Novel common genetic susceptibility loci for colorectal cancer

Abstract

Background: Previous genome-wide association studies (GWAS) have identified 42 loci (P<5x10-8) associated with the risk of colorectal cancer (CRC). Expanded consortium efforts facilitating the discovery of additional susceptibility loci may capture unexplained familial risk. Methods: We conducted a GWAS in European-descent CRC cases and controls using a discovery-replication design, followed by examination of novel findings in a multiethnic sample (cumulative N=163,315). In the discovery stage (36,948 cases/30,864 controls), we identified genetic variants with minor allele frequency ≥1% associated with risk of CRC using logistic regression followed by a fixed effects inverse variance weighted meta-analysis. All novel independent variants reaching genome-wide statistical significance (two-sided P<5x10-8) were tested for replication in separate European-ancestry samples (12,952 cases/48,383 controls). Next, we examined the generalizability of discovered variants in East Asians, African-Americans, and Hispanics (12,085 cases/22,083 controls). Finally, we examined the contributions of novel risk variants to familial relative risk and examined the prediction capabilities of a polygenic risk score. All statistical tests were two-sided. Results: The discovery GWAS identified eleven variants associated with CRC at P<5x10-8, of which nine (at 4q22.2/5p15.33/5p13.1/ 6p21.31/6p12.1/10q11.23/12q24.21/16q24.1/ 20q13.13) independently replicated at P<0.05. Multiethnic follow-up supported the generalizability of discovery findings. These results provide a 14.7% increase in familial relative risk explained by common risk alleles from 10.3% (95% CI 7.9 – 13.7%; known variants) to 11.85% (95% CI 9.2 – 15.5%; known and novel variants). A polygenic risk score identifies 4.3% of the population at an odds ratio of at least 2.0. Conclusions: This study provides insight into the architecture of common genetic variation contributing to CRC etiology and improves risk prediction for individualized screening.