Screening properties of trend tests in genetic association studies

• Published in: Scientific reports Vol. 13; no. 1; p. 9139
• Main Authors: Jiang, Zhenzhen, Guo, Hongping, Wang, Jinjuan
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 05.06.2023; Nature Publishing Group; Nature Portfolio
• Subjects: 631/208; 692/308; Algorithms; Association analysis; Case studies; Case-Control Studies; Diabetes; Diabetes mellitus (insulin dependent); Genetic Association Studies; Genetic diversity; Genetic Predisposition to Disease; Genetic variance; Genome-wide association studies; Genome-Wide Association Study - methods; Genomes; Genotype & phenotype; Humanities and Social Sciences; Humans; Mathematics; Models, Genetic; multidisciplinary; Polymorphism, Single Nucleotide; Science; Science (multidisciplinary); Single-nucleotide polymorphism; Statistics
• ISSN: 2045-2322; 2045-2322
• 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.