Detecting PCOS susceptibility loci from genome-wide association studies via iterative trend correlation based feature screening.
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ABSTRACT: BACKGROUND:Feature screening plays a critical role in handling ultrahigh dimensional data analyses when the number of features exponentially exceeds the number of observations. It is increasingly common in biomedical research to have case-control (binary) response and an extremely large-scale categorical features. However, the approach considering such data types is limited in extant literature. In this article, we propose a new feature screening approach based on the iterative trend correlation (ITC-SIS, for short) to detect important susceptibility loci that are associated with the polycystic ovary syndrome (PCOS) affection status by screening 731,442 SNP features that were collected from the genome-wide association studies. RESULTS:We prove that the trend correlation based screening approach satisfies the theoretical strong screening consistency property under a set of reasonable conditions, which provides an appealing theoretical support for its outperformance. We demonstrate that the finite sample performance of ITC-SIS is accurate and fast through various simulation designs. CONCLUSION:ITC-SIS serves as a good alternative method to detect disease susceptibility loci for clinic genomic data.
SUBMITTER: Dai X
PROVIDER: S-EPMC7199379 | biostudies-literature | 2020 May
REPOSITORIES: biostudies-literature
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