Electronic Resource
Genome-wide Modeling of Polygenic Risk Score in Colorectal Cancer Risk.
| العنوان: | Genome-wide Modeling of Polygenic Risk Score in Colorectal Cancer Risk. |
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| المؤلفون: | Huyghe J.R., Thomas M., Sakoda L.C., Hoffmeister M., Rosenthal E.A., Lee J.K., van Duijnhoven F.J.B., Platz E.A., Wu A.H., Dampier C.H., de la Chapelle A., Wolk A., Joshi A.D., Burnett-Hartman A., Gsur A., Lindblom A., Castells A., Win A.K., Namjou B., Van Guelpen B., Tangen C.M., He Q., Li C.I., Schafmayer C., Joshu C.E., Ulrich C.M., Bishop D.T., Buchanan D.D., Schaid D., Drew D.A., Muller D.C., Duggan D., Crosslin D.R., Albanes D., Giovannucci E.L., Larson E., Qu F., Mentch F., Giles G.G., Hakonarson H., Hampel H., Stanaway I.B., Figueiredo J.C., Minnier J., Chang-Claude J., Hampe J., Harley J.B., Visvanathan K., Curtis K.R., Offit K., Li L., Le Marchand L., Vodickova L., Gunter M.J., Jenkins M.A., Slattery M.L., Lemire M., Woods M.O., Song M., Murphy N., Lindor N.M., Dikilitas O., Pharoah P.D.P., Campbell P.T., Newcomb P.A., Milne R.L., MacInnis R.J., Castellvi-Bel S., Ogino S., Berndt S.I., Bezieau S., Thibodeau S.N., Gallinger S.J., Zaidi S.H., Harrison T.A., Keku T.O., Hudson T.J., Vymetalkova V., Moreno V., Martin V., Arndt V., Wei W.-Q., Chung W., Su Y.-R., Hayes R.B., White E., Vodicka P., Casey G., Gruber S.B., Schoen R.E., Chan A.T., Potter J.D., Brenner H., Jarvik G.P., Corley D.A., Peters U., Hsu L. |
| بيانات النشر: | Cell Press (E-mail: subs@cell.com) United States 2020-10-16 |
| نوع الوثيقة: | Electronic Resource |
| مستخلص: | Accurate colorectal cancer (CRC) risk prediction models are critical for identifying individuals at low and high risk of developing CRC, as they can then be offered targeted screening and interventions to address their risks of developing disease (if they are in a high-risk group) and avoid unnecessary screening and interventions (if they are in a low-risk group). As it is likely that thousands of genetic variants contribute to CRC risk, it is clinically important to investigate whether these genetic variants can be used jointly for CRC risk prediction. In this paper, we derived and compared different approaches to generating predictive polygenic risk scores (PRS) from genome-wide association studies (GWASs) including 55,105 CRC-affected case subjects and 65,079 control subjects of European ancestry. We built the PRS in three ways, using (1) 140 previously identified and validated CRC loci; (2) SNP selection based on linkage disequilibrium (LD) clumping followed by machine-learning approaches; and (3) LDpred, a Bayesian approach for genome-wide risk prediction. We tested the PRS in an independent cohort of 101,987 individuals with 1,699 CRC-affected case subjects. The discriminatory accuracy, calculated by the age- and sex-adjusted area under the receiver operating characteristics curve (AUC), was highest for the LDpred-derived PRS (AUC = 0.654) including nearly 1.2 M genetic variants (the proportion of causal genetic variants for CRC assumed to be 0.003), whereas the PRS of the 140 known variants identified from GWASs had the lowest AUC (AUC = 0.629). Based on the LDpred-derived PRS, we are able to identify 30% of individuals without a family history as having risk for CRC similar to those with a family history of CRC, whereas the PRS based on known GWAS variants identified only top 10% as having a similar relative risk. About 90% of these individuals have no family history and would have been considered average risk under current screening guidelines, but might be |
| مصطلحات الفهرس: | receiver operating characteristic, prediction, priority journal, risk assessment, sex factor, single nucleotide polymorphism, very elderly, age, aged, article, Bayes theorem, cancer risk, cancer screening, case control study, colorectal cancer/di [Diagnosis], colorectal cancer/et [Etiology], controlled study, early diagnosis, European, family history, female, gene linkage disequilibrium, genetic model, genetic risk score, genetic variability, genome-wide association study, high risk population, human, machine learning, major clinical study, male, mathematical computing, measurement accuracy, population based case control study, practice guideline, Article |
| URL: | American Journal of Human Genetics Click here for full text options LibKey Link |
| الاتاحة: | Open access content. Open access content Copyright 2020 Elsevier B.V., All rights reserved. |
| Other Numbers: | AUSHL oai:repository.monashhealth.org:1/29066 American Journal of Human Genetics. 107 (3) (pp 432-444), 2020. Date of Publication: 3 September 2020. 0002-9297 https://repository.monashhealth.org/monashhealthjspui/handle/1/29066 32758450 [http://www.ncbi.nlm.nih.gov/pubmed/?term=32758450] 2007636504 (Thomas, Sakoda, He, Li, Qu, Huyghe, Curtis, Newcomb, Harrison, Su, White, Potter, Peters, Hsu) Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, United States (Sakoda, Lee, Corley) Division of Research, Kaiser Permanente Northern California, Oakland, CA 94612, United States (Hoffmeister, Arndt, Brenner) Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany (Rosenthal, Stanaway, Jarvik) Department of Medicine (Medical Genetics), University of Washington Medical Center, Seattle, WA 98195, United States (van Duijnhoven) Division of Human Nutrition and Health, Wageningen University & Research, Wageningen 176700, Netherlands (Platz, Joshu, Visvanathan) Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, and the Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins, Baltimore, MD 21287, United States (Wu) University of Southern California, Preventative Medicine, Los Angeles, CA 90089, United States (Dampier) Department of Surgery, University of Virginia Health System, Charlottesville, VA 22903, United States (de la Chapelle) Department of Cancer Biology and Genetics and the Comprehensive Cancer Center, The Ohio State University, Columbus, OH 43210, United States (Wolk) Institute of Environmental Medicine, Karolinska Institutet, Stockholm 17177, Sweden (Joshi, Drew, Song, Chan) Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, United States (Joshi, Giovannucci, Ogino, Chan) Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, United States (Burnett-Hartman) Institute for Health Research, Kaiser Permanente Colorado, Denver, CO 80014, United States (Gsur) Institute of Cancer Research, Department of Medicine I, Medical University Vienna, Vienna 1090, Austria (Lindblom) Department of Clinical Genetics, Karolinska University Hospital, S Peters U.; upeters@fredhutch.org Hsu L.; lih@fredhutch.org 1305134197 |
| المصدر المساهم: | MONASH HEALTH LIBRS From OAIster®, provided by the OCLC Cooperative. |
| رقم الانضمام: | edsoai.on1305134197 |
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