AbstractBackground: Red meat and/or processed meat are established colorectal cancer risk factors. Genome-wide association studies (GWAS) have reported more than 200 variants associated with colorectal cancer risk. We used functional annotation data to identify subsets of variants within known pathways to construct pathway-based polygenic risk scores (pPRS) to assess interactions with meat intake. Methods: A pooled sample of 30,812 cases and 40,504 colorectal cancer controls from 27 studies was analyzed. Quantiles for red and processed meat intake were constructed. A total of 204 GWAS variants were annotated to genes with Annotation Query (AnnoQ) and assessed for overrepresentation in PANTHER-reported pathways. pPRSs were constructed from significantly overrepresented pathways. Covariate-adjusted logistic regression models evaluated interactions between pPRS and red or processed meat intake in relation to colorectal cancer risk. Results: A total of 30 variants were overrepresented in four pathways: presenilin/Alzheimer disease, cadherin/WNT signaling, gonadotropin-releasing hormone receptor, and transforming growth factor-β (TGF-β) signaling. We found a significant interaction between TGF-β pPRS and red meat intake [ORint = 0.95; 95% confidence interval (CI) = 0.92–0.98; P = 0.003). When variants in the TGF-β pathway were assessed, we observed significant interactions of red meat with rs2337113 [intron SMAD family member 7 (SMAD7) gene, Chr18] and rs2208603 [intergenic region bone morphogenetic protein 5 (BMP5), Chr6; P = 0.0005 and 0.036, respectively]. There was no evidence of pPRS × red meat interactions for other pathways or with processed meat. Conclusions: This pathway-based interaction analysis revealed a statistically significant interaction between variants in the TGF-β pathway and red meat consumption that influences colorectal cancer risk. Impact: These findings shed light on the possible mechanistic link between red meat consumption and colorectal cancer risk.

A polygenic risk score (PRS) is used to quantify the combined disease risk of many genetic variants. For complex human traits there is interest in determining whether the PRS modifies, i.e. interacts with, important environmental (E) risk factors. Detection of a PRS by environment (PRS x E) interaction may provide clues to underlying biology and can be useful in developing targeted prevention strategies for modifiable risk factors. The standard PRS may include a subset of variants that interact with E but a much larger subset of variants that affect disease without regard to E. This latter subset will dilute the underlying signal in former subset, leading to reduced power to detect PRS x E interaction. We explore the use of pathway-defined PRS (pPRS) scores, using state of the art tools to annotate subsets of variants to genomic pathways. We demonstrate via simulation that testing targeted pPRS x E interaction can yield substantially greater power than testing overall PRS x E interaction. We also analyze a large study (N = 78,253) of colorectal cancer (CRC) where E = non-steroidal anti-inflammatory drugs (NSAIDs), a well-established protective exposure. While no evidence of overall PRS x NSAIDs interaction (p = 0.41) is observed, a significant pPRS x NSAIDs interaction (p = 0.0003) is identified based on SNPs within the TGF-β/ gonadotropin releasing hormone receptor (GRHR) pathway. NSAIDS is protective (OR=0.84) for those at the 5th percentile of the TGF-β/GRHR pPRS (low genetic risk, OR), but significantly more protective (OR=0.70) for those at the 95th percentile (high genetic risk). From a biological perspective, this suggests that NSAIDs may act to reduce CRC risk specifically through genes in these pathways. From a population health perspective, our result suggests that focusing on genes within these pathways may be effective at identifying those for whom NSAIDs-based CRC-prevention efforts may be most effective.

Background:High intake of red and/or processed meat are established colorectal cancer (CRC) risk factors. Genome-wide association studies (GWAS) have reported 204 variants (G) associated with CRC risk. We used functional annotation data to identify subsets of variants within known pathways and constructed pathway-based Polygenic Risk Scores (pPRS) to model pPRS x environment (E) interactions. Methods:A pooled sample of 30,812 cases and 40,504 CRC controls of European ancestry from 27 studies were analyzed. Quantiles for red and processed meat intake were constructed. The 204 GWAS variants were annotated to genes with AnnoQ and assessed for overrepresentation in PANTHER-reported pathways. pPRS's were constructed from significantly overrepresented pathways. Covariate-adjusted logistic regression models evaluated pPRSxE interactions with red or processed meat intake in relation to CRC risk. Results:A total of 30 variants were overrepresented in four pathways: Alzheimer disease-presenilin, Cadherin/WNT-signaling, Gonadotropin-releasing hormone receptor, and TGF-β signaling. We found a significant interaction between TGF-β-pPRS and red meat intake (p = 0.003). When variants in the TGF-β pathway were assessed, significant interactions with red meat for rs2337113 (intron SMAD7 gene, Chr18), and rs2208603 (intergenic region BMP5, Chr6) (p = 0.013 & 0.011, respectively) were observed. We did not find evidence of pPRS x red meat interactions for other pathways or with processed meat. Conclusions:This pathway-based interaction analysis revealed a significant interaction between variants in the TGF-β pathway and red meat consumption that impacts CRC risk. Impact:These findings shed light into the possible mechanistic link between CRC risk and red meat consumption.