rs961253

This study analyzes 243 rectal cancer patients to determine if colorectal cancer (CRC) susceptibility SNPs are associated with rectal cancer prognosis. SNPs on 8q24 (rs6983267 near MYC), 18q21 (rs12953717, rs4464148 in SMAD7), and 20q13 (rs4925386 in LAMA5) are found to be associated with disease-free survival and overall survival in rectal cancer patients, with some alleles associated with better or worse prognosis despite their effects on CRC risk.

This case-control study in Japanese population evaluated CRC risk prediction models using SNPs identified in European and East Asian GWAS. An 11-SNP model combining 6 European-identified SNPs (rs6983267, rs4779584, rs4444235, rs9929218, rs10936599, rs16969681) with 5 East Asian-identified SNPs (rs704017, rs11196172, rs10774214, rs647161, rs2423279) showed significantly improved discrimination capacity compared to a 6-SNP model alone (derivation AUC 0.6392 vs 0.6125, P=0.0039; replication AUC 0.5695 vs 0.5310, P=0.0018), with cumulative risk at age 80 estimated at 13% in high-risk versus 6% in low-risk genetic groups.

Genome-wide investigation of gene-environment interactions in colorectal cancer using a two-tiered case-only/case-control design. In 314 sporadic CRC cases (stage I) and 259 familial CRC cases plus 1,002 controls (stage II), rs1944511 showed a significant interaction with overweight (OR=2.00, p=0.042 after multiple testing correction). Several other SNPs showed nominally significant G×E interactions with overweight, smoking, and alcohol consumption. Among candidate CRC-associated SNPs, rs9929218 showed the strongest interaction with alcohol consumption (nominal p=0.008).

Meta-analysis of genome-wide association studies examining colorectal cancer susceptibility in 2,906 cases and 3,416 controls (GWAS) with replication in 8,161 cases and 9,101 controls. Eight of ten previously identified SNPs showed associations (p-values 0.02 to 1.8×10⁻⁸), and the study identified marginal evidence for a second independent signal in BMP2 (rs4813802, combined p=7.3×10⁻⁵) and a novel association with TERT-CLPTM1L (rs2853668, combined p=1.9×10⁻⁴).

This paper applies a bias correction method for odds ratio estimates from GWAS discovery data, demonstrating that the 'winner's curse' affects initial effect size estimates. The authors applied conditional maximum likelihood estimation to correct bias in GWAS findings from multiple complex diseases (breast cancer, colorectal cancer, lung cancer, prostate cancer, type I and II diabetes) and show that bias-adjusted odds ratios are substantially more consistent with subsequent replication studies, with selection-adjusted confidence intervals providing better uncertainty quantification than uncorrected estimates.