Powerful SNP-Set Analysis for Case-Control Genome-wide Association Studies
Powerful SNP-Set Analysis for Case-Control Genome-wide Association Studies
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DOI：
10.1016/j.ajhg.2010.05.002
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发表时间：
2010-06-11
影响因子：
9.8
通讯作者：
Lin, Xihong
中科院分区：
文献类型：
--
作者：
Wu, Michael C.;Kraft, Peter;Lin, Xihong
GWA已成为识别与疾病风险相关的遗传变异的流行工具。病例对照GWA的标准分析涉及评估每个基因分型SNP与疾病风险之间的关联。但是，这种方法在检测多SNP和上皮效应方面的可重复性有限和困难。作为替代性分析策略，我们建议将SNP组合在一起，以基因组或单倍型块等基因组特征的距离，然后测试每个SNP集的关节效应。每个SNP集的测试通过基于Logistic内核机器的测试进行，该测试基于一个统计框架，该框架允许灵活地建模上位和非线性SNP效应。这种灵活性和自然调整协变量效应的能力是我们测试的重要特征，与单个SNP测试和现有多标记测试相比，它具有吸引力。使用基于国际HAPMAP项目的模拟数据，我们表明，在广泛的设置下，SNP-stet测试可以提高对标准个人SNP分析的功率。特别是，当疾病敏感性变体与基因分型SNP之间的中位相关性时，我们的方法比单个SNP分析具有更高的功率。当相关性较低时，单个-SNP分析和SNP-set分析往往具有低功率。我们采用SNP - 设定分析来分析易感性（CGEM）乳腺癌GWAS发现相数据数据的癌症遗传标记。
GWAS have emerged as popular tools for identifying genetic variants that are associated with disease risk. Standard analysis of a case-control GWAS involves assessing the association between each individual genotyped SNP and disease risk. However, this approach suffers from limited reproducibility and difficulties in detecting multi-SNP and epistatic effects. As an alternative analytical strategy, we propose grouping SNPs together into SNP sets on the basis of proximity to genomic features such as genes or haplotype blocks, then testing the joint effect of each SNP set. Testing of each SNP set proceeds via the logistic kernel-machine-based test, which is based on a statistical framework that allows for flexible modeling of epistatic and nonlinear SNP effects. This flexibility and the ability to naturally adjust for covariate effects are important features of our test that make it appealing in comparison to individual SNP tests and existing multimarker tests. Using simulated data based on the International HapMap Project, we show that SNP-set testing can have improved power over standard individual-SNP analysis under a wide range of settings. In particular, we find that our approach has higher power than individual-SNP analysis when the median correlation between the disease-susceptibility variant and the genotyped SNPs is moderate to high. When the correlation is low, both individual-SNP analysis and the SNP-set analysis tend to have low power. We apply SNP-set analysis to analyze the Cancer Genetic Markers of Susceptibility (CGEMS) breast cancer GWAS discovery-phase data.