Genome-wide association study of colorectal cancer in Hispanics

This manuscript describes the first large-scale genome-wide association study of colorectal cancer in Hispanics and Latinos. Our results demonstrate the broad replication of known susceptibility regions and the importance of fine-mapping in ethnic minority populations.Genome-wide association studies (GWAS) have identified 58 susceptibility alleles across 37 regions associated with the risk of colorectal cancer (CRC) with P < 5x10(-8). Most studies have been conducted in non-Hispanic whites and East Asians; however, the generalizability of these findings and the potential for ethnic-specific risk variation in Hispanic and Latino (HL) individuals have been largely understudied. We describe the first GWAS of common genetic variation contributing to CRC risk in HL (1611 CRC cases and 4330 controls). We also examine known susceptibility alleles and implement imputation-based fine-mapping to identify potential ethnicity-specific association signals in known risk regions. We discovered 17 variants across 4 independent regions that merit further investigation due to suggestive CRC associations (P < 1x10(-6)) at 1p34.3 (rs7528276; Odds Ratio (OR) = 1.86 [95% confidence interval (CI): 1.47-2.36); P = 2.5x10(-7)], 2q23.3 (rs1367374; OR = 1.37 (95% CI: 1.21-1.55); P = 4.0x10(-7)), 14q24.2 (rs143046984; OR = 1.65 (95% CI: 1.36-2.01); P = 4.1x10(-7)) and 16q12.2 [rs142319636; OR = 1.69 (95% CI: 1.37-2.08); P=7.8x10(-7)]. Among the 57 previously published CRC susceptibility alleles with minor allele frequency a parts per thousand yen1%, 76.5% of SNPs had a consistent direction of effect and 19 (33.3%) were nominally statistically significant (P < 0.05). Further, rs185423955 and rs60892987 were identified as novel secondary susceptibility variants at 3q26.2 (P = 5.3x10(-5)) and 11q12.2 (P = 6.8x10(-5)), respectively. Our findings demonstrate the importance of fine mapping in HL. These results are informative for variant prioritization in functional studies and future risk prediction modeling in minority populations.