Using volcano plots and regularized-chi statistics in genetic association studies
Using volcano plots and regularized-chi statistics in genetic association studies
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DOI：
10.1016/j.compbiolchem.2013.02.003
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发表时间：
2014-02-01
影响因子：
3.1
通讯作者：
Yang, Yaning
中科院分区：
文献类型：
--
作者：
Li, Wentian;Freudenberg, Jan;Yang, Yaning
通常需要劳动密集型实验来从基因组中与疾病相关的变异体列表中识别致病变异体。为了设计这样的实验，候选变种根据它们与疾病的遗传联系的强度进行排序。然而，两种常用的遗传关联测量，优势比(OR)和p值可能会以不同的顺序对变异进行排序。为了将这两种方法整合到一个单一的分析中，这里我们将火山图方法从基因表达分析转移到遗传关联研究。在最初的背景下，火山图是褶皱变化和t检验统计量(或p值的对数)的散点图，后者对样本量更敏感。在遗传关联研究中，OR和皮尔逊的卡方统计量(或等价的其平方根，X；或标准化对数(OR))可以类似地用于火山图，允许他们的目测检查。此外，这些曲线图的几何解释导致了一种直观的方法，通过OR和卡方统计量的组合来过滤结果，我们称其为正则化卡方统计量。该方法在火山图上用一条光滑的曲线代替对应于独立截止点的直角线来选择相关标志进行OR和卡方统计。与卡方检验统计量相比，正则化的卡方检验包含了相对更多的来自小等位基因频率较低的变异体的信号。由于罕见的变异往往具有更强的功能效应，正规化的chi更适合于确定候选基因的优先顺序。(C)2013爱思唯尔有限公司。保留所有权利。
Labor intensive experiments are typically required to identify the causal disease variants from a list of disease associated variants in the genome. For designing such experiments, candidate variants are ranked by their strength of genetic association with the disease. However, the two commonly used measures of genetic association, the odds-ratio (OR) and p-value may rank variants in different order. To integrate these two measures into a single analysis, here we transfer the volcano plot methodology from gene expression analysis to genetic association studies. In its original setting, volcano plots are scatter plots of fold-change and t-test statistic (or -log of the p-value), with the latter being more sensitive to sample size. In genetic association studies, the OR and Pearson's chi-square statistic (or equivalently its square root, chi; or the standardized log(OR)) can be analogously used in a volcano plot, allowing for their visual inspection. Moreover, the geometric interpretation of these plots leads to an intuitive method for filtering results by a combination of both OR and chi-square statistic, which we term "regularized-chi". This method selects associated markers by a smooth curve in the volcano plot instead of the rightangled lines which corresponds to independent cutoffs for OR and chi-square statistic. The regularized-chi incorporates relatively more signals from variants with lower minor-allele-frequencies than chi-square test statistic. As rare variants tend to have stronger functional effects, regularized-chi is better suited to the task of prioritization of candidate genes. (C) 2013 Elsevier Ltd. All rights reserved.