Rare-Variant Kernel Machine Test for Longitudinal Data from Population and Family Samples.
Rare-Variant Kernel Machine Test for Longitudinal Data from Population and Family Samples.
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作者：
Yan Q;Weeks DE;Tiwari HK;Yi N;Zhang K;Gao G;Lin WY;Lou XY;Chen W;Liu N
据报道，核机（KM）测试在基于集的罕见变异关联测试中表现良好。已经进行了许多研究来测量多个时间点的表型，但标准KM方法仅适用于单个时间点的表型。此外，基于家族的设计已广泛应用于遗传关联研究；因此，所采用的数据分析方法必须适当处理家族关系。一个罕见的变异测试目前不存在纵向数据从家庭样本。因此，在本文中，我们的目标是引入一种罕见变异的关联检验，其中包括对群体或家庭样本的多重纵向表型测量。该方法使用基于线性混合模型框架的KM回归，适用于群体（L-KM）或家族样本（LF-KM）的纵向数据。在我们基于种群的仿真研究中，与其他竞争方法相比，L-KM在我们考虑的所有场景中都能很好地控制I型错误率并增加功率。相反，在基于家庭的模拟研究中，我们发现当L-KM直接应用于家庭样本时，I型错误率会膨胀，而LF-KM保留了期望的I型错误率，并且总体上具有最佳的功率性能。最后，我们通过分析来自遗传分析研讨会18 （GAW18）的罕见变异和血压之间关联研究的数据来说明我们提出的LF-KM方法的实用性。我们提出了一种在群体和家庭样本中进行罕见变异关联测试的方法，使用在每个受试者的多个时间点测量的表型。在我们的仿真研究中，该方法具有最佳的功率性能。
The kernel machine (KM) test reportedly performs well in the set-based association test of rare variants. Many studies have been conducted to measure phenotypes at multiple time points, but the standard KM methodology has only been available for phenotypes at a single time point. In addition, family-based designs have been widely used in genetic association studies; therefore, the data analysis method used must appropriately handle familial relatedness. A rare variant test does not currently exist for longitudinal data from family samples. Therefore, in this paper, we aim to introduce an association test for rare variants, which includes multiple longitudinal phenotype measurements for either population or family samples. This approach uses KM regression based on the linear mixed model framework and is applicable to longitudinal data from either population (L-KM) or family samples (LF-KM). In our population-based simulation studies, L-KM has good control of Type I error rate and increased power in all the scenarios we considered, compared with other competing methods. Conversely, in the family-based simulation studies, we found an inflated Type I error rate when L-KM was applied directly to the family samples, whereas LF-KM retained the desired Type I error rate and had the best power performance overall. Finally, we illustrate the utility of our proposed LF-KM approach by analyzing data from an association study between rare variants and blood pressure from the Genetic Analysis Workshop 18 (GAW18). We propose a method for rare-variant association testing in population and family samples, using phenotypes measured at multiple time points for each subject. The proposed method has the best power performance compared to competing approaches in our simulation study.