Staff Publications

Staff Publications

  • external user (warningwarning)
  • Log in as
  • language uk
  • About

    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

    We have a manual that explains all the features 

Record number 549068
Title Genetic variant predictors of gene expression provide new insight into risk of colorectal cancer
Author(s) 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; Kühn, Tilman; Küry, Sébastien; Lejbkowicz, Flavio; Marchand, Loic Le; Milne, Roger L.; Li, Christopher I.; Lindblom, Annika; Lindor, Noralane M.; Martín, Vicente; McNeil, Caroline E.; Melas, Marilena; Moreno, Victor; Newcomb, Polly A.; Offit, Kenneth; Pharaoh, Paul D.P.; Potter, John D.; Qu, Chenxu; Riboli, Elio; Rennert, Gad; Sala, Núria; Schafmayer, Clemens; Scacheri, Peter C.; Schmit, Stephanie L.; Severi, Gianluca; Slattery, Martha L.; Smith, Joshua D.; Trichopoulou, Antonia; Tumino, Rosario; Ulrich, Cornelia M.; Duijnhoven, Fränzel J.B. van; Guelpen, Bethany Van; Weinstein, Stephanie J.; White, Emily; Wolk, Alicja; Woods, Michael O.; Wu, Anna H.; Abecasis, Goncalo R.; Casey, Graham; Nickerson, Deborah A.; Gruber, Stephen B.; Hsu, Li; Zheng, Wei; Peters, Ulrike
Source Human Genetics 138 (2019)4. - ISSN 0340-6717 - p. 307 - 326.
DOI https://doi.org/10.1007/s00439-019-01989-8
Department(s) VLAG
Global Nutrition
Nutrition and Disease
Publication type Refereed Article in a scientific journal
Publication year 2019
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.
Comments
There are no comments yet. You can post the first one!
Post a comment
 
Please log in to use this service. Login as Wageningen University & Research user or guest user in upper right hand corner of this page.