Introduction: Studies that examine the role of rare variants in both simple and complex disease are increasingly common. Though the usual approach of testing rare variants in aggregate sets is more powerful than testing individual variants, it is of interest to identify the variants that are plausible drivers of the association. We present a novel method for prioritization of rare variants after a significant aggregate test by quantifying the influence of the variant on the aggregate test of association. Methods: In addition to providing a measure used to rank variants, we use outlier detection methods to present the computationally efficient Rare Variant Influential Filtering Tool (RIFT) to identify a subset of variants that influence the disease association. We evaluated several outlier detection methods that vary based on the underlying variance measure: interquartile range (Tukey fences), median absolute deviation, and SD. We performed 1,000 simulations for 50 regions of size 3 kb and compared the true and false positive rates. We compared RIFT using the Inner Tukey to 2 existing methods: adaptive combination of p values (ADA) and a Bayesian hierarchical model (BeviMed). Finally, we applied this method to data from our targeted resequencing study in idiopathic pulmonary fibrosis (IPF). Results: All outlier detection methods observed higher sensitivity to detect uncommon variants (0.001 #x3c; minor allele frequency, MAF #x3e; 0.03) compared to very rare variants (MAF #x3c;0.001). For uncommon variants, RIFT had a lower median false positive rate compared to the ADA. ADA and RIFT had significantly higher true positive rates than that observed for BeviMed. When applied to 2 regions found previously associated with IPF including 100 rare variants, we identified 6 polymorphisms with the greatest evidence for influencing the association with IPF. Discussion: In summary, RIFT has a high true positive rate while maintaining a low false positive rate for identifying polymorphisms influencing rare variant association tests. This work provides an approach to obtain greater resolution of the rare variant signals within significant aggregate sets; this information can provide an objective measure to prioritize variants for follow-up experimental studies and insight into the biological pathways involved.
Hum Hered

中文翻译：

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在重要的聚合稀有变异测试中鉴定有影响的变异


简介：检查罕见变异在简单和复杂疾病中的作用的研究越来越普遍。尽管测试聚合集中的罕见变体的常用方法比测试单个变体更强大，但识别出可能是关联驱动因素的变体是有意义的。我们提出了一种新颖的方法，通过量化变异对关联聚合测试的影响，在显着的聚合测试后对罕见变异进行优先级排序。方法：除了提供用于对变异进行排名的测量方法之外，我们还使用离群值检测方法来提供计算效率高的稀有变异影响过滤工具（RIFT），以识别影响疾病关联的变异子集。我们评估了几种异常值检测方法，这些方法根据基础方差度量而变化：四分位数范围（Tukey 栅栏）、中值绝对偏差和 SD。我们对 50 个大小为 3 kb 的区域进行了 1,000 次模拟，并比较了真阳性率和假阳性率。我们将使用 Inner Tukey 的 RIFT 与 2 种现有方法进行了比较： p值的自适应组合 (ADA) 和贝叶斯分层模型 (BeviMed)。最后，我们将该方法应用于特发性肺纤维化（IPF）靶向重测序研究的数据。结果：与非常罕见的变异 (MAF #x3c;0.001) 相比，所有异常值检测方法检测不常见变异 (0.001 #x3c; 次要等位基因频率，MAF #x3e; 0.03) 的灵敏度更高。对于不常见的变异，与 ADA 相比，RIFT 的中位假阳性率较低。 ADA 和 RIFT 的真阳性率明显高于 BeviMed 观察到的真阳性率。 当应用于先前发现的与 IPF 相关的 2 个区域（包括 100 个罕见变异）时，我们鉴定了 6 个多态性，它们具有影响与 IPF 关联的最有力证据。讨论：综上所述，RIFT 在识别影响罕见变异关联测试的多态性方面具有较高的真阳性率，同时保持较低的假阳性率。这项工作提供了一种在重要聚合集中获得更高分辨率的罕见变异信号的方法；这些信息可以提供客观的衡量标准，以确定后续实验研究中变体的优先顺序，并深入了解所涉及的生物途径。
 赫里德