cumulative burden of colorectal cancer-associated genetic

Gastroenterology 2020;158:1274–1286

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Cumulative Burden of Colorectal Cancer–Associated GeneticVariants Is More Strongly Associated With Early-Onset vsLate-Onset Cancer
Alexi N. Archambault,1 Yu-Ru Su,2 Jihyoun Jeon,3 Minta Thomas,2 Yi Lin,2 David V. Conti,4
Aung Ko Win,5 Lori C. Sakoda,2,6 Iris Lansdorp-Vogelaar,7 Elisabeth F. P. Peterse,7

Ann G. Zauber,8 David Duggan,9 Andreana N. Holowatyj,10 Jeroen R. Huyghe,2

Hermann Brenner,11,12,13 Michelle Cotterchio,14 Stéphane Bézieau,15 Stephanie L. Schmit,4,16

Christopher K. Edlund,4 Melissa C. Southey,17 Robert J. MacInnis,5,18 Peter T. Campbell,19

Jenny Chang-Claude,20,21 Martha L. Slattery,22 Andrew T. Chan,23,24,25,26,27,28

Amit D. Joshi,25,27 Mingyang Song,29 Yin Cao,25,30 Michael O. Woods,31 Emily White,2,32

Stephanie J. Weinstein,33 Cornelia M. Ulrich,34 Michael Hoffmeister,11 Stephanie A. Bien,2

Tabitha A. Harrison,2 Jochen Hampe,35 Christopher I. Li,2 Clemens Schafmayer,36

Kenneth Offit,37,38 Paul D. Pharoah,39 Victor Moreno,40,41,42 Annika Lindblom,43,44

Alicja Wolk,45 Anna H. Wu,4 Li Li,46 Marc J. Gunter,47 Andrea Gsur,48 Temitope O. Keku,49

Rachel Pearlman,50 D. Timothy Bishop,51 Sergi Castellví-Bel,52 Leticia Moreira,52

Pavel Vodicka,53,54,55 Ellen Kampman,56 Graham G. Giles,5,16 Demetrius Albanes,32

John A. Baron,57 Sonja I. Berndt,32 Stefanie Brezina,48 Stephan Buch,34

Daniel D. Buchanan,5,58,59,60 Antonia Trichopoulou,61 Gianluca Severi,62

María-Dolores Chirlaque,41,63 Maria-José Sánchez,64 Domenico Palli,65 Tilman Kühn,20

Neil Murphy,66 Amanda J. Cross,67 Andrea N. Burnett-Hartman,68 Stephen J. Chanock,32

Albert de la Chapelle,69 Douglas F. Easton,38 Faye Elliott,51 Dallas R. English,5,16

Edith J. M. Feskens,56 Liesel M. FitzGerald,18,70 Phyllis J. Goodman,71 John L. Hopper,5,72

Thomas J. Hudson,73 David J. Hunter,27,74 Eric J. Jacobs,19 Corinne E. Joshu,75

Sébastien Küry,18 Sanford D. Markowitz,45 Roger L. Milne,5,18 Elizabeth A. Platz,75

Gad Rennert,76,77,78 Hedy S. Rennert,76,77,78 Fredrick R. Schumacher,79 Robert S. Sandler,49

Daniela Seminara,80 Catherine M. Tangen,71 Stephen N. Thibodeau,81 Amanda E. Toland,69

Franzel J. B. van Duijnhoven,56 Kala Visvanathan,75 Ludmila Vodickova,53,54,55 John D. Potter,2

Satu Männistö,82 Korbinian Weigl,11,83 Jane Figueiredo,4,84 Vicente Martín,41,85

Susanna C. Larsson,44 Patrick S. Parfrey,86 Wen-Yi Huang,33 Heinz-Josef Lenz,87

Jose E. Castelao,88 Manuela Gago-Dominguez,89,90 Victor Muñoz-Garzón,91

Christoph Mancao,92 Christopher A. Haiman,4 Lynne R. Wilkens,93 Erin Siegel,16

Elizabeth Barry,94 Ban Younghusband,31 Bethany Van Guelpen,95,96 Sophia Harlid,96

Anne Zeleniuch-Jacquotte,1 Peter S. Liang,97 Mengmeng Du,8 Graham Casey,98

Noralane M. Lindor,99 Loic Le Marchand,93 Steven J. Gallinger,100 Mark A. Jenkins,5

Polly A. Newcomb,2,101 Stephen B. Gruber,102 Robert E. Schoen,103 Heather Hampel,50

Douglas A. Corley,6,§ Li Hsu,2,104,§ Ulrike Peters,2,31,§ and Richard B. Hayes1,§

1Division of Epidemiology, Department of Population Health, New York University School of Medicine, New York, New York;2Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington; 3Department ofEpidemiology, University of Michigan, Ann Arbor, Michigan; 4Department of Preventive Medicine, USC Norris ComprehensiveCancer Center, Keck School of Medicine, University of Southern California, Los Angeles, California; 5Centre for Epidemiologyand Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria,Australia; 6Division of Research, Kaiser Permanente Northern California, Oakland, California; 7Department of Public Health,Erasmus MC, University Medical Center, Rotterdam, The Netherlands; 8Department of Epidemiology and Biostatistics,Memorial Sloan Kettering Cancer Center, New York, New York; 9Translational Genomics Research Institute, An Affiliate of Cityof Hope, Phoenix, Arizona; 10Huntsman Cancer Institute and Department of Population Health Sciences, University of Utah,Salt Lake City, Utah; 11Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ),

http://crossmark.crossref.org/dialog/?doi=10.1053/j.gastro.2019.12.012&domain=pdf

April 2020 Polygenic Risk Score Is Associated With Risk for Early-Onset CRC 1275

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Heidelberg, Germany; 12Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center forTumor Diseases (NCT), Heidelberg, Germany; 13German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ),Heidelberg, Germany; 14Population Health and Prevention, Cancer Care Ontario, Toronto, Ontario, Canada; 15Service deGénétique Médicale, Centre Hospitalier Universitaire (CHU) Nantes, Nantes, France; 16Department of Cancer Epidemiology, H.Lee Moffitt Cancer Center and Research Institute, Tampa, Florida; 17Genetic Epidemiology Laboratory, Department ofPathology, The University of Melbourne, Melbourne, Australia; 18Cancer Epidemiology and Intelligence Division, CancerCouncil Victoria, Melbourne, Victoria, Australia; 19Behavioral and Epidemiology Research Group, American Cancer Society,Atlanta, Georgia; 20Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany;21University Medical Centre Hamburg-Eppendorf, University Cancer Centre Hamburg (UCCH), Hamburg, Germany;22Department of Internal Medicine, University of Utah, Salt Lake City, Utah; 23Division of Gastroenterology, MassachusettsGeneral Hospital and Harvard Medical School, Boston, Massachusetts; 24Channing Division of Network Medicine, Brigham andWomen’s Hospital and Harvard Medical School, Boston, Massachusetts; 25Clinical and Translational Epidemiology Unit,Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts; 26Broad Institute of Harvard and MIT,Cambridge, Massachusetts; 27Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University,Boston, Massachusetts; 28Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health,Harvard University, Boston, Massachusetts; 29Department of Nutrition, Harvard T.H. Chan School of Public Health, HarvardUniversity, Boston, Massachusetts; 30Division of Public Health Sciences, Department of Surgery, Washington University in St.Louis, St. Louis, Missouri; 31Memorial University of Newfoundland, Discipline of Genetics, St. John’s, Newfoundland, Canada;32Department of Epidemiology, University of Washington School of Public Health, Seattle, Washington; 33Division of CancerEpidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland; 34Huntsman CancerInstitute and Department of Population Health Sciences, University of Utah, Salt Lake City, Utah; 35Department of Medicine I,University Hospital Dresden, Technische Universität Dresden (TU Dresden), Dresden, Germany; 36Department of General andThoracic Surgery, University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany; 37Clinical Genetics Service,Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York; 38Department of Medicine, WeillCornell Medical College, New York, New York; 39Department of Public Health and Primary Care, University of Cambridge,Cambridge, UK; 40Cancer Prevention and Control Program, Catalan Institute of Oncology-IDIBELL, L’Hospitalet de Llobregat,Barcelona, Spain; 41CIBER in Epidemiology and Public Health (CIBERESP), Madrid, Spain; 42Department of Clinical Sciences,Faculty of Medicine, University of Barcelona, Barcelona, Spain; 43Department of Clinical Genetics, Karolinska UniversityHospital, Stockholm, Sweden; 44Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden;45Institute of Environmental Medicine, Karolinska Institute, Stockholm, Sweden; 46Department of Family Medicine, University ofVirginia, Charlottesville, Virginia; 47Nutrition and Metabolism Section, International Agency for Research on Cancer, WorldHealth Organization, Lyon, France; 48Institute of Cancer Research, Department of Medicine I, Medical University of Vienna,Vienna, Austria; 49Center for Gastrointestinal Biology and Disease, University of North Carolina, Chapel Hill, North Carolina;50Division of Human Genetics, Department of Internal Medicine, The Ohio State University Comprehensive Cancer Center,Columbus, Ohio; 51Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, UK; 52Gastroenterology Department,Hospital Clínic, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Centro de Investigación Biomédica en Redde Enfermedades Hepáticas y Digestivas (CIBEREHD), University of Barcelona, Barcelona, Spain; 53Department of MolecularBiology of Cancer, Institute of Experimental Medicine of the Czech Academy of Sciences, Prague, Czech Republic; 54Instituteof Biology and Medical Genetics, First Faculty of Medicine, Charles University, Prague, Czech Republic; 55Faculty of Medicineand Biomedical Center in Pilsen, Charles University, Pilsen, Czech Republic; 56Division of Human Nutrition and Health,Wageningen University & Research, Wageningen, The Netherlands; 57Department of Medicine, University of North CarolinaSchool of Medicine, Chapel Hill, North Carolina; 58Colorectal Oncogenomics Group, Department of Clinical Pathology, TheUniversity of Melbourne, Parkville, Victoria, Australia; 59Genomic Medicine and Family Cancer Clinic, The Royal MelbourneHospital, Parkville, Victoria, Australia; 60University of Melbourne Centre for Cancer Research, Victorian Comprehensive CancerCentre, Parkville, Victoria, Australia; 61Hellenic Health Foundation, Athens, Greece; 62Centre de Recherche en Épidémiologie etSanté des Populations (CESP, Inserm U1018), Facultés de Médecine, Université Paris-Saclay, Gustave Roussy, Villejuif,France; 63Department of Epidemiology, Regional Health Council, IMIB-Arrixaca, Murcia University, Murcia, Spain; 64EscuelaAndaluza de Salud Pública, CIBER de Epidemiología y Salud Pública, Granada, Spain; 65Cancer Risk Factors and Life-StyleEpidemiology Unit, Institute for Cancer Research, Prevention and Clinical Network - ISPRO, Florence, Italy; 66Section ofNutrition and Metabolism, International Agency for Research on Cancer, Lyon, France; 67School of Public Health, ImperialCollege London, London, UK; 68Institute for Health Research, Kaiser Permanente Colorado, Aurora, Colorado; 69Department ofCancer Biology and Genetics, Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio; 70Menzies Institutefor Medical Research, University of Tasmania, Hobart, Tasmania, Australia; 71SWOG Statistical Center, Fred HutchinsonCancer Research Center, Seattle, Washington; 72Department of Epidemiology, School of Public Health and Institute of Healthand Environment, Seoul National University, Seoul, South Korea; 73Ontario Institute for Cancer Research, Toronto, Ontario,Canada; 74Nuffield Department of Population Health, University of Oxford, Oxford, UK; 75Department of Epidemiology, JohnsHopkins Bloomberg School of Public Health, Baltimore, Maryland; 76Department of Community Medicine and Epidemiology,Lady Davis Carmel Medical Center, Haifa, Israel; 77Ruth and Bruce Rappaport Faculty of Medicine, Technion-Israel Institute ofTechnology, Haifa, Israel; 78Clalit National Cancer Control Center, Haifa, Israel; 79Department of Population and QuantitativeHealth Sciences, Case Western Reserve University, Cleveland, Ohio; 80Division of Cancer Control and Population Sciences,National Cancer Institute, Bethesda, Maryland; 81Division of Laboratory Genetics, Department of Laboratory Medicine andPathology, Mayo Clinic, Rochester, Minnesota; 82Department of Public Health Solutions, National Institute for Health andWelfare, Helsinki, Finland; 83Medical Faculty, University of Heidelberg, Heidelberg, Germany; 84Department of Medicine,Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, California; 85Biomedicine Institute(IBIOMED), University of León, León, Spain; 86The Clinical Epidemiology Unit, Memorial University Medical School, St. John’s,Newfoundland, Canada; 87Division of Medical Oncology, Norris Comprehensive Cancer Center, Keck School of Medicine,University of Southern California, Los Angeles, California; 88Instituto de Investigación Sanitaria Galicia Sur (IISGS), Xerencia de

1276 Archambault et al Gastroenterology Vol. 158, No. 5

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Xestion Integrada de Vigo-SERGAS, Oncology and Genetics Unit, Vigo, Spain; 89Genomic Medicine Group, GalicianFoundation of Genomic Medicine, Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS), Complejo HospitalarioUniversitario de Santiago, SERGAS, Santiago de Compostela, Spain; 90Moores Cancer Center, University of California SanDiego, La Jolla, California; 91Radiotherapy Department, Complejo Hospitalario Universitario de Vigo, SERGAS, Vigo, Spain;92Genentech, Inc., Basel, Switzerland; 93Epidemiology Program, University of Hawaii Cancer Center, Honolulu, Hawaii;94Department of Epidemiology, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire; 95Wallenberg Centre forMolecular Medicine, Umeå University, Umeå, Sweden; 96Department of Radiation Sciences, Oncology Unit, Umeå University,Umeå, Sweden; 97Department of Medicine, New York University School of Medicine, New York, New York; 98Center for PublicHealth Genomics, University of Virginia, Charlottesville, Virginia; 99Department of Health Science Research, Mayo Clinic,Scottsdale, Arizona; 100Lunenfeld Tanenbaum Research Institute, Mount Sinai Hospital, University of Toronto, Toronto, Ontario,Canada; 101School of Public Health, University of Washington, Seattle, Washington; 102Center for Precision Medicine &Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, California;103Department of Medicine and Epidemiology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania; and104Department of Biostatistics, University of Washington, Seattle, Washington

See Covering the Cover synopsis on page 1182.

§ Authors share co-senior authorship

Abbreviations used in this paper: CI, confidence interval; CRC, colorectalcancer; GWAS, genome-wide association studies; OR, odds ratio; PRS,polygenic risk score; RPGEH, Research Program on Genes, Environmentand Health; SD, standard deviation; SNP, single nucleotide polymorphism.

Most current article

© 2020 by the AGA Institute0016-5085/$36.00

https://doi.org/10.1053/j.gastro.2019.12.012

BACKGROUND & AIMS: Early-onset colorectal cancer (CRC, inpersons younger than 50 years old) is increasing in incidence;yet, in the absence of a family history of CRC, this populationlacks harmonized recommendations for prevention. We aimedto determine whether a polygenic risk score (PRS) developedfrom 95 CRC-associated common genetic risk variants wasassociated with risk for early-onset CRC. METHODS: Westudied risk for CRC associated with a weighted PRS in 12,197participants younger than 50 years old vs 95,865 participants50 years or older. PRS was calculated based on single nucleo-tide polymorphisms associated with CRC in a large-scalegenome-wide association study as of January 2019. Partici-pants were pooled from 3 large consortia that provided clinicaland genotyping data: the Colon Cancer Family Registry, theColorectal Transdisciplinary Study, and the Genetics andEpidemiology of Colorectal Cancer Consortium and were all ofgenetically defined European descent. Findings were replicatedin an independent cohort of 72,573 participants. RESULTS:Overall associations with CRC per standard deviation of PRSwere significant for early-onset cancer, and were strongercompared with late-onset cancer (P for interaction ¼ .01);when we compared the highest PRS quartile with the lowest,risk increased 3.7-fold for early-onset CRC (95% CI 3.28–4.24)vs 2.9-fold for late-onset CRC (95% CI 2.80–3.04). This asso-ciation was strongest for participants without a first-degree

family history of CRC (P for interaction ¼ 5.61 � 10–5). Whenwe compared the highest with the lowest quartiles in thisgroup, risk increased 4.3-fold for early-onset CRC (95% CI3.61–5.01) vs 2.9-fold for late-onset CRC (95% CI 2.70–3.00).Sensitivity analyses were consistent with these findings.CONCLUSIONS: In an analysis of associations with CRC perstandard deviation of PRS, we found the cumulative burden ofCRC-associated common genetic variants to associate withearly-onset cancer, and to be more strongly associated withearly-onset than late-onset cancer, particularly in the absenceof CRC family history. Analyses of PRS, along with environ-mental and lifestyle risk factors, might identify younger in-dividuals who would benefit from preventive measures.

Keywords: Colon Cancer; SNP; Penetrance; EOCRC.



olorectal cancer (CRC) incidence and mortality have
WHAT YOU NEED TO KNOW
BACKGROUND AND CONTEXT

Although early-onset colorectal cancer (CRC, in personsyounger than 50 years old) is increasing in incidence;yet, in the absence of a family history of CRC, thispopulation lacks harmonized recommendations forprevention.

NEW FINDINGS

In an analysis of associations with CRC per standarddeviation of polygenic risk score (PRS), we found thecumulative burden of CRC-associated common geneticvariants to associate with early-onset cancer, and to bemore strongly associated with early-onset than late-onset cancer—particularly in the absence of CRC familyhistory.

LIMITATIONS

Further studies are needed to identify genetic factorsassociated with early-onset CRC.

IMPACT

Analyses of PRS, along with environmental and lifestylerisk factors, might identify younger individuals whowould benefit from preventative measures.

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Cbeen declining in the United States over the pastseveral decades.1 These reductions are largely attributed tosuccesses in CRC early detection, surveillance, and treat-ment for this disease.2,3 In contrast to these overall trends,the incidence of CRC in individuals younger than 50 years(early-onset disease) has been increasing in the UnitedStates and elsewhere4: early-onset CRC incidence in theUnited States has increased by an average of 1.8% annuallyfrom 1992 to 2012, and is projected to account for 10% to25% of newly diagnosed CRC by 2030.1,5–10 Furthermore,early-onset CRC tends to present with higher pathologicgrade, distant disease, and a greater incidence of recurrenceand metastatic disease.5 In response to this newly recog-nized disease burden, the US Preventive Services TaskForce,11 the American Cancer Society,12 the U.S. Multi-Society Task Force on Colorectal Cancer,13 and other pro-fessional bodies14 have initiated discussions on the merits ofrevising recent consensus CRC prevention guidelines toinclude early detection of average-risk individuals youngerthan 50 years. Although the American Cancer Society rec-ommends lowering the screening age to 45 years for in-dividuals at average risk,12 others recommend targetingonly high-risk groups for early detection.13,15

Weighing against the potential benefits of CRC earlydetection and prevention programs targeted to those agedyounger than 50 years are concerns about adverse sideeffects and associated costs.14,16 New approaches to dis-ease prevention in younger adults are warranted, andassessing germline genetic variants, along with otherknown risk factors, could facilitate tailored early detectionof high-risk individuals due to their genetic makeup andlifestyle. To date, genetic research on factors associatedwith early-onset CRC has been limited largely to the raremonogenic, high-penetrance genetic syndromes associatedwith this disease in high-risk families, while the frequentlyoccurring low-penetrance polymorphisms have beenunderstudied.

Here, we report on CRC risks for early- (<50 years ofage) and late-onset disease (�50 years of age) associatedwith a polygenic risk score (PRS) developed from 95 com-mon genetic risk variants identified in previous CRCgenome-wide association studies (GWAS). Our researchprovides the first substantive evidence that early-onset CRCexhibits differential genetic risks, compared with late-onsetdisease, due to low-penetrance, common genetic poly-morphisms. The findings of our research may contribute tothe identification of individuals susceptible to early-onsetCRC for tailored early detection or other preventiveinterventions.

MethodsStudy Participants

We studied 108,062 participants in the discovery dataset,including 50,023 CRC cases and 58,039 controls. Participantsfor this study were pooled from 3 large consortia that providedclinical and genotyping data: the Colon Cancer Family Registry,the Colorectal Transdisciplinary Study, and the Genetics and

Epidemiology of Colorectal Cancer Consortium (Table 1 andSupplementary Table 1) (for additional study information, seeearlier publications17–20). All analyses were restricted to par-ticipants of genetically defined European descent. Family his-tory of CRC was ascertained through self-report or interviewer-administered questionnaire, and defined as having 1 or morefirst-degree relatives with CRC. Participant recruitment acrossall studies occurred between the 1990s and the early 2010s. Allstudy participants provided written informed consent andstudies were approved by their respective institutional reviewboards (see Supplementary Information).

Genotyping and Single Nucleotide PolymorphismSelection

We included 95 CRC-risk-associated single nucleotidepolymorphisms (SNPs) that reached genome-wide significance(P � 5 � 10–8), in large-scale GWAS, as of January 2019. Nonew discovery of CRC-related SNPs was carried out here. In-dividual participant and genotype data for the 95 SNPs wereextracted from GWAS and imputed to the Haplotype ReferenceConsortium panel, which provides high-quality, accurateimputation for variants with a minor allele frequency as low as0.1%.21 For details, see Huyghe et al.17 Additional informationon SNPs can be located in Supplementary Table 2.

Statistical AnalysisFor cases and controls, we compared baseline participant

characteristics between individuals who had a reference age of<50 years with those with a reference age of �50 years. Forcases, reference age was defined as the age of diagnosis of firstprimary CRC. For controls, reference age was defined as the ageat selection.

Genotyped SNPs were coded as 0, 1, or 2 copies of the riskallele. Imputed SNPs were coded for the expected number of

Table 1.Baseline Study Characteristics of the Discovery and Replication Datasets

Discoverydataset

Replicationdataset

Cases(n ¼ 50,023)

Controls(n ¼ 58,039)

Allparticipants

CRCcases

<50 y old �50 y old <50 y old �50 y old Eligible cohort CRC cases <50 y old �50 y old

N 5479 44,544 6718 51,321 72,573 1093 25 1068Age, mean (SD) 43.1 (5.6) 66.5 (8.7) 41.3 (7.2) 65.3 (8.3) 71.5 (13.1) 73.1 (10.8) 45.2 (3.3) 73.7 (10.1)Sex, n (%)

Male 2767 (50.5) 24,145 (54.2) 3272 (48.7) 26,886 (52.4) 30,160 (41.6) 526 (48.1) 9 (36.0) 517 (48.4)Female 2706 (49.4) 20,336 (45.7) 3446 (51.3) 24,435 (47.6) 42,413 (58.4) 567 (51.9) 16 (64.0) 551 (51.6)Missing 6 (0.1) 63 (0.1) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0)

Family History of CRC, n (%)Yes 944 (17.2) 5558 (12.5) 578 (8.6) 5330 (10.4) 6956 (9.6) 204 (18.7) 7 (28.0) 197 (18.4)No 3159 (57.7) 24,028 (53.9) 4130 (61.5) 28,317 (55.2) 65,617 (90.4) 889 (81.3) 18 (72.0) 871 (81.6)Missing 1376 (25.1) 14,958 (33.6) 2010 (29.9) 17,674 (34.4) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0)

Tumor site, n (%)Proximal colon 1231 (22.5) 12,978 (29.1) – – – – – –

Distal colon 1442 (26.3) 12,036 (27.0) – – – – – –

Rectum 1920 (35.0) 12,918 (29.0) – – – – – –

Missing 886 (16.2) 6612 (14.8) – – – – – –

PRS, n (%)Quartile 1 693 (12.6) 6227 (14.0) 1659 (24.7) 12,863 (25.1) 18,175 (25.0) 163 (14.9) 2 (8.0) 161 (15.1)Quartile 2 1048 (19.1) 8824 (19.8) 1666 (24.8) 12,848 (25.0) 18,150 (25.0) 232 (21.2) 4 (16.0) 228 (21.3)Quartile 3 1396 (25.5) 11,877 (26.7) 1674 (24.9) 12,824 (25.0) 18,132 (25.0) 287 (26.3) 7 (28.0) 280 (26.2)Quartile 4 2342 (42.7) 17,616 (39.5) 1719 (25.6) 12,786 (24.9) 18,116 (25.0) 411 (37.6) 12 (48.0) 399 (37.4)


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copies of the risk allele, as imputed dosages. Potential populationsubstructure within the Genetics and Epidemiology of ColorectalCancer Consortium, Colon Cancer Family Registry, and Colo-rectal Transdisciplinary Study was accounted for throughadjustment by principal components (PCs) of genetic ancestry.To develop the weighted PRS, we used log-odds ratios derivedfrom the literature for 55 of the SNPs, and for the remaining 40SNPs that were first identified within this discovery dataset, wecomputed log-odds ratios from a regression model fit with CRCas the outcome (1 vs 0) and the following independent variables:95 SNPs, age (in years), sex, principal components, and genotypeplatform. For the 40 SNPs identified within this discoverydataset, we then implemented a conservative winner’s curseadjustment of the log-odds ratios from the risk model, usingZhong and Prentice’s approach.22 We then weighted the PRS forindividuals, by multiplying the number of risk alleles for eachSNP by their adjusted log-odds ratios, summing and recoding asa percentile based on the distribution in the controls. The finalPRS was modeled as a continuous variable per 1 standard de-viation (SD), transformed to the standard normal distribution.Odds ratios (ORs) and 95% confidence intervals (CIs) were alsoestimated comparing quartiles of PRS.

We used unconditional logistic regression to assess theassociation between the PRS and CRC for those with a referenceage <50 years and for those with a reference age �50 years. Allmodels additionally included sex, reference age in years, prin-cipal components, and genotype platform. Further adjustmentby study was not warranted, as extensive genome-wide ana-lyses with and without adjusting for study have been con-ducted, with the results being consistent.17 To test fordifferences in associations across age, an interaction term was

included for age category (<50, �50) and PRS (continuous).Models were also examined separately by first-degree familyhistory of CRC. We evaluated the discriminatory accuracy of therisk prediction models by calculating the area under thereceiver operating characteristic curve for 5-year diagnosticage groups, adjusting for sex, PCs, and genotype platform, usingthe adjusted.ROC function from the R Package ROCt.

For the larger group with no first-degree family history ofCRC, additional subgroup analyses were performed includingestimation of CRC risk within specific reference age groups (15–39, 40–49, 50–59, 60–69, and 70–79 years) and by disease site(proximal colon, distal colon, and rectum). The interaction termused to assess differences in associations across age categoriesconsisted of age as a continuous variable and PRS (continuous).Multinomial logistic regression was used to assess risk differ-entials by disease site within age strata. Analyses werecompleted using the R statistical software program version 3.5.1.

Sensitivity AnalysesReplication accounting for cases with Lynch

syndrome. Screening of CRC cases for the presence of Lynchsyndrome was systematically carried out for CRC casesrecruited through the Ohio State University Medical Center(Supplementary Table 1: hereditary nonpolyposis colorectalcancer, Ohio Colorectal Cancer Prevention Initiative, and OhioState University Medical Center) as described in detail else-where.23–25 All cases were screened for mismatch repair defi-ciency using immunohistochemical analysis. Cases withprobable characteristics of Lynch syndrome were subjected toadditional genetic testing for conclusively determining a


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diagnosis of Lynch syndrome based on the presence of one ormore germline high-penetrance mutations in DNA mismatchrepair genes (MLH1, MSH2, MSH6, PMS2) or the EPCAM gene.

Using unconditional logistic regression in these studies, weevaluated the association between the PRS and CRC for thoseaged <50 years and for those �50 years, with consideration ofLynch syndrome status among cases. All models additionallyincluded sex, reference age in years, and principal components.To test for differences in associations across age, an interactionterm was included for age category (<50, �50) and PRS(continuous).

Replication in an independent cohort. To indepen-dently replicate the association of this PRS with younger andolder-onset CRC, we studied all 72,573 participants of Euro-pean ancestry who were genotyped in the Research Program onGenes, Environment and Health (RPGEH), a cohort comprisedof Kaiser Permanente Northern California health plan mem-bers.26,27 This cohort was not included in the discovery of anyof the 95 CRC genetic risk variants. Cancer history was deter-mined from initiation of health plan membership by linkage tothe Kaiser Permanente Northern California Cancer Registry,which adheres to the National Cancer Institute’s Surveillance,Epidemiology, and End Results Program standards.

Family history of CRC, defined as having 1 or more first-degree relatives with CRC, was ascertained through a baselinestudy questionnaire, electronic family history data in the med-ical records, and International Classification of Diseases codesZ80.0 (Family history of malignant neoplasm of digestive or-gans) and V16.0 (Cancer family history, gastrointestinal tract).Analyses were restricted to participants of genetically definedEuropean descent. All study participants provided writteninformed consent, and the study was approved by the KaiserPermanente Northern California Institutional Review Board.

RPGEH biospecimens were genotyped using the AffymetrixAxiom platform. Details on the calling and quality control canbe found elsewhere.28 Consistent with genetic data in the dis-covery set, we imputed the genotyped data to the HaplotypeReference Consortium. To develop the PRS for this replication,we used 94 SNPs from the discovery dataset, as describedpreviously, and, for 1 unmatched SNP (rs755229494), weincluded the best available surrogate (rs112334046, R2 ¼ 0.40,minor allele frequency ¼ 0.0026).

For the longitudinal replication cohort, we used Cox pro-portional hazards models to assess the association of PRS withCRC, which was not feasible for the discovery dataset because itincluded case-control data. The coefficients from the model fitwith 95 SNPs in the discovery dataset were used to fit the PRSin the replication analysis, thereby reducing potential foroverfitting. The observed time was defined from the age ofinitial Kaiser Permanente Northern California enrollment to theearliest of age at CRC diagnosis, death, or end of follow-up(the RPGEH cohort was followed until December 31, 2016).The replication models also included sex and principal com-ponents to account for potential population substructure. Es-timates of absolute risk are inferred using Kaplan-Meier plotsproduced using RPGEH data.

ResultsEarly-onset CRC cases (n ¼ 5,479) had a mean age at

diagnosis of 43.1 years, and the older-onset cases(n ¼ 44,544) had a mean age at diagnosis of 66.5 years

(Table 1). Men and women were approximately equallyrepresented across cases and controls. A first-degree familyhistory of CRC, among those ascertained for family history,was reported for 17.2% of early-onset and 12.5% for late-onset CRC cases, and, respectively, for 8.6% of youngerand 10.4% for older controls. Family history informationwas missing for >25% of participants; all of whom werefrom 9 studies that did not query participants on familyhistory and therefore were not included in our familyhistory–specific analyses. Younger-onset cases tended tohave fewer proximal colon tumors and a greater prepon-derance of tumors in the rectum. Both early-onset and late-onset CRC cases showed marked skewing toward higherPRS values compared with controls, when representedas quartiles (Table 1) and as a continuous score(Supplementary Figure 1).

We found that associations with risk for CRC per SD ofPRS were significant among participants <50 years of age,and were stronger compared with participants aged �50years (P for interaction ¼ 0.01). Contrasting the highest PRSquartile with the lowest, risks were 3.7-fold higher (OR3.73; 95% CI 3.28–4.24) for early-onset CRC and 2.9-foldhigher (OR 2.92; 95% CI 2.80–3.04) for late-onset disease(Table 2 and Figure 1A). For the larger group of participantswho reported a negative first-degree family history of CRC,PRS-associated risks for CRC among participants aged <50years were also stronger than those for individuals aged�50 years (P for interaction ¼ 5.61 � 10–5); riskscomparing the highest with the lowest quartile of PRS were4.3-fold (OR 4.26; 95% CI 3.61–5.01) for early-onset CRCand 2.9-fold (OR 2.85; 95% CI 2.70–3.00) for late-onsetdisease (Table 2 and Figure 1B). In contrast, for thesmaller group of participants who reported a positive first-degree family history of CRC, risks per SD of PRS tended tobe greater for older individuals (P for interaction ¼ 0.003);risks in the highest quartile for PRS were 1.7-fold (OR 1.70;95% CI 1.17–2.47) for early-onset CRC, and 2.5-fold (OR2.47; 95% CI 2.18–2.79) for late-onset disease (Table 2 andFigure 1C). The discriminatory capabilities for prediction(i.e., area under the curve) of these models across the entireage spectrum tended to be highest for early-onset in-dividuals without a family history of CRC, ranging from 0.64to 0.65 (Supplementary Table 3).

As the PRS displayed the strongest association for early-onset CRC without a first-degree family history, we investi-gated whether certain subgroups could account for thesestrong effects. When stratified further by age at diagnosis,CRC risks were 1.7-fold (OR per SD of PRS 1.74; 95% CI 1.55–1.96) for those diagnosed aged 15 to 39 years and 1.8-fold(OR per SD of PRS 1.75; 95% CI 1.64–1.87) for those diag-nosed aged 40 to 49 years of age. For participants diagnosedat �50 years of age, the related CRC risks were 1.6-fold (ORper SD of PRS 1.60; 95% CI 1.54–1.67) for participants aged50 to 59 years, 1.5-fold (OR per SD of PRS 1.52; 95% CI 1.48–1.57) for individuals 60 to 69 years old, and 1.4-fold (OR perSD of PRS 1.44; 95% CI 1.39–1.49) for those diagnosed be-tween 70 and 79 years, with age and PRS exhibiting statis-tical interaction across the entire study age range(Supplementary Table 4, P for interaction ¼ 3.44 � 10–10).

Table 2.Risk Estimates for Early-Onset vs Late-Onset CRC Associated With a 95-SNP PRS in the Discovery Dataset

PRS n (cases) n (controls) OR (95% CI) P valueP value forinteractiona

All subjects 0.0137<50 y old

per 1 SD 5479 6718 1.64 (1.57–1.72) 6.00E-107Quartile 1 (ref) 693 1659 1.00Quartile 2 1048 1666 1.64 (1.43–1.89) 2.07E-12Quartile 3 1396 1674 2.19 (1.91–2.50) 2.17E-30Quartile 4 2342 1719 3.73 (3.28–4.24) 1.13E-89

�50 y oldper 1 SD 44,544 51,321 1.52 (1.50–1.54) < 2.23E-308Quartile 1 (ref) 6227 12,863 1.00Quartile 2 8824 12,848 1.45 (1.39–1.51) 8.55E-62Quartile 3 11,877 12,824 1.95 (1.87–2.03) 1.37E-208Quartile 4 17,616 12,786 2.92 (2.80–3.04) < 2.23E-308

Negative family history 5.61E-05<50 y old


�50 y oldper 1 SD 24,028 28,317 1.50 (1.47–1.53) < 2.23E-308Quartile 1 (ref) 3529 7341 1.00Quartile 2 4869 7083 1.44 (1.36–1.53) 1.85E-36Quartile 3 6494 7058 1.92 (1.82–2.03) 6.17E-119Quartile 4 9136 6835 2.85 (2.70–3.00) < 2.23E-308

Positive family history 0.0028<50 y old

per 1 SD 944 578 1.19 (1.05–1.35) 0.0063Quartile 1 (ref) 133 105 1.00Quartile 2 203 133 1.58 (1.05–2.36) 0.0265Quartile 3 208 152 1.22 (0.82–1.83) 0.3277Quartile 4 400 188 1.70 (1.17–2.47) 0.0052

�50 y oldper 1 SD 5558 5330 1.42 (1.36–1.48) 7.02E-57Quartile 1 (ref) 690 1134 1.00Quartile 2 1037 1264 1.42 (1.24–1.63) 5.85E-07Quartile 3 1478 1343 1.81 (1.59–2.07) 8.44E-19Quartile 4 2353 1589 2.47 (2.18–2.79) 2.70E-45

NOTE. The logistic regression models include age, sex, principal components, genotype platform, and polygenic risk score.aP value produced from interaction term with continuous PRS (per SD) and age (<50 vs �50 years).


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Furthermore, as found for all cancer sites (Table 2 andFigure 1), the PRS was also more strongly associated withrisks for early-onset, compared with late-onset, cancers ofthe proximal colon, distal colon, and rectum (SupplementaryTable 5 and Supplementary Figure 2), with the greatest riskdifferentials observed for cancers of the distal colon andrectum (Supplementary Table 6).

Sensitivity AnalysesReplication accounting for cases with Lynch

syndrome. A total of 37 Lynch cases <50 years of age(6.4%, among 574 cases) and 54 Lynch cases �50 years ofage (2.1%, among 2525 cases) were identified in the Ohio-based studies. Removing Lynch cases from the analysisdemonstrated that the relatively small number of thesecases did not substantially impact the relationship of PRS

with CRC (Table 3). After exclusion of Lynch cases, risks forearly-onset CRC per SD of PRS remained similarly increasedin participants <50 years of age (OR per SD of PRS 1.82;95% CI 1.61–2.06) and were greater compared with par-ticipants aged �50 years (OR per SD of PRS 1.49; 95% CI1.39–1.60; P for interaction ¼ 0.01). These trends heldparticularly for participants who reported a negative first-degree family history of CRC (aged <50 years, OR per SDof PRS 1.83; 95% CI 1.60–2.09; aged �50 years, OR per SDof PRS 1.46; 95% CI 1.35–1.57; P for interaction ¼ 0.01).

Replication in an independent cohort. In RPGEH,early-onset CRC cases (n ¼ 25) had a mean age of 45.2years, whereas the older-onset cases (n ¼ 1068) had a meanage of 73.7 years (Table 1). More women participated thanmen. A first-degree family history of CRC was reported for28.0% of early-onset and 18.4% of late-onset CRC cases,


compared with 9.6% for the cohort overall. Consistent withthe discovery dataset, the distributions of PRS for bothearly- and late-onset CRC cases were skewed toward higherPRS quartiles compared with controls. Right-censoring was

Figure 1. Risk estimatesfor early-onset versus late-onset CRC associatedwith a 95-SNP PRS in thediscovery dataset. (A)Model includes all studyparticipants regardless offirst-degree family historyof CRC. (B) Model includesstudy participants withouta first-degree family his-tory of CRC. (C) Model in-cludes study participantswith a first-degree familyhistory of CRC. Modelswere adjusted for age, sex,principal components, ge-notype platform, and PRS.The interaction P valuereported was producedfrom a model including aninteraction term with acontinuous PRS (per SD)and age (<50 years vs�50 years).

due to either death (15%, n ¼ 11,165) or loss to follow-up(1%, n ¼ 735).

Hazard ratio estimates for PRS and CRC in the inde-pendent replication (Table 4) were consistent with findings

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Table 3.Risk Estimates for Early-Onset vs Late-Onset CRC Associated With a 95-SNP PRS Among Participants With andWithout Lynch Syndrome, in the Ohio Cohort

PRS per 1 SD n (cases) n (controls) OR (95% CI) P value P value for interactiona

Including Lynch and non-Lynch casesAll subjects 0.0369<50 y old 574 979 1.73 (1.54–1.95) 1.39E-19�50 y old 2525 1463 1.47 (1.37–1.58) 1.77E-28

Negative family history 0.0106<50 y old 449 931 1.81 (1.59–2.07) 9.64E-19�50 y old 1885 1271 1.45 (1.34–1.56) 1.16E-21

Positive family history 0.1517<50 y old 106 48 1.28 (0.84–1.97) 0.2530�50 y old 565 192 1.55 (1.30–1.84) 1.12E-06

Excluding Lynch casesAll subjects 0.0149<50 y old 537 979 1.82 (1.61–2.06) 2.63E-21�50 y old 2471 1463 1.49 (1.39–1.60) 1.11E-29

Negative family history 0.0107<50 y old 438 931 1.83 (1.60–2.09) 7.50E-19�50 y old 1856 1271 1.46 (1.35–1.57) 4.30E-22

Positive family history 0.5627<50 y old 80 48 1.53 (0.98–2.41) 0.0635�50 y old 540 192 1.61 (1.34–1.92) 2.34E-07

NOTE. The logistic regression models include age, sex, principal components, and polygenic risk score.aP value produced from interaction term with continuous PRS (per SD) and age (<50 vs �50 years).


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from the discovery dataset (Table 2), overall (aged <50years, HR per SD of PRS 1.73; 95% CI 1.17–2.56; aged �50years, HR per SD of PRS 1.43; 95% CI 1.34–1.51) and forindividuals who reported a negative first-degree familyhistory of CRC (aged <50 years, HR per SD of PRS 1.76; 95%CI 1.11–2.78; aged �50 years, HR per SD of PRS 1.42; 95%CI 1.33–1.52). Although the effects seen for younger andolder individuals were consistent with our primary analysis,the specific evaluation of whether these effects differ by age(<50 vs �50 years) was underpowered in RPGEH, due tothe limited number of early-onset CRC cases in this cohort.

Table 4.Risk Estimates for Early-Onset vs Late-Onset CRC Ass

PRS n in eligible cohort n (cases)

All subjects<50 y old

per 1 SD 26,983 25 1�50 y old

per 1 SD 67,792 1068 1Negative family history<50 y old

per 1 SD 24,472 18 1�50 y old

per 1 SD 61,129 871 1Positive family history<50 y old

per 1 SD 2511 7 1�50 y old

per 1 SD 6668 202 1

NOTE. The Cox models include sex, principal components, andHR, hazard ratio.aP value produced from interaction term with continuous PRS

Numbers of early-onset CRC among individuals with a first-degree family history of CRC in the replication dataset weretoo few for a meaningful interpretation of the analysis.Kaplan-Meier survival plots, stratified by family history, aredisplayed in Figure 2, consistent with the hypothesized PRS-related probability gradients across the full age range.

DiscussionOur study, including more than 50,000 CRC cases and

50,000 controls, demonstrated that a PRS, derived from

ociated With a 95-SNP PRS in the RPGEH Replication Cohort

HR (95% CI) P value P value for interactiona

0.3291

.73 (1.17–2.56) 0.0056

.43 (1.34–1.51) 2.77E-310.3681

.76 (1.11–2.78) 0.0161

.42 (1.33–1.52) 2.85E-250.6920

.56 (0.75–3.26) 0.2334

.34 (1.17–1.54) 2.87E-05

polygenic risk score.

(per SD) and age (<50 versus �50 years).

Figure 2. Absolute riskestimates of being diag-nosed with CRC acrossthe age stratum by PRSpercentile among in-dividuals in the RPGEHcohort. (A) Among in-dividuals with a first-degree relative with CRC.(B) Among individualswithout a family history ofCRC.


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common genetic variants, successfully identifies partici-pants at increased risk for early-onset CRC, particularlyamong individuals without a family history of CRC; inaddition, the PRS was more strongly associated with early-onset cancer compared with late-onset CRC. ThePRS-associated risks were found for early-onset cancer ofthe proximal and distal colon, and the rectum, with a modestincreased propensity for the nonproximal cancers. Weconfirmed the overall findings for early-onset CRC in asubstudy from Ohio, where Lynch syndrome cases wereexcluded from the analysis. The results from these case-control studies were also supported by a smaller, prospec-tive study that showed increased PRS-associated risks forearly-onset CRC, particularly in those negative for CRCfamily history. Our findings may have important clinicalrelevance, as they could contribute, along with other life-style and environmental risk factors, to tailored screening inpeople aged <50 years who are currently not targeted forearly detection and for whom CRC rates have increased overthe past decades.

The development of a PRS to evaluate the overallpredictive power of common risk loci for CRC has previ-ously been carried out29–31; however, few studies evalu-ated specifically for association of common

polymorphisms with early-onset CRC.32–36 These smallerstudies, involving 10 to 33 SNPs, pointed to some indi-vidual loci differentially associated with early-onset CRC;however, our much larger study, which included 95 lociidentified from GWAS (Supplementary Table 2), showedthat risks related to an individual’s cumulative genetic riskprofile for at-risk alleles, as reflected in the PRS, weremuch greater than the contributions of individual SNPs. Acaveat to using these 95 variants in a PRS intended fordiscriminating early-onset CRC risk is that they are pro-duced from GWAS analyses not specific to early-onsetdisease; adequately powered GWAS analyses specific forearly-onset CRC have yet to be performed. Therefore,although our PRS positively identifies those at heightenedrisk for early-onset CRC, there is still room for improvingits discriminatory accuracy. Furthermore, combining agenetic PRS with lifestyle and environmental risk factorscould potentially contribute to even greater precision inidentification of individuals who may benefit from earlier-onset CRC screening.37

Given that early-onset CRC is increasing in incidence andis commonly diagnosed at later stages, which carries apoorer prognosis, recommendations have been made tolower the screening age to 45 for individuals at average


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risk.12 Consideration of early detection for early-onsetcancer is dependent, however, on a number of factors,including differentials in CRC risk in absolute terms, pro-jected benefits, potential harms such as colonic perforation,and costs; therefore, potentially tempering some enthu-siasm for lowering the CRC screening age and calling foridentification of high-risk groups for more targeted earlydetection.16,38,39 Our study highlights the potential utility ofa PRS in CRC risk stratification for people <50 years of age,which might inform precision cancer screening in thispopulation that currently lacks consistent early detectionrecommendations, particularly for those without a familyhistory of CRC.

This study is unique in the large size of the study popu-lation, particularly for those <50 years of age, allowing forevaluation of PRS-related risks overall, and by family history,refined age groups, and tumor site. Major results for associ-ation of the PRS with early-onset cancer were also replicatedin an independent community-based cohort, although thenumber of early-onset cases in that cohort was limited.Limitations of our study include the lack of CRC family historyinformation on a substantial subset of study participants;however, missingness was defined by study and thereforeunlikely to introduce bias. Also, our PRS was generated andvalidated in individuals of European ancestry, currentlylimiting its applicability for different ancestral groups, until aPRS is developed and validated in diverse populations.Another limitation is that we did not systematically take intoaccount the genetic mutations related to Lynch and otherrarer hereditary cancer syndromes23,34,40–42; however, oursensitivity analysis, in the Ohio investigations where this in-formation was systematically assessed, indicated that risksassociated with PRS remained very similar after the removalof Lynch cases from the analysis. Nevertheless, furtherresearch is needed on the combined utility for risk predictionof rare and common variants in thosewith orwithout a familyhistory of CRC, as it can be expected that accounting for bothPRS and high-penetrance genes will further improve riskstratification.43,44 There remains more to be discoveredabout the genetics of CRC, particularly for early-onset disease,as substantial heritability for CRC remains unexplained andgenetic effects are typically stronger for early-onset die-sase.45,46 As more risk loci will be discovered, the predictivepower of the PRS is expected to further improve, and to betested in clinical trials.

In conclusion, we demonstrated that a PRS, derived fromcommon genetic variants, successfully stratifies individualsfor early-onset CRC based on genetic risk, particularly amongindividuals who report a negative first-degree family historyof CRC. Furthermore, the associations between the PRS andCRC are greater for young-onset than for older-onset disease.The PRS may contribute, along with lifestyle and environ-mental risk profiling, toward prioritizing individuals atincreased susceptibility to early-onset CRC for personalizedscreening regimens or other intervention strategies. Early-onset CRC is increasing in the United States and elsewhere;by selecting high-risk individuals <50 years of age, we canreduce the burden on early detection programs and poten-tially provide more individualized prevention approaches.

Supplementary MaterialNote: To access the supplementary material accompanyingthis article, visit the online version of Gastroenterology atwww.gastrojournal.org, and at https://doi.org/10.1053/j.gastro.2019.12.012.

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Received July 18, 2019. Accepted December 9, 2019.

CorrespondenceAddress correspondence to: Richard B. Hayes, DDS, PhD, NYU LangoneHealth, 180 Madison Avenue, Room 415, New York, New York 10016.e-mail: [email protected]; or Ulrike Peters, PhD, MPH, FredHutchinson Cancer Research Center, 1100 Fairview Avenue N, M4-B402,Seattle, Washington 98109–1024. e-mail: [email protected].

AcknowledgmentsAuthor Contributions: Alexi N Archambault: Study concept and design,Analysis and interpretation of data, Drafting of the manuscript, Criticalrevision of the manuscript for important intellectual content, Statisticalanalysis.Yu-Ru Su: Study concept and design, Analysis and interpretation ofdata, Critical revision of the manuscript for important intellectual content,Statistical analysis. Jihyoun Jeon: Study concept and design, Analysis andinterpretation of data, Critical revision of the manuscript for importantintellectual content, Statistical analysis. Minta Thomas: Study concept anddesign, Analysis and interpretation of data, Critical revision of the manuscriptfor important intellectual content, Statistical analysis. Yi Lin: Study concept



































































































































mailto:[email protected]



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and design, Analysis and interpretation of data, Critical revision of themanuscript for important intellectual content, Statistical analysis. David VConti: Critical revision of the manuscript for important intellectual content.Aung Ko Win: Critical revision of the manuscript for important intellectualcontent. Lori C Sakoda: Critical revision of the manuscript for importantintellectual content. Iris Lansdorp-Vogelaar- Critical revision of themanuscript for important intellectual content. Elisabeth F.P. Peterse: Criticalrevision of the manuscript for important intellectual content. Ann G Zauber:Critical revision of the manuscript for important intellectual content. DavidDuggan- Critical revision of the manuscript for important intellectual content.Andreana N Holowatyj: Critical revision of the manuscript for importantintellectual content. Jeroen R Huyghe: Critical revision of the manuscript forimportant intellectual content. 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Conflicts of interestThe authors disclose no conflicts.

FundingThis study was supported by National Cancer Institute, National Institutes ofHealth Grants R03-CA215775–01A1, R01-CA206279–03.

Supplementary Methods

Study DescriptionsSample numbers of our discovery dataset study partic-

ipants contributing to the whole-genome sequencing areprovided in Supplementary Table 1. The discovery datasetanalyses comprised existing genotyping and clinical datafrom 42 studies that have been described in detail pre-viously.17–20 Newly generated genotype data from theResearch Program on Genes, Environment and Health(RPGEH) cohort formed our replication dataset.

FundingASTERISK: a Hospital Clinical Research Program (PHRC-

BRD09/C) from the University Hospital Center of Nantes(CHU de Nantes) and supported by the Regional Council ofPays de la Loire, the Groupement des Entreprises Françaisesdans la Lutte contre le Cancer (GEFLUC), the AssociationAnne de Bretagne Génétique and the Ligue Régionale Contrele Cancer (LRCC).

The ATBC Study was supported by the US Public HealthService contracts (N01-CN-45165, N01-RC-45035, N01-RC-37004, and HHSN261201000006C) from the National Can-cer Institute.

COLO2&3: National Institutes of Health (R01 CA60987).ColoCare: This work was supported by the National In-

stitutes of Health (NIH) (grant numbers R01 CA189184 [Li/Ulrich], U01 CA206110 [Ulrich/Li/Siegel/Figueireido/Col-ditz], 2P30CA015704-40 [Gilliland], R01 CA207371 [Ulrich/Li]), NIH P30 CA42014 (Ulrich), the Matthias Lackas-Foundation, the German Consortium for Translational Can-cer Research, and by TRANSCAN (JTC2012-MetaboCCC,JTC2013-FOCUS).

The Colon Cancer Family Registry (CFR) Illumina GWASwas supported by funding from the National Cancer Insti-tute (NCI), NIH (grant numbers U01 CA122839, R01CA143247). The Colon CFR/CORECT Affymetrix AxiomGWAS and OncoArray GWAS were supported by fundingfrom NCI, NIH (grant number U19 CA148107 to S Gruber).The Colon CFR participant recruitment and collection ofdata and biospecimens used in this study were supportedby the National Cancer Institute, NIH (grant number U01CA167551) and through cooperative agreements with thefollowing Colon CFR centers: Australasian Colorectal CFR(NCI/NIH grant numbers U01 CA074778 and U01/U24CA097735), USC Consortium Colorectal CFR (NCI/NIH grantnumbers U01/U24 CA074799), Mayo Clinic CooperativeFamily Registry for Colon Cancer Studies (NCI/NIH grantnumber U01/U24 CA074800), Ontario Familial ColorectalCancer Registry (NCI/NIH grant number U01/U24CA074783), Seattle Colorectal CFR (NCI/NIH grant numberU01/U24 CA074794), and University of Hawaii ColorectalCFR (NCI/NIH grant number U01/U24 CA074806). Addi-tional support for case ascertainment was provided fromthe Surveillance, Epidemiology and End Results (SEER)Program of the National Cancer Institute to Fred HutchinsonCancer Research Center (Control Nos. N01-CN-67009 and

N01-PC-35142, and Contract No. HHSN2612013000121),the Hawai‘i Department of Health (Control Nos. N01-PC-67001 and N01-PC-35137, and Contract No.HHSN26120100037C, and the California Department ofPublic Health (contracts HHSN261201000035C awarded tothe University of Southern California, and the following statecancer registries: AZ, CO, MN, NC, NH, and by the VictoriaCancer Registry and Ontario Cancer Registry.

COLON: The COLON study is sponsored by WereldKanker Onderzoek Fonds, including funds from grant 2014/1179 as part of the World Cancer Research Fund Interna-tional Regular Grant Programme, by Alpe d’Huzes and theDutch Cancer Society (UM 2012–5653, UW 2013-5927,UW2015-7946), and by TRANSCAN (JTC2012-MetaboCCC,JTC2013-FOCUS). The NQplus study is sponsored by aZonMW investment grant (98-10030); by PREVIEW, theproject PREVention of diabetes through lifestyle interven-tion and population studies in Europe and around the World(PREVIEW) project which received funding from the Euro-pean Union Seventh Framework Programme (FP7/2007–2013) under grant no. 312057; by funds from TI Food andNutrition (cardiovascular health theme), a public–privatepartnership on pre-competitive research in food and nutri-tion; and by FOODBALL, the Food Biomarker Alliance, aproject from JPI Healthy Diet for a Healthy Life.

Colorectal Cancer Transdisciplinary (CORECT) Study:The CORECT Study was supported by NCI/NIH, USDepartment of Health and Human Services (grant numbersU19 CA148107, R01 CA81488, P30 CA014089, R01CA197350,; P01 CA196569; R01 CA201407) and NationalInstitutes of Environmental Health Sciences, NIH (grantnumber T32 ES013678).

CORSA: This study was funded by FFG BRIDGE (grant829675, to Andrea Gsur), the “Herzfelder’sche Familien-stiftung” (grant to Andrea Gsur) and was supported byCOST Actions BM1206 and CA17118.

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 Institu-tional Review Board approval.

CRCGEN: Colorectal Cancer Genetics & Genomics, Span-ish study was supported by Instituto de Salud Carlos III, co-funded by FEDER funds –a way to build Europe– (grantsPI14-613 and PI09-1286), Agency for Management of Uni-versity and Research Grants (AGAUR) of the Catalan Gov-ernment (grant 2017SGR723), and Junta de Castilla y León(grant LE22A10-2). Sample collection of this work wassupported by the Xarxa de Bancs de Tumors de Catalunyasponsored 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 theGrant Agency of the Czech Republic (grants CZ GA CR:GAP304/10/1286 and 1585) and by the Grant Agency ofthe Ministry of Health of the Czech Republic (grants AZV 15-27580A and AZV 17-30920A).

DACHS: This work was supported by the GermanResearch 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,

April 2020 Polygenic Risk Score Is Associated With Risk for Early-Onset CRC 1286.e1

RO 2270/8-1 and BR 1704/17-1), the InterdisciplinaryResearch Program of the National Center for Tumor Dis-eases (NCT), Germany, and the German Federal Ministry ofEducation 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 (DG-SANCO) and the Inter-national Agency for Research on Cancer. The national co-horts are supported by Danish Cancer Society (Denmark);Ligue Contre le Cancer, Institut Gustave Roussy, MutuelleGénérale de l’Education Nationale, Institut National de laSanté 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 Krebsfor-schungszentrum and Federal Ministry of Education andResearch (Germany); the Hellenic Health Foundation(Greece); Associazione Italiana per la Ricerca sul Cancro-AIRC-Italy and National Research Council (Italy); DutchMinistry of Public Health, Welfare and Sports (VWS),Netherlands Cancer Registry (NKR), LK Research Funds,Dutch Prevention Funds, Dutch ZON (Zorg OnderzoekNederland), World Cancer Research Fund (WCRF), StatisticsNetherlands (The Netherlands); ERC-2009-AdG 232997 andNordforsk, 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, BasqueCountry, Murcia and Navarra, ISCIII RETIC (RD06/0020)(Spain); Swedish Cancer Society, Swedish Research Counciland County Councils of Skåne and Västerbotten (Sweden);Cancer Research UK (14136 to EPIC-Norfolk; C570/A16491and C8221/A19170 to EPIC-Oxford), Medical ResearchCouncil (1000143 to EPIC-Norfolk, MR/M012190/1 toEPIC-Oxford) (United Kingdom).

EPICOLON: This work was supported by grants fromFondo de Investigación Sanitaria/FEDER (PI08/0024, PI08/1276, PS09/02368, P111/00219, PI11/00681, PI14/00173,PI14/00230, PI17/00509, 17/00878, Acción Transversal deCáncer), Xunta de Galicia (PGIDIT07PXIB9101209PR),Ministerio de Economia y Competitividad (SAF07-64873,SAF 2010-19273, SAF2014-54453R), Fundación Científicade la Asociación Española contra el Cáncer(GCB13131592CAST), Beca Grupo de Trabajo “Oncología”AEG (Asociación Española de Gastroenterología), FundaciónPrivada Olga Torres, FP7 CHIBCHA Consortium, Agència deGestió d’Ajuts Universitaris i de Recerca (AGAUR, General-itat de Catalunya, 2014SGR135, 2014SGR255, 2017SGR21,2017SGR653), Catalan Tumour Bank Network (Pla Directord’Oncologia, Generalitat de Catalunya), PERIS (SLT002/16/00398, Generalitat de Catalunya), CERCA Programme(Generalitat de Catalunya) and COST Action BM1206.CIBERehd is funded by the Instituto de Salud Carlos III.

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

Harvard cohorts (HPFS, NHS, PHS): HPFS is supportedby the NIH (P01 CA055075, UM1 CA167552, U01CA167552, R01 CA137178, R01 CA151993, R35 CA197735,K07 CA190673, and P50 CA127003), NHS by the NIH (R01CA137178, P01 CA087969, UM1 CA186107, R01 CA151993,R35 CA197735, K07 CA190673, and P50 CA127003) andPHS by the NIH (R01 CA042182).

Genetics and Epidemiology of Colorectal Cancer Con-sortium (GECCO): National Cancer Institute, NIH, U.S.Department of Health and Human Services (U01 CA164930,R01 CA201407). This research was funded in part throughthe NIH/NCI Cancer Center Support Grant P30 CA015704.

Kentucky: This work was supported by the followinggrant support: 1) Clinical Investigator Award from DamonRunyon Cancer Research Foundation (CI-8) and 2) NCIR01CA136726; and, we would like to acknowledge the staffat the Kentucky Cancer Registry

Kiel: This work was supported by institutional fundsfrom the Medical Faculties of the Christian-Albrechts Uni-versity Kiel and the Technical University Dresden.

LCCS: The Leeds Colorectal Cancer Study was funded bythe Food Standards Agency and Cancer Research UK Pro-gramme Award (C588/A19167).

MCCS cohort recruitment was funded by VicHealth andCancer Council Victoria. The MCCS was further supportedby Australian NHMRC grants 509348, 209057, 251553 and504711 and by infrastructure provided by Cancer CouncilVictoria. Cases and their vital status were ascertainedthrough the Victorian Cancer Registry (VCR) and theAustralian Institute of Health and Welfare (AIHW), includingthe National Death Index and the Australian CancerDatabase.

MEC: NIH (R37 CA54281, P01 CA033619, and R01CA063464).

MECC: This work was supported by the NIH, U.S.Department of Health and Human Services (R01 CA81488to SBG and GR).

MSKCC: The work at Sloan Kettering in New York wassupported by the Robert and Kate Niehaus Center forInherited Cancer Genomics and the Romeo Milio Founda-tion. Moffitt: This work was supported by funding from theNIH (grant numbers R01 CA189184, P30 CA076292),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 TotalCancer Care Initiative, Collaborative Data Services Core, andTissue Core at the H. Lee Moffitt Cancer Center & ResearchInstitute, a National Cancer Institute-designated Compre-hensive Cancer Center (grant number P30 CA076292).

NCCCS I & II: We acknowledge funding support for thisproject from the NIH, R01 CA66635 and P30 DK034987.

NFCCR: This work was supported by an InterdisciplinaryHealth Research Team award from the Canadian Institutesof Health Research (CRT 43821); the NIH, US Department ofHealth and Human Serivces (U01 CA74783); and NationalCancer Institute of Canada grants (18223 and 18226). Theauthors wish to acknowledge the contribution of AlexandreBelisle and the genotyping team of the McGill University andGénome Québec Innovation Centre, Montréal, Canada, for

1286.e2 Archambault et al Gastroenterology Vol. 158, No. 5

genotyping the Sequenom panel in the NFCCR samples.Funding was provided to Michael O. Woods by the CanadianCancer Society Research Institute.

NSHDS: Swedish Research Council; Swedish Cancer So-ciety; Cutting-Edge Research Grant and other grants fromthe County Council of Västerbotten, Sweden; WallenbergCentre for Molecular Medicine at Umeå University; Lion’sCancer Research Foundation at Umeå University; the CancerResearch Foundation in Northern Sweden; and the Facultyof Medicine, Umeå University, Umeå, Sweden.

OFCCR: NIH, through funding allocated to the OntarioRegistry for Studies of Familial Colorectal Cancer (U01CA074783); see CCFR section above. Additional fundingtoward genetic analyses of OFCCR includes the OntarioResearch Fund, the Canadian Institutes of Health Research,and the Ontario Institute for Cancer Research, throughgenerous support from the Ontario Ministry of Research andInnovation.

OSUMC: Funding was provided by Pelotonia and the NCI(CA16058 and CA67941).

PLCO: Intramural Research Program of the Division ofCancer Epidemiology and Genetics and supported by con-tracts from the Division of Cancer Prevention, NationalCancer Institute, NIH, DHHS. Funding was provided by NIH,Genes, Environment and Health Initiative (GEI) Z01 CP010200, NIH U01 HG004446, and NIH GEI U01 HG 004438.

PMH: NIH (R01 CA076366 to P.A. Newcomb).RPGEH: Data used in this study were provided by the

Kaiser Permanente Research Bank (KPRB) from the KPRBcollection, which includes the Kaiser Permanente RPGEHand the Genetic Epidemiology Research on Adult Health andAging (GERA) data, funded by the NIH [RC2 AG036607(Schaefer and Risch)], The Ellison Medical Foundation, andthe Kaiser Permanente Community Benefits Program. Ac-cess to data used in this study may be obtained by appli-cation to the KPRB via [email protected].

SEARCH: The University of Cambridge has receivedsalary support in respect of PDPP from the NHS in the Eastof England through the Clinical Academic Reserve. CancerResearch UK (C490/A16561); the UK National Institute forHealth Research Biomedical Research Centres at the Uni-versity of Cambridge.

The Swedish Low-risk Colorectal Cancer Study: Thestudy was supported by grants from the Swedish researchcouncil; 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 SwedishMen: This work is supported by the Swedish ResearchCouncil /Infrastructure grant, the Swedish Cancer Founda-tion, and the Karolinska Institutés Distinguished ProfessorAward to Alicja Wolk.

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

VITAL: NIH (K05 CA154337).WHI: The WHI program is funded by the National

Heart, Lung, and Blood Institute, NIH, U.S. Department ofHealth and Human Services through contracts

HHSN268201100046C, HHSN268201100001C, HHSN268201100002C, HHSN268201100003C, HHSN268201100004C,and HHSN271201100004C.

AcknowledgmentsASTERISK: We are very grateful to Dr Bruno Buecher

without whom this project would not have existed. We alsothank all those who agreed to participate in this study,including the patients and the healthy control persons, aswell as all the physicians, technicians and students.

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

CORSA: We thank all those who agreed to participate inthe CORSA study, including the patients and the controlpersons, as well as all the physicians and students.

CPS-II: The authors thank the CPS-II participants andStudy Management Group for their invaluable contributionsto this research. The authors also acknowledge the contri-bution to this study from central cancer registries sup-ported through the Centers for Disease Control andPrevention National Program of Cancer Registries, andcancer registries supported by the NCI SEER program.

Czech Republic CCS: We are thankful to all clinicians inmajor hospitals in the Czech Republic, without whom thestudy would not be practicable. We are also sincerelygrateful to all patients participating in this study.

DACHS: We thank all participants and cooperating cli-nicians, and Ute Handte-Daub, Utz Benscheid, MuhabbetCelik and Ursula Eilber for excellent technical assistance.

EDRN: We acknowledge all the following contributors tothe development of the resource: University of PittsburghSchool of Medicine, Department of Gastroenterology, Hep-atology and Nutrition: Lynda Dzubinski; University ofPittsburgh School of Medicine, Department of Pathology:Michelle Bisceglia; and University of Pittsburgh School ofMedicine, Department of Biomedical Informatics.

EPICOLON: We are sincerely grateful to all patientsparticipating in this study who were recruited as part of theEPICOLON project. We acknowledge the Spanish NationalDNA Bank, Biobank of Hospital Clínic–IDIBAPS and Bio-banco Vasco for the availability of the samples. The workwas carried out (in part) at the Esther Koplowitz Centre,Barcelona.

Harvard cohorts: The study protocol was approved bythe institutional review boards of the Brigham and Women’sHospital and Harvard T.H. Chan School of Public Health, andthose of participating registries as required. We thank theparticipants and staff of the HPFS, NHS, and PHS for theirvaluable contributions as well as the following state cancerregistries 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 authorsassume full responsibility for analyses and interpretation ofthese data.

LCCS: We acknowledge the contributions of JenniferBarrett, Robin Waxman, Gillian Smith, and Emma North-wood in conducting this study.



NCCCS I & II: We thank the study participants, and theNC Colorectal Cancer Study staff.

NSHDS: We thank all participants in the NSHDS cohorts,the staff at the Department of Biobank Research, UmeåUniversity, the staff at Biobanken norr, Västerbotten CountyCouncil, and the scientists managing the Northern SwedenDiet Database. We also thank Prof Richard Palmqvist andDr Björn Gylling at the Department of Medical Biosciences,and Dr Robin Myte, formerly at the Department of RadiationSciences, all at Umeå University, Sweden, for theirvaluable contributions in defining the nested case-controlstudy.

PLCO: The authors thank the PLCO Cancer ScreeningTrial screening center investigators and the staff from

Information Management Services Inc and Westat Inc. Mostimportantly, we thank the study participants for their con-tributions that made this study possible.

PMH: The authors thank the study participants and staffof the Hormones and Colon Cancer study.

SEARCH: SEARCH team.UK Biobank: This research has been conducted using the

UK Biobank Resource under Application Number 8614.WHI: The authors thank the WHI investigators and staff

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


http://www.whi.org/researchers/Documents%20%20Write%20a%20Paper/WHI%20Investigator%20Short%20List.pdf



Supplementary Figure 1. Distribution of the PRS acrosscases and controls. (A) Plot includes all cases and controlswith a CRC diagnosis at <50 years of age. (B) Plot includesall cases and controls with a CRC diagnosis at �50 years ofage.

Supplementary Figure 2. Risk estimates for early-onsetversus late-onset CRC associated with a 95-SNP PRSacross disease site among participants with a negative familyhistory of CRC in the discovery dataset. (A) Model includes allcases with CRC diagnosis within the proximal colon. (B)Model includes all cases with CRC diagnosis within the distalcolon. (C) Model includes all cases with CRC diagnosis withinthe rectum. Models were adjusted for age, sex, principalcomponents, genotype platform, and PRS. The interaction Pvalue reported was produced from a model including aninteraction term with a continuous PRS (per SD) and age (<50years vs �50 years).


Supplementary Table 1.Study specifics of the individual discovery datasets

Studyacronym Study name

Family history Cases Controls

No Yes Total <50 ya �50 ya Total <50 ya �50 ya

ASTERISK3 The french Association STudy EvaluatingRISK for sporadic colorectal cancer

869 23 892 54 828 947 66 881

ATBC2 Alpha-Tocopherol, Beta CaroteneCancer Prevention Study

150 3 147 0 147 30 0 30

CCFR2 Colon Cancer Family Registry 4406 1574 3812 1867 1941 2171 755 1415Colo2&33 Hawai’i Colorectal Cancer Studies 2 & 3 187 24 87 11 76 124 12 112ColoCare2 ColoCare Consortium 0 0 356 74 282 36 10 26COLON1 Colorectal Cancer: Longitudinal

Observational study on Nutritionaland lifestyle factors that influencecolorectal tumor recurrence, survivaland quality of life

1165 165 639 34 605 691 40 651

CORSA1 Colorectal Cancer Study of Austria 2166 126 2708 344 2332 1953 391 1562CPS-II1 American Cancer Society Cancer

Prevention Study II nested case-control study

851 122 540 0 540 536 0 536

Czech RepublicCCS1

Czech Republic Colorectal Cancer Study 2420 407 1688 162 1526 1616 770 846

DACHS1 Darmkrebs: Chancen der Verhütungdurch Screening

5596 809 3590 170 3420 2819 120 2699

DALS3 Diet, Activity, and Lifestyle Study 1972 302 1104 83 1021 1170 89 1081EDRN1 Early Detection Research Network 514 75 277 44 233 313 32 281EPIC1 European Prospective Investigation into

Cancer and Nutrition0 0 2105 91 2014 2321 28 2293

EPICOLON1 The EPICOLON Consortium 582 17 267 63 204 342 17 325ESTHER-VERDI2 Epidemiologische Studie zu Chancen

der Verhütung, Früherkennung undoptimierten Therapie chronischerErkrankungen in der älterenBevölkerung, Verlauf derdiagnotischen Abklärung beiKrebspatienten

0 0 397 8 389 420 1 419

HNPCC, OCCPI,and OSUMC1

Columbus-area HNPCC study, OhioColorectal Cancer PreventionInitiative, and Ohio State UniversityMedical Center

4545 912 3108 574 2525 2443 979 1463

HPFS1 Health Professionals Follow-up Study 1227 233 716 7 708 744 8 736Kentucky3 Kentucky Case-Control Study 1468 372 1035 109 926 1132 76 1056Kiel2 PopGen Biobank 0 0 1103 119 894 0 0 0LCCS1 Leeds Colorectal Cancer Study 1545 327 1486 88 1388 696 19 677MCCS2 Melbourne Collaborative Cohort Study 1138 204 709 11 698 633 13 620MEC2 Multiethnic Cohort study 527 82 389 3 386 427 4 423MECC2 Molecular Epidemiology of Colorectal

Cancer Study3722 409 2880 113 2767 2214 71 2143

MSKCC2 Memorial Sloan Kettering Cancer CenterCohort

0 0 68 16 52 0 0 0

NCCCS I1 North Carolina Colon Cancer Study-I 615 96 252 22 230 467 30 437NCCCS II1 North Carolina Colon Cancer Study-II 1098 155 596 84 497 687 60 627NFCCR3 Newfoundland Case-Control Study 584 56 184 17 167 456 67 389NHS1 Nurses’ Health Study 2204 390 1062 31 1031 1532 49 1483NHS II2 Nurses’ Health Study 0 0 87 22 65 80 24 56NSHDS1 The Northern Sweden Health and

Disease Study790 16 406 20 386 412 20 392

OFCCR3 Ontario Familial Colorectal CancerRegistry

742 225 593 41 552 522 13 509

PHS3 Physicians’ Health Study 0 0 375 7 368 389 7 382PLCO1 Prostate, Lung, Colorectal, and Ovarian

Cancer Screening Trial2643 353 1003 0 1003 2015 0 2015

PMH-CCFR3 Postmenopausal HormonesSupplementary Study to the CCFR

351 47 276 1 275 122 0 122


Supplementary Table 1.Continued

Studyacronym Study name

Family history Cases Controls

No Yes Total <50 ya �50 ya Total <50 ya �50 ya

SEARCH2 Studies of Epidemiology and RiskFactors in Cancer Heredity

899 337 4139 451 3615 115 19 96

SLRCCS2 Swedish Low-Risk Colorectal CancerStudy

0 0 2504 113 2390 2281 513 726

SMC andCOSM2

Swedish Mammography Cohort andCohort of Swedish Men

951 113 562 6 556 828 26 802

Spain2 The Spanish study (University Hospital ofBellvitge, Hospital of Leon)

1356 147 727 46 681 786 84 702

UK Biobank1 United Kingdom Biobank 9010 3528 5356 573 4783 21407 2305 19102USC-HRT-CRC2 Los Angeles County Cancer Surveillance

Program0 0 321 0 321 387 0 387

VITAL3 VITamins And Lifestyle 473 77 279 0 279 287 0 287WHI1 Women’s Health Initiative 2968 697 1443 0 1443 2532 0 2532

NOTE. Participants from individual studies with missing information were excluded from the analysis.Additional study information can be found in the following:1Huyghe JR, et al. Discovery of common and rare genetic risk variants for colorectal cancer. Nat Genet 2019;51:76–87, https://doi.org/10.1038/s41588-018-0286-6 (2018).2Schmit SL, et al. Novel common genetic susceptibility loci for colorectal cancer. J Natl Cancer Inst 2019;111:146–157,https://doi.org/10.1093/jnci/djy099 (2018).3Schumacher FR, et al. Genome-wide association study of colorectal cancer identifies six new susceptibility loci. Nat Commun2015;6:7138, https://doi.org/10.1038/ncomms8138.aAge at CRC diagnosis.


https://doi.org/10.1038/s41588-018-0286-6

https://doi.org/10.1038/s41588-018-0286-6

https://doi.org/10.1093/jnci/djy099

https://doi.org/10.1038/ncomms8138

Supplementary Table 2.Associations Between 95 Risk Variants and Risk of CRC in the Study Population

SNP Locus Variant Risk/other alleleRisk allele

freqUnadjusted beta

(95% CI)Adjusted betaa

(95% CI)

rs72647484 1p36.12 1:22587728 T/C 0.9107 0.0504 (0.0192–0.0816) -rs4360494 1p34.3 1:38455891 G/C 0.4539 0.0523 (0.0349–0.0697) 0.0379 (0.0013–0.0689)rs12144319 1p32.3 1:55246035 C/T 0.2548 0.0665 (0.0469–0.0861) 0.0661 (0.0013–0.0689)rs6678517 1q25.3 1:183002639 A/G 0.5898 0.073 (0.0556–0.0904) -rs17011141 1q41 1:222112634 G/A 0.2087 0.0877 (0.0665–0.1089) -rs448513 2q24.2 2:159964552 C/T 0.326 0.0511 (0.0329–0.0693) 0.0054 (0.0013–0.0689)rs11884596 2q33.1 2:199612407 C/T 0.3823 0.0535 (0.0355–0.0715) 0.0342 (0.0013–0.0689)rs983402 2q33.1 2:199781586 T/C 0.3312 0.0627 (0.0441–0.0813) 0.0622 (0.0013–0.0689)rs3731861 2q35 2:219191256 T/C 0.6295 0.0613 (0.0435–0.0791) -rs35470271 3p22.1 3:40915239 G/A 0.154 0.0994 (0.0759–0.1229) -rs72942485 3q13.2 3:112999560 G/A 0.9802 0.1761 (0.1153–0.2369) 0.0545 (1.0013–1.0689)rs10049390 3q22.2 3:133701119 A/G 0.7353 0.0597 (0.0399–0.0795) 0.0455 (0.0013–0.0689)rs9876206 3q26.2 3:169517436 C/T 0.7507 0.0453 (0.0255–0.0651) -rs13149359 4q22.2 4:94938618 A/C 0.3663 0.052 (0.0342–0.0698) -rs1391441 4q24 4:106128760 A/G 0.672 0.0522 (0.0342–0.0702) 0.0148 (0.0013–0.0689)rs11727676 4q31.21 4:145659064 C/T 0.098 0.0842 (0.0544–0.114) 0.0093 (0.0013–0.0689)rs78368589 5p15.33 5:1240204 T/C 0.0597 0.1119 (0.0745–0.1493) 0.0786 (1.0013–1.0689)rs2735940 5p15.33 5:1296486 G/A 0.4952 0.0865 (0.0691–0.1039) -rs7708610 5p13.1 5:40102443 A/G 0.3564 0.0545 (0.0363–0.0727) 0.0384 (0.0013–0.0689)rs12514517 5p13.1 5:40280076 A/G 0.288 0.1013 (0.0819–0.1207) -rs145364999 5q21.1 5:98206082 T/A 0.9969 0.5559 (0.3683–0.7435) 0.3496 (5.0013–5.0689)rs755229494 5q22.2 5:112097351 G/A 0.0011 0.6286 (0.4534–0.8038) -rs4976270 5q31.1 5:134467220 C/T 0.5501 0.0693 (0.0521–0.0865) -rs2516420 6p21.33 6:31449620 C/T 0.9263 0.1118 (0.0775–0.1461) 0.1091 (1.0013–1.0689)rs9271695 6p21.32 6:32593080 G/A 0.7954 0.0889 (0.0658–0.112) 0.0889 (0.0013–0.0689)rs16878812 6p21.31 6:35569562 A/G 0.8861 0.0778 (0.0502–0.1054) -rs9470361 6p21.2 6:36623379 A/G 0.2488 0.054 (0.0342–0.0738) -rs62404966 6p12.1 6:55712124 C/T 0.7623 0.0724 (0.0514–0.0934) -rs12672022 7p13 7:45136423 T/C 0.8345 0.065 (0.0417–0.0883) 0.0067 (0.0013–0.0689)rs16892766 8q23.3 8:117630683 C/A 0.0829 0.2099 (0.1723–0.2475) -rs6469654 8q23.3 8:117632965 G/C 0.2288 0.0677 (0.0436–0.0918) -rs117079142 8q24.11 8:117790914 A/C 0.0432 0.1139 (0.0688–0.159) -rs6983267 8q24.21 8:128413305 G/T 0.5228 0.1052 (0.0795–0.1309) -rs4313119 8q24.21 8:128571855 G/T 0.7486 0.0608 (0.041–0.0806) 0.0518 (0.0013–0.0689)rs1537372 9p21.3 9:22103183 G/T 0.5692 0.0504 (0.033–0.0678) 0.012 (0.0013–0.0689)rs34405347 9q22.33 9:101679752 T/G 0.9034 0.0818 (0.0528–0.1108) 0.0089 (0.0013–0.0689)rs10980628 9q31.3 9:113671403 C/T 0.2106 0.0637 (0.0427–0.0847) 0.0511 (0.0013–0.0689)rs11255841 10p14 10:8739580 T/A 0.703 0.1064 (0.0864–0.1264) -rs10821907 10q11.23 10:52648454 C/T 0.8276 0.073 (0.0501–0.0959) -rs704017 10q22.3 10:80819132 G/A 0.5846 0.0765 (0.0589–0.0941) -rs11190164 10q24.2 10:101351704 G/A 0.2626 0.0889 (0.0685–0.1093) -rs12246635 10q25.2 10:114288619 C/T 0.0983 0.0975 (0.0693–0.1257) -rs11196170 10q25.2 10:114722621 A/G 0.2178 0.0527 (0.0319–0.0735) -rs174533 11q12.2 11:61549025 G/A 0.6739 0.0636 (0.0452–0.082) -rs7121958 11q13.4 11:74280012 G/T 0.5105 0.078 (0.0608–0.0952) -rs61389091 11q13.4 11:74427921 C/T 0.9606 0.1934 (0.1469–0.2399) 0.1934 (1.0013–1.0689)rs2186607 11q22.1 11:101656397 T/A 0.5178 0.0537 (0.0365–0.0709) 0.0483 (0.0013–0.0689)rs3087967 11q23.1 11:111156836 T/C 0.2911 0.1122 (0.0934–0.131) -rs35808169 12p13.32 12:4368607 C/T 0.1721 0.089 (0.0661–0.1119) -rs3217810 12p13.32 12:4388271 T/C 0.1253 0.1181 (0.0891–0.1471) -rs3217874 12p13.32 12:4400808 T/C 0.4282 0.055 (0.037–0.073) 0.0453 (0.0013–0.0689)rs2250430 12p13.31 12:6421174 T/A 0.7095 0.0597 (0.0399–0.0795) -rs11610543 12q12 12:43134191 G/A 0.5013 0.053 (0.0359–0.0701) 0.0474 (0.0013–0.0689)rs12372718 12q13.12 12:51171090 G/A 0.3924 0.0896 (0.072–0.1072) -rs4759277 12q13.3 12:57533690 A/C 0.3546 0.053 (0.035–0.071) 0.0285 (0.0013–0.0689)rs597808 12q24.12 12:111973358 G/A 0.5166 0.0737 (0.0561–0.0913) -rs7300312 12q24.21 12:115890922 C/T 0.5719 0.066 (0.0488–0.0832) -rs7333607 13q13.3 13:37462010 G/A 0.235 0.0758 (0.0552–0.0964) 0.0758 (0.0013–0.0689)rs78341008 13q22.1 13:73791554 C/T 0.0719 0.0982 (0.0635–0.1329) 0.0109 (0.0013–0.0689)rs8000189 13q34 13:111075881 T/C 0.6401 0.0549 (0.0371–0.0727) 0.0473 (0.0013–0.0689)


Supplementary Table 2.Continued

SNP Locus Variant Risk/other alleleRisk allele

freqUnadjusted beta

(95% CI)Adjusted betaa

(95% CI)

rs35107139 14q22.2 14:54419106 C/A 0.4235 0.0912 (0.0734–0.109) -rs4901473 14q22.2 14:54445157 G/A 0.378 0.0465 (0.0287–0.0643) -rs17094983 14q23.1 14:59189361 G/A 0.8773 0.0691 (0.0415–0.0967) 0.0062 (0.0013–0.0689)rs12708491 15q13.3 15:32992836 G/A 0.5872 0.0464 (0.0278–0.065) -rs2293581 15q13.3 15:33010736 A/G 0.2116 0.1248 (0.103–0.1466) -rs17816465 15q13.3 15:33156386 A/G 0.2055 0.0705 (0.0489–0.0921) 0.069 (0.0013–0.0689)rs56324967 15q22.33 15:67402824 C/T 0.6757 0.0689 (0.0505–0.0873) 0.0689 (0.0013–0.0689)rs9924886 16q22.1 16:68743939 A/C 0.7321 0.055 (0.0356–0.0744) -rs9930005 16q23.2 16:80043258 C/A 0.4303 0.0498 (0.0324–0.0672) 0.0061 (0.0013–0.0689)rs12149163 16q24.1 16:86339315 T/C 0.4976 0.0487 (0.0315–0.0659) -rs62042090 16q24.1 16:86703949 T/C 0.2164 0.0481 (0.0271–0.0691) -rs4968127 17p13.3 17:809643 G/A 0.3684 0.0514 (0.0328–0.07) -rs1078643 17p12 17:10707241 A/G 0.7636 0.0748 (0.0534–0.0962) 0.0747 (0.0013–0.0689)rs983318 17q24.3 17:70413253 A/G 0.2526 0.0595 (0.0395–0.0795) 0.0397 (0.0013–0.0689)rs75954926 17q25.3 17:81061048 G/A 0.6568 0.0882 (0.0684–0.108) 0.0882 (0.0013–0.0689)rs11874392 18q21.1 18:46453156 A/T 0.545 0.1606 (0.1434–0.1778) -rs34797592 19p13.11 19:16417198 T/C 0.1182 0.0868 (0.0596–0.114) 0.0824 (0.0013–0.0689)rs28840750 19q13.11 19:33519927 T/G 0.948 0.1939 (0.1555–0.2323) -rs1963413 19q13.2 19:41871573 A/G 0.6119 0.0441 (0.0265–0.0617) -rs73068325 19q13.43 19:59079096 T/C 0.1826 0.0632 (0.0407–0.0857) 0.0066 (0.0013–0.0689)rs189583 20p12.3 20:6376457 G/C 0.3298 0.0795 (0.0607–0.0983) -rs994308 20p12.3 20:6603622 C/T 0.5939 0.0627 (0.0449–0.0805) 0.0626 (0.0013–0.0689)rs4813802 20p12.3 20:6699595 G/T 0.3561 0.0819 (0.0635–0.1003) -rs28488 20p12.3 20:6762221 T/C 0.6388 0.0714 (0.053–0.0898) 0.0714 (0.0013–0.0689)rs11087784 20p12.3 20:7740976 G/A 0.1523 0.0874 (0.0639–0.1109) -rs6031311 20q13.12 20:42666475 T/C 0.7591 0.0597 (0.0395–0.0799) 0.0362 (0.0013–0.0689)rs6066825 20q13.13 20:47340117 A/G 0.6448 0.0719 (0.0539–0.0899) -rs6063514 20q13.13 20:49055318 C/T 0.6086 0.0547 (0.0367–0.0727) -rs1741640 20q13.33 20:60932414 C/T 0.7652 0.1146 (0.0936–0.1356) -rs2738783 20q13.33 20:62308612 T/G 0.2029 0.0593 (0.0379–0.0807) 0.006 (0.0013–0.0689)rs6781752 3p14.1 3:66365163 A/G 0.205 0.0597 (0.0379–0.0815) -rs62396735 6p21.1 6:41702582 C/T 0.2908 0.033 (0.0142–0.0518) -rs2710310 12p13.2 12:12035649 C/T 0.7596 0.0145 (-0.0057–0.0347) -rs377429877 13q13.2 13:34092164 C/T 0.6117 0.0468 (0.0288–0.0648) -rs6058093 20q11.22 20:33213196 C/A 0.4942 0.045 (0.0278–0.0622) -

aAdjusted for “winner’s curse” (Zhong H, Prentice RL. Bias-reduced estimators and confidence intervals for odds ratios ingenome-wide association studies. Biostatistics 2008;9:621–634.)


Supplementary Table 3.Area Under the Curve Estimates forCRC Associated With a 95-SNPPRS per SD Across Categories ofAge at CRC Diagnosis

Ages, y Family history negative Family history positive

<40 0.65 0.5440–44 0.64 0.6145–49 0.65 0.5650–54 0.63 0.5955–59 0.63 0.6260–64 0.61 0.6265–69 0.62 0.59> 70 0.60 0.58

NOTE. Models adjusted for age, sex, genotype platform, andprincipal components.

Supplementary Table 4.Risk Estimates for CRC Associated With a 95-SNP PRS per SD Across Categories of Age at CRCDiagnosis Among Participants With a Negative Family History of CRC in the Discovery Dataset

Age, y N (cases) N (controls) OR (95% CI) P value P value for interactiona

3.44E-1015–39 678 1321 1.74 (1.55–1.96) 1.34E-2040–49 2481 2809 1.75 (1.64–1.87) 2.71E-6250–59 5125 7074 1.60 (1.54–1.67) 5.35E-11260–69 9065 11,710 1.52 (1.48–1.57) 5.06E-15870–79 7647 7630 1.44 (1.39–1.49) 2.79E-98

NOTE. The logistic regression models include age, sex, principal components, genotype platform, and polygenic risk score.aP value produced from interaction term with continuous PRS (per SD) and continuous age.


Supplementary Table 5.Risk Estimates for Early-Onset vs Late-Onset CRC Associated With a 95-SNP PRS Across DiseaseSite Among Participants With a Negative Family History of CRC in the Discovery Dataset

PRS n (cases) n (controls) OR (95% CI) P value P value for interactiona

Proximal colon .0124<50 y old

per 1 SD 706 4130 1.66 (1.50–1.82) 3.60E-25Quartile 1 (ref) 97 1085 1.00Quartile 2 138 1025 1.63 (1.21–2.21) 0.0015Quartile 3 192 1001 2.42 (1.82–3.23) 1.44E-09Quartile 4 279 1019 3.80 (2.88–5.01) 4.25E-21

�50 y oldper 1 SD 7603 28,317 1.42 (1.38–1.46) 3.24E-135Quartile 1 (ref) 1215 7341 1.00Quartile 2 1593 7083 1.38 (1.27–1.51) 4.90E-14Quartile 3 2080 7058 1.79 (1.65–1.94) 1.05E-44Quartile 4 2715 6835 2.45 (2.27–2.66) 1.43E-110

Distal colon .0071<50 y old

per 1 SD 930 4130 1.85 (1.70–2.02) 6.87E-44Quartile 1 (ref) 110 1085 1.00Quartile 2 172 1025 1.61 (1.22–2.13) 0.0008Quartile 3 230 1001 2.46 (1.89–3.21) 3.34E-11Quartile 4 418 1019 4.79 (3.72–6.17) 4.53E-34


Rectal .0017<50 y old



NOTE. The logistic regression models include age, sex, principal components, genotype platform, and polygenic risk score.aP value produced from interaction term with continuous PRS (per SD) and age (<50 vs �50 y).


Supplementary Table 6.OR (95% CI) of 1 SD PRSAssociated With CRC Risk byDisease Site Among ParticipantsWith a Negative Family History ofCRC in the Discovery Dataset

OR (95% CI) P value

<50 y oldDistal colon vs proximal colon 1.12 (1.01–1.25) .0290Rectum vs proximal colon 1.12 (1.01–1.24) .0331�50 y oldDistal colon vs proximal colon 1.11 (1.08–1.15) 7.31E-10Rectum vs proximal colon 1.09 (1.05–1.13) 2.85E-06

NOTE. The multinomial logistic regression models includeage, sex, principal components, genotype platform, andpolygenic risk score.
