Analysis of rare genetic variants has focused on region-based analysis wherein a subset of the variants within a genomic region is tested for association with a complex trait. Two important practical challenges have emerged. First, it is difficult to choose which test to use. Second, it is unclear which group of variants within a region should be tested. Both depend on the unknown true state of nature. Therefore, we develop the Multi-Kernel SKAT (MK-SKAT) which tests across a range of rare variant tests and groupings. Specifically, we demonstrate that several popular rare variant tests are special cases of the sequence kernel association test which compares pair-wise similarity in trait value to similarity in the rare variant genotypes between subjects as measured through a kernel function. Choosing a particular test is equivalent to choosing a kernel. Similarly, choosing which group of variants to test also reduces to choosing a kernel. Thus, MK-SKAT uses perturbation to test across a range of kernels. Simulations and real data analyses show that our framework controls type I error while maintaining high power across settings: MK-SKAT loses power when compared to the kernel for a particular scenario but has much greater power than poor choices.

中文翻译：

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使用多内核序列内核关联测试 (MK-SKAT) 进行跨方法和阈值的罕见变异测试

对罕见遗传变异的分析侧重于基于区域的分析，其中测试基因组区域内的变异子集与复杂性状的关联。出现了两个重要的实际挑战。首先，很难选择使用哪种测试。其次，不清楚应该测试一个区域内的哪一组变异。两者都取决于未知的真实自然状态。因此，我们开发了多内核 SKAT (MK-SKAT)，它可以测试一系列罕见的变体测试和分组。具体来说，我们证明了几种流行的稀有变异测试是序列核关联测试的特例，该测试将性状值的成对相似性与通过核函数测量的受试者之间稀有变异基因型的相似性进行比较。选择一个特定的测试相当于选择一个内核。同样，选择要测试的变体组也简化为选择内核。因此，MK-SKAT 使用扰动来测试一系列内核。模拟和真实数据分析表明，我们的框架控制 I 类错误，同时保持跨设置的高功率：与特定场景的内核相比，MK-SKAT 会失去功率，但比糟糕的选择具有更大的功率。