This table includes the overall sample description stratified by colorectal cancer (CRC) status and smoking status.

This file includes the expression imputation statistics and included SNPs from the elastin net models.

Supplementary Figure from Beyond GWAS of Colorectal Cancer: Evidence of Interaction with Alcohol Consumption and Putative Causal Variant for the 10q24.2 Region

This file details the two-step interaction tests, and the gene-based aggregate test.

This file includes the association parameters (OR [95% CI]) for the identified SNPs with significant interaction term for smoking intensity by genotypes.

This table includes the association parameters of smoking habits for colorectal cancer risk stratified by study type.

This figure depicts the flowchart of the two-step interaction test.

This figure depicts the two-step EDGE test plots of prioritized SNPs.

This figure depicts the LocusZoom plots for SNPs interacting with smoking intensity for colorectal cancer risk.

This file includes the association parameters of the interaction component (OR [95% CI]) for the identified SNPs for smoking habits stratified by tumor molecular markers.

This file includes the association parameters (OR [95% CI]) for the identified SNPs for smoking habits stratified by study type, sex and tumour site.

This file includes the association parameters (OR [95% CI]) for the identified SNPs with suggestive interaction term for smoking status by genotypes.

BACKGROUND:Colorectal cancer (CRC) is a significant public health concern, highlighting the critical need for identifying novel intervention targets for its prevention. METHODS:We conducted genome-wide interaction analyses for 15 exposures with established or putative CRC risk [body mass index (BMI), height, physical activity, smoking, type 2 diabetes, use of menopausal hormone therapy, non-steroidal anti-inflammatory drugs, and intake of alcohol, calcium, fibre, folate, fruits, processed meat, red meat, and vegetables], and used interaction estimates to explore pathways and genes underlying CRC risk. The adaptive combination of Bayes Factors (ADABF), and over-representation analysis (ORA) were used for pathway analyses, and findings were further investigated using publicly available resources [hallmarks of cancer, Open Targets Platform (OTP)]. FINDINGS:A total of 1973 pathways using ADABF, and 840 pathways using ORA, out of the 2950 analysed, were enriched (P < 0.05) for at least one exposure, as well as 1227 genes within the enriched pathways. Data were available for 811/1227 coding genes in the OTP, 241 of which were supported by strong relative abundance of prior evidence (overall OTP score > 0.05). Fifty percent of the genes (617/1227) mapped to at least one hallmark of cancer, most of which (388/617) pertained to the Sustaining Proliferative Signalling hallmark. Our findings reflect previously established pathways for CRC risk and highlight the emerging importance of several less studied genes. Common pathways were found for several combinations of exposures, potentially suggesting common underlying mechanisms. INTERPRETATION:The results of the present analysis provide a basis for further functional research. If confirmed, they may help elucidate the etiological associations between risk factors and CRC risk and ultimately inform personalized prevention strategies. FUNDING:This study was funded by Cancer Research UK (CRUK; grant number:PPRCPJT∖100005) and World Cancer Research Fund International (WCRF; IIG_FULL_2020_022). Funding for grant IIG_FULL_2020_022 was obtained from Wereld Kanker Onderzoek Fonds (WKOF) as part of the World Cancer Research Fund International grant programme. Full funding details for the individual consortia are provided in the acknowledgements.

This file includes the association parameters (OR [95% CI]) for the imputed gene expression levels of genes associated with lead SNPs.

This table includes the smoking habits descriptives per studies included in the analysis.

This file includes the studies included in the analysis stratifing individuals by tumour features.

This file includes the association parameters of the interaction component (OR [95% CI]) for the identified SNPs for smoking habits further adjusted for body-mass index.

This figure depicts the quantile-quantile plots for the standard 1-df interaction tests.

This figure depicts the LocusZoom plots for the SNP interacting with smoking status for colorectal cancer risk.

Obesity is a major risk factor for a myriad of diseases, affecting >600 million people worldwide. Genome-wide association studies (GWASs) have identified hundreds of genetic variants that influence body mass index (BMI), a commonly used metric to assess obesity risk. Most variants are non-coding and likely act through regulating genes nearby. Here, we apply multiple computational methods to prioritize the likely causal gene(s) within each of the 536 previously reported GWAS-identified BMI-associated loci. We performed summary-data-based Mendelian randomization (SMR), FINEMAP, DEPICT, MAGMA, transcriptome-wide association studies (TWASs), mutation significance cutoff (MSC), polygenic priority score (PoPS), and the nearest gene strategy. Results of each method were weighted based on their success in identifying genes known to be implicated in obesity, ranking all prioritized genes according to a confidence score (minimum: 0; max: 28). We identified 292 high-scoring genes (≥11) in 264 loci, including genes known to play a role in body weight regulation (e.g., DGKI, ANKRD26, MC4R, LEPR, BDNF, GIPR, AKT3, KAT8, MTOR) and genes related to comorbidities (e.g., FGFR1, ISL1, TFAP2B, PARK2, TCF7L2, GSK3B). For most of the high-scoring genes, however, we found limited or no evidence for a role in obesity, including the top-scoring gene BPTF. Many of the top-scoring genes seem to act through a neuronal regulation of body weight, whereas others affect peripheral pathways, including circadian rhythm, insulin secretion, and glucose and carbohydrate homeostasis. The characterization of these likely causal genes can increase our understanding of the underlying biology and offer avenues to develop therapeutics for weight loss.