bNEAT: a Bayesian network method for detecting epistatic interactions in genome-wide association studies
<p>Abstract</p> <p>Background</p> <p>Detecting epistatic interactions plays a significant role in improving pathogenesis, prevention, diagnosis and treatment of complex human diseases. A recent study in automatic detection of epistatic interactions shows that Markov Bla...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2011-07-01
|
| Series: | BMC Genomics |
| _version_ | 1831599331890692096 |
|---|---|
| author | Chen Xue-wen Han Bing |
| author_facet | Chen Xue-wen Han Bing |
| author_sort | Chen Xue-wen |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>Detecting epistatic interactions plays a significant role in improving pathogenesis, prevention, diagnosis and treatment of complex human diseases. A recent study in automatic detection of epistatic interactions shows that Markov Blanket-based methods are capable of finding genetic variants strongly associated with common diseases and reducing false positives when the number of instances is large. Unfortunately, a typical dataset from genome-wide association studies consists of very limited number of examples, where current methods including Markov Blanket-based method may perform poorly.</p> <p>Results</p> <p>To address small sample problems, we propose a Bayesian network-based approach (bNEAT) to detect epistatic interactions. The proposed method also employs a Branch-and-Bound technique for learning. We apply the proposed method to simulated datasets based on four disease models and a real dataset. Experimental results show that our method outperforms Markov Blanket-based methods and other commonly-used methods, especially when the number of samples is small.</p> <p>Conclusions</p> <p>Our results show bNEAT can obtain a strong power regardless of the number of samples and is especially suitable for detecting epistatic interactions with slight or no marginal effects. The merits of the proposed approach lie in two aspects: a suitable score for Bayesian network structure learning that can reflect higher-order epistatic interactions and a heuristic Bayesian network structure learning method.</p> |
| first_indexed | 2024-12-18T14:21:58Z |
| format | Article |
| id | doaj.art-bf7fb90f39e5411cbcf075020d031f7a |
| institution | Directory Open Access Journal |
| issn | 1471-2164 |
| language | English |
| last_indexed | 2024-12-18T14:21:58Z |
| publishDate | 2011-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Genomics |
| spelling | doaj.art-bf7fb90f39e5411cbcf075020d031f7a2022-12-21T21:04:50ZengBMCBMC Genomics1471-21642011-07-0112Suppl 2S910.1186/1471-2164-12-S2-S9bNEAT: a Bayesian network method for detecting epistatic interactions in genome-wide association studiesChen Xue-wenHan Bing<p>Abstract</p> <p>Background</p> <p>Detecting epistatic interactions plays a significant role in improving pathogenesis, prevention, diagnosis and treatment of complex human diseases. A recent study in automatic detection of epistatic interactions shows that Markov Blanket-based methods are capable of finding genetic variants strongly associated with common diseases and reducing false positives when the number of instances is large. Unfortunately, a typical dataset from genome-wide association studies consists of very limited number of examples, where current methods including Markov Blanket-based method may perform poorly.</p> <p>Results</p> <p>To address small sample problems, we propose a Bayesian network-based approach (bNEAT) to detect epistatic interactions. The proposed method also employs a Branch-and-Bound technique for learning. We apply the proposed method to simulated datasets based on four disease models and a real dataset. Experimental results show that our method outperforms Markov Blanket-based methods and other commonly-used methods, especially when the number of samples is small.</p> <p>Conclusions</p> <p>Our results show bNEAT can obtain a strong power regardless of the number of samples and is especially suitable for detecting epistatic interactions with slight or no marginal effects. The merits of the proposed approach lie in two aspects: a suitable score for Bayesian network structure learning that can reflect higher-order epistatic interactions and a heuristic Bayesian network structure learning method.</p> |
| spellingShingle | Chen Xue-wen Han Bing bNEAT: a Bayesian network method for detecting epistatic interactions in genome-wide association studies BMC Genomics |
| title | bNEAT: a Bayesian network method for detecting epistatic interactions in genome-wide association studies |
| title_full | bNEAT: a Bayesian network method for detecting epistatic interactions in genome-wide association studies |
| title_fullStr | bNEAT: a Bayesian network method for detecting epistatic interactions in genome-wide association studies |
| title_full_unstemmed | bNEAT: a Bayesian network method for detecting epistatic interactions in genome-wide association studies |
| title_short | bNEAT: a Bayesian network method for detecting epistatic interactions in genome-wide association studies |
| title_sort | bneat a bayesian network method for detecting epistatic interactions in genome wide association studies |
| work_keys_str_mv | AT chenxuewen bneatabayesiannetworkmethodfordetectingepistaticinteractionsingenomewideassociationstudies AT hanbing bneatabayesiannetworkmethodfordetectingepistaticinteractionsingenomewideassociationstudies |