Background:

A positive association between circulating C-reactive protein (CRP) and colorectal cancer survival was reported in observational studies, which are susceptible to unmeasured confounding and reverse causality. We used a Mendelian randomization approach to evaluate the association between genetically predicted CRP concentrations and colorectal cancer–specific survival.

Methods:

We used individual-level data for 16,918 eligible colorectal cancer cases of European ancestry from 15 studies within the International Survival Analysis of Colorectal Cancer Consortium. We calculated a genetic-risk score based on 52 CRP-associated genetic variants identified from genome-wide association studies. Because of the non-collapsibility of hazard ratios from Cox proportional hazards models, we used the additive hazards model to calculate hazard differences (HD) and 95% confidence intervals (CI) for the association between genetically predicted CRP concentrations and colorectal cancer–specific survival, overall and by stage at diagnosis and tumor location. Analyses were adjusted for age at diagnosis, sex, body mass index, genotyping platform, study, and principal components.

Results:

Of the 5,395 (32%) deaths accrued over up to 10 years of follow-up, 3,808 (23%) were due to colorectal cancer. Genetically predicted CRP concentration was not associated with colorectal cancer–specific survival (HD, −1.15; 95% CI, −2.76 to 0.47 per 100,000 person-years; P = 0.16). Similarly, no associations were observed in subgroup analyses by stage at diagnosis or tumor location.

Conclusions:

Despite adequate power to detect moderate associations, our results did not support a causal effect of circulating CRP concentrations on colorectal cancer–specific survival.

Impact:

Future research evaluating genetically determined levels of other circulating inflammatory biomarkers (i.e., IL6) with colorectal cancer survival outcomes is needed.

Chronic inflammation plays an important role in colorectal cancer development and progression (1). Elevated level of inflammation after colorectal cancer diagnosis may lead to increased expression of proinflammatory mediators and promote tumor growth and progression (2).

C-reactive protein (CRP) is an abundant acute-phase protein produced mainly by hepatocytes in response to pro-inflammatory cytokines (3). Observational studies of colorectal cancer outcomes have reported positive associations between prediagnostic and preoperative concentrations of CRP and larger tumor size, metastases, and survival (4–8). These studies, however, may have been subject to bias as most were unadjusted or insufficiently adjusted for potential confounders and factors related to inflammation and survival, such as adiposity, use of NSAIDs, and smoking. Furthermore, disease progression itself could lead to enhanced tumor-associated inflammation and elevated concentrations of circulating pro-inflammatory markers. Thus, reverse causation is also a potential source of bias.

Most studies of CRP and colorectal cancer only had a single measurement of CRP, which may not represent lifelong levels of chronic inflammation. Mendelian randomization uses inherited germline genetic markers known to be associated with the risk factor of interest, in this case circulating CRP concentrations. These genetic variants can serve as non-modifiable markers of long-term susceptibility to chronic inflammation. Because of the natural random assortment of alleles during gamete formation, genetic variants are not affected by environmental factors that occur after conception and are non-modifiable by disease progression (9). Over the last of 15 years, genome-wide association studies (GWAS) have accumulated robust evidence on genetic variants associated with various inflammatory biomarkers, including CRP (10, 11). “Mendelian randomization” has become a common approach for observational studies of inflammatory biomarkers in association with cancer risk, providing a way to minimize reverse causality and residual confounding. However, Mendelian randomization studies of inflammatory biomarkers and cancer survival are scarce (12).

In this study, we aimed to test the association of genetically predicted concentrations of CRP with colorectal cancer–specific survival using a Mendelian randomization approach. As a secondary aim, we evaluated stage- and tumor site–specific associations between genetically predicted circulating CRP concentration and colorectal cancer survival. To achieve this, we used the existing data on germline genetic variants and epidemiological and clinical factors from the International Survival Analysis in Colorectal Cancer Consortium (ISACC).

Study sample

We included individuals diagnosed with incident, invasive colorectal cancer from ISACC, a consortium consisting of clinical trials, case–control, and cohort studies from North America, Europe, and Australia. Of the 26,282 eligible ISACC participants who had GWAS and survival data available (Fig. 1), we excluded individuals whose GWAS data did not pass QC (n = 1,154), whose epidemiologic data were not available (n = 217), and those with non-European ancestry (n = 1,200) for this analysis. Further exclusion of studies and individuals without data on colorectal cancer–specific survival outcome (n = 6,793) resulted in a total of 16,918 subjects included in these analyses from the following 15 studies: Colon Cancer Family Registry (CCFR; ref. 13), Cancer Prevention Study-II (CPS-II; ref. 14), German Darmkrebs: Chancen der Verhütung Durch Screening (DACHS; ref. 15), Diet Activity and Lifestyle Study (DALS; ref. 16), Early Detection Research Network (EDRN; ref. 17), European Prospective Investigation into Cancer (EPIC; ref. 18), Health Professionals Follow-up Study (HPFS; ref. 19), Melbourne Collaborative Cohort Study (MCCS; ref. 20), Nurses' Health Study (NHS; ref. 21), North Central Cancer Treatment Group (NCCTG) N9741 randomized trial (ClinicalTrials.gov Identifier: NCT00003594; ref. 22), Physician's Health Study (PHS; ref. 23), Prostate, Lung, Colorectal, and Ovarian Study (PLCO; ref. 24), UK Biobank (UKB; ref. 25), VITamins And Lifestyle Study (VITAL; ref. 26), and Women's Health Initiative (WHI; ref. 27). Study-specific details are summarized in Supplementary Table S1. All studies were approved by their respective Institutional Review Boards and participants provided written informed consent.

Ascertainment of environmental variables and survival outcomes

Demographic and epidemiologic factors were collected using self- or interviewer-administered questionnaires at enrollment according to study-specific protocols. A multistep data-harmonization process was conducted centrally to define epidemiologic and clinicopathological variables in the same way across studies, as described previously (28). Information on cancer diagnosis, such as age at diagnosis, tumor location (proximal, distal colon, or rectum), and stage at diagnosis [local: American Joint Committee of Cancer (AJCC) stage I; regional: AJCC stage II/III; or distant: AJCC stage IV], was obtained from cancer registries and/or medical records.

All study participants were followed for vital status. Date and cause of death were ascertained through linkages to the National Death Index or cancer registries (CCFR, CPSII, DACHS, DALS, EPIC, MCCS, UKB, VITAL) or via active follow-up with dates/cause of death verified by the review of death certificates and/or medical records (HPFS, NHS, PHS, PLCO, WHI, N9741). Time to event was defined as days between colorectal cancer diagnosis and death, last date of contact, or the end of study follow-up. To evaluate 10-year colorectal cancer–specific survival, we censored cases at 10 years from the date of colorectal cancer diagnosis. Cases who died from causes other than colorectal cancer within 10 years from diagnosis were censored at the time of death. We used the International Classification of Diseases-9 (ICD-9) or ICD-10 (depending on year of linkage) to define colorectal cancer–specific deaths (ICD-9: 153.0–153.4, 153.6–153.9, or 154.0–154.1; ICD-10: C18.0–20.0 or C26.0).

Genotyping, quality control, and imputation

Details of genotyping and quality control (QC) methods have been reported previously (29–33). Briefly, genomic DNA was extracted from blood or buccal samples using conventional methods. Genotyping was performed using multiple platforms (Supplementary Table S1). All genotype data underwent standardized QC procedures, including the exclusion of samples and SNPs with low call rates (<98%), chromosomal anomalies, samples with discrepancies in self-reported and genetically determined sex, and SNPs out of Hardy–Weinberg Equilibrium. To investigate population structure, we used Plink (v1.9) to conduct principal components analysis (PCA). We restricted our analytic sample to participants with estimated European ancestry based on the PCA due to the low numbers of participants of other ancestries (detailed in Supplementary Methods). We imputed genotypes to infer unobserved genotypes and increase the density of genetic variants. All samples were first phased using SHAPEIT2 (34) and imputed to the Haplotype Reference Consortium (HRC) panel (35) using the University of Michigan Imputation Server (36).

Selection of instrumental variables

The Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) is the largest GWAS of circulating CRP concentrations to date, analyzing 204,402 individuals of European descent (11). It reported 48 lead genetic variants from the HapMap GWAS and four additional variants from the 1000 Genome GWAS that were associated with CRP at the genome-wide statistical significance (P < 5 × 10−8). Together these 52 SNPs explained 6.5% of the variance in circulating CRP (11).

We also searched for, but did not identify additional variants from the NHGRI-EBI GWAS Catalog (ref. 37; downloaded on 03/09/20) that met the following criteria: (i) association with CRP at a genome-wide statistical significance level (P < 5 × 10−8); (ii) study population of European ancestry; (iii) not in LD (R2 < 0.3) with previously selected SNPs; and (iv) available information on effect sizes and standard errors.

We included the 52 variants as instrumental variables in our Mendelian randomization analyses. The imputation quality (r2) of all 52 CRP-associated SNPs in our data was greater than 0.8. We then calculated a 52-SNP genetic-risk score (GRS; ref. 38) by taking the sum of the number of risk (CRP-increasing) alleles for each of the 52 genetic variants weighted by the β coefficients reported by the CHARGE study (11). The β coefficients represent the change in the natural log-transformed CRP per copy increment in the risk allele (Table 1).

Statistical analysis

The genetic variants selected as an instrumental variable in a Mendelian randomization analysis need to meet three assumptions: (i) they are robustly associated with the exposure (“relevance”), (ii) they do not share a common cause with the outcome (“exchangeability”), and (iii) they affect the outcome only through the exposure (“exclusion restriction”).

We first verified the “relevance” assumption by evaluating the association between GRS and post-diagnosis circulating CRP concentrations in a subset of colorectal cancer cases from Seattle CCFR (n = 285) whose CRP levels were measured in between one to three years after diagnosis to rule out active treatment effects (39). We estimated the proportion of variance (R2) explained by the 52 genetic variants and calculated the F statistic, a measure of instrument strength, based on R2, the sample size (n), and the number of instruments (k) as described in the formula: |$F={{{R^2}\over{{R^2}+1}}{\vskip 2pt*}{{n-k-1}\over {k}} }$|⁠. A strong instrumental variable is defined as having F ≥ 10 (40).

For the second “exchangeability” assumption, we examined several epidemiologic and clinicopathological factors that may confound the CRP-survival association, including smoking, body mass index (BMI), NSAID use, tumor location, and stage at diagnosis. Each was assessed for association with the GRS. BMI was statistically significantly associated with the GRS, and therefore it was included as an additional adjustment variable in the following Mendelian randomization analysis. No other variables were statistically significantly associated with GRS.

The “exclusion restriction” assumption was assessed in a series of sensitivity analyses. We used MR-Egger regression to assess the horizontal pleiotropic effect. The test of a non-zero intercept indicates whether there are averaged pleiotropic effects (41). We also restricted the instrumental variable to rs2794520 in the CRP gene to minimize the probability of horizontal pleiotropy. This variant itself explained 1.4% of the variance in circulating CRP (11).

We performed the Mendelian randomization analyses using a two-stage regression approach (42). Additive hazards model offers a flexible alternative for modeling associations on the hazard scale: A hazard difference (HD), unlike the hazard ratio (HR) from the Cox proportional hazards model, is a collapsible effect measure over strata of unmeasured and unknown confounders (42, 43). We used additive hazards models to calculate HD and 95% confidence intervals (CI) for the associations between CRP-associated GRS and colorectal cancer–specific survival. The R package “timereg” was used for fitting additive hazards models (44).

We also evaluated the association between genetically determined CRP-circulating concentration and colorectal cancer–specific survival using the inverse-variance weighted (IVW) method (45), MR-Egger regression (41), and the estimator from the weighted median approach (46) based on summary statistics on SNP-specific associations with colorectal cancer survival. In secondary analyses, we evaluated the associations between genetically predicted concentrations of CRP and colorectal cancer–specific survival according to tumor stage and location.

In the sensitivity analyses, Cox proportional hazards models were used for hypothesis testing. We also compared results with and without adjustment of BMI in addition to age at diagnosis, sex, genotyping platform, study, and the first nine principal components. All analyses were conducted using R version 3.6.0.

Currently, there is no available power calculation tool for survival outcomes in Mendelian randomization analysis, we first took a conservative approach treating colorectal cancer–specific survival as a binary outcome and used the methods described by Burgess (47). With a total of 16,916 colorectal cancer cases and 23% colorectal cancer–specific deaths occurring over up to 10 years follow-up, we have more than 85% power to detect an OR of 1.25 for the association between CRP and colorectal cancer–specific survival at a significance level of 0.05, assuming 5.9% variance of CRP explained by the genetic variance.

In addition, we ran a simulation using the additive hazards model for power calculation. With the number of colorectal cancer cases and 3,808 colorectal cancer–related deaths accrued over a 10-year follow-up, the population-averaged hazard was estimated to be 3,808/(16,918 × 10) = 0.023 per person × year. We have at least 83% power to detect a 25% difference in hazard (HD, 0.0058) for every 1 SD increase of CRP assuming 5.9% of the variance of CRP was explained by GRS. The R code for the simulation is included in the Supplementary Materials.

We included 16,918 eligible colorectal cancer cases from ISACC in this study (Fig. 1). Study participants were diagnosed at a median of 67 years of age, and 49.7% were female. Over the maximum 10-year follow up, there were 5,395 (32%) deaths accrued with 3,808 (23%) due to colorectal cancer. Study-specific summaries are shown in Supplementary Table S1. SNP-specific associations with circulating CRP concentrations and colorectal cancer–specific survival are summarized in Table 1.

In evaluating the “relevance” assumption, we observed strong associations between the GRS and circulating CRP concentrations in a subset of the study participants (n = 285). A 1 U increase in GRS was associated with a 1.22 U increase in the natural log-transformed CRP (95% CI, 0.65–1.80; P = 4.33 × 10−5) and explained 5.9% of the variance of the natural log-transformed CRP concentrations. The estimated F statistic was 20.2, indicating a strong instrumental variable.

Among the 16,918 participants from ISACC, the distribution of the CRP-associated GRS calculated on the basis of individual-level data is shown in Supplementary Fig. S2. On basis of additive hazards model, we observed that 1 U increase in GRS was associated with 1.15 fewer deaths due to colorectal cancer per 100,000 patients each year (HD, −1.15; 95% CI, −2.76 to 0.47 per 100,000 person-year, Table 2). However, it did not reach statistical significance (P = 0.16). No associations between quartiles of GRS and colorectal cancer–specific survival were observed (Table 2). Results based on IVW, MR-Egger, and weighted median approaches using summary statistics were consistent with those based on individual GRS data (Table 2). Sensitivity analyses using Cox proportional hazards models for hypothesis testing showed similar null associations between GRS and colorectal cancer–specific survival (HR, 0.90; 95% CI, 0.79–1.02; P = 0.10, Table 2).

We further evaluated this association by stage at diagnosis and tumor location, and found no evidence of statistically significant association in these subgroup analyses using Cox proportional hazards models, whereas the additive hazards model did not converge due to limited number of events in subgroups (Table 3). Among individuals diagnosed with colon cancer, we observed a borderline significant association: 1 U increase in GRS was associated with improved colorectal cancer–specific survival (HR, 0.87; 95% CI, 0.75–1.00; P = 0.06; Table 3).

We plotted the SNP-specific associations with colorectal cancer–specific survival against coefficients of SNP–CRP associations (Fig. 2). After conducting MR-Egger regression analysis, we found that the intercept was not statistically significantly different from zero (β0 = 1.28 × 10−7; 95% CI, −1.23 × 10−6 to 1.48 × 10−6; P = 0.85) when using additive hazards models. This suggested no horizontal pleiotropic effect. The MR-Egger regression using Cox proportional hazards estimates (Fig. 2B) yielded similar results compared with the one using additive hazards models (Fig. 2A). We then restricted the instrumental variable to rs2794520 in the CRP gene and repeated the Mendelian randomization analysis. A null association with colorectal cancer survival was observed (additive hazards model: HD, −0.049 per 100,000 person-year; P = 0.88; Cox proportional hazards model: HR, 0.99; 95% CI, 0.94–1.04; P = 0.60).

In this large Mendelian randomization study, we did not find evidence of an association between genetically predicted CRP-circulating concentration and colorectal cancer–specific survival in a cohort of individuals diagnosed with incident invasive colorectal cancer and followed up for survival. No associations were observed in subgroups defined by tumor stage at diagnosis and location. Our findings do not support a causal relationship between circulating CRP and colorectal cancer–specific survival.

Previous studies of CRP and colorectal cancer incidence and survival do not provide convincing evidence of causation. For colorectal cancer risk, meta-analyses of prediagnostic circulating CRP concentrations showed that a 1-unit change in natural logarithm CRP was associated with a 12% increased risk of developing colorectal cancer (48). Conversely, we showed in a large multi-consortium Mendelian randomization study with more than 30,400 cases and 22,800 controls no association between genetically determined CRP concentrations and colorectal cancer risk (49). For colorectal cancer–specific survival, results from observational studies of circulating CRP concentration were inconsistent. Some studies observed that circulating CRP concentration measured before surgery was not statistically significantly associated with survival after multivariable adjustment (7, 8). Other studies observed that elevated concentrations of pre-operative (4–6, 50) and post-treatment (51, 52) CRP were associated with worse colorectal cancer survival outcomes. However, the CRP measures in these studies were crude. Several of these studies used CRP ≥10 mg/L as the cutoff to dichotomize circulating CRP concentrations (4, 5, 50). Elevated CRP concentrations ≥10 mg/L are likely driven by acute inflammatory conditions other than chronic inflammation. Similarly, in our recent study, circulating concentration of CRP was no longer associated with colorectal cancer survival after we excluded colorectal cancer cases who had post-treatment CRP >10 mg/L (39).

In this study, we used genetic variants as proxies of circulating CRP concentrations that can help address potential biases due to residual confounding and reverse causality, but existing evidence on colorectal cancer survival outcomes is limited. Slattery and colleagues (53) evaluated four tag SNPs in the CRP gene in relation to colorectal cancer survival among 1,574 cases; however, none were statistically significantly associated with colorectal cancer–specific survival within 5 years after diagnosis. Another study with 421 colorectal cancer cases of East Asian ancestry showed that two SNPs from the CRP gene were associated with colorectal cancer survival: rs3093059 was associated with disease-free survival, whereas rs1205 was associated with colorectal cancer–specific survival (54). Although these two variants were not included in our study, we evaluated rs2794520, at CRP locus that is in high LD with these two SNPs. The allele frequencies of these SNPs are twice as common in the East Asian population (ASN) compared with the European population (EUR): rs3093059 (ASN: 0.14; EUR: 0.07), rs2794520, and rs1205 (ASN: 0.60; EUR: 0.31). This could partially explain the different study findings.

There are some limitations when interpreting our study results. First, the restriction of our study sample to individuals diagnosed with colorectal cancer by design could be a potential source of selection bias (also known as collider bias) particularly if CRP is causally associated with increased risk of developing colorectal cancer. By conditioning on the collider-colorectal cancer risk (selecting only colorectal cancer cases into the study sample), it can induce an association between genetic variants and risk factors of colorectal cancer. However, evidence from our previous Mendelian randomization study suggests that CRP is not causally associated with colorectal cancer risk (49). To further address this potential selection bias, we evaluated the associations between the genetic variants with both potential confounders of CRP and colorectal cancer survival associations and common risk factors of colorectal cancer risk. BMI was identified as the only variable being statistically significantly associated with the GRS for CRP in our study sample and was adjusted for in all analyses. However, as BMI is an inheritable trait that shares some genetic susceptibilities with CRP, we also assessed whether there was potential bias due to BMI adjustment (55, 56) and compared main analysis with and without adjustment of BMI (Supplementary Table S2). We observed minimal changes due to BMI adjustment. Second, the 52-SNP GRS for CRP explained only less than 6% of the variance of the natural log-transformed CRP concentrations. The null results of our study cannot rule out a weaker causal effect of CRP on colorectal cancer–specific survival. Third, the genetic variants shown to be robustly associated with circulating CRP were identified from a GWAS based on study sample from the general population. The SNP–CRP associations may be different in a sample of colorectal cancer cases. Although we evaluated the “relevance” assumption in a subset of our study sample and observed a strong association between the CRP-associated GRS and post-diagnostic circulating CRP concentrations among colorectal cancer cases, the small sample size limited the statistical power to evaluate SNP-specific associations with CRP among colorectal cancer cases. In addition, our subgroup analyses had insufficient statistical power even though our main analysis was well powered. The limited number of events in subgroups also led to convergence issues when using the additive hazards model. Finally, because the study sample was limited to individuals with European ancestry, our findings may not be generalizable to other racial/ethnicity groups.

Our study also has many strengths. This is the first study that evaluates circulating biomarkers in relation to colorectal cancer survival using a Mendelian randomization approach. Our large sample size possessed adequate statistical power to detect associations with moderate effect sizes. Also, the well-characterized study sample with individual-level genotype data and detailed information on epidemiologic and clinic factors allowed us to compare study results with those based on summary statistics, to evaluate the “exchangeability” assumptions, and to conduct subgroup analysis by stage at diagnosis and tumor location; however, we were not able to account for several clinical prognostic factors for colorectal cancer survival, such as treatment, due to data availability. A subset of study participants had data on both genotypes and circulating CRP concentrations allowing us to evaluate the “relevance” assumption. By carefully examining the three assumptions, our Mendelian randomization study is less susceptible to confounding and reverse causality compared with observational studies.

In summary, our study did not find evidence of an association between genetically predicted circulating CRP concentration and colorectal cancer–specific survival, overall or in subgroups defined by stage at diagnosis or tumor location. Future research should be conducted to determine whether other circulating inflammatory biomarkers, such as IL6, are associated with colorectal cancer survival outcomes to better understand chronic inflammation and disease progression among patients with colorectal cancer.

G. Casey reports grants from NIH during the conduct of the study, as well as grants from NIH outside the submitted work. G.G. Giles reports grants from National Health and Medical Research Council (Australia) during the conduct of the study. R.M. Goldberg reports grants from National Cancer Institute during the conduct of the study, as well as personal fees from Amgen, Taiho, AstraZeneca, Merck, Novartis, Genentech, Adaptimmune, and Bayer outside the submitted work. R.K. Pai reports personal fees from PathAI, Alimentiv Inc., Allergan, Eli Lilly, AbbVie, and Genentech outside the submitted work. L.C. Sakoda reports grants from National Cancer Institute during the conduct of the study, as well as grants from National Cancer Institute outside the submitted work. R.E. Schoen reports grants from NIH during the conduct of the study. Q. Shi reports personal fees from Regeneron Pharmaceuticals, Inc., Chugai Pharmaceutical Co., Ltd., Boehringer Ingelheim Pharmaceuticals, Inc., and Merck & Co.; grants from Roche/Genentech and BMS/Celgene; and personal fees from Yiviva Inc., Johnson & Johnson, and Amgen outside the submitted work. M.L. Slattery reports grants from National Cancer Institute during the conduct of the study. L. Le Marchand reports grants from NCI during the conduct of the study. A.T. Chan reports personal fees from Bayer Pharma AG, Pfizer Inc., and Boehringer Ingelheim outside the submitted work. No disclosures were reported by the other authors.

X. Hua: Conceptualization, software, formal analysis, visualization, methodology, writing–original draft. J.Y. Dai: Conceptualization, methodology, writing–review and editing. S. Lindström: Conceptualization, methodology, writing–review and editing. T.A. Harrison: Methodology, project administration, writing–review and editing. Y. Lin: Data curation, methodology, writing–review and editing. S.R. Alberts: Resources, data curation, writing–review and editing. E. Alwers: Data curation, writing–review and editing. S.I. Berndt: Resources, data curation, writing–review and editing. H. Brenner: Resources, data curation, writing–review and editing. D. D. Buchanan: Resources, data curation, writing–review and editing. P.T. Campbell: Resources, data curation, writing–review and editing. G. Casey: Resources, data curation, writing–review and editing. J. Chang-Claude: Resources, data curation, writing–review and editing. S. Gallinger: Resources, data curation, writing–review and editing. G.G. Giles: Resources, data curation, writing–review and editing. R.M. Goldberg: Resources, data curation, writing–review and editing. M.J. Gunter: Resources, data curation, writing–review and editing. M. Hoffmeister: Resources, data curation, writing–review and editing. M.A. Jenkins: Resources, data curation, writing–review and editing. A.D. Joshi: Methodology, writing–review and editing. W. Ma: Methodology, writing–review and editing. R.L. Milne: Resources, data curation, writing–review and editing. N. Murphy: Methodology, writing–review and editing. R.K. Pai: Resources, data curation, writing–review and editing. L.C. Sakoda: Resources, data curation, writing–review and editing. R.E. Schoen: Resources, data curation, writing–review and editing. Q. Shi: Resources, data curation, writing–review and editing. M.L. Slattery: Resources, data curation, writing–review and editing. M. Song: Methodology, writing–review and editing. E. White: Resources, data curation, writing–review and editing. L. Le Marchand: Resources, data curation, writing–review and editing. A.T. Chan: Resources, funding acquisition, writing–review and editing. U. Peters: Resources, supervision, funding acquisition, writing–review and editing. P.A. Newcomb: Resources, supervision, funding acquisition, writing–review and editing.

ISACC: The authors would like to thank all those at the ISACC Coordinating Center for helping bring together the data and people that made this project possible. This research was funded by in part through National Cancer Institute, National Institutes of Health, U.S. Department of Health and Human Services (R01 CA176272) and the NIH/NCI Cancer Center Support grant P30 CA015704. Scientific Computing Infrastructure at Fred Hutch funded by ORIP grant S10OD028685. CCFR: The Colon CFR graciously thanks the generous contributions of their 42,505 study participants, dedication of study staff, and the financial support from the U.S. National Cancer Institute, without which this important registry would not exist. CCFR (www.coloncfr.org) is supported in part by funding from the National Cancer Institute (NCI), National Institutes of Health (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 SBG). The CCFR Set-4 (Illumina OncoArray 600K SNP array) was supported by NIH award U19 CA148107 and by the Center for Inherited Disease Research (CIDR), which is funded by the NIH to the Johns Hopkins University, contract number HHSN268201200008I. The content of this article 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 U.S. government, any cancer registry, or the CCFR. SCCFR: The authors would like to thank the study participants and staff of the Seattle Colon Cancer Family Registry and the Hormones and Colon Cancer study (CORE Studies). [OFCCR ARCTIC]: 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 (to S. Gallinger), and through generous support from the Ontario Ministry of Research and Innovation. [SCCFR (Illumina HumanCytoSNP (300k)]: The SCCFR Illumina HumanCytoSNP array was supported through NCI award R01 CA076366 (to P.A. Newcomb). [CCFR Set-1 and/or Set-2 scan (Illumina Human 1M, 1M-Duo, and/or Omni1-Quad)]: 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). [CCFR Set-3 scan (Affymetrix Axiom CORECT Set array)]: The CCFR Set-3 (Affymetrix Axiom CORECT Set array) was supported by NIH award U19 CA148107 and R01 CA81488 (to SBG). [CCFR Set-4 scan (Illumina OncoArray 600K SNP array)]: The CCFR Set-4 (Illumina OncoArray 600K SNP array) was supported by NIH award U19 CA148107 (to SBG) and by the Center for Inherited Disease Research (CIDR), which is funded by the NIH to the Johns Hopkins University, contract number HHSN268201200008I. CPS-II: The authors thank the CPS-II participants and Study Management Group for their invaluable contributions to this research. The authors would also like to 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. The American Cancer Society funds the creation, maintenance, and updating of the CPS-II cohort. This study was conducted with Institutional Review Board approval. DACHS: We thank all participants and cooperating clinicians, and everyone who provided excellent technical assistance. This work was supported by the German Research Council (BR 1704/6-1, BR 1704/6-3, BR1704/6-4, 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 German Federal Ministry of Education and Research (01KH0404, 01ER0814, 01ER0815, 01ER1505A, and 01ER1505B), the Interdisciplinary Research Program of the National Center for Tumor Diseases (NCT), Germany, and German Cancer Research Center. DALS: National Institutes of Health (R01 CA48998; to M.L. Slattery). EDRN: We acknowledge all contributors to the development of the resource at University of Pittsburgh School of Medicine, Department of Gastroenterology, Department of Pathology, Hepatology and Nutrition and Biomedical Informatics. This work is funded and supported by the NCI, EDRN Grant (U01 CA 84968-06). 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. 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 Center of Excellence program 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, UK). Harvard cohorts (HPFS, NHS, and 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. We would like to 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. HPFS is supported by the National Institutes of Health (P01 CA055075, UM1 CA167552, U01 CA167552, R01 CA137178, R01 CA151993, and R35 CA197735), NHS by the National Institutes of Health (R01 CA137178, P01 CA087969, UM1 CA186107, R01 CA151993, and R35 CA197735), and PHS by the National Institutes of Health (R01 CA042182). 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. 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, we thank the study participants for their contributions that made this study possible. Cancer incidence data have been provided by the District of Columbia Cancer Registry, Georgia Cancer Registry, Hawaii Cancer Registry, Minnesota Cancer Surveillance System, Missouri Cancer Registry, Nevada Central Cancer Registry, Pennsylvania Cancer Registry, Texas Cancer Registry, Virginia Cancer Registry, and Wisconsin Cancer Reporting System. All are supported in part by funds from the Center for Disease Control and Prevention, National Program for Central Registries, local states, or by the National Cancer Institute, Surveillance, Epidemiology, and End Results program. The results reported here and the conclusions derived are the sole responsibility of the authors. Intramural Research Program of the Division of Cancer Epidemiology and Genetics and supported by contracts from the Division of Cancer Prevention, National Cancer Institute, NIH, DHHS. Funding was provided by National Institutes of Health (NIH), Genes, Environment and Health Initiative (GEI) Z01 CP 010200, NIH U01 HG004446, and NIH GEI U01 HG 004438. UK Biobank: The authors thank all the participants and staff of UK Biobank for making such a wonderful resource available for research. This study has been conducted under Application Number 8614. VITAL is supported by National Institutes of Health (K05 CA154337). 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. The WHI program is funded by the National Heart, Lung, and Blood Institute, National Institutes of Health, U.S. Department of Health and Human Services through contracts HHSN268201100046C, HHSN268201100001C, HHSN268201100002C, HHSN268201100003C, HHSN268201100004C, and HHSN271201100004C.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

1.
Terzic
J
,
Grivennikov
S
,
Karin
E
,
Karin
M
. 
Inflammation and colon cancer
.
Gastroenterology
2010
;
138
:
2101
14
.
2.
Klintrup
K
,
Makinen
JM
,
Kauppila
S
,
Vare
PO
,
Melkko
J
,
Tuominen
H
, et al
Inflammation and prognosis in colorectal cancer
.
Eur J Cancer
2005
;
41
:
2645
54
.
3.
Ansar
W
,
Ghosh
S
. 
C-reactive protein and the biology of disease
.
Immunol Res
2013
;
56
:
131
42
.
4.
Takasu
C
,
Shimada
M
,
Kurita
N
,
Iwata
T
,
Nishioka
M
,
Morimoto
S
, et al
Impact of C-reactive protein on prognosis of patients with colorectal carcinoma
.
Hepatogastroenterology
2013
;
60
:
507
11
.
5.
Crozier
JE
,
McKee
RF
,
McArdle
CS
,
Angerson
WJ
,
Anderson
JH
,
Horgan
PG
, et al
The presence of a systemic inflammatory response predicts poorer survival in patients receiving adjuvant 5-FU chemotherapy following potentially curative resection for colorectal cancer
.
Br J Cancer
2006
;
94
:
1833
6
.
6.
Koike
Y
,
Miki
C
,
Okugawa
Y
,
Yokoe
T
,
Toiyama
Y
,
Tanaka
K
, et al
Preoperative C-reactive protein as a prognostic and therapeutic marker for colorectal cancer
.
J Surg Oncol
2008
;
98
:
540
4
.
7.
Chung
YC
,
Chang
YF
. 
Serum C-reactive protein correlates with survival in colorectal cancer patients but is not an independent prognostic indicator
.
Eur J Gastroenterol Hepatol
2003
;
15
:
369
73
.
8.
Volkova
E
,
Willis
JA
,
Wells
JE
,
Robinson
BA
,
Dachs
GU
,
Currie
MJ
. 
Association of angiopoietin-2, C-reactive protein and markers of obesity and insulin resistance with survival outcome in colorectal cancer
.
Br J Cancer
2011
;
104
:
51
9
.
9.
Davey Smith
G
,
Ebrahim
S
. 
‘Mendelian randomization': can genetic epidemiology contribute to understanding environmental determinants of disease?
Int J Epidemiol
2003
;
32
:
1
22
.
10.
Dehghan
A
,
Dupuis
J
,
Barbalic
M
,
Bis
JC
,
Eiriksdottir
G
,
Lu
C
, et al
Meta-analysis of genome-wide association studies in >80,000 subjects identifies multiple loci for C-reactive protein levels
.
Circulation
2011
;
123
:
731
8
.
11.
Ligthart
S
,
Vaez
A
,
Vosa
U
,
Stathopoulou
MG
,
de Vries
PS
,
Prins
BP
, et al
Genome analyses of >200,000 individuals identify 58 loci for chronic inflammation and highlight pathways that link inflammation and complex disorders
.
Am J Hum Genet
2018
;
103
:
691
706
.
12.
Guo
Q
,
Burgess
S
,
Turman
C
,
Bolla
MK
,
Wang
Q
,
Lush
M
, et al
Body mass index and breast cancer survival: a Mendelian randomization analysis
.
Int J Epidemiol
2017
;
46
:
1814
22
.
13.
Newcomb
PA
,
Baron
J
,
Cotterchio
M
,
Gallinger
S
,
Grove
J
,
Haile
R
, et al
Colon Cancer Family Registry: an international resource for studies of the genetic epidemiology of colon cancer
.
Cancer Epidemiol Biomarkers Prev
2007
;
16
:
2331
43
.
14.
Calle
EE
,
Rodriguez
C
,
Jacobs
EJ
,
Almon
ML
,
Chao
A
,
McCullough
ML
, et al
The American Cancer Society Cancer Prevention Study II Nutrition Cohort: rationale, study design, and baseline characteristics
.
Cancer
2002
;
94
:
2490
501
.
15.
Brenner
H
,
Chang-Claude
J
,
Seiler
CM
,
Rickert
A
,
Hoffmeister
M
. 
Protection from colorectal cancer after colonoscopy: a population-based, case–control study
.
Ann Intern Med
2011
;
154
:
22
30
.
16.
Slattery
ML
,
Potter
J
,
Caan
B
,
Edwards
S
,
Coates
A
,
Ma
KN
, et al
Energy balance and colon cancer—beyond physical activity
.
Cancer Res
1997
;
57
:
75
80
.
17.
Amin
W
,
Singh
H
,
Dzubinski
LA
,
Schoen
RE
,
Parwani
AV
. 
Design and utilization of the colorectal and pancreatic neoplasm virtual biorepository: an early detection research network initiative
.
J Pathol Inform
2010
;
1
:
22
.
18.
Riboli
E
,
Kaaks
R
. 
The EPIC Project: rationale and study design. European Prospective Investigation into Cancer and Nutrition
.
Int J Epidemiol
1997
;
26
:
S6
14
.
19.
Rimm
EB
,
Stampfer
MJ
,
Colditz
GA
,
Chute
CG
,
Litin
LB
,
Willett
WC
. 
Validity of self-reported waist and hip circumferences in men and women
.
Epidemiology
1990
;
1
:
466
73
.
20.
Giles
GG
,
English
DR
. 
The Melbourne Collaborative Cohort Study
.
IARC Sci Publ
2002
;
156
:
69
70
.
21.
Colditz
GA
,
Manson
JE
,
Hankinson
SE
. 
The Nurses' Health Study: 20-year contribution to the understanding of health among women
.
J Womens Health
1997
;
6
:
49
62
.
22.
Goldberg
RM
,
Sargent
DJ
,
Morton
RF
,
Fuchs
CS
,
Ramanathan
RK
,
Williamson
SK
, et al
A randomized controlled trial of fluorouracil plus leucovorin, irinotecan, and oxaliplatin combinations in patients with previously untreated metastatic colorectal cancer
.
J Clin Oncol
2004
;
22
:
23
30
.
23.
Christen
WG
,
Gaziano
JM
,
Hennekens
CH
. 
Design of Physicians' Health Study II–a randomized trial of beta-carotene, vitamins E and C, and multivitamins, in prevention of cancer, cardiovascular disease, and eye disease, and review of results of completed trials
.
Ann Epidemiol
2000
;
10
:
125
34
.
24.
Prorok
PC
,
Andriole
GL
,
Bresalier
RS
,
Buys
SS
,
Chia
D
,
Crawford
ED
, et al
Design of the prostate, lung, colorectal and ovarian (PLCO) cancer screening trial
.
Control Clin Trials
2000
;
21
:
273S
309S
.
25.
Sudlow
C
,
Gallacher
J
,
Allen
N
,
Beral
V
,
Burton
P
,
Danesh
J
, et al
UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age
.
PLoS Med
2015
;
12
:
e1001779
.
26.
White
E
,
Patterson
RE
,
Kristal
AR
,
Thornquist
M
,
King
I
,
Shattuck
AL
, et al
VITamins and lifestyle cohort study: study design and characteristics of supplement users
.
Am J Epidemiol
2004
;
159
:
83
93
.
27.
Group TWsHIS
. 
Design of the Women's Health Initiative clinical trial and observational study
.
Control Clin Trials
1998
;
19
:
61
109
.
28.
Hutter
CM
,
Chang-Claude
J
,
Slattery
ML
,
Pflugeisen
BM
,
Lin
Y
,
Duggan
D
, et al
Characterization of gene-environment interactions for colorectal cancer susceptibility loci
.
Cancer Res
2012
;
72
:
2036
44
.
29.
Peters
U
,
Jiao
S
,
Schumacher
FR
,
Hutter
CM
,
Aragaki
AK
,
Baron
JA
, et al
Identification of genetic susceptibility loci for colorectal tumors in a genome-wide meta-analysis
.
Gastroenterology
2013
;
144
:
799
807
.
30.
Huyghe
JR
,
Bien
SA
,
Harrison
TA
,
Kang
HM
,
Chen
S
,
Schmit
SL
, et al
Discovery of common and rare genetic risk variants for colorectal cancer
.
Nat Genet
2019
;
51
:
76
87
.
31.
Bycroft
C
,
Freeman
C
,
Petkova
D
,
Band
G
,
Elliott
LT
,
Sharp
K
, et al
The UK Biobank resource with deep phenotyping and genomic data
.
Nature
2018
;
562
:
203
9
.
32.
Schmit
SL
,
Edlund
CK
,
Schumacher
FR
,
Gong
J
,
Harrison
TA
,
Huyghe
JR
, et al
Novel common genetic susceptibility loci for colorectal cancer
.
J Natl Cancer Inst
2019
;
111
:
146
57
.
33.
Schumacher
FR
,
Schmit
SL
,
Jiao
S
,
Edlund
CK
,
Wang
H
,
Zhang
B
, et al
Genome-wide association study of colorectal cancer identifies six new susceptibility loci
.
Nat Commun
2015
;
6
:
7138
.
34.
Delaneau
O
,
Marchini
J
,
McVean
GA
,
Donnelly
P
,
Lunter
G
,
Marchini
JL
, et al
Integrating sequence and array data to create an improved 1000 Genomes Project haplotype reference panel
.
Nat Commun
2014
;
5
:
3934
.
35.
McCarthy
S
,
Das
S
,
Kretzschmar
W
,
Delaneau
O
,
Wood
AR
,
Teumer
A
, et al
A reference panel of 64,976 haplotypes for genotype imputation
.
Nat Genet
2016
;
48
:
1279
83
.
36.
Das
S
,
Forer
L
,
Schönherr
S
,
Sidore
C
,
Locke
AE
,
Kwong
A
, et al
Next-generation genotype imputation service and methods
.
Nat Genet
2016
;
48
:
1284
7
.
37.
Buniello
A
,
MacArthur
JAL
,
Cerezo
M
,
Harris
LW
,
Hayhurst
J
,
Malangone
C
, et al
The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019
.
Nucleic Acids Res
2019
;
47
:
D1005
d12
.
38.
Burgess
S
,
Dudbridge
F
,
Thompson
SG
. 
Combining information on multiple instrumental variables in Mendelian randomization: comparison of allele score and summarized data methods
.
Stat Med
2016
;
35
:
1880
906
.
39.
Hua
X
,
Kratz
M
,
Newcomb
PA
. 
Associations between post-treatment inflammatory biomarkers and survival among stage II–III colorectal cancer patients
.
Cancer Epidemiol Biomarkers Prev
2020
;
29
:
691
.
40.
Staiger
DO
,
Stock
JH
.
Instrumental variables regression with weak instruments
.
Mass., USA
:
National Bureau of Economic Research Cambridge
; 
1994
.
41.
Bowden
J
,
Davey Smith
G
,
Burgess
S
. 
Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression
.
Int J Epidemiol
2015
;
44
:
512
25
.
42.
Tchetgen Tchetgen
EJ
,
Walter
S
,
Vansteelandt
S
,
Martinussen
T
,
Glymour
M
. 
Instrumental variable estimation in a survival context
.
Epidemiology
2015
;
26
:
402
10
.
43.
Sjolander
A
,
Dahlqwist
E
,
Zetterqvist
J
. 
A note on the noncollapsibility of rate differences and rate ratios
.
Epidemiology
2016
;
27
:
356
9
.
44.
Scheike
TH
,
Zhang
MJ
. 
Analyzing competing risk data using the R timereg package
.
J Stat Softw
2011
;
38
.
2
.
45.
Burgess
S
,
Butterworth
A
,
Thompson
SG
. 
Mendelian randomization analysis with multiple genetic variants using summarized data
.
Genet Epidemiol
2013
;
37
:
658
65
.
46.
Bowden
J
,
Davey Smith
G
,
Haycock
PC
,
Burgess
S
. 
Consistent estimation in mendelian randomization with some invalid instruments using a weighted median estimator
.
Genet Epidemiol
2016
;
40
:
304
14
.
47.
Burgess
S
. 
Sample size and power calculations in Mendelian randomization with a single instrumental variable and a binary outcome
.
Int J Epidemiol
2014
;
43
:
922
9
.
48.
Zhou
B
,
Shu
B
,
Yang
J
,
Liu
J
,
Xi
T
,
Xing
Y
. 
C-reactive protein, interleukin-6 and the risk of colorectal cancer: a meta-analysis
.
Cancer Causes Control
2014
;
25
:
1397
405
.
49.
Wang
X
,
Dai
JY
,
Albanes
D
,
Arndt
V
,
Berndt
SI
,
Bezieau
S
, et al
Mendelian randomization analysis of C-reactive protein on colorectal cancer risk
.
Int J Epidemiol
2019
;
48
:
767
80
.
50.
Li
C
,
Xu
Q
,
Chen
L
,
Luo
C
,
Ying
J
,
Liu
J
. 
C-reactive protein (CRP) as a prognostic factor for colorectal cancer after surgical resection of pulmonary metastases
.
Bull Cancer
2017
;
104
:
232
6
.
51.
Cooney
RV
,
Chai
W
,
Franke
AA
,
Wilkens
LR
,
Kolonel
LN
,
Le Marchand
L
. 
C-reactive protein, lipid-soluble micronutrients, and survival in colorectal cancer patients
.
Cancer Epidemiol Biomarkers Prev
2013
;
22
:
1278
88
.
52.
Matsubara
D
,
Arita
T
,
Nakanishi
M
,
Kuriu
Y
,
Murayama
Y
,
Kudou
M
, et al
The impact of postoperative inflammation on recurrence in patients with colorectal cancer
.
Int J Clin Oncol
2020
;
25
:
602
13
.
53.
Slattery
ML
,
Curtin
K
,
Poole
EM
,
Duggan
DJ
,
Samowitz
WS
,
Peters
U
, et al
Genetic variation in C-reactive protein in relation to colon and rectal cancer risk and survival
.
Int J Cancer
2011
;
128
:
2726
34
.
54.
Yang
SH
,
Huang
CJ
,
Chang
SC
,
Lin
JK
. 
Association of C-reactive protein gene polymorphisms and colorectal cancer
.
Ann Surg Oncol
2011
;
18
:
1907
15
.
55.
Aschard
H
,
Vilhjálmsson
BJ
,
Joshi
AD
,
Price
AL
,
Kraft
P
. 
Adjusting for heritable covariates can bias effect estimates in genome-wide association studies
.
Am J Hum Genet
2015
;
96
:
329
39
.
56.
Aschard
H
,
Guillemot
V
,
Vilhjalmsson
B
,
Patel
CJ
,
Skurnik
D
,
Ye
CJ
, et al
Covariate selection for association screening in multiphenotype genetic studies
.
Nat Genet
2017
;
49
:
1789
95
.