Genetic variant predictors of gene expression provide new insight into risk of colorectal cancer

Stephanie A. Bien*, Yu-Ru Su, David V. Conti, Tabitha A. Harrison, Conghui Qu, Xingyi Guo, Yingchang Lu, Demetrius Albanes, Paul L. Auer, Barbara L. Banbury, Sonja I. Berndt, Stéphane Bézieau, Hermann Brenner, Daniel D. Buchanan, Bette J. Caan, Peter T. Campbell, Christopher S. Carlson, Andrew T. Chan, Jenny Chang-Claude, Sai ChenCharles M. Connolly, Douglas F. Easton, Edith J.M. Feskens, Steven Gallinger, Graham G. Giles, Marc J. Gunter, Jochen Hampe, Jeroen R. Huyghe, Michael Hoffmeister, Thomas J. Hudson, Eric J. Jacobs, Mark A. Jenkins, Ellen Kampman, Hyun Min Kang, Tilman Kühn, Sébastien Küry, Flavio Lejbkowicz, Loic Le Marchand, Roger L. Milne, Christopher I. Li, Annika Lindblom, Noralane M. Lindor, Vicente Martín, Caroline E. McNeil, Marilena Melas, Victor Moreno, Polly A. Newcomb, Kenneth Offit, Paul D.P. Pharaoh, John D. Potter, Chenxu Qu, Elio Riboli, Gad Rennert, Núria Sala, Clemens Schafmayer, Peter C. Scacheri, Stephanie L. Schmit, Gianluca Severi, Martha L. Slattery, Joshua D. Smith, Antonia Trichopoulou, Rosario Tumino, Cornelia M. Ulrich, Fränzel J.B. van Duijnhoven, Bethany Van Guelpen, Stephanie J. Weinstein, Emily White, Alicja Wolk, Michael O. Woods, Anna H. Wu, Goncalo R. Abecasis, Graham Casey, Deborah A. Nickerson, Stephen B. Gruber, Li Hsu, Wei Zheng, Ulrike Peters

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

5 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)307-326
Number of pages20
JournalHuman Genetics
Volume138
Issue number4
Early online date28 Feb 2019
DOIs
Publication statusPublished - Apr 2019

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    Bien, S. A., Su, Y-R., Conti, D. V., Harrison, T. A., Qu, C., Guo, X., ... Peters, U. (2019). Genetic variant predictors of gene expression provide new insight into risk of colorectal cancer. Human Genetics, 138(4), 307-326. https://doi.org/10.1007/s00439-019-01989-8