Trimming, weighting, and grouping SNPs in human case-control association studies
Trimming, weighting, and grouping SNPs in human case-control association studies
复制标题
DOI：
10.1101/gr.204001
复制
发表时间：
2001-12-01
期刊：
影响因子：
7
通讯作者：
Ott, J
中科院分区：
文献类型：
--
作者：
Hoh, J;Wille, A;Ott, J
寻找复杂性状背后的基因一直很困难，而且常常令人失望。这些困难的主要原因是，几个基因，每一个相当小的影响，可能是相互作用产生的性状。因此，我们必须在全基因组中寻找找到这些基因的好机会。然而，使用数万个SNP标记进行此操作会大大增加假阳性结果的总体概率，并且当前将此类错误概率限制在可接受水平的方法往往会降低检测弱基因的能力。研究大量SNP不可避免地引入错误（例如，在基因分型中），这将扭曲分析结果。在这里，我们提出了一个简单的策略，规避了许多这些问题。我们开发了一种集合关联方法来融合相关的信息来源，如等位基因关联和Hardy-Weinberg不平衡。信息在基因组中的多个标记和基因上组合，通过修剪改善质量控制，并且适当的测试策略限制了总体假阳性率。与其他可用的方法相比，我们的方法在真实的数据应用中检测与不同基因中的SNP标记集的关联已经显示出显著的成功。
The search for genes underlying complex traits has been difficult and often disappointing. The main reason for these difficulties is that several genes, each with rather small effect, might be interacting to produce the trait. Therefore, we must search the whole genome for a good chance to find these genes. Doing this with tens of thousands of SNP markers, however, greatly increases the overall probability of false-positive results, and current methods limiting such error probabilities to acceptable levels tend to reduce the power of detecting weak genes. Investigating large numbers of SNPs inevitably introduces errors (e.g., in genotyping), which will distort analysis results. Here we propose a simple strategy that circumvents many of these problems. We develop a set-association method to blend relevant sources of information such as allelic association and Hardy-Weinberg disequilibrium. Information is combined over multiple markers and genes in the genome, quality control is improved by trimming, and an appropriate testing strategy limits the overall false-positive rate. In contrast to other available methods, Our method to detect association to sets of SNP markers in different genes in a real data application has shown remarkable success.