SNP set analysis for detecting disease association using exon sequence data.
SNP set analysis for detecting disease association using exon sequence data.
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DOI：
10.1186/1753-6561-5-s9-s91
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发表时间：
2011-11-29
期刊：
影响因子：
--
通讯作者：
Wang P
中科院分区：
文献类型：
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作者：
Wang R;Peng J;Wang P
罕见的变异被认为在疾病病因学中发挥着重要作用。高通量测序技术的最新进展使研究人员能够系统地描述常见和罕见变异的遗传效应。我们介绍了几种基于Logistic回归模型和Logistic核机器模型同时测试单核苷酸多态(SNP)集合中常见和罕见变异的效果的方法。其中一些模型还考虑了基因-环境相互作用和SNP-SNP相互作用。我们使用来自基因分析研讨会17的无关个体数据来说明这些方法的性能。使用所提出的方法一致地选择了三个真实的疾病基因(Flt1、PIK3C3和KDR)。此外，与Logistic回归模型相比，Logistic核机模型更强大，这可能是因为它们通过正则化减少了有效的参数数量。我们的结果还表明，筛查步骤在减少假阳性发现数量方面是有效的，这通常是关联研究的一大担忧。
Rare variants are believed to play an important role in disease etiology. Recent advances in high-throughput sequencing technology enable investigators to systematically characterize the genetic effects of both common and rare variants. We introduce several approaches that simultaneously test the effects of common and rare variants within a single-nucleotide polymorphism (SNP) set based on logistic regression models and logistic kernel machine models. Gene-environment interactions and SNP-SNP interactions are also considered in some of these models. We illustrate the performance of these methods using the unrelated individuals data from Genetic Analysis Workshop 17. Three true disease genes (FLT1, PIK3C3, and KDR) were consistently selected using the proposed methods. In addition, compared to logistic regression models, the logistic kernel machine models were more powerful, presumably because they reduced the effective number of parameters through regularization. Our results also suggest that a screening step is effective in decreasing the number of false-positive findings, which is often a big concern for association studies.