Reconsidering association testing methods using single-variant test statistics as alternatives to pooling tests for sequence data with rare variants.
Reconsidering association testing methods using single-variant test statistics as alternatives to pooling tests for sequence data with rare variants.
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重新考虑使用单变量测试统计量的重新考虑关联测试方法是乘坐稀有变体的序列数据的汇总测试。
DOI：
10.1371/journal.pone.0030238
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发表时间：
2012
期刊：
影响因子：
3.7
通讯作者：
Martin ER
中科院分区：
文献类型：
--
作者：
Kinnamon DD;Hershberger RE;Martin ER
关联测试将少量等位基因汇集到一个位点的负担测量中，已被提议用于病例对照研究，使用包含罕见变异的序列数据。然而，这种池化测试对中性和保护性变异的纳入并不稳健，这可能掩盖风险变异的关联信号。早期提出合并检验的研究摒弃了基于不现实比较的非负单变量检验统计的全基因座推断方法。然而，这种方法对于包括中性和保护性变异是稳健的，因此可能比以前认识到的更有用。事实上，最近提出的一些在不同框架内推导的方法相当于对单变量得分统计的加权平方和进行推理。在这项研究中，我们比较了两种现有的使用非负单变量检验统计的全基因座推理方法和两种被广泛引用的池化检验在更现实的条件下。我们建立了一个具有一个罕见风险和一个罕见中性变异的简单模型的分析结果，这表明在大多数现实情况下，池化检验甚至不如bonferroni校正的单变异检验有效。我们还使用具有实际小等位基因频率和连锁不平衡谱的变异、具有多个罕见风险变异和广泛中性变异的疾病模型以及不同缺失基因型率的疾病模型进行了模拟。在所有考虑的情况下，使用非负单变量检验统计的现有方法的功效与两个被广泛引用的池化检验相当或大于两个。此外，在只有罕见风险变异的疾病模型中，基于基因座最大单变异Cochran-Armitage趋势卡方统计量的现有方法与最近提出的一些方法密切相关的另一种现有方法的功效相当或大于另一种现有方法。我们的结论是，使用单变量测试统计的高效全基因座推理应该被重新考虑作为设计具有罕见变异的序列数据的强大关联测试的有用框架。
Association tests that pool minor alleles into a measure of burden at a locus have been proposed for case-control studies using sequence data containing rare variants. However, such pooling tests are not robust to the inclusion of neutral and protective variants, which can mask the association signal from risk variants. Early studies proposing pooling tests dismissed methods for locus-wide inference using nonnegative single-variant test statistics based on unrealistic comparisons. However, such methods are robust to the inclusion of neutral and protective variants and therefore may be more useful than previously appreciated. In fact, some recently proposed methods derived within different frameworks are equivalent to performing inference on weighted sums of squared single-variant score statistics. In this study, we compared two existing methods for locus-wide inference using nonnegative single-variant test statistics to two widely cited pooling tests under more realistic conditions. We established analytic results for a simple model with one rare risk and one rare neutral variant, which demonstrated that pooling tests were less powerful than even Bonferroni-corrected single-variant tests in most realistic situations. We also performed simulations using variants with realistic minor allele frequency and linkage disequilibrium spectra, disease models with multiple rare risk variants and extensive neutral variation, and varying rates of missing genotypes. In all scenarios considered, existing methods using nonnegative single-variant test statistics had power comparable to or greater than two widely cited pooling tests. Moreover, in disease models with only rare risk variants, an existing method based on the maximum single-variant Cochran-Armitage trend chi-square statistic in the locus had power comparable to or greater than another existing method closely related to some recently proposed methods. We conclude that efficient locus-wide inference using single-variant test statistics should be reconsidered as a useful framework for devising powerful association tests in sequence data with rare variants.
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影响因子：
9.8
作者：
Li, Bingshan;Leal, Suzanne M.
通讯作者：
Leal, Suzanne M.
DOI：
10.1007/978-3-540-37654-5_2
发表时间：
2010-01-01
期刊：
VOGEL AND MOTULSKY'S HUMAN GENETICS, FOURTH EDITION
影响因子：
--
作者：
Antonarakis, Stylianos E.
通讯作者：
Antonarakis, Stylianos E.
影响因子：
4.5
作者：
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通讯作者：
Daly MJ
影响因子：
30.8
作者：
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影响因子：
2.1
作者：
Morris, Andrew P.;Zeggini, Eleftheria
通讯作者：
Zeggini, Eleftheria