Testing for an unusual distribution of rare variants.
Testing for an unusual distribution of rare variants.
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DOI：
10.1371/journal.pgen.1001322
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发表时间：
2011-03
期刊：
影响因子：
4.5
通讯作者：
Daly MJ
中科院分区：
文献类型：
--
作者：
Neale BM;Rivas MA;Voight BF;Altshuler D;Devlin B;Orho-Melander M;Kathiresan S;Purcell SM;Roeder K;Daly MJ
技术的进步使高通量测序成为医学遗传学的主要发现工具，特别是用于检测罕见变异。尽管如此，这种方法仍然面临着分析的挑战，即非常罕见的变异的影响只能作为一个群体进行有效评估。另一个复杂的问题是，任何给定的罕见变异都可能没有影响，可能增加风险，或者可能具有保护性。在这里，我们提出了C-alpha检验统计量作为一种新的方法来测试这种混合效应在一组罕见的变体的存在。与现有的负担测试不同，C-alpha通过测试方差而不是均值，在目标集包含风险和保护性变量时保持一致的功效。通过病例/对照数据的模拟和分析，我们证明了相对于评估个体中罕见变异负担的现有方法的良好功效。测序技术的发展现在使我们能够分析所有的遗传变异，其中大部分是极其罕见的。我们建议测试我们在病例与对照中观察到的罕见变异的分布。为此，我们提出了一种新的应用程序的C-alpha统计来测试这些罕见的变异。C-alpha旨在确定在病例和对照中观察到的一组变体是否是混合物，使得一些变体赋予风险或保护或表型中性。预计风险变异在病例中更常见;保护性变异在对照中更常见。c-α对这种不平衡很敏感，不管它的来源是风险性的、保护性的，还是两者兼而有之，但它非常适合保护性和风险性变体的混合。APOB的变异很好地说明了一种混合物，即某些罕见的变异会增加甘油三酯水平，而另一些则会降低甘油三酯水平。C-α的标志性特征是，它使用在病例和对照中观察到的变异分布来检测混合物的存在，从而暗示基因或途径是疾病的风险因素。
Technological advances make it possible to use high-throughput sequencing as a primary discovery tool of medical genetics, specifically for assaying rare variation. Still this approach faces the analytic challenge that the influence of very rare variants can only be evaluated effectively as a group. A further complication is that any given rare variant could have no effect, could increase risk, or could be protective. We propose here the C-alpha test statistic as a novel approach for testing for the presence of this mixture of effects across a set of rare variants. Unlike existing burden tests, C-alpha, by testing the variance rather than the mean, maintains consistent power when the target set contains both risk and protective variants. Through simulations and analysis of case/control data, we demonstrate good power relative to existing methods that assess the burden of rare variants in individuals. Developments in sequencing technology now enable us to assay all genetic variation, much of which is extremely rare. We propose to test the distribution of rare variants we observe in cases versus controls. To do so, we present a novel application of the C-alpha statistic to test these rare variants. C-alpha aims to determine whether the set of variants observed in cases and controls is a mixture, such that some of the variants confer risk or protection or are phenotypically neutral. Risk variants are expected to be more common in cases; protective variants more common in controls. C-alpha is sensitive to this imbalance, regardless of its origin—risk, protective, or both—but is ideally suited for a mixture of protective and risk variants. Variation in APOB nicely illustrates a mixture, in that certain rare variants increase triglyceride levels while others decrease it. The hallmark feature of C-alpha is that it uses the distribution of variation observed in cases and controls to detect the presence of a mixture, thus implicating genes or pathways as risk factors for disease.
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DOI：
10.1073/pnas.0812824106
发表时间：
2009-03-10
影响因子：
11.1
作者：
Kryukov, Gregory V.;Shpunt, Alexander;Sunyaev, Shamil R.
通讯作者：
Sunyaev, Shamil R.
影响因子：
7
作者：
Ng, PC;Henikoff, S
通讯作者：
Henikoff, S
影响因子：
9.8
作者：
Li, Bingshan;Leal, Suzanne M.
通讯作者：
Leal, Suzanne M.
影响因子：
30.8
作者：
Jonathan, C;Pertsemlidis, A;Hobbs, HH
通讯作者：
Hobbs, HH
影响因子：
3.9
作者：
Mort, Matthew;Evani, Uday S.;Mooney, Sean D.
通讯作者：
Mooney, Sean D.