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

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?

SUBMITTER: Bien SA

PROVIDER: S-EPMC6483948 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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

Bien Stephanie A SA Su Yu-Ru YR Conti David V DV Harrison Tabitha A TA Qu Conghui C Guo Xingyi X Lu Yingchang Y Albanes Demetrius D Auer Paul L PL Banbury Barbara L BL Berndt Sonja I SI Bézieau Stéphane S Brenner Hermann H Buchanan Daniel D DD Caan Bette J BJ Campbell Peter T PT Carlson Christopher S CS Chan Andrew T AT Chang-Claude Jenny J Chen Sai S Connolly Charles M CM Easton Douglas F DF Feskens Edith J M EJM Gallinger Steven S Giles Graham G GG Gunter Marc J MJ Hampe Jochen J Huyghe Jeroen R JR Hoffmeister Michael M Hudson Thomas J TJ Jacobs Eric J EJ Jenkins Mark A MA Kampman Ellen E Kang Hyun Min HM Kühn Tilman T Küry Sébastien S Lejbkowicz Flavio F Le Marchand Loic L Milne Roger L RL Li Li L Li Christopher I CI Lindblom Annika A Lindor Noralane M NM Martín Vicente V McNeil Caroline E CE Melas Marilena M Moreno Victor V Newcomb Polly A PA Offit Kenneth K Pharaoh Paul D P PDP Potter John D JD Qu Chenxu C Riboli Elio E Rennert Gad G Sala Núria N Schafmayer Clemens C Scacheri Peter C PC Schmit Stephanie L SL Severi Gianluca G Slattery Martha L ML Smith Joshua D JD Trichopoulou Antonia A Tumino Rosario R Ulrich Cornelia M CM van Duijnhoven Fränzel J B FJB Van Guelpen Bethany B Weinstein Stephanie J SJ White Emily E Wolk Alicja A Woods Michael O MO Wu Anna H AH Abecasis Goncalo R GR Casey Graham G Nickerson Deborah A DA Gruber Stephen B SB Hsu Li L Zheng Wei W Peters Ulrike U

Human genetics 20190228 4

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 t ...[more]

PMID: 30820706

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Project description:Observational studies have shown higher folate consumption to be associated with lower risk of colorectal cancer (CRC). Understanding whether and how genetic risk factors interact with folate could further elucidate the underlying mechanism. Aggregating functionally relevant genetic variants in set-based variant testing has higher power to detect gene-environment (G × E) interactions and may provide information on the underlying biological pathway. We investigated interactions between folate consumption and predicted gene expression on colorectal cancer risk across the genome. We used variant weights from the PrediXcan models of colon tissue-specific gene expression as a priori variant information for a set-based G × E approach. We harmonized total folate intake (mcg/day) based on dietary intake and supplemental use across cohort and case-control studies and calculated sex and study specific quantiles. Analyses were performed using a mixed effects score tests for interactions between folate and genetically predicted expression of 4839 genes with available genetically predicted expression. We pooled results across 23 studies for a total of 13,498 cases with colorectal tumors and 13,918 controls of European ancestry. We used a false discovery rate of 0.2 to identify genes with suggestive evidence of an interaction. We found suggestive evidence of interaction with folate intake on CRC risk for genes including glutathione S-Transferase Alpha 1 (GSTA1; p = 4.3E-4), Tonsuko Like, DNA Repair Protein (TONSL; p = 4.3E-4), and Aspartylglucosaminidase (AGA: p = 4.5E-4). We identified three genes involved in preventing or repairing DNA damage that may interact with folate consumption to alter CRC risk. Glutathione is an antioxidant, preventing cellular damage and is a downstream metabolite of homocysteine and metabolized by GSTA1. TONSL is part of a complex that functions in the recovery of double strand breaks and AGA plays a role in lysosomal breakdown of glycoprotein.

Dataset Information

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

Publications

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

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