Detecting epistatic SNPs associated with complex diseases via a Bayesian classification tree search method.
Detecting epistatic SNPs associated with complex diseases via a Bayesian classification tree search method.
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DOI：
10.1111/j.1469-1809.2010.00627.x
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发表时间：
2011-01
影响因子：
1.9
通讯作者：
Zhao H
中科院分区：
文献类型：
--
作者：
Chen M;Cho J;Zhao H
已知复杂的表型与遗传因素之间的相互作用有关。越来越多的证据表明，基因与基因之间的相互作用会导致许多常见的人类疾病。因此，识别多种多态性的潜在相互作用对于理解疾病病因的生物学和生化过程可能很重要。然而，尽管主要集中于单基因座分析的全基因组关联研究取得了巨大成功，但检测这些相互作用仍然具有挑战性，特别是当易感基因座的边际效应较弱和/或涉及多个遗传因素时。在这里，我们描述了贝叶斯分类树模型来检测病例对照关联研究中的此类相互作用。我们表明，该方法有可能揭示涉及显示弱至中度边际效应的多态性的相互作用以及涉及两个以上基因座的多因素相互作用。
Complex phenotypes are known to be associated with interactions among genetic factors. A growing body of evidence suggests that gene–gene interactions contribute to many common human diseases. Identifying potential interactions of multiple polymorphisms thus may be important to understand the biology and biochemical processes of the disease etiology. However, despite the great success of genome-wide association studies that mostly focus on single locus analysis, it is challenging to detect these interactions, especially when the marginal effects of the susceptible loci are weak and/or they involve several genetic factors. Here we describe a Bayesian classification tree model to detect such interactions in case-control association studies. We show that this method has the potential to uncover interactions involving polymorphisms showing weak to moderate marginal effects as well as multi-factorial interactions involving more than two loci.
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DOI：
10.1038/nrg2579
发表时间：
2009-06
期刊：
Nature reviews. Genetics
影响因子：
--
作者：
Cordell HJ
通讯作者：
Cordell HJ
影响因子：
9.8
作者：
Culverhouse, R;Suarez, BK;Reich, T
通讯作者：
Reich, T
影响因子：
1.8
作者：
Moore, JH
通讯作者：
Moore, JH
影响因子：
9.8
作者：
Ritchie, MD;Hahn, LW;Moore, JH
通讯作者：
Moore, JH
影响因子：
56.9
作者：
Duerr, Richard H.;Taylor, Kent D.;Cho, Judy H.
通讯作者：
Cho, Judy H.