Testing association between disease and multiple SNPs in a candidate gene
Testing association between disease and multiple SNPs in a candidate gene
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DOI：
10.1002/gepi.20219
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发表时间：
2007-07-01
影响因子：
2.1
通讯作者：
Conti, David V.
中科院分区：
文献类型：
--
作者：
Gauderman, W. James;Murcray, Cassandra;Conti, David V.
目前的技术允许研究人员获得候选基因座内多个单核苷酸多态(SNPs)的基因类型。已经开发了许多方法来在与疾病的相关性测试中使用这种数据，从基于基因型的测试到基于单倍型的测试。我们开发了一种新的方法，包括两个基本步骤。在第一步中，我们使用主成分(PC)分析来计算SNPs的组合，这些SNPs捕捉到该基因座内的潜在关联结构。第二步是在疾病关联性测试中直接使用PC。PC方法捕获候选区域内的连锁不平衡信息，但不需要单倍型分析中隐含的困难计算。我们通过模拟证明，PC方法通常与基于基因型和基于单倍型的方法一样或更强大。我们还基于儿童健康研究的数据，分析了儿童呼吸道症状与谷氨酸硫酮-S-转移酶P1基因座的四个SNPs的关联。我们观察到，使用PC方法(p=0.044)比使用基于基因型的方法(p=0.13)或基于单倍型的方法(p=0.052)有更强的相关性证据。
Current technology allows investigators to obtain genotypes at multiple single nucleotide polymorphism (SNPS) within a candidate locus. Many approaches have been developed for using such data in a test of association with disease, ranging from genotype-based to haplotype-based tests. We develop a new approach that involves two basic steps. In the first step, we use principal components (PCs) analysis to compute combinations of SNPs that capture the underlying correlation structure within the locus. The second step uses the PCs directly in a test of disease association. The PC approach captures linkage-disequilibrium information within a candidate region, but does not require the difficult computing implicit in a haplotype analysis. We demonstrate by simulation that the PC approach is typically as or more powerful than both genotype- and haplotype-based approaches. We also analyze association between respiratory symptoms in children and four SNPs in the Gluta thione-S-Transf erase P1 locus, based on data from the Children's Health Study. We observe stronger evidence of an association using the PC approach (p = 0.044) than using either a genotype-based (p = 0.13) or haplotypebased (p = 0.052) approach.