Weighted pedigree-based statistics for testing the association of rare variants.
Weighted pedigree-based statistics for testing the association of rare variants.
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DOI：
10.1186/1471-2164-13-667
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发表时间：
2012-11-24
期刊：
影响因子：
4.4
通讯作者：
Xiong M
中科院分区：
文献类型：
--
作者：
Shugart YY;Zhu Y;Guo W;Xiong M
随着下一代测序（NGS）技术的出现，研究人员现在正在生成大量关于高维基因组变异的数据，这些数据的分析可能会揭示涉及疾病复杂病因的罕见变异。然而，阻碍这些发现的是，目前罕见变异的统计数据是为基于人口的数据而设计的。在本文中，我们介绍了一个基于家系的统计量，专门设计用于测试罕见的变异，以家庭为基础的数据。基于谱系的统计数据的额外力量源于这样一个事实，即虽然与疾病或感兴趣的性状相关的罕见变异在人群中只偶尔发生，但在有多个受影响个体的家庭中，这些变异是丰富的。请注意，虽然所提出的统计可以在有统计加权和没有统计加权的情况下应用，但我们的模拟表明，当应用加权（WSS和VT）时，其功效会增加。我们的工作假设是，由于罕见变异集中在有多个受影响个体的家庭中，因此基于谱系的统计数据应该比基于人群的统计数据更有效地检测罕见变异。为了评估我们新的基于谱系的统计在关联研究中的表现，我们开发了一个基于序列的关联研究的一般框架，该框架能够处理来自各种类型的谱系和不相关个体的数据。简而言之，我们开发了一个将基于人口的统计数据转换为基于家庭的关联检验的程序。此外，我们修改了两个现有的测试，加权平方和测试和可变阈值测试，并适用于我们的家庭为基础的崩溃方法。我们证明了新的基于家庭的测试比相应的基于人口的测试更强大，它们产生了合理的I型错误率。为了证明可行性，我们将新开发的测试应用于Frachial Heart Study（FHS）的基于家系的GWAS数据集。FHS-GWAS数据包含大约5000个频率小于0.05的不常见变异。这些数据中的潜在关联发现证明了软件PB-STAR的可行性（注意，PB-STAR现在可免费向公众提供）。我们的测试表明，在分析罕见变异时，基于谱系的设计比基于人群的病例对照设计更有效。我们进一步证明，基于谱系的统计数据检测罕见变异的能力与谱系内受影响个体的比例直接相关。
With the advent of next-generation sequencing (NGS) technologies, researchers are now generating a deluge of data on high dimensional genomic variations, whose analysis is likely to reveal rare variants involved in the complex etiology of disease. Standing in the way of such discoveries, however, is the fact that statistics for rare variants are currently designed for use with population-based data. In this paper, we introduce a pedigree-based statistic specifically designed to test for rare variants in family-based data. The additional power of pedigree-based statistics stems from the fact that while rare variants related to diseases or traits of interest occur only infrequently in populations, in families with multiple affected individuals, such variants are enriched. Note that while the proposed statistic can be applied with and without statistical weighting, our simulations show that its power increases when weighting (WSS and VT) are applied. Our working hypothesis was that, since rare variants are concentrated in families with multiple affected individuals, pedigree-based statistics should detect rare variants more powerfully than population-based statistics. To evaluate how well our new pedigree-based statistics perform in association studies, we develop a general framework for sequence-based association studies capable of handling data from pedigrees of various types and also from unrelated individuals. In short, we developed a procedure for transforming population-based statistics into tests for family-based associations. Furthermore, we modify two existing tests, the weighted sum-square test and the variable-threshold test, and apply both to our family-based collapsing methods. We demonstrate that the new family-based tests are more powerful than corresponding population-based test and they generate a reasonable type I error rate. To demonstrate feasibility, we apply the newly developed tests to a pedigree-based GWAS data set from the Framingham Heart Study (FHS). FHS-GWAS data contain approximately 5000 uncommon variants with frequencies less than 0.05. Potential association findings in these data demonstrate the feasibility of the software PB-STAR (note, PB-STAR is now freely available to the public). Our tests show that when analyzing for rare variants, a pedigree-based design is more powerful than a population-based case–control design. We further demonstrate that a pedigree-based statistic’s power to detect rare variants increases in direct relation to the proportion of affected individuals within the pedigree.
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DOI：
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发表时间：
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期刊：
Clinical and translational science
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