Local Joint Testing Improves Power and Identifies Hidden Heritability in Association Studies
Local Joint Testing Improves Power and Identifies Hidden Heritability in Association Studies
复制标题
局部联合测试提高了功效并识别了关联研究中隐藏的遗传力
作者：
Brielin C. Brown;A. Price;N. Patsopoulos;N. Zaitlen
越来越多的证据表明，复杂的人类表型是高度多基因的，许多基因座含有多个致病变异，但大多数遗传关联研究都是孤立地检查每个SNP。虽然这导致了数千种疾病关联的发现，但发现的变异只占疾病遗传性的一小部分。已经提出了替代的多SNP方法，但是诸如多重测试校正、对基因分型错误的敏感性以及对潜在遗传结构的优化等问题仍然存在。在这里，我们描述了一个本地的联合测试程序，完成多重测试校正，利用我们称之为连锁掩蔽的遗传现象，其中SNP之间的连锁不平衡隐藏在标准的关联方法下的信号。我们发现，当地联合测试的原始威康信托病例控制联盟（WTCCC）的数据集，导致发现22个相关位点，5比边际的方法。这些基因座后来在后续研究中发现，其中包含数千个额外的个体。我们发现，这些基因座显着增加了遗传力解释的全基因组的显着关联在WTCCC数据集。此外，我们表明，在gEUVADIS数据集的顺式表达QTL（eQTL）研究中，局部联合测试增加了包含显着eQTL的基因数量，比边际分析增加了10.7%。我们的多假设校正和联合测试框架可以在一个名为Jester的Python软件包中找到，可以在github.com/brielin/Jester上找到。
There is mounting evidence that complex human phenotypes are highly polygenic, with many loci harboring multiple causal variants, yet most genetic association studies examine each SNP in isolation. While this has led to the discovery of thousands of disease associations, discovered variants account for only a small fraction of disease heritability. Alternative multi-SNP methods have been proposed, but issues such as multiple-testing correction, sensitivity to genotyping error, and optimization for the underlying genetic architectures remain. Here we describe a local joint-testing procedure, complete with multiple-testing correction, that leverages a genetic phenomenon we call linkage masking wherein linkage disequilibrium between SNPs hides their signal under standard association methods. We show that local joint testing on the original Wellcome Trust Case Control Consortium (WTCCC) data set leads to the discovery of 22 associated loci, 5 more than the marginal approach. These loci were later found in follow-up studies containing thousands of additional individuals. We find that these loci significantly increase the heritability explained by genome-wide significant associations in the WTCCC data set. Furthermore, we show that local joint testing in a cis-expression QTL (eQTL) study of the gEUVADIS data set increases the number of genes containing significant eQTL by 10.7% over marginal analyses. Our multiple-hypothesis correction and joint-testing framework are available in a python software package called Jester, available at github.com/brielin/Jester.
影响因子：
64.8
作者：
通讯作者：
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影响因子：
4.8
作者：
Buschbeck, M;Eickhoff, J;Ullrich, A
通讯作者：
Ullrich, A
影响因子：
9.8
作者：
Ionita-Laza, Iuliana;Lee, Seunggeun;Lin, Xihong
通讯作者：
Lin, Xihong