Li Bingshan, Leal Suzanne M
Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA.
Am J Hum Genet. 2008 Sep;83(3):311-21. doi: 10.1016/j.ajhg.2008.06.024. Epub 2008 Aug 7.
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.
尽管使用标签单核苷酸多态性（tagSNPs）的全基因组关联研究是检测常见变异的有力方法，但对于检测与罕见变异的关联而言，其效能不足。近期研究表明，常见疾病可能归因于具有广泛等位基因频率范围（从罕见到常见）的功能性变异。识别罕见变异的有效方法是通过直接测序。具有成本效益的测序技术的发展使关联研究能够使用来自候选基因以及未来来自整个基因组的序列数据。虽然用于分析常见变异的方法适用于序列数据，但其性能可能并非最优。在本研究中，结果表明，将各个变异的基因型合并并应用单变量检验的合并法对于分析罕见变异很有效，而多变量分析对于包含非因果变异具有稳健性。这两种方法都优于使用单变量检验单独分析每个变异。为了统一合并法和多标记检验的优点，我们开发了联合多变量和合并（CMC）法，并证明了CMC法既有效又稳健。CMC法可应用于候选基因或全基因组序列数据。
