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Title: Genetic variant predictors of gene expression provide new insight into risk of colorectal cancer
Author: Bien, Stephanie A.
Su, Yu-Ru
Conti, David V.
Harrison, Tabitha A.
Qu, Conghui
Guo, Xingyi
Lu, Yingchang
Albanes, Demetrius
Auer, Paul L.
Banbury, Barbara L.
Berndt, Sonja I.
Bézieau, Stéphane
Brenner, Hermann
Buchanan, Daniel D.
Caan, Bette J.
Campbell, Peter T.
Carlson, Christopher S.
Chan, Andrew T.
Chang-Claude, Jenny
Chen, Sai
Connolly, Charles M.
Easton, Douglas F.
Feskens, Edith J. M.
Gallinger, Steven
Giles, Graham G.
Gunter, Marc J.
Hampe, Jochen
Huyghe, Jeroen R.
Hoffmeister, Michael
Hudson, Thomas J.
Jacobs, Eric J.
Jenkins, Mark A.
Kampman, Ellen
Kang, Hyun Min
Kuehn, Tilman
Kury, Sebastien
Lejbkowicz, Flavio
Marchand, Loïc Le
Milne, Roger L.
Li, Li
Li, Christopher I.
Lindblom, Annika
Lindo, Noralane M.
Martín Sánchez, Vicente
McNeil, Caroline E.
Melas, Marilena
Moreno Aguado, Víctor
Newcomb, Polly A.
Offit, Kenneth
Pharaoh, Paul D.
Potter, John D.
Qu, Chenxu
Riboli, Elio
Rennert, Gad
Sala Serra, Núria
Schafmayer, Clemens
Scacheri, Peter C.
Schmit, Stephanie L.
Severi, Gianluca
Slattery, Martha L.
Smith, Joshua D.
Trichopoulou, Antonia
Tumino, Rosario
Ulrich, Cornelia M.
van Duijnhoven, Franzel J. B.
Van Guelpen, Bethany
Weinstein, Stephanie J.
White, Emily
Wolk, Alicja
Woods, Michael O.
Wu, Anna H.
Abecasis, Gonçalo R.
Casey, Graham
Nickerson, Deborah A.
Gruber, Stephen B.
Hsu, Li
Zheng, Wei
Peters, Ulrike
Keywords: Càncer colorectal
Expressió gènica
Colorectal cancer
Gene expression
Issue Date: 1-Apr-2019
Publisher: Springer Verlag
Abstract: Genome-wide association studies have reported 56 independently associated colorectal cancer (CRC) risk variants, most of which are non-coding and believed to exert their effects by modulating gene expression. The computational method PrediXcan uses cis-regulatory variant predictors to impute expression and perform gene-level association tests in GWAS without directly measured transcriptomes. In this study, we used reference datasets from colon (n = 169) and whole blood (n = 922) transcriptomes to test CRC association with genetically determined expression levels in a genome-wide analysis of 12,186 cases and 14,718 controls. Three novel associations were discovered from colon transverse models at FDR ≤ 0.2 and further evaluated in an independent replication including 32,825 cases and 39,933 controls. After adjusting for multiple comparisons, we found statistically significant associations using colon transcriptome models with TRIM4 (discovery P = 2.2 × 10− 4, replication P = 0.01), and PYGL (discovery P = 2.3 × 10− 4, replication P = 6.7 × 10− 4). Interestingly, both genes encode proteins that influence redox homeostasis and are related to cellular metabolic reprogramming in tumors, implicating a novel CRC pathway linked to cell growth and proliferation. Defining CRC risk regions as one megabase up- and downstream of one of the 56 independent risk variants, we defined 44 non-overlapping CRC-risk regions. Among these risk regions, we identified genes associated with CRC (P < 0.05) in 34/44 CRC-risk regions. Importantly, CRC association was found for two genes in the previously reported 2q25 locus, CXCR1 and CXCR2, which are potential cancer therapeutic targets. These findings provide strong candidate genes to prioritize for subsequent laboratory follow-up of GWAS loci. This study is the first to implement PrediXcan in a large colorectal cancer study and findings highlight the utility of integrating transcriptome data in GWAS for discovery of, and biological insight into, risk loci.
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It is part of: Human Genetics, 2019, vol. 138, num. 4, p. 307-326
Related resource:
ISSN: 0340-6717
Appears in Collections:Articles publicats en revistes (Ciències Clíniques)
Articles publicats en revistes (Institut d'lnvestigació Biomèdica de Bellvitge (IDIBELL))

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