Whole genome and exome sequencing studies have become increasingly available and are being used to identify rare genetic variants associated with health and disease outcomes. Investigators routinely use mixed models to account for genetic relatedness or other clustering variables (e.g. family or household) when testing genetic associations. However, no existing tests of the association of a rare variant association with a binary outcome in the presence of correlated data controls the Type 1 error where there are (1) few carriers of the rare allele, (2) a small proportion of cases relative to controls, and (3) covariates to adjust for. Here, we address all three issues in developing the carriers-only test framework for testing rare variant association with a binary trait. In this framework, we estimate outcome probabilities under the null hypothesis, and then use them, within the carriers, to test variant associations. We extend the BinomiRare test, which was previously proposed for independent observations, and develop the Conway-Maxwell-Poisson (CMP) test, and study their properties in simulations. We show that the BinomiRare test always controls the type 1 error, while the CMP test sometimes does not. We then use the BinomiRare test to test the association of rare genetic variants in target genes with small vessel disease stroke, short sleep, and venous thromboembolism, in whole-genome sequence data from the Trans-Omics for Precision Medicine program.

中文翻译：

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BinomiRare：仅携带者的测试，用于将稀有遗传变异与混合模型和任何病例对照比例的​​二元结果相关联

全基因组和外显子组测序研究已变得越来越有用，并被用于鉴定与健康和疾病结果相关的罕见遗传变异。在测试遗传关联时，研究人员通常使用混合模型来说明遗传相关性或其他聚类变量（例如家庭或家庭）。但是，在存在相关数据的情况下，尚无关于稀有变异关联与二元结果的关联的现有测试可控制1型错误，其中（1）稀有等位基因的携带者很少，（2）相对病例的比例很小（3）进行调整的协变量。在这里，我们解决了开发仅运营商的测试框架中的所有三个问题，该框架用于测试具有二进制特征的稀有变异关联。在这个框架中 我们估计了原假设下的结果概率，然后在携带者中使用它们来测试变异关联。我们扩展了先前为独立观测而提出的BinomiRare检验，并开发了Conway-Maxwell-Poisson（CMP）检验，并在模拟中研究了它们的特性。我们显示，BinomiRare测试始终控制类型1错误，而CMP测试有时则不能。然后，我们使用BinomiRare检验，在Trans-Omics for Precision Medicine程序的全基因组序列数据中，测试目标基因中的罕见遗传变异与小血管疾病，中风，短暂的睡眠和静脉血栓栓塞的相关性。并开发Conway-Maxwell-Poisson（CMP）测试，并在仿真中研究其性能。我们显示，BinomiRare测试始终控制类型1错误，而CMP测试有时则不能。然后，我们使用BinomiRare检验，在Trans-Omics for Precision Medicine程序的全基因组序列数据中，测试目标基因中的罕见遗传变异与小血管疾病，中风，短暂的睡眠和静脉血栓栓塞的相关性。并开发Conway-Maxwell-Poisson（CMP）测试，并在仿真中研究其性能。我们显示，BinomiRare测试始终控制类型1错误，而CMP测试有时则不能。然后，我们使用BinomiRare检验，在Trans-Omics for Precision Medicine程序的全基因组序列数据中，测试目标基因中的罕见遗传变异与小血管疾病，中风，短暂的睡眠和静脉血栓栓塞的相关性。