Pooled Association Tests for Rare Variants in Exon-Resequencing Studies
Pooled Association Tests for Rare Variants in Exon-Resequencing Studies
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DOI：
10.1016/j.ajhg.2010.04.005
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发表时间：
2010-06-11
影响因子：
9.8
通讯作者：
Sunyaev, Shamil R.
中科院分区：
文献类型：
--
作者：
Price, Alkes L.;Kryukov, Gregory V.;Sunyaev, Shamil R.
深度测序将很快在大型疾病样本中生成全面的序列信息。虽然检测与单个罕见变异相关性的能力有限，但将基因或途径的变异合并到复合测试中提供了识别易感基因的替代策略。我们描述了一种统计方法，用于检测蛋白质编码基因中多个罕见变异与数量性状或二分性状的关联。该方法是基于回归的表型值对个人的基因型得分受到一个可变的等位基因频率阈值，结合计算预测的功能效应的错义变体。通过具有可变阈值的排列检验评估统计显著性。我们使用了严格的群体遗传学模拟框架来评估该方法的功效，并将该方法应用于三项疾病研究的经验测序数据。
Deep sequencing will soon generate comprehensive sequence information in large disease samples. Although the power to detect association with an individual rare variant is limited, pooling variants by gene or pathway into a composite test provides an alternative strategy for identifying susceptibility genes. We describe a statistical method for detecting association of multiple rare variants in protein-coding genes with a quantitative or dichotomous trait. The approach is based on the regression of phenotypic values on individuals' genotype scores subject to a variable allele-frequency threshold, incorporating computational predictions of the functional effects of missense variants. Statistical significance is assessed by permutation testing with variable thresholds. We used a rigorous population-genetics simulation framework to evaluate the power of the method, and we applied the method to empirical sequencing data from three disease studies.