ArticleBMC bioinformatics2020
Detecting PCOS susceptibility loci from genome-wide association studies via iterative trend correlation based feature screening.
Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Contribution of labor related gene subtype classification on heterogeneity of polycystic ovary syndrome.PloS one · 2023Article
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Authors and funding
3 authors.
Funding
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Abstract
backgroundFeature 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.
resultsWe 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.
conclusionITC-SIS serves as a good alternative method to detect disease susceptibility loci for clinic genomic data.
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