background</p> <p>The interactions among genetic factors related to diseases are called epistasis. With the availability of genotyped data from genome-wide association studies, it is now possible to computationally unravel epistasis related to the susceptibility to common complex human diseases such as asthma, diabetes, and hypertension. However, the difficulties of detecting epistatic interaction arose from the large number of genetic factors and the enormous size of possible combinations of genetic factors. Most computational methods to detect epistatic interactions are predictor-based methods and can not find true causal factor elements. Moreover, they are both time-consuming and sample-consuming.</p> <p>Results</p> <p>We propose a new and fast Markov Blanket-based method, FEPI-MB (Fast EPistatic Interactions detection using Markov Blanket), for epistatic interactions detection. The Markov Blanket is a minimal set of variables that can completely shield the target variable from all other variables. Learning of Markov blankets can be used to detect epistatic interactions by a heuristic search for a minimal set of SNPs, which may cause the disease. Experimental results on both simulated data sets and a real data set demonstrate that FEPI-MB significantly outperforms other existing methods and is capable of finding SNPs that have a strong association with common diseases.</p> <p>Conclusions</p> <p>FEPI-MB algorithm outperforms other computational methods for detection of epistatic interactions in terms of both the power and sample-efficiency. Moreover, compared to other Markov Blanket learning methods, FEPI-MB is more time-efficient and achieves a better performance.</p>
collection DOAJ
language English
format Article
sources DOAJ
author Han Bing
Chen Xue-wen
Talebizadeh Zohreh
spellingShingle Han Bing
Chen Xue-wen
Talebizadeh Zohreh
FEPI-MB: identifying SNPs-disease association using a Markov Blanket-based approach
BMC Bioinformatics
author_facet Han Bing
Chen Xue-wen
Talebizadeh Zohreh
author_sort Han Bing
title FEPI-MB: identifying SNPs-disease association using a Markov Blanket-based approach
title_short FEPI-MB: identifying SNPs-disease association using a Markov Blanket-based approach
title_full FEPI-MB: identifying SNPs-disease association using a Markov Blanket-based approach
title_fullStr FEPI-MB: identifying SNPs-disease association using a Markov Blanket-based approach
title_full_unstemmed FEPI-MB: identifying SNPs-disease association using a Markov Blanket-based approach
title_sort fepi-mb: identifying snps-disease association using a markov blanket-based approach
publisher BMC
series BMC Bioinformatics
issn 1471-2105
publishDate 2011-11-01
description <p>Abstract</p> <p>Background</p> <p>The interactions among genetic factors related to diseases are called epistasis. With the availability of genotyped data from genome-wide association studies, it is now possible to computationally unravel epistasis related to the susceptibility to common complex human diseases such as asthma, diabetes, and hypertension. However, the difficulties of detecting epistatic interaction arose from the large number of genetic factors and the enormous size of possible combinations of genetic factors. Most computational methods to detect epistatic interactions are predictor-based methods and can not find true causal factor elements. Moreover, they are both time-consuming and sample-consuming.</p> <p>Results</p> <p>We propose a new and fast Markov Blanket-based method, FEPI-MB (Fast EPistatic Interactions detection using Markov Blanket), for epistatic interactions detection. The Markov Blanket is a minimal set of variables that can completely shield the target variable from all other variables. Learning of Markov blankets can be used to detect epistatic interactions by a heuristic search for a minimal set of SNPs, which may cause the disease. Experimental results on both simulated data sets and a real data set demonstrate that FEPI-MB significantly outperforms other existing methods and is capable of finding SNPs that have a strong association with common diseases.</p> <p>Conclusions</p> <p>FEPI-MB algorithm outperforms other computational methods for detection of epistatic interactions in terms of both the power and sample-efficiency. Moreover, compared to other Markov Blanket learning methods, FEPI-MB is more time-efficient and achieves a better performance.</p>
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