Graphical-model Based Multiple Testing under Dependence, with Applications to Genome-wide Association Studies
Graphical-model Based Multiple Testing under Dependence, with Applications to Genome-wide Association Studies
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发表时间：
2012-08
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通讯作者：
Jie Liu;Chunming Zhang;C. McCarty;P. Peissig;E. Burnside;David Page
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作者：
Jie Liu;Chunming Zhang;C. McCarty;P. Peissig;E. Burnside;David Page
大规模的多个测试任务经常表现出依赖性，利用单个测试之间的依赖性仍然是统计学中一个具有挑战性和重要的问题。随着图形模型的发展，使用图形模型进行依赖下的多重测试是可行的。我们提出了一种基于马尔可夫-随机场耦合混合模型的多重测试方法。假设的基础真值由一个潜在的二元马尔可夫随机场表示，观察到的检验统计量表现为耦合的混合变量。我们的模型中的参数可以通过一种新的电磁算法自动学习。我们使用MCMC算法来推断每个假设为零的后验概率（称为局部显著性指数），并且可以相应地控制错误发现率。仿真结果表明，采用该方法可以大大提高多次测试的数值性能。我们将该程序应用于真实世界的乳腺癌全基因组关联研究，并确定了几个具有强关联证据的snp。
Large-scale multiple testing tasks often exhibit dependence, and leveraging the dependence between individual tests is still one challenging and important problem in statistics. With recent advances in graphical models, it is feasible to use them to perform multiple testing under dependence. We propose a multiple testing procedure which is based on a Markov-random-field-coupled mixture model. The ground truth of hypotheses is represented by a latent binary Markov random-field, and the observed test statistics appear as the coupled mixture variables. The parameters in our model can be automatically learned by a novel EM algorithm. We use an MCMC algorithm to infer the posterior probability that each hypothesis is null (termed local index of significance), and the false discovery rate can be controlled accordingly. Simulations show that the numerical performance of multiple testing can be improved substantially by using our procedure. We apply the procedure to a real-world genome-wide association study on breast cancer, and we identify several SNPs with strong association evidence.