Petersen, Paneen S.

Abstract Background 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 (GxE) interactions and may provide information on the underlying biological pathway. Objective We investigated interactions between folate consumption and predicted gene expression on colorectal cancer risk across the genome. Methods We used variant weights from the PrediXcan models of colon tissue-specific gene expression as a priori variant information for a set-based GxE 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 4,839 genes with available genetically predicted expression. We meta-analyzed results across 23 studies for a total of 13,498 cases with colorectal tumors and 13,918 controls of European ancestry. Results We found suggestive evidence of interaction with folate intake 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). 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. Conclusion We identified three genes involved in preventing or repairing DNA damage that may interact with folate consumption to alter CRC risk.

Abstract Background: Regular use of nonsteroidal anti-inflammatory drugs (NSAID) is associated with lower risk of colorectal cancer. Genome-wide interaction analysis on single variants (G × E) has identified several SNPs that may interact with NSAIDs to confer colorectal cancer risk, but variations in gene expression levels may also modify the effect of NSAID use. Therefore, we tested interactions between NSAID use and predicted gene expression levels in relation to colorectal cancer risk. Methods: Genetically predicted gene expressions were tested for interaction with NSAID use on colorectal cancer risk among 19,258 colorectal cancer cases and 18,597 controls from 21 observational studies. A Mixed Score Test for Interactions (MiSTi) approach was used to jointly assess G × E effects which are modeled via fixed interaction effects of the weighted burden within each gene set (burden) and residual G × E effects (variance). A false discovery rate (FDR) at 0.2 was applied to correct for multiple testing. Results: Among the 4,840 genes tested, genetically predicted expression levels of four genes modified the effect of any NSAID use on colorectal cancer risk, including DPP10 (PG×E = 1.96 × 10−4), KRT16 (PG×E = 2.3 × 10−4), CD14 (PG×E = 9.38 × 10−4), and CYP27A1 (PG×E = 1.44 × 10−3). There was a significant interaction between expression level of RP11-89N17 and regular use of aspirin only on colorectal cancer risk (PG×E = 3.23 × 10−5). No interactions were observed between predicted gene expression and nonaspirin NSAID use at FDR < 0.2. Conclusions: By incorporating functional information, we discovered several novel genes that interacted with NSAID use. Impact: These findings provide preliminary support that could help understand the chemopreventive mechanisms of NSAIDs on colorectal cancer.

AbstractBackgroundBody mass index (BMI) and diabetes are established risk factors for colorectal cancer (CRC), likely through perturbations in metabolic traits (e.g. insulin resistance and glucose homeostasis). Identification of interactions between variation in genes and these metabolic risk factors may identify novel biologic insights into CRC etiology.MethodsTo improve statistical power and interpretation for gene‐environment interaction (G × E) testing, we tested genetic variants that regulate expression of a gene together for interaction with BMI (kg/m2) and diabetes on CRC risk among 26 017 cases and 20 692 controls. Each variant was weighted based on PrediXcan analysis of gene expression data from colon tissue generated in the Genotype‐Tissue Expression Project for all genes with heritability ≥1%. We used a mixed‐effects model to jointly measure the G × E interaction in a gene by partitioning the interactions into the predicted gene expression levels (fixed effects), and residual G × E effects (random effects). G × BMI analyses were stratified by sex as BMI‐CRC associations differ by sex. We used false discovery rates to account for multiple comparisons and reported all results with FDR <0.2.ResultsAmong 4839 genes tested, genetically predicted expressions of FOXA1 (P = 3.15 × 10−5), PSMC5 (P = 4.51 × 10−4) and CD33 (P = 2.71 × 10−4) modified the association of BMI on CRC risk for men; KIAA0753 (P = 2.29 × 10−5) and SCN1B (P = 2.76 × 10−4) modified the association of BMI on CRC risk for women; and PTPN2 modified the association between diabetes and CRC risk in both sexes (P = 2.31 × 10−5).ConclusionsAggregating G × E interactions and incorporating functional information, we discovered novel genes that may interact with BMI and diabetes on CRC risk.

Abstract Genome-wide association studies of colorectal cancer (CRC) have identified over 50 susceptibility loci. These variants represent only a small fraction of total heritability for CRC. Gene-environment (GxE) interaction studies may help identify novel loci and biological interactions that give insight to the pathogenesis of CRC. Previous genome-wide GxE studies with dietary factors have identified interactions between loci and processed meat consumption and alcohol; however, limited statistical power remains a primary concern. Set-based SNP testing has the potential to increase statistical power to detect GxE interactions by aggregating functionally relevant SNPs. In this large pooled analysis using 14 case-control and nested case-control studies, we incorporated functional information from the transcriptome prediction tool, PrediXcan, into a novel set-based approach for testing GxE interactions. We used variant weights from the PrediXcan models of tissue-specific gene expression in the transverse colon as a priori variant information for a set-based GxE approach. We restricted our analysis to variants in the PrediXcan transverse colon gene models (n = 4,842). This discovery phase included 10,360 CRC and advanced adenoma cases, and 11,183 controls of European ancestry from the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO) and the Colon Cancer Family Registry. All 14 studies were analyzed together in a pooled data set using the Mixed Effects Score Tests for interactions. We tested for gene interactions with sex- and study- specific quartiles of dietary intake of red meat (servings/day), processed meat (servings/day), vegetables (servings/day), fruits (servings/day), and fiber (g/day). We detected two genes with suggestive interactions (false discovery rate (FDR) &lt; 0.2) with intake of red meat and risk of CRC: Superoxide Dismutase 2 (SOD2) and Ubiquitin Conjugating Enzyme E2 H (UBE2H). No interactions at FDR &lt; 0.2 were observed for processed meat, vegetables, fruits, or fiber. The SOD2 gene, which encodes an enzyme important in apoptotic signaling and clearing of reactive oxygen species, may regulate response to colonic exposure to heme iron or increased bile acid from high-fat content in red meat. UBE2H is part of the ubiquitin-proteasome system that has a role in Wnt signaling, which can be mediated by heme iron in red meat and is commonly found dysregulated in cancer. These findings highlight the efficacy of integrating functional information and set-based testing for novel discovery of genes interacting with known dietary risk factors of CRC. We plan to replicate these findings in additional studies. Citation Format: Paneen S. Petersen, Yu-Ru Su, Sonja I. Berndt, Stephanie A. Bien, Hermann Brenner, Graham Casey, Andrew T. Chan, Jenny Chang-Claude, Jane C. Figueiredo, Steven J. Gallinger, Robert W. Haile, Tabitha A. Harrison, Michael Hoffmeister, Mark A. Jenkins, Amit D. Joshi, Sébastien Küry, Loic Le Marchand, Yi Lin, Noralane M. Lindor, Polly A. Newcomb, John D. Potter, Robert Schoen, Martha L. Slattery, Stephen N. Thibodeau, Emily White, Li Hsu, Ulrike Peters, CCFR, GECCO. Interactions between genetic predictors of gene expression and dietary factors associated with risk of colorectal cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 5268.