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A new GWAS and meta-analysis with 1000Genomes imputation identifies novel risk variants for colorectal cancer

Al-Tassan, Nada A., Whiffin, Nicola, Hosking, Fay J., Palles, Claire, Farrington, Susan M., Dobbins, Sara E., Harris, Rebecca, Gorman, Maggie, Tenesa, Albert, Meyer, Brian F., Wakil, Salma M., Kinnersley, Ben, Campbell, Harry, Martin, Lynn, Smith, Christopher G., Idziaszczyk, Shelley Alexis, Barclay, Ella, Maughan, Timothy Stanley, Kaplan, Richard, Kerr, Rachel, Kerr, David, Buchannan, Daniel D., Ko Win, Aung, Hopper, John, Jenkins, Mark, Lindor, Noralane M., Newcomb, Polly A., Gallinger, Steve, Conti, David, Schumacher, Fred, Casey, Graham, Dunlop, Malcolm G., Tomlinson, Ian P., Cheadle, Jeremy Peter and Houlston, Richard S. 2015. A new GWAS and meta-analysis with 1000Genomes imputation identifies novel risk variants for colorectal cancer. Scientific Reports 5 , 10442. 10.1038/srep10442

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Abstract

Genome-wide association studies (GWAS) of colorectal cancer (CRC) have identified 23 susceptibility loci thus far. Analyses of previously conducted GWAS indicate additional risk loci are yet to be discovered. To identify novel CRC susceptibility loci, we conducted a new GWAS and performed a meta-analysis with five published GWAS (totalling 7,577 cases and 9,979 controls of European ancestry), imputing genotypes utilising the 1000 Genomes Project. The combined analysis identified new, significant associations with CRC at 1p36.2 marked by rs72647484 (minor allele frequency [MAF] = 0.09) near CDC42 and WNT4 (P = 1.21 × 10−8, odds ratio [OR] = 1.21 ) and at 16q24.1 marked by rs16941835 (MAF = 0.21, P = 5.06 × 10−8; OR = 1.15) within the long non-coding RNA (lncRNA) RP11-58A18.1 and ~500 kb from the nearest coding gene FOXL1. Additionally we identified a promising association at 10p13 with rs10904849 intronic to CUBN (MAF = 0.32, P = 7.01 × 10-8; OR = 1.14). These findings provide further insights into the genetic and biological basis of inherited genetic susceptibility to CRC. Additionally, our analysis further demonstrates that imputation can be used to exploit GWAS data to identify novel disease-causing variants.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Biosciences
Medicine
Subjects: R Medicine > RC Internal medicine > RC0254 Neoplasms. Tumors. Oncology (including Cancer)
Publisher: Nature Publishing Group
ISSN: 2045-2322
Date of First Compliant Deposit: 30 March 2016
Date of Acceptance: 13 April 2015
Last Modified: 15 Mar 2019 09:19
URI: http://orca.cf.ac.uk/id/eprint/74685

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Cited 49 times in Scopus. View in Scopus. Powered By Scopus® Data

Cited 5 times in Web of Science. View in Web of Science.

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