Bayesian inference of epistatic interactions in case-control studies
Bayesian inference of epistatic interactions in case-control studies
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DOI：
10.1038/ng2110
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发表时间：
2007-09-01
期刊：
影响因子：
30.8
通讯作者：
Liu, Jun S.
中科院分区：
文献类型：
--
作者：
Zhang, Yu;Liu, Jun S.
人类基因组中多种遗传变异之间的上位相互作用可能在确定个体对常见疾病的易感性方面很重要。虽然现有的一些用于识别遗传相互作用的计算方法在小规模研究中是有效的，但我们在这里提出了一种用于全基因组病例对照研究的方法，称为“巴氏上位关联作图”（BEAM）。BEAM通过一个贝叶斯分割模型处理疾病相关的标志物及其相互作用，并通过马尔可夫链蒙特卡罗计算每个标志物组与疾病相关的后验概率。在年龄相关性黄斑变性全基因组关联数据集上测试这一点，我们证明了该方法比现有方法显着更强大，并且具有数千个标记的全基因组病例对照上位性映射在计算和统计上都是可行的。
Epistatic interactions among multiple genetic variants in the human genome may be important in determining individual susceptibility to common diseases. Although some existing computational methods for identifying genetic interactions have been effective for small- scale studies, we here propose a method, denoted ' bayesian epistasis association mapping' ( BEAM), for genome- wide case- control studies. BEAM treats the disease- associated markers and their interactions via a bayesian partitioning model and computes, via Markov chain Monte Carlo, the posterior probability that each marker set is associated with the disease. Testing this on an age- related macular degeneration genome- wide association data set, we demonstrate that the method is significantly more powerful than existing approaches and that genome- wide case- control epistasis mapping with many thousands of markers is both computationally and statistically feasible.