A variable selection method for genome-wide association studies
A variable selection method for genome-wide association studies
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DOI：
10.1093/bioinformatics/btq600
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发表时间：
2011-01-01
期刊：
影响因子：
5.8
通讯作者：
Lin, Dan-Yu
中科院分区：
文献类型：
--
作者：
He, Qianchuan;Lin, Dan-Yu
动机：涉及 50 万或更多单核苷酸多态性 (SNP) 的全基因组关联研究 (GWAS) 允许以整体方式对复杂疾病进行遗传剖析。一次分析一个 SNP 的常见做法并没有完全实现 GWAS 识别多种因果变异和预测疾病风险的潜力。现有的 GWAS 数据联合分析方法往往会错过与疾病无关且具有较高错误发现率 (FDR) 的因果 SNP。结果：我们引入了 GWASelect，这是一种统计功能强大且计算高效的变量选择方法，旨在解决 GWAS 数据的独特挑战。该方法以先前选择的 SNP 为条件，迭代搜索潜在的 SNP，因此能够捕获与疾病边缘相关以及与疾病边缘不相关的因果 SNP。该方法内置了特殊的重采样机制，以减少误报结果。模拟研究表明，GWASelect 在广泛的连锁不平衡模式下表现良好，并且在捕获因果变异方面比现有方法更强大，同时具有较低的 FDR。此外，基于 GWASelect 的回归模型往往比现有方法能够更准确地预测疾病风险。 Wellcome Trust 病例控制联盟 (WTCCC) 的数据说明了 GWASelect 的优势。
Motivation: Genome-wide association studies (GWAS) involving half a million or more single nucleotide polymorphisms (SNPs) allow genetic dissection of complex diseases in a holistic manner. The common practice of analyzing one SNP at a time does not fully realize the potential of GWAS to identify multiple causal variants and to predict risk of disease. Existing methods for joint analysis of GWAS data tend to miss causal SNPs that are marginally uncorrelated with disease and have high false discovery rates (FDRs).Results: We introduce GWASelect, a statistically powerful and computationally efficient variable selection method designed to tackle the unique challenges of GWAS data. This method searches iteratively over the potential SNPs conditional on previously selected SNPs and is thus capable of capturing causal SNPs that are marginally correlated with disease as well as those that are marginally uncorrelated with disease. A special resampling mechanism is built into the method to reduce false positive findings. Simulation studies demonstrate that the GWASelect performs well under a wide spectrum of linkage disequilibrium patterns and can be substantially more powerful than existing methods in capturing causal variants while having a lower FDR. In addition, the regression models based on the GWASelect tend to yield more accurate prediction of disease risk than existing methods. The advantages of the GWASelect are illustrated with the Wellcome Trust Case-Control Consortium (WTCCC) data.