bNEAT: a Bayesian network method for detecting epistatic interactions in genome-wide association studies.
bNEAT: a Bayesian network method for detecting epistatic interactions in genome-wide association studies.
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DOI：
10.1186/1471-2164-12-s2-s9
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发表时间：
2011
期刊：
影响因子：
4.4
通讯作者：
Chen XW
中科院分区：
文献类型：
--
作者：
Han B;Chen XW
上位性相互作用的检测对于人类复杂疾病的发病机制、预防、诊断和治疗具有重要意义。最近一项关于上位相互作用自动检测的研究表明，基于马尔可夫毯的方法能够发现与常见疾病密切相关的遗传变异，并在实例数量较大时减少假阳性。不幸的是，来自全基因组关联研究的典型数据集由非常有限数量的示例组成，其中包括基于马尔可夫毯的方法在内的当前方法可能表现不佳。为了解决小样本问题，我们提出了一种基于贝叶斯网络的方法（bNEAT）来检测上位相互作用。该方法还采用了分支定界技术进行学习。我们将所提出的方法应用于基于四种疾病模型和一个真实的数据集的模拟数据集。实验结果表明，该方法的性能优于基于马尔可夫毯的方法和其他常用的方法，特别是当样本数较少时。我们的研究结果表明，bNEAT可以获得一个强大的权力，无论样本的数量，特别适合于检测上位相互作用轻微或没有边际效应。该方法的优点在于两个方面：一是能够反映高阶上位性相互作用的贝叶斯网络结构学习得分，二是启发式贝叶斯网络结构学习方法。
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. 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. 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.