Methods for detecting associations with rare variants for common diseases: Application to analysis of sequence data
Methods for detecting associations with rare variants for common diseases: Application to analysis of sequence data
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DOI：
10.1016/j.ajhg.2008.06.024
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发表时间：
2008-09-12
影响因子：
9.8
通讯作者：
Leal, Suzanne M.
中科院分区：
文献类型：
--
作者：
Li, Bingshan;Leal, Suzanne M.
虽然使用tagSNPs的全基因组关联研究是检测常见变异的有力方法，但它们在检测与罕见变异的关联方面能力不足。最近的研究表明，常见疾病可能是由于具有广泛等位基因频率的功能变体，从罕见到常见。鉴定罕见变异的一种有效方法是直接测序。成本效益测序技术的发展使关联研究能够使用候选基因的序列数据，并在未来，从整个基因组。虽然用于分析常见变异的方法适用于序列数据，但它们的性能可能不是最佳的。在这项研究中，它表明，折叠的方法，其中包括跨变量折叠基因型和应用单变量检验，是强大的分析罕见的变异，而多变量分析是强大的，对非因果变异的列入。这两种方法都上级单变量检验单独分析每个变量。为了统一崩溃和多标记测试的优势，我们开发了组合多元和崩溃（CMC）方法，并证明了CMC方法既强大又稳健。CMC方法可以应用于候选基因或全基因组序列数据。
Although whole-genome association studies using tagSNPs are a powerful approach for detecting common variants, they are underpowered for detecting associations with rare variants. Recent studies have demonstrated that common diseases can be due to functional variants with a wide spectrum of allele frequencies, ranging from rare to common. An effective way to identify rare variants is through direct sequencing. The development of cost-effective sequencing technologies enables association studies to use sequence data from candidate genes and, in the future, from the entire genome. Although methods used for analysis of common variants are applicable to sequence data, their performance might not be optimal. In this study, it is shown that the collapsing method, which involves collapsing genotypes across variants and applying a univariate test, is powerful for analyzing rare variants, whereas multivariate analysis is robust against inclusion of noncausal variants. Both methods are superior to analyzing each variant individually with univariate tests. In order to unify the advantages of both collapsing and multiple-marker tests, we developed the Combined Multivariate and Collapsing (CMC) method and demonstrated that the CMC method is both powerful and robust. The CMC method can be applied to either candidate-gene or whole-genome sequence data.