The effects of linkage disequilibrium in large scale SNP datasets for MDR.
The effects of linkage disequilibrium in large scale SNP datasets for MDR.
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大规模SNP数据集对MDR的连锁不平衡的影响。
DOI：
10.1186/1756-0381-4-11
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发表时间：
2011-05-05
期刊：
影响因子：
4.5
通讯作者：
Ritchie MD
中科院分区：
文献类型：
--
作者：
Grady BJ;Torstenson ES;Ritchie MD
在分析大规模基因组数据集时，一个重要的考虑因素是分析方法识别疾病准确预测模型的能力。当试图从这些分析方法中评估灵敏度时，迄今为止的混杂因素是连锁不平衡（LD）的存在。在这项研究中，我们研究了LD对多因素重复性降低（MDR）软件包的灵敏度的影响。在多个单基因座和双基因座场景中模拟LD的四个相对量，其中LD块内的功能SNP的位置变化。用MDR分析模拟数据以确定该方法在不同背景下的灵敏度，其中该方法的灵敏度被衡量为该方法将正确的一个或两个基因座模型鉴定为最佳总体模型的100次中的次数。随着LD量的增加，MDR检测正确功能SNP的灵敏度下降，但检测疾病信号和发现间接关联的灵敏度增加。更高水平的LD开始混淆MDR算法，并导致识别直接关联的灵敏度下降;然而，它不影响检测间接关联的能力。对MDR产生的解决方案模型的仔细检查表明，MDR可以在正确的LD块中识别位点;尽管它并不总是功能性SNP。因此，应仔细检查LD数据集的MDR分析结果，以考虑数据集的潜在LD结构。
In the analysis of large-scale genomic datasets, an important consideration is the power of analytical methods to identify accurate predictive models of disease. When trying to assess sensitivity from such analytical methods, a confounding factor up to this point has been the presence of linkage disequilibrium (LD). In this study, we examined the effect of LD on the sensitivity of the Multifactor Dimensionality Reduction (MDR) software package. Four relative amounts of LD were simulated in multiple one- and two-locus scenarios for which the position of the functional SNP(s) within LD blocks varied. Simulated data was analyzed with MDR to determine the sensitivity of the method in different contexts, where the sensitivity of the method was gauged as the number of times out of 100 that the method identifies the correct one- or two-locus model as the best overall model. As the amount of LD increases, the sensitivity of MDR to detect the correct functional SNP drops but the sensitivity to detect the disease signal and find an indirect association increases. Higher levels of LD begin to confound the MDR algorithm and lead to a drop in sensitivity with respect to the identification of a direct association; it does not, however, affect the ability to detect indirect association. Careful examination of the solution models generated by MDR reveals that MDR can identify loci in the correct LD block; though it is not always the functional SNP. As such, the results of MDR analysis in datasets with LD should be carefully examined to consider the underlying LD structure of the dataset.
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