Machine learning study of SNPs in noncoding regions to predict non-small cell lung cancer susceptibility
Non-small cell lung cancer (NSCLC) is the most common pathological subtype of lung cancer. Both environmental and genetic factors have been reported to impact the lung cancer susceptibility. We conducted a genome-wide association study (GWAS) of 287 NSCLC patients and 467 healthy controls in a Chinese population using the Illumina Genome-Wide Asian Screening Array Chip on 712,095 SNPs (single nucleotide polymorphisms). Using logistic regression modeling, GWAS identified 17 new noncoding region SNP loci associated with the NSCLC risk, and the top three (rs80040741, rs9568547, rs6010259) were under a stringent p-value (
Source: Clinical Oncology - Category: Radiology Authors: Yan Huang, Ting Bao, Tingting Zhang, Guiyi Ji, Youjuan Wang, Zhihao Ling, Weimin Li Tags: Original Article Source Type: research
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