The Generalized Higher Criticism for Testing SNP-Set Effects in Genetic Association Studies.
The Generalized Higher Criticism for Testing SNP-Set Effects in Genetic Association Studies.
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DOI：
10.1080/01621459.2016.1192039
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发表时间：
2017
影响因子：
3.7
通讯作者：
Lin X
中科院分区：
文献类型：
--
作者：
Barnett I;Mukherjee R;Lin X
研究基因、遗传通路和网络对复杂疾病风险的影响具有重大意义。这些遗传构建体各自包含多个SNP，这些SNP通常是相关的并且共同起作用，并且数量可能很大。然而，基因构建体中只有稀疏的SNP子集通常与感兴趣的疾病相关。在这篇文章中，我们提出了广义高级批评（GHC）来测试SNP集和疾病结果之间的关联。更高的批评是传统上用于高维信号检测设置的测试，当边缘测试统计量是独立的，参数的数量非常大。然而，这些假设并不总是在遗传关联研究中成立，由于SNP之间的连锁不平衡和每个遗传构建体中SNP集合中SNP的有限数量。拟议的GHC通过允许SNP集中的SNP之间的任意相关结构，同时对SNP集中任何有限数量的SNP执行准确的分析p值计算，克服了更高批评的局限性。得到了GHC检验的检测边界。我们比较经验，使用模拟的GHC方法与现有的SNP集测试在一系列的遗传区域与不同的相关性结构和信号稀疏的权力。我们应用所提出的方法来分析CGEM乳腺癌全基因组关联研究。本文的补充材料可在网上查阅。
It is of substantial interest to study the effects of genes, genetic pathways, and networks on the risk of complex diseases. These genetic constructs each contain multiple SNPs, which are often correlated and function jointly, and might be large in number. However, only a sparse subset of SNPs in a genetic construct is generally associated with the disease of interest. In this article, we propose the generalized higher criticism (GHC) to test for the association between an SNP set and a disease outcome. The higher criticism is a test traditionally used in high-dimensional signal detection settings when marginal test statistics are independent and the number of parameters is very large. However, these assumptions do not always hold in genetic association studies, due to linkage disequilibrium among SNPs and the finite number of SNPs in an SNP set in each genetic construct. The proposed GHC overcomes the limitations of the higher criticism by allowing for arbitrary correlation structures among the SNPs in an SNP-set, while performing accurate analytic p-value calculations for any finite number of SNPs in the SNP-set. We obtain the detection boundary of the GHC test. We compared empirically using simulations the power of the GHC method with existing SNP-set tests over a range of genetic regions with varied correlation structures and signal sparsity. We apply the proposed methods to analyze the CGEM breast cancer genome-wide association study. Supplementary materials for this article are available online.
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