Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, People's Republic of China.
University of Chinese Academy of Sciences, Beijing, 100049, People's Republic of China.
Sci Rep. 2023 Jun 5;13(1):9139. doi: 10.1038/s41598-023-35929-4.
In genome-wide association study, extracting disease-associated genetic variants among millions of single nucleotide polymorphisms is of great importance. When the response is a binary variable, the Cochran-Armitage trend tests and associated MAX test are among the most widely used methods for association analysis. However, the theoretical guarantees for applying these methods to variable screening have not been built. To fill this gap, we propose screening procedures based on adjusted versions of these methods and prove their sure screening properties and ranking consistency properties. Extensive simulations are conducted to compare the performances of different screening procedures and demonstrate the robustness and efficiency of MAX test-based screening procedure. A case study on a dataset of type 1 diabetes further verifies their effectiveness.
在全基因组关联研究中，从数百万个单核苷酸多态性中提取与疾病相关的遗传变异是非常重要的。当反应变量是二分类变量时， Cochran-Armitage 趋势检验和相关的 MAX 检验是最广泛使用的关联分析方法之一。然而，这些方法应用于变量筛选的理论保证尚未建立。为了填补这一空白，我们提出了基于这些方法调整版本的筛选程序，并证明了它们的确定筛选特性和排序一致性特性。通过广泛的模拟比较了不同筛选程序的性能，并验证了基于 MAX 检验的筛选程序的稳健性和效率。对 1 型糖尿病数据集的案例研究进一步验证了它们的有效性。
