A Combined Proteomics and Mendelian Randomization Approach to Investigate the Effects of Aspirin-Targeted Proteins on Colorectal Cancer

Colorectal cancer is the fourth most common cancer worldwide (1). Observational studies as well as randomized controlled trials (RCT) using aspirin for the prevention of vascular events have shown that aspirin use is associated with a decrease in colorectal cancer incidence and mortality (2–5). This was primarily thought to be through the acetylation of the COX enzymes thereby inhibiting their action (6). These enzymes are involved in the COX/prostaglandin E2(PGE2) signaling pathway, which is frequently upregulated in colorectal cancer, driving many of the hallmarks of cancer (7, 8).

Evidence for COX-independent mechanisms have also emerged, such as the prevention of NFκB activation, inhibition of the extracellular-signal–regulated kinase (ERK) signaling pathway, cell-cycle progression inhibition, and possible induction of autophagy (7, 9). An aspirin derivative that does not inhibit COX reduced the mean number of aberrant crypt foci (an early lesion in colorectal carcinogenesis) in a mouse model of colorectal cancer more than aspirin itself (10). Furthermore, aspirin was able to inhibit proliferation and induce apoptosis in COX-2–negative colon cancer cell lines as well as reducing angiogenesis in 3D assays where COX inhibitors showed no effect (11–13). Clinically, aspirin has been shown to reduce tumor recurrence in phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA)-mutant cancer, whereas rofecoxib (a COX-2 selective inhibitor) showed no effect (14) and has also been shown to improve survival in patients with human leukocyte antigen (HLA) class I antigen expression, regardless of COX-2 expression (15). There are now a significant number of studies that indicate the mechanism behind the action of aspirin on colorectal cancer risk is still not fully understood and that multiple mechanisms are involved (16).

In conventional epidemiologic studies, it is often difficult to determine causality due to limitations of confounding and reverse causation. While RCTs can overcome these limitations, they are generally limited to assessing the causal role of health interventions or pharmaceutical agents on disease outcomes, rather than understanding biological mechanisms. Furthermore, in the context of cancer, RCTs for cancer primary prevention are not always feasible, as they require long-term follow-up for the cancer to develop. Mendelian randomization (MR) is an epidemiologic method that applies a similar notion of randomization as in the RCT to evaluate causality. In MR, genetic variants (most commonly single-nucleotide polymorphisms; SNPs) are used to proxy an exposure of interest (17). As genetic variants are randomly assorted at conception, an individual's genetic makeup is unlikely to be influenced by exposures later on in life, thus reducing the possibility of confounding and reverse causation (18). These SNPs instrumenting exposure instruments can then be used to test for association with an outcome of interest.

More recently, the increase in genome-wide association studies for molecular traits has identified SNPs that are associated with protein and mRNA expression levels, thereby providing protein quantitative trait loci (pQTL) and expression quantitative trait loci (eQTL; refs. 19, 20), which may be used to investigate the causal mechanism of drug targets on disease risk (21). Such methods can complement laboratory experiments to better understand the mechanism of action of drugs on cancer growth and progression.

Because of evidence showing that aspirin may prevent adenoma formation (22) and adenomas being the precursors of most colorectal cancers (23), we focused on a colorectal adenoma cell line (RG/C2) in this study and identified altered protein expression in relation to aspirin treatment. Findings were then taken forward into an MR analysis to investigate which proteins targeted by aspirin may be causally implicated in reducing risk of colorectal cancer incidence, thereby providing insight into alternative mechanisms/pathways for the action of aspirin.

The S/RG/C2 (referred to as RG/C2 henceforth whereby the prefix “S” denotes that they are from a sporadic tumor; RRID:CVCL_IQ11) colorectal adenoma cell line was derived in the Colorectal Tumour Biology group and is described in detail elsewhere (24). These cells express WT full-length APC (25) as well as wild-type KRAS and PIK3CA (26), but express mutant TP53 (25–27). RG/C2s were cultured in DMEM (Life Technologies) and supplemented with 20% FBS (Life Technologies), l-glutamine (2 mmol/L; Life Technologies), penicillin (100 U/mL; Life Technologies), streptomycin (100 μg/mL; Life Technologies) and insulin (0.2 U/mL; Sigma-Aldrich). Cells were Mycoplasma tested (Mycoalert Plus Mycoplasma Detection Kit; Lonza Group) before generation of proteomic data and experiments were performed within 10 passages. Aspirin (Sigma-Aldrich) was dissolved in fresh growth medium and diluted to form concentrations of 2 mmol/L and 4 mmol/L. Concentrations of aspirin between 0.1–2 mmol/L are known to be typical therapeutic ranges in vivo (13). While the 2 mmol/L dose is similar to clinically relevant doses of aspirin, we also treated with 4 mmol/L to identify more consistent and apparent effects of the drug.

A stable isotope labeling with amino acids in cell culture (SILAC) approach was carried out on RG/C2 cells treated with 0 mmol/L, 2 mmol/L and 4 mmol/L aspirin for 24 hours. Control cells (0 mmol/L aspirin) were cultured with an l-arginine and l-lysine (light labeling), 2 mmol/L treated cells were cultured with ²H₄-lysine and ₁₃C₆-arginine (medium labeling) and 4 mmol/L treated cells were cultured with ¹⁵N₂¹³C₆-lysine and ¹⁵N₄¹³C₆-arginine (heavy labeling; Cambridge Isotope Laboratory). These methods were based on the SILAC-based mass spectrometry approach by Trinkle-Mulcahy and colleagues (2008; ref. 28).

Cells were cultured with aspirin and the isotopes for 24 hours before extracting protein lysates. This experiment was carried out in duplicate. Lysates from the three conditions were pooled in a 1:1:1 ratio, separated by SDS-PAGE and then subjected to in-gel tryptic digestion. The resulting peptides were analyzed by LC-MS using an LTQ Orbitrap Velos mass spectrometer (Thermo Fisher Scientific) and the mass spectral data analyzed using Proteome Discoverer software v1.4 (Thermo Fisher Scientific). Details of SILAC labeling and proteomics have been published previously (29) and are mentioned in the Supplementary Methods. To determine proteins whose expression is altered due to aspirin treatment, we applied a threshold of a 1.4-fold change between 4 mmol/L/control and 2 mmol/L/control, as suggested previously (30). Results were also limited to a variability of <100% and a peptide count of at least 2.

To assess the effect of protein/mRNA expression of aspirin targets on risk of colorectal cancer, we used a two-sample MR approach. First, SNPs were identified to instrument/proxy for protein/mRNA expression of the proteins shown to be altered in cell culture. SNP associations were then obtained for CRC risk before two-sample MR was carried out to identify how increases in protein/mRNA expression (pQTLs/eQTLs) (sample 1) affected risk of colorectal cancer (sample 2) using the statistical methods described below.

Protein quantitative trait loci (pQTL) were obtained from the INTERVAL study (19). The original study is comprised of about 50,000 individuals within a randomized trial conducted to evaluate the effect of varying intervals between blood donations and how this affects outcomes such as quality of life (31). Relative protein measurements were taken using SOMAscan assays for 3,622 plasma proteins in a subset of 3,301 participants, randomly chosen. All participants provided consent before joining the study and ethics were approved by the Research Ethics Service (11/EE/0538; ref. 19). Genotyping and imputation (using a combined 1000 Genomes Phase 3-UK10K as the reference panel) of these individuals provided measures for 10,572,814 variants that passed quality control and were taken forward in a GWAS analysis to identify pQTLs for the measured proteins (details of quality control are mentioned elsewhere; ref. 19). pQTLs were extracted from summary level data and used to instrument/proxy a SD change in protein expression (19). To adjust for multiple testing, a Bonferroni correction (0.05/10,572,814 = 4.72 × 10⁻⁹) was applied and pQTLs below this P value threshold were used to proxy for protein expression in our analysis (32).

In the absence of a relevant pQTL for the protein of interest, an equivalent mRNA expression GWAS was used instead. Summary level data for expression quantitative trait loci (eQTL) were extracted from the eQTLGEN consortium consisting of 31,684 individuals from 37 datasets, of which 26,886 samples were from whole blood and 4,798 from peripheral blood mononuclear cells (PBMC). Because of the differing methods for genotyping between the studies, variants for each transcript ranged between 2,337–31,684 variants (20). For this reason, a Bonferroni correction threshold was adjusted depending on the number of variants measured for each transcript (0.05/number of variants; ref. 32). eQTLs were standardized and meta-analyzed through a Z-transformation, therefore eQTL effect sizes are reported as SD changes (20).

Although cis (within 1 Mb of the gene transcription start sit) associations are more likely to play a role in regulating gene/protein expression due to their close proximity to the gene start site and influencing binding affinity of regulatory proteins (33), we used both cis- and trans-QTLs in this analysis to instrument/proxy for expression. Once suitable pQTLs/eQTLs were identified, linkage disequilibiurm (LD) clumping at an R² of 0.001 was carried out to remove SNPs that are inherited together and so that only the SNP most strongly associated with the mRNA/protein expression within a 10,000 kb window was used.

Genetic association summary statistics for colorectal cancer, comprising 55,168 colorectal cancer cases and 65,160 controls, were obtained from the Colon Cancer Family Registry (CCFR), Colorectal Transdisciplinary (CORECT) and Genetics and Epidemiology of Colorectal Cancer (GECCO) consortia and UK Biobank (34–36). Quality control procedures have been described elsewhere (34). Written informed consent was provided by all participants and ethics for each study were approved by research ethics committee or respective institutional review boards (34).

Analyses were carried out in R version 3.2.3 using the MR-Base TwoSampleMR R package (github.com/MRCIEU/TwoSampleMR; ref. 37), which allows the formatting, harmonization, and analysis of summary statistics. The package reassigns alleles so that the effect allele has a positive association with the exposure and so represents an increase in protein/mRNA expression. In turn, allele harmonization ensures that the same allele (that predicts increased expression) is the effect allele in the outcome dataset as well. In the case of palindromic SNPs (represented by either A/T or G/C on both the forward and reverse alleles) these were also harmonized where possible based on allele frequencies. If allele frequencies for the effect allele and the other allele were similar, thus making harmonization difficult, these SNPs were dropped from the analysis (37).

Separate MR analyses were carried for cis and trans pQTLs as well as cis- and trans-eQTLs. For proteins with just one pQTL or eQTL, Wald ratios (SNP–outcome estimate ÷ SNP–exposure estimate) were calculated to give a causal estimate for risk of colorectal cancer per SD increase in mRNA/protein expression. Where more than one QTL was available as an instrument/proxy for the exposure (mRNA/protein levels), a weighted mean of the ratio estimates weighted by the inverse variance of the ratio estimates [inverse-variance weighted (IVW) method] was used (38).

When one genetic variant used to proxy for an exposure is invalid, for example, due to horizontal pleiotropy (where a genetic variant affects the outcome through an alternative exposure/pathway of interest; ref. 17), then the estimator from the IVW method becomes biased (39). As a sensitivity analysis, alternative MR methods were used when more than 2 SNPs were available as instruments for mRNA/protein expression (MR Egger, simple mode, weighted mode, and weighted median; refs. 37, 40, 41). Unlike the IVW method, the MR Egger method is not constrained to pass through an effect size of 0, thereby allowing the assessment of horizontal pleiotropy through the y-intercept (39, 42). The weighted median approach is useful as it allows a consistent estimate even if 50% of the SNPs proxying protein/mRNA expression are invalid instruments (41) and the mode estimate also provides a consistent causal–effect estimate even if the majority of the instruments are invalid, as the estimate depends on the largest number of similar instruments (40).

To investigate the early changes that could reduce cancer risk, we investigated the proteome of aspirin-treated adenoma-derived cells to identify new targets of aspirin that may alter the risk of colorectal cancer by combining these proteomic results with an MR analysis. After applying a filtering threshold based on fold change and variability in expression, we identified 125 proteins whose expression appeared to be regulated by aspirin treatment (Fig. 1; Supplementary Table S1), although 5 were uncharacterized from mass spectrometry and therefore excluded from the analysis.

Of the 120 proteins, expression of 28 proteins was measured in the INTERVAL study, of which 12 proteins had pQTLs that were below the Bonferroni significance threshold (0.05/10,572,814 = 4.73 × 10⁻⁹). From these 12 proteins, cis pQTLs were available for 3 proteins and trans pQTLs for 10 proteins (Supplementary Table S2). In the absence of available pQTLs, eQTLs for the transcripts of the identified proteins were used instead. Of the 108 proteins with no pQTLs available, expression of 89 mRNAs were measured in the eQTLGen consortium, of which 77 proteins had eQTLs that were below the Bonferroni significance threshold. From these 77 proteins, cis eQTLs were available for 71 proteins and trans eQTLs were available for 37 proteins (Supplementary Table S3). In total, there were 318 unique SNPs proxying for protein and mRNA expression, of which outcome summary statistics were available for 305 SNPs to test for association between 99 mRNA/proteins against risk of colorectal cancer.

Using the datasets summarized in Table 1, two-sample MR analysis using the Wald ratio or IVW method was conducted to test the effect of increased mRNA/protein expression on the risk of colorectal cancer incidence using cis and trans pQTLs (Supplementary Table S4) as well as cis and trans eQTLs (Supplementary Table S5). In total, 99 proteins were tested for association with colorectal cancer incidence. To correct for multiple testing, a Bonferroni-adjusted threshold of significance was applied (0.05/99 = 5.05 × 10⁻⁴), but we also considered associations of a nominal significance (P < 0.05) to identify possible pathways and mechanisms of aspirin's action. Overall, 1 protein with cis eQTLs and 2 with trans eQTLs were associated with colorectal cancer incidence at P < 5.05 × 10⁻⁴ and a further 3 proteins with cis eQTLs, 1 with a trans eQTL and 1 instrumented by a trans pQTL were associated with colorectal cancer incidence at a P < 0.05.

Increased mRNA expression of Human Leukocyte Antigen A (HLA-A) and mini chromosome maintenance 6 (MCM6) instrumented by cis eQTLs were found to be associated with an increased risk of colorectal cancer incidence (OR 1.28; 95% CI, 1.04–1.58; P: 0.02 and OR 1.08; 95% CI, 1.03–1.13; P: 9.23 × 10⁻⁴ per SD increase in mRNA expression, respectively). An SD increase in mRNA expression of fatty acid desaturase 2 (FADS2) and DNA polymerase delta subunit 2 (POLD2) instrumented by cis eQTLs was associated with a decrease in risk of colorectal cancer incidence (OR 0.94; 95% CI, 0.90–0.97; P: 2.50 × 10⁻⁴ and OR 0.84; 95% CI, 0.75–0.94; P: 1.17 × 10⁻³, respectively; Fig. 2; Table 2). For FADS2 and POLD2, results were consistent using other MR methods (weighted median, weighted mode, and simple mode) and the MR Egger test shows no evidence of pleiotropy (Supplementary Table S6; Supplementary Fig. S1). From the cis eQTL analysis, only results for FADS2 survived the Bonferroni significance threshold.

Proteins instrumented by trans eQTLs include ribonucleoside-diphosphate reductase subunit M2 (RRM2), stathmin-1 (STMN1) and lipin 1 (LPIN1). An increase in RRM2 was estimated to increase the risk of cancer incidence (OR 3.33; 95% CI, 2.46–4.50; P: 6.25 × 10⁻¹⁵ per SD increase in mRNA expression), whereas an increase in STMN1 and LPIN1 was associated with decreases in the risk of colorectal cancer incidence (OR 0.72; 95% CI, 0.54–0.97; P: 0.03 and OR 0.40; 95% CI, 0.32–0.50; P: 5.50 × 10⁻¹⁶ per SD increase in mRNA expression, respectively). From the trans eQTL analysis, results for RRM2 and LPIN1 both survived the Bonferroni significance threshold.

For proteins instrumented by pQTLs, ADP ribosylation factor interacting protein 2 (ARFIP2) proxied using a trans pQTL conferred an increased risk of colorectal cancer incidence (OR 1.15; 95% CI, 1.01–1.29; P: 0.03 per SD increase in protein expression).

Overall, the directions of effects between HLA-A, MCM6, RRM2, and ARFIP2 and colorectal cancer risk obtained from our MR analysis concur with those anticipated given the protective role of aspirin on colorectal cancer and the effect of aspirin treatment on expression of these proteins. Aspirin reduces the protein expression of HLA-A, MCM6, RRM2, and ARFIP2 (fold change in protein expression with 4 mmol/L aspirin treatment compared with control: 0.55, 0.65, 0.36, and 0.69, respectively; Table 2) and aspirin intake is associated with a decreased risk of colorectal cancer (2–4). Our MR analysis shows that increased expression of these proteins is associated with an increased risk of colorectal cancer incidence. Taken together, our results indicate that a possible mechanism through which aspirin decreases the risk of colorectal cancer incidence is through the downregulation of HLA-A, MCM6, RRM2, and ARFIP2. The direction of effect was less consistent for the other 4 proteins (FADS2, POLD2, STMN1, and LPIN1) showing opposite results to what we would expect based on the proteomic results (Table 2).

Evidence for the use of aspirin in the prevention of colorectal cancer is increasing (2–5). However, the mechanism through which it functions is still not fully understood. By combining both a proteomic-based approach as well as an MR analysis, our results provide mechanistic insights into how aspirin could decrease the risk of colorectal cancer.

Using a SILAC-based proteomics approach, 120 proteins appear to be regulated at 24 hours by 4 mmol/L and 2 mmol/L aspirin treatment. Genetic variants (pQTLs and eQTLs) were identified and used to proxy for protein and mRNA expression levels of the identified proteins to test for evidence of a causal effect on colorectal cancer incidence. When no pQTL was available for a protein, eQTLs were used instead.

Overall, 4 cis eQTLs, 3 trans eQTLs, and 1 trans pQTL were associated with cancer incidence at a P < 0.05. Increased expression of HLA-A and MCM6 proxied by cis eQTLs were associated with an increase in the risk of colorectal cancer incidence and an increase in RRM2 and ARFIP2 (proxied by a trans eQTL and trans pQTL, respectively) also conferred an increased risk. Therefore, suppressing the expression of these four proteins could decrease the risk of CRC. As the proteomic results showed that aspirin treatment decreases the expression of these proteins, this could be a potential mechanism by which aspirin reduces the risk of colorectal cancer. However, only results for RRM2 survive the Bonferroni significance threshold, indicating that further studies are required to verify these results.

The proteins MCM6 and RRM2 are both involved in repair of DNA damage. MCM6 is part of a helicase complex involved in unwinding DNA and is involved in repair of double stranded breaks (DSB) in homologous recombination through interaction with RAD51. This interaction is required for chromatin localisation and formation of foci for DNA damage recovery (43). Likewise, RRM2 is part of a protein complex called ribonucleotide reductase, that catalyzes the biosynthesis of dNTPs and is therefore required for DNA replication and damage repair (44).

Cancer cells commonly lose the DNA damage response, which results in the accumulation of mutations that may be oncogenic (45). Because of this, tumor cells end up relying on a reduced number of repair pathways and are therefore more sensitive to inhibition of DNA damage repair pathways when compared with normal cells which have full capability of DNA repair (46). Drugs that target these other pathways have been shown to selectively kill the cancer cells which is known as synthetic lethality (47, 48). It may be that by reducing the expression of DNA repair proteins, which combined with DNA damage response proteins that are already mutated during tumor progression, aspirin can induce cell death in the developing tumor cells reducing the risk of developing cancer.

The MR results for the proteins ARFIP2 and HLA-A also concur with our SILAC proteomic results. ARFIP2 is a protein previously shown to play a role in membrane ruffling and actin polymerization, therefore regulating the actin cytoskeleton (49). The remodeling of the actin cytoskeleton is known to be involved in cancer metastasis (50). This is of particular interest as aspirin reduces the odds of colorectal adenocarcinoma metastasis by 64% (OR, 0.36; 95% CI, 0.18–0.74; ref. 51) and this may be through the reduction in ARFIP2 expression. With regards to HLA-A expression and cancer risk, results from a cohort study showed that aspirin was more chemopreventative in tumors that expressed HLA class I antigen [which includes HLA-A, HLA-B, and HLA-C; rate ratio (RR) 0.53; 95% CI, 0.38–0.74] and this association was no longer apparent in tumors that lacked expression of this protein (15). Our MR analysis showed that an increase in HLA-A was associated with increased colorectal cancer risk, and that aspirin may reduce this risk through a reduction in HLA-A expression; however, further investigation is required before any conclusions can be drawn.

Our MR analysis results also showed that increased mRNA expression of FADS2, POLD2, LPIN1, and STMN1 all decreased the risk of colorectal cancer, indicating that decreased expression increases the risk of cancer. Our proteomic results showed that aspirin decreases the expression of these proteins and aspirin is known to decrease cancer risk. The exact meaning behind the inconsistencies in direction of effect is unclear, but may be related to the dosage used in this study. A randomized trial of aspirin to prevent adenomas showed that lower doses reduced adenoma risk more than higher doses, suggesting that lower doses of aspirin may affect mRNA/protein expression differently than higher doses (52, 53). Furthermore, the genetic instruments used to proxy for 1SD in POLD2, LPIN1, and STMN1 expression explain little of the variance in mRNA expression (0.05%, 0.08%, and 0.04%, respectively) indicating that SNPs that explain more of the variance are required before any conclusions can be made.

Further limitations also exist in our analysis. First, the exact correlation between eQTLs and pQTLs has not been fully determined. Second, it is difficult to interpret results using trans eQTLs and pQTLs without clear confirmation that these SNPs directly influence the gene/protein expression. It may be that they indirectly influence expression, for example, trans eQTLs may regulate gene expression by affecting expression of a nearby cis gene, which is in fact a transcription factor that is regulating the expression of the trans gene (54). Third, both the pQTL and eQTL associations were carried out using blood plasma, whole blood samples or PBMCs (19, 20); therefore, these SNPs estimate changes in gene and protein expression in circulating immune cells or plasma proteins, respectively. Our SILAC approach identified cellular proteins affected by aspirin treatment; however, the pQTLs used in this analysis proxied levels of plasma proteins. We believe that if the expression of cellular proteins is affected by aspirin, then this in turn will affect the amount of protein secreted into the plasma. We acknowledge that pQTLs for cellular proteins in colorectal tissue would be more appropriate for this analysis; however, studies that have measured cellular pQTLs are small and involve lymphoblastoid cell lines, rather than primary tissues of interest (55, 56). Also, the specificity of eQTLs/pQTLs for specific tissues is unclear. As found by the Genotype-Tissue Expression (GTEx) study, cis eQTLs are either shared across tissues or are specific to a small number of tissues (57). Therefore, the use of these eQTLs and pQTLs measured in the blood may not be fully suitable as proxies for mRNA and protein expression in the epithelium of the colon and rectum.

Furthermore, the units for the eQTLs and pQTLs represent SD changes in expression, making interpretation of the results difficult. However, we can interpret the direction of effect as well as the statistical significance of the association (P values) for these analyses. Moreover, pQTLs and eQTLs could not be identified for 20 of the proteins found to be regulated by aspirin in our proteomic approach, therefore we could not test the association of their expression with colorectal cancer risk. Finally, apart from the association of FADS2 with colorectal cancer incidence, the other associations proxied by cis eQTLs found by our study are not below the Bonferroni threshold of significance (P ≤ 4.63 × 10⁻⁴).

MR is commonly used to proxy for a drug's effect on risk of various outcomes after identification of its target. Genetic variants that predict lower function of 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase are commonly used to investigate the effect of lowering LDL cholesterol via the use of statins on outcomes such as ovarian cancer, Alzheimer disease, or coronary heart disease (58–60). These studies involve investigation of a drug's effect via a known target on an outcome. However, this approach would be difficult to apply in the case of drugs with pleiotropic targets such as aspirin. Therefore, to identify all possible targets of aspirin, a proteomic approach was first applied and targets that may affect risk of cancer were identified through using MR. To our knowledge, this is the first study that combines basic science and MR to generate hypotheses of a drug's mechanism of action in cancer.

Further experiments need to be conducted to confirm the effect of aspirin on gene and protein expression and the consequent effect this may have on hypothesized pathways such as DNA repair before definitive conclusions can be made. However, the potential of this unbiased approach to gain mechanistic insight is clear, allowing hypothesis driven research will better inform the clinical use of aspirin for the prevention of colorectal cancer.

J.A. Baron reports grants from NCI during the conduct of the study; in addition, J.A. Baron, with Dartmouth College, has a patent for Chemopreventive use of aspirin. G. Casey reports grants from NCI during the conduct of the study. E.A. Platz reports personal fees from Kaiser Permanente Northern California, Division of Research outside the submitted work; in addition, E.A. Platz is the editor-in-chief of CEBP. C.M. Ulrich reports grants from NIH during the conduct of the study. V. Moreno reports grants from NIH, Agency for Management of University and Research Grants (AGAUR), and grants from Instituto de Salud Carlos III during the conduct of the study. L.C. Sakoda reports grants from NCI during the conduct of the study. M.L. Slattery reports grants from University of Utah during the conduct of the study. R.E. Schoen reports grants from NIH during the conduct of the study. A.T. Chan reports grants and personal fees from Bayer Pharma AG and Pfizer Inc., and personal fees from Boehringer Ingelheim outside the submitted work. G.G. Giles reports grants from National Health and Medical Research Council (Australia) during the conduct of the study. Z.K. Stadler reports personal fees from Genentech/Roche, Novartis, RegenexBio, Neurogene, Optos Plc, Regeneron, Allergan, Gyroscope Tx, and Adverum outside the submitted work. B. Van Guelpen reports grants from Swedish Research Council, Swedish Cancer Society, Knut and Alice Wallenberg Foundation, Lion's Cancer Research Foundation at Umeå University, and Cancer Research Foundation in Northern Sweden during the conduct of the study. H. Hampel reports personal fees and other from Invitae and personal fees from Genome Medical and Promega outside the submitted work. A. Wolk reports grants from The Swedish Research Council and Swedish Cancer Foundation during the conduct of the study. D.A. Drew reports grants from NIH during the conduct of the study. C.L. Relton reports personal fees from Wellcome Trust outside the submitted work. No disclosures were reported by the other authors.

A. Nounu: Conceptualization, formal analysis, investigation, methodology, writing–original draft, writing–review and editing. A. Greenhough: Conceptualization, data curation, investigation, methodology, writing–review and editing. K.J. Heesom: Resources, data curation, writing–review and editing. R.C. Richmond: Supervision, methodology, writing–review and editing. J. Zheng: Methodology, writing–review and editing. S.J. Weinstein: Data curation, writing–review and editing. D. Albanes: Data curation, writing–review and editing. J.A. Baron: Data curation, writing–review and editing. J.L. Hopper: Data curation. J.C. Figueiredo: Data curation. P.A. Newcomb: Data curation. N.M. Lindor: Data curation. G. Casey: Data curation. E.A. Platz: Data curation, writing–review and editing. L. Le Marchand: Data curation, writing–review and editing. C.M. Ulrich: Data curation. C.I. Li: Data curation. F.J.B. van Duijnhoven: Data curation, writing–review and editing. A. Gsur: Data curation. P.T. Campbell: Data curation. V. Moreno: Data curation. P. Vodicka: Data curation. L. Vodickova: Data curation. H. Brenner: Data curation, writing–review and editing. J. Chang-Claude: Data curation. M. Hoffmeister: Data curation. L.C. Sakoda: Data curation, writing–review and editing. M.L. Slattery: Data curation. R.E. Schoen: Data curation. M.J. Gunter: Data curation. S. Castellví-Bel: Data curation, writing–review and editing. H.R. Kim: Data curation. S.-S. Kweon: Data curation. A.T. Chan: Data curation, writing–review and editing. L. Li: Data curation. W. Zheng: Data curation. D.T. Bishop: Data curation. D.D. Buchanan: Data curation. G.G. Giles: Data curation. S.B. Gruber: Data curation. G. Rennert: Data curation. Z.K. Stadler: Data curation. T.A. Harrison: Data curation. Y. Lin: Data curation. T.O. Keku: Data curation. M.O. Woods: Data curation. C. Schafmayer: Data curation. B. Van Guelpen: Data curation. S. Gallinger: Data curation. H. Hampel: Data curation. S.I. Berndt: Data curation. P.D.P. Pharoah: Data curation. A. Lindblom: Data curation. A. Wolk: Data curation, writing–review and editing. A.H. Wu: Data curation. E. White: Data curation, writing–review and editing. U. Peters: Data curation, writing–review and editing. D.A. Drew: Data curation. D. Scherer: Conceptualization, writing–review and editing. J.L. Bermejo: Conceptualization. A.C. Williams: Conceptualization, supervision, funding acquisition, writing–review and editing. C.L. Relton: conceptualization, supervision, funding acquisition, writing–review and editing.

Author funding: This work was funded by a PhD studentship from the Medical Research Council (to A. Nounu), a Cancer Research UK Programme Grant (C19/A11975; to A.C. Williams), an MRC Research grant (MR/R017247/1; to A.C. Williams), and by the John James Bristol Foundation. Further funding was provided by The UK Medical Research Council Integrative Epidemiology Unit (MC_UU_12013_2; to C.L. Relton) and Cancer Research UK (C18281/A19169; to C.L. Relton), the Wellcome Trust (217487/Z/19/Z; to A. Greenhough), and Bowel Cancer UK (19PT0039; to A. Greenhough). R.C. Richmond is a de Pass Vice Chancellor Research Fellow at the University of Bristol. This work was also supported by the German Federal Ministry of Education and Research (01KT1510; to D. Scherer).

Consortia funding: Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO): NCI, NIH, U.S. Department of Health and Human Services (U01 CA164930, U01 CA137088, R01 CA059045, R21 CA191312, R01201407; to U. Peters). Genotyping/sequencing services were provided by the Center for Inherited Disease Research (CIDR; X01-HG008596 and X-01-HG007585). CIDR is fully funded through a federal contract from the NIH to The Johns Hopkins University, contract number HHSN268201200008I. This research was funded in part through the NIH/NCI Cancer Center Support Grant P30 CA015704.

ASTERISK: a Hospital Clinical Research Program (PHRC-BRD09/C) from the University Hospital Center of Nantes (CHU de Nantes) and supported by the Regional Council of Pays de la Loire, the Groupement des Entreprises Françaises dans la Lutte contre le Cancer (GEFLUC), the Association Anne de Bretagne Génétique and the Ligue Régionale Contre le Cancer (LRCC).

The ATBC Study is supported by the Intramural Research Program of the U.S. NCI, NIH.

CLUE funding was from the NCI (U01 CA86308, Early Detection Research Network; P30 CA006973), National Institute on Aging (U01 AG18033), and the American Institute for Cancer Research. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the US government. COLO2&3: NIH (R01 CA60987; to L. Le Marchand).

ColoCare: This work was supported by the NIH (R01 CA189184, to C.I. Li and C.M. Ulrich; U01 CA206110, to C.M. Ulrich, C.I. Li, and J.C. Figueiredo; 2P30CA015704- 40, R01 CA207371, to C.M. Ulrich and C.I. Li), the Matthias Lackas-Foundation, the German Consortium for Translational Cancer Research, and the EU TRANSCAN initiative.

The Colon Cancer Family Registry (CCFR, www.coloncfr.org) is supported in part by funding from the NCI, NIH (award U01 CA167551). The CCFR Set-1 (Illumina 1M/1M-Duo) and Set-2 (Illumina Omni1-Quad) scans were supported by NIH awards U01 CA122839 and R01 CA143247 (to G. Casey). The CCFR Set-3 (Affymetrix Axiom CORECT Set array) was supported by NIH award U19 CA148107 and R01 CA81488 (to S.B. Gruber). The CCFR Set-4 (Illumina OncoArray 600K SNP array) was supported by NIH award U19 CA148107 (to S.B. Gruber) and by the Center for Inherited Disease Research (CIDR), which is funded by the NIH to the Johns Hopkins University, contract number HHSN268201200008I. The SCCFR Illumina HumanCytoSNP array was supported through NCI award R01 CA076366 (to P.A. Newcomb). Additional funding for the OFCCR/ARCTIC was through award GL201-043 from the Ontario Research Fund, award 112746 from the Canadian Institutes of Health Research, through a Cancer Risk Evaluation (CaRE) Program grant from the Canadian Cancer Society, and through generous support from the Ontario Ministry of Research and Innovation. The content of this manuscript does not necessarily reflect the views or policies of the NCI, NIH, or any of the collaborating centers in the Colon Cancer Family Registry (CCFR), nor does mention of trade names, commercial products, or organizations imply endorsement by the US Government, any cancer registry, or the CCFR.

COLON: The COLON study is sponsored by Wereld Kanker Onderzoek Fonds, including funds from grant 2014/1179 as part of the World Cancer Research Fund International Regular Grant Programme, by Alpe d'Huzes and the Dutch Cancer Society (UM 2012-5653, UW 2013-5927, UW2015-7946), and by TRANSCAN (JTC2012-MetaboCCC, JTC2013-FOCUS). The Nqplus study is sponsored by a ZonMW investment grant (98-10030); by PREVIEW, the project PREVention of diabetes through lifestyle intervention and population studies in Europe and around the World (PREVIEW) project, which received funding from the European Union Seventh Framework Programme (FP7/2007–2013) under grant no. 312057; by funds from TI Food and Nutrition (cardiovascular health theme), a public–private partnership on precompetitive research in food and nutrition; and by FOODBALL, the Food Biomarker Alliance, a project from JPI Healthy Diet for a Healthy Life.

Colorectal Cancer Transdisciplinary (CORECT) Study: The CORECT Study was supported by the NCI/NIH, U.S. Department of Health and Human Services (grant numbers U19 CA148107, R01 CA81488, P30 CA014089, R01 CA197350, to S.B. Gruber; P01 T32 ES013678; R01 CA201407, to U. Peters) and National Institutes of Environmental Health Sciences, National Institutes of Health (grant number T32 ES013678).

CORSA: “Österreichische Nationalbank Jubiläumsfondsprojekt” (12511) and Austrian Research Funding Agency (FFG) grant 829675.

CPS-II: The American Cancer Society funds the creation, maintenance, and updating of the Cancer Prevention Study-II (CPS-II) cohort. This study was conducted with Institutional Review Board approval.

CRCGEN: Colorectal Cancer Genetics & Genomics, Spanish study was supported by Instituto de Salud Carlos III, cofunded by FEDER funds–a way to build Europe–(grants PI14-613 and PI09-1286), Agency for Management of University and Research Grants (AGAUR) of the Catalan Government (grant 2017SGR723), and Junta de Castilla y León (grant LE22A10-2). Sample collection of this work was supported by the Xarxa de Bancs de Tumors de Catalunya sponsored by Pla Director d'Oncología de Catalunya (XBTC), Plataforma Biobancos PT13/0010/0013 and ICOBIOBANC, sponsored by the Catalan Institute of Oncology.

Czech Republic CCS: This work was supported by the Grant Agency of the Czech Republic (grants CZ GA CR: GAP304/10/1286 and 1585) and by the Grant Agency of the Ministry of Health of the Czech Republic (grants AZV 15-27580A and AZV 17-30920A).

DACHS: This work was supported by the German Research Council (BR 1704/6-1, BR 1704/6-3, BR 1704/6-4, CH 117/1-1, HO 5117/2-1, HE 5998/2-1, KL 2354/3-1, RO 2270/8-1 and BR 1704/17-1), the Interdisciplinary Research Program of the National Center for Tumor Diseases (NCT), Germany, and the German Federal Ministry of Education and Research (01KH0404, 01ER0814, 01ER0815, 01ER1505A and 01ER1505B).

DALS: NIH (R01 CA48998, to M.L. Slattery).

EDRN: This work is funded and supported by the NCI, EDRN Grant (U01 CA 84968-06).

EPIC: The coordination of EPIC is financially supported by the European Commission (DGSANCO) and the International Agency for Research on Cancer. The national cohorts are supported by Danish Cancer Society (Denmark); Ligue Contre le Cancer, Institut Gustave Roussy, Mutuelle Générale de l'Education Nationale, Institut National de la Santé et de la Recherche Médicale (INSERM) (France); German Cancer Aid, German Cancer Research Center (DKFZ), Federal Ministry of Education and Research (BMBF), Deutsche Krebshilfe, Deutsches Krebsforschungszentrum and Federal Ministry of Education and Research (Germany); the Hellenic Health Foundation (Greece); Associazione Italiana per la Ricerca sul Cancro-AIRCItaly and National Research Council (Italy); Dutch Ministry of Public Health, Welfare and Sports (VWS), Netherlands Cancer Registry (NKR), LK Research Funds, Dutch Prevention Funds, Dutch ZON (Zorg Onderzoek Nederland), World Cancer Research Fund (WCRF), Statistics Netherlands (The Netherlands); ERC-2009-AdG 232997 and Nordforsk, Nordic Centre of Excellence programme on Food, Nutrition and Health (Norway); Health Research Fund (FIS), PI13/00061 to Granada, PI13/01162 to EPIC-Murcia, Regional Governments of Andalucía, Asturias, Basque Country, Murcia and Navarra, ISCIII RETIC (RD06/0020) (Spain); Swedish Cancer Society, Swedish Research Council and County Councils of Skåne and Västerbotten (Sweden); Cancer Research UK (14136 to EPIC-Norfolk; C570/A16491 and C8221/A19170 to EPIC-Oxford), Medical Research Council (1000143 to EPIC-Norfolk, MR/M012190/1 to EPICOxford) (United Kingdom).

EPICOLON: This work was supported by grants from Fondo de Investigación Sanitaria/FEDER (PI08/0024, PI08/1276, PS09/02368, PI11/00219, PI11/00681, PI14/00173, PI14/00230, PI17/00509, 17/00878, Acción Transversal de Cáncer), Xunta de Galicia (PGIDIT07PXIB9101209PR), Ministerio de Economia y Competitividad (SAF07-64873, SAF 2010-19273, SAF2014-54453R), Fundación Científica de la Asociación Española contra el Cáncer (GCB13131592CAST), Beca Grupo de Trabajo “Oncología” AEG (Asociación Española de Gastroenterología), Fundación Privada Olga Torres, FP7 CHIBCHA Consortium, Agència de Gestió d'Ajuts Universitaris i de Recerca (AGAUR, Generalitat de Catalunya, 2014SGR135, 2014SGR255, 2017SGR21, 2017SGR653), Catalan Tumour Bank Network (Pla Director d'Oncologia, Generalitat de Catalunya), PERIS (SLT002/16/00398, Generalitat de Catalunya), CERCA Programme (Generalitat de Catalunya) and COST Action BM1206 and CA17118. CIBERehd is funded by the Instituto de Salud Carlos III.

ESTHER/VERDI. This work was supported by grants from the Baden-Württemberg Ministry of Science, Research and Arts and the German Cancer Aid.

Harvard cohorts (HPFS, NHS, PHS): HPFS is supported by the NIH (R01 CA137178 and P50 CA127003, to A.T. Chan; P01 CA055075, UM1 CA167552, U01 CA167552, R01 CA151993, R35 CA197735 and K07 CA190673), NHS by the NIH (R01 CA137178 and P50 CA127003, to A.T. Chan; P01 CA087969, UM1 CA186107, R01 CA151993, R35 CA197735, K07CA190673) and PHS by the NIH (R01 CA042182, to J. Ma).

Hawaii Adenoma Study: NCI grants R01 CA72520.

HCES-CRC: the Hwasun Cancer Epidemiology Study–Colon and Rectum Cancer (HCES-CRC; grants from Chonnam National University Hwasun Hospital, HCRI15011-1).

Kentucky: This work was supported by the following grant support: Clinical Investigator Award from Damon Runyon Cancer Research Foundation (CI-8); NCI R01 CA136726 (to L. Li).

LCCS: The Leeds Colorectal Cancer Study was funded by the Food Standards Agency and Cancer Research UK Programme Award (C588/A19167).

MCCS cohort recruitment was funded by VicHealth and Cancer Council Victoria. The MCCS was further supported by Australian NHMRC grants 509348, 209057, 251553 and 504711 and by infrastructure provided by Cancer Council Victoria. Cases and their vital status were ascertained through the Victorian Cancer Registry (VCR) and the Australian Institute of Health and Welfare (AIHW), including the National Death Index and the Australian Cancer Database.

Multiethnic Cohort (MEC) Study: NIH (U01 CA164973, to L. Le Marchand; R37 CA54281, P01 CA033619, and R01 CA063464).

MECC: This work was supported by the NIH, U.S. Department of Health and Human Services (R01 CA81488, to S.B. Gruber and G. Rennert).

MSKCC: The work at Sloan Kettering in New York was supported by the Robert and Kate Niehaus Center for Inherited Cancer Genomics and the Romeo Milio Foundation. Moffitt: This work was supported by funding from the NIH (grant numbers R01 CA189184, to C.I. Li and C.M. Ulrich; P30 CA076292, to H. Lee Moffitt Cancer Center and Research Institute), Florida Department of Health Bankhead-Coley Grant 09BN-13, and the University of South Florida Oehler Foundation. Moffitt contributions were supported in part by the Total Cancer Care Initiative, Collaborative Data Services Core, and Tissue Core at the H. Lee Moffitt Cancer Center & Research Institute, an NCI-designated Comprehensive Cancer Center (grant number P30 CA076292 to H. Lee Moffitt Cancer Center and Research Institute).

NCCCS I & II: The authors acknowledge funding support for this project from the NIH (R01 CA66635 and P30 DK034987).

NFCCR: This work was supported by an Interdisciplinary Health Research Team award from the Canadian Institutes of Health Research (CRT 43821); the NIH, U.S. Department of Health and Human Services (U01 CA74783; to S. Gallinger); and National Cancer Institute of Canada grants (18223 and 18226). The authors acknowledge the contribution of Alexandre Belisle and the genotyping team of the McGill University and Génome Québec Innovation Centre, Montréal, Canada, for genotyping the Sequenom panel in the NFCCR samples. Funding was provided to M.O. Woods by the Canadian Cancer Society Research Institute.

NSHDS: Swedish Research Council; Swedish Cancer Society; Cutting-Edge Research Grant and other grants from Region Västerbotten; Knut and Alice Wallenberg Foundation; Lion's Cancer Research Foundation at Umeå University; the Cancer Research Foundation in Northern Sweden; and the Faculty of Medicine, Umeå University, Umeå, Sweden.

OFCCR: The Ontario Familial Colorectal Cancer Registry was supported in part by the NCI of the National NIH under award U01 CA167551 and award U01/U24 CA074783 (to S. Gallinger). Additional funding for the OFCCR and ARCTIC testing and genetic analysis was through a Canadian Cancer Society CaRE (Cancer Risk Evaluation) program grant and Ontario Research Fund award GL201-043, through the Canadian Institutes of Health Research award 112746, and through generous support from the Ontario Ministry of Research and Innovation. OSUMC: OCCPI funding was provided by Pelotonia and HNPCC funding was provided by the NCI (CA16058 and CA67941).

PLCO: Intramural Research Program of the Division of Cancer Epidemiology and Genetics and supported by contracts from the Division of Cancer Prevention, NCI, NIH, DHHS. Funding was provided by NIH, Genes, Environment and Health Initiative (GEI) Z01 CP 010200, NIH U01 HG004446, and NIH GEI U01 HG 004438.

SCCFR: The Seattle Colon Cancer Family Registry was supported in part by the National Cancer Institute (NCI) of the National Institutes of Health (NIH) under awards U01 P30 CA015704, U01 CA074794, and U24 CA074794 (to P.A. Newcomb) and R01 CA076366 (to P.A. Newcomb).

SEARCH: The University of Cambridge has received salary support in respect of PDPP from the NHS in the East of England through the Clinical Academic Reserve. Cancer Research UK (C490/A16561); the UK National Institute for Health Research Biomedical Research Centres at the University of Cambridge.

SELECT: Research reported in this publication was supported in part by the NCI of the NIH under Award Numbers U10 CA37429 and UM1 CA182883. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

SMS: This work was supported by the NCI (grant P01 CA074184, to P.A. Newcomb; grants R01 CA097325, R03 CA153323, and K05 CA152715, to P.A. Newcomb; and the National Center for Advancing Translational Sciences at the NIH (grant KL2 TR000421).

The Swedish Low-risk Colorectal Cancer Study: The study was supported by grants from the Swedish Research Council; K2015-55X-22674-01-4, K2008-55X-20157-03-3, K2006-72X-20157-01-2 and the Stockholm County Council (ALF project).

Swedish Mammography Cohort and Cohort of Swedish Men: This work is supported by the Swedish Research Council/Infrastructure grant, the Swedish Cancer Foundation, and the Karolinska Institutés Distinguished Professor Award (to A. Wolk).

UK Biobank: This research has been conducted using the UK Biobank Resource under Application Number 8614.

VITAL: NIH (K05 CA154337, to E. White).

WHI: The WHI program is funded by the National Heart, Lung, and Blood Institute, NIH, U.S. Department of Health and Human Services through contracts HHSN268201100046C, HHSN268201100001C, HHSN268201100002C, HHSN268201100003C, HHSN268201100004C, and HHSN271201100004C.

ASTERISK: The authors are very grateful to Dr. Bruno Buecher, without whom this project would not have existed. They also thank all those who agreed to participate in this study, including the patients and the healthy control persons, as well as all the physicians, technicians, and students.

CCFR: The authors are grateful to Mark A. Jenkins, who helped obtain the NCI grant U01 CA167551.

CLUE: The authors appreciate the continued efforts of the staff members at the Johns Hopkins George W. Comstock Center for Public Health Research and Prevention in the conduct of the CLUE II study. They thank the participants in CLUE. Cancer incidence data for CLUE were provided by the Maryland Cancer Registry, Center for Cancer Surveillance and Control (Maryland Department of Health, 201 W. Preston Street, Room 400, Baltimore, MD 21201, http://phpa.dhmh.maryland.gov/cancer, 410-767-4055). They acknowledge the State of Maryland, the Maryland Cigarette Restitution Fund, and the National Program of Cancer Registries of the Centers for Disease Control and Prevention for the funds that support the collection and availability of the cancer registry data.

COLON and NQplus: The authors thank the COLON and NQplus investigators at Wageningen University and Research and the involved clinicians in the participating hospitals.

CORSA: The authors kindly thank all those who contributed to the screening project Burgenland against CRC. Furthermore, they are grateful to Doris Mejri and Monika Hunjadi for laboratory assistance.

CPS-II: The authors thank the CPS-II participants and Study Management Group for their invaluable contributions to this research. The authors also acknowledge the contribution to this study from central cancer registries supported through the Centers for Disease Control and Prevention National Program of Cancer Registries, and cancer registries supported by the National Cancer Institute Surveillance Epidemiology and End Results program.

Czech Republic CCS: The authors are thankful to all clinicians in major hospitals in the Czech Republic, without whom the study would not be practicable. They are also sincerely grateful to all patients participating in this study.

DACHS: The authors thank all participants and cooperating clinicians, and Ute Handte-Daub, Utz Benscheid, Muhabbet Celik, and Ursula Eilber for excellent technical assistance.

EDRN: The authors acknowledge all the following contributors to the development of the resource: University of Pittsburgh School of Medicine, Department of Gastroenterology, Hepatology and Nutrition: Lynda Dzubinski; University of Pittsburgh School of Medicine, Department of Pathology: Michelle Bisceglia; and University of Pittsburgh School of Medicine, Department of Biomedical Informatics.

EPIC: Where authors are identified as personnel of the International Agency for Research on Cancer/World Health Organization, the authors alone are responsible for the views expressed in this article and they do not necessarily represent the decisions, policy or views of the International Agency for Research on Cancer/World Health Organization.

EPICOLON: The authors are sincerely grateful to all patients participating in this study who were recruited as part of the EPICOLON project. They acknowledge the Spanish National DNA Bank, Biobank of Hospital Clínic–IDIBAPS and Biobanco Vasco for the availability of the samples. The work was carried out (in part) at the Esther Koplowitz Centre, Barcelona.

Harvard cohorts (HPFS, NHS, PHS): The study protocol was approved by the institutional review boards of the Brigham and Women's Hospital and Harvard T.H. Chan School of Public Health, and those of participating registries as required. The authors thank the participants and staff of the HPFS, NHS, and PHS for their valuable contributions, as well as the following state cancer registries for their help: AL, AZ, AR, CA, CO, CT, DE, FL, GA, ID, IL, IN, IA, KY, LA, ME, MD, MA, MI, NE, NH, NJ, NY, NC, ND, OH, OK, OR, PA, RI, SC, TN, TX, VA, WA, WY. The authors assume full responsibility for analyses and interpretation of these data. The authors are grateful to Edward L Giovannucci, Walter C. Willett, Shuji Ogino, Reiko Nishihara, and Meir J. Stampfer, who helped secure NIH grant P01 CA055075, U01 CA167552, R35 CA197735, K07 CA190673, and UM1 CA186107, respectively. Edward L. Giovannucci and Shuji Ogino also secured NIH grants P01 CA087969 and R01 CA151993, respectively.

Kentucky: The authors acknowledge the staff at the Kentucky Cancer Registry.

LCCS: The authors acknowledge the contributions of Jennifer Barrett, Robin Waxman, Gillian Smith, and Emma Northwood in conducting this study.

NCCCS I & II: The authors thank the study participants, and the NC Colorectal Cancer Study staff.

NSHDS investigators thank the Biobank Research Unit at Umeå University, the Västerbotten Intervention Programme, the Northern Sweden MONICA study, and Region Västerbotten for providing data and samples and acknowledge the contribution from Biobank Sweden, supported by the Swedish Research Council (VR 2017-00650).

PLCO: The authors thank the PLCO Cancer Screening Trial screening center investigators and the staff from Information Management Services Inc and Westat Inc. Most importantly, they thank the study participants for their contributions that made this study possible.

SCCFR: The authors thank the study participants and staff of the Hormones and Colon Cancer and Seattle Cancer Family Registry studies (CORE Studies).

SEARCH: The authors thank the SEARCH team.

SELECT: The authors thank the research and clinical staff at the sites that participated on SELECT study, without whom the trial would not have been successful. They are also grateful to the 35,533 dedicated men who participated in SELECT.

WHI: The authors thank the WHI investigators and staff for their dedication, and the study participants for making the program possible. A full listing of WHI investigators can be found at http://www.whi.org/researchers/Documents%20%20Write%20a%20Paper/WHI%20Investigator%20Short%20List.pdf

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