Artifact due to differential error when cases and controls are imputed from different platforms.
Artifact due to differential error when cases and controls are imputed from different platforms.
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DOI：
10.1007/s00439-011-1054-1
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发表时间：
2012-01
期刊：
影响因子：
5.3
通讯作者：
Kraft P
中科院分区：
文献类型：
--
作者：
Sinnott JA;Kraft P
在全基因组关联研究中纳入先前的基因型对照可以节省成本，但也可能产生设计偏差。当病例和对照在不同的平台上进行基因分型时，提供全基因组覆盖所需的插补将引入不同的测量误差，并可能导致假阳性。我们比较了来自护士健康研究的两个健康对照组在不同平台上的基因型频率（Affymetrix 6.0 [n= 1672]和Illumina HumanHap550 [n= 1038]）。使用标准的输入质量过滤器，我们观察到2,347,809个snp中有9,841个（0.4%）在5 × 10−8水平上显著。我们探索了三种控制这种I型误差膨胀的方法。一种方法是使用主成分去除平台效应；另一种是限制最高质量的snp；第三种方法是将一些控制组与病例一起进行基因分型，以排除统计伪产物的snp。第一种方法不能降低I类错误率；另外两种方法可以显著降低错误率，尽管它们都需要将部分snp排除在分析之外。理想情况下，我们描述的偏差将在设计阶段通过在每个平台上对足够数量的病例和对照进行基因分型来消除。研究人员使用imputation将不同平台上的基因分型样本与严重不平衡的病例对照比结合起来，他们应该意识到I型错误率膨胀的可能性，并应用适当的质量过滤器。每个发现的具有全基因组意义的SNP都应该在另一个平台上进行验证，以验证其重要性不是研究设计的产物。
Including previously-genotyped controls in a genome-wide association study can provide cost-savings, but can also create design biases. When cases and controls are genotyped on different platforms, the imputation needed to provide genome-wide coverage will introduce differential measurement error and may lead to false positives. We compared genotype frequencies of two healthy control groups from the Nurses’ Health Study genotyped on different platforms (Affymetrix 6.0 [n=1,672] and Illumina HumanHap550 [n=1,038]). Using standard imputation quality filters, we observed 9,841 SNPs out of 2,347,809 (0.4%) significant at the 5 × 10−8 level. We explored three methods for controlling for this Type I error inflation. One method was to remove platform effects using principal components; another was to restrict to SNPs of highest quality imputation; and a third was to genotype some controls alongside cases to exclude SNPs that are statistical artifact. The first method could not reduce the Type I error rate; the other two could dramatically reduce the error rate, although both required that a portion of SNPs be excluded from analysis. Ideally, the biases we describe would be eliminated at the design stage, by genotyping sufficient numbers of cases and controls on each platform. Researchers using imputation to combine samples genotyped on different platforms with severely unbalanced case-control ratios should be aware of the potential for inflated Type I error rates and apply appropriate quality filters. Every SNP found with genome-wide significance should be validated on another platform to verify that its significance is not an artifact of study design.
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影响因子：
2.1
作者：
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