Salomon, M.P., W.L. Li, C.K. Edlund, J. Morrison, B.K. Fortini, A.K. Win, D.V. Conti, D.C. Thomas, D. Duggan, D.D. Buchanan, M.A. Jenkins, J.L. Hopper, S. Gallinger, L. Le Marchand, P.A. Newcomb, G. Casey, and P. Marjoram. “GWASeq: targeted re-sequencing follow up to GWAS” BMC Genomics 17(1), 2016, 176.
Abstract: For the last decade the conceptual framework of the Genome-Wide Association Study (GWAS) has dominated the investigation of human disease and other complex traits. While GWAS have been successful in identifying a large number of variants associated with various phenotypes, the overall amount of heritability explained by these variants remains small. This raises the question of how best to follow up on a GWAS, localize causal variants accounting for GWAS hits, and as a consequence explain more of the so-called "missing" heritability. Advances in high throughput sequencing technologies now allow for the efficient and cost-effective collection of vast amounts of fine-scale genomic data to complement GWAS. We investigate these issues using a colon cancer dataset. After QC, our data consisted of 1993 cases, 899 controls. Using marginal tests of associations, we identify 10 variants distributed among six targeted regions that are significantly associated with colorectal cancer, with eight of the variants being novel to this study. Additionally, we perform so-called 'SNP-set' tests of association and identify two sets of variants that implicate both common and rare variants in the etiology of colorectal cancer. Here we present a large-scale targeted re-sequencing resource focusing on genomic regions implicated in colorectal cancer susceptibility previously identified in several GWAS, which aims to 1) provide fine-scale targeted sequencing data for fine-mapping and 2) provide data resources to address methodological questions regarding the design of sequencing-based follow-up studies to GWAS. Additionally, we show that this strategy successfully identifies novel variants associated with colorectal cancer susceptibility and can implicate both common and rare variants.
Schmit, S.L., F.R. Schumacher, C.K. Edlund, D.V. Conti, U. Ihenacho, P. Wan, D. Van Den Berg, G. Casey, B.K. Fortini, H.J. Lenz, T. Tusié-Luna, C.A. Aguilar-Salinas, H. Moreno Macías, A. Huerta-Chagoya, M.L. Ordóñez-Sánchez, R. Rodríguez-Guillén, I. Cruz-Bautista, M. Rodríguez-Torres, L.L. Muñóz-Hernández, O. Arellano-Campos, D. Gómez, U. Alvirde, C. González-Villalpando, M.E. González-Villalpando, L. Le Marchand, C.A. Haiman, and J.C. Figueiredo. “Genome-wide Association Study of Colorectal Cancer in Hispanics” Carcinogenesis 37(6), 2016, 547-556.
Abstract: Genome-wide association studies (GWAS) have identified 58 susceptibility alleles across 37 regions associated with the risk of colorectal cancer (CRC) with P < 5×10(-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 < 1×10(-6)) at 1p34.3 (rs7528276; Odds Ratio (OR) = 1.86 [95% confidence interval (CI): 1.47-2.36); P = 2.5×10(-7)], 2q23.3 (rs1367374; OR = 1.37 (95% CI: 1.21-1.55); P = 4.0×10(-7)), 14q24.2 (rs143046984; OR = 1.65 (95% CI: 1.36-2.01); P = 4.1×10(-7)) and 16q12.2 [rs142319636; OR = 1.69 (95% CI: 1.37-2.08); P=7.8×10(-7)]. Among the 57 previously published CRC susceptibility alleles with minor allele frequency ≥1%, 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 = 5.3×10(-5)) and 11q12.2 (P = 6.8×10(-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.