Abstract
Since the publication of the first genome-wide association study for cancer in 2007, thousands of common alleles that are associated with the risk of cancer have been identified. The relative risk associated with individual variants is small and of limited clinical significance. However, the combined effect of multiple risk variants as captured by polygenic scores (PGSs) may be much greater and therefore provide risk discrimination that is clinically useful. We review the considerable research efforts over the past 15 years for developing statistical methods for PGSs and their application in large-scale genome-wide association studies to develop PGSs for various cancers. We review the predictive performance of these PGSs and the multiple challenges currently limiting the clinical application of PGSs. Despite this, PGSs are beginning to be incorporated into clinical multifactorial risk prediction models to stratify risk in both clinical trials and clinical implementation studies.
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References
Sung, H. et al. Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 71, 209–249 (2021).
Brown, G. R. et al. A review of inherited cancer susceptibility syndromes. J. Am. Acad. Physician Assist. 33, 10–16 (2020).
Antoniou, A. C. et al. Breast-cancer risk in families with mutations in PALB2. N. Engl. J. Med. 371, 497–506 (2014).
Short, E., Thomas, L. E., Hurley, J., Jose, S. & Sampson, J. R. Inherited predisposition to colorectal cancer: towards a more complete picture. J. Med. Genet. 52, 791–796 (2015).
Mur, P. et al. Role of POLE and POLD1 in familial cancer. Genet. Med. 22, 2089–2100 (2020).
Yang, X. et al. Cancer risks associated with germline PALB2 pathogenic variants: an international study of 524 families. J. Clin. Oncol. 38, 674–685 (2020).
Yang, X. et al. Ovarian and breast cancer risks associated with pathogenic variants in RAD51C and RAD51D. J. Natl Cancer Inst. 112, 1242–1250 (2020).
Breast Cancer Association, C. et al. Breast cancer risk genes — association analysis in more than 113,000 women. N. Engl. J. Med. 384, 428–439 (2021).
Buniello, A. et al. The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Res. 47, D1005–D1012 (2019). This publication describes the GWAS Catalog, a publicly available resource collecting all published human GWASs; as of 5 July 2023, the GWAS Catalog contained 6,367 publications, 516,944 top associations and 62,987 sets of full summary statistics.
Lee, A. et al. BOADICEA: a comprehensive breast cancer risk prediction model incorporating genetic and nongenetic risk factors. Genet. Med. 21, 1708–1718 (2019).
Carver, T. et al. CanRisk Tool — a web interface for the prediction of breast and ovarian cancer risk and the likelihood of carrying genetic pathogenic variants. Cancer Epidemiol. Biomark. Prev. 30, 469–473 (2021).
Lee, A. et al. Enhancing the BOADICEA cancer risk prediction model to incorporate new data on RAD51C, RAD51D, BARD1 updates to tumour pathology and cancer incidence. J. Med. Genet. 59, 1206–1218 (2022).
Yang, X. et al. Prospective validation of the BOADICEA multifactorial breast cancer risk prediction model in a large prospective cohort study. J. Med. Genet. 59, 1196–1205 (2022).
Yang, J. et al. Common SNPs explain a large proportion of the heritability for human height. Nat. Genet. 42, 565–569 (2010).
Bulik-Sullivan, B. et al. An atlas of genetic correlations across human diseases and traits. Nat. Genet. 47, 1236–1241 (2015).
Zhang, Y. D. et al. Assessment of polygenic architecture and risk prediction based on common variants across fourteen cancers. Nat. Commun. 11, 3353 (2020). This comprehensive polygenicity analysis of 14 cancers reveals the polygenic risk prediction diversity for individual cancers, projects the yields of future GWASs and highlights the potential clinical utility of PGS in population risk stratification.
Mucci, L. A. et al. Familial risk and heritability of cancer among twins in Nordic countries. J. Am. Med. Assoc. 315, 68–76 (2016).
Vilhjalmsson, B. J. et al. Modeling linkage disequilibrium increases accuracy of polygenic risk scores. Am. J. Hum. Genet. 97, 576–592 (2015).
Ge, T., Chen, C. Y., Ni, Y., Feng, Y. A. & Smoller, J. W. Polygenic prediction via Bayesian regression and continuous shrinkage priors. Nat. Commun. 10, 1776 (2019).
Yang, S. & Zhou, X. Accurate and scalable construction of polygenic scores in large Biobank Data Sets. Am. J. Hum. Genet. 106, 679–693 (2020).
Zhang, Y., Qi, G., Park, J. H. & Chatterjee, N. Estimation of complex effect-size distributions using summary-level statistics from genome-wide association studies across 32 complex traits. Nat. Genet. 50, 1318–1326 (2018). The authors of this paper estimate the effect-size distributions of common variants on the basis of summary-level GWAS statistics across 32 complex traits and use this to project the potential ability of SNP discovery and risk prediction for future GWASs.
Zhu, X. & Stephens, M. Bayesian large-scale multiple regression with summary statistics from genome-wide association studies. Ann. Appl. Stat. 11, 1561–1592 (2017).
Lloyd-Jones, L. R. et al. Improved polygenic prediction by Bayesian multiple regression on summary statistics. Nat. Commun. 10, 5086 (2019).
Pavlou, M., Ambler, G., Seaman, S., De Iorio, M. & Omar, R. Z. Review and evaluation of penalised regression methods for risk prediction in low-dimensional data with few events. Stat. Med. 35, 1159–1177 (2016).
Dareng, E. O. et al. Polygenic risk modeling for prediction of epithelial ovarian cancer risk. Eur. J. Hum. Genet. 30, 349–362 (2022).
Choi, S. W., Mak, T. S. & O’Reilly, P. F. Tutorial: a guide to performing polygenic risk score analyses. Nat. Protoc. 15, 2759–2772 (2020).
Pharoah, P. D. et al. Polygenic susceptibility to breast cancer and implications for prevention. Nat. Genet. 31, 33–36 (2002). This hallmark publication describes the potential for the polygenic risk model on the basis of common genetic variants to discriminate risk in common cancers and demonstrates its potential clinical application in risk-stratified population disease prevention and early detection.
Chatterjee, N., Shi, J. & Garcia-Closas, M. Developing and evaluating polygenic risk prediction models for stratified disease prevention. Nat. Rev. Genet. 17, 392–406 (2016).
Fritsche, L. G. et al. Cancer PRSweb: an online repository with polygenic risk scores for major cancer traits and their evaluation in two independent biobanks. Am. J. Hum. Genet. 107, 815–836 (2020).
Lambert, S. A. et al. The Polygenic Score Catalog as an open database for reproducibility and systematic evaluation. Nat. Genet. 53, 420–425 (2021).
Kachuri, L. et al. Pan-cancer analysis demonstrates that integrating polygenic risk scores with modifiable risk factors improves risk prediction. Nat. Commun. 11, 6084 (2020).
Rashkin, S. R. et al. Pan-cancer study detects genetic risk variants and shared genetic basis in two large cohorts. Nat. Commun. 11, 4423 (2020).
Sampson, J. N. et al. Analysis of heritability and shared heritability based on genome-wide association studies for thirteen cancer types. J. Natl Cancer Inst. 107, djv279 (2015).
Lindstrom, S. et al. Quantifying the genetic correlation between multiple cancer types. Cancer Epidemiol. Biomark. Prev. 26, 1427–1435 (2017).
Martin, A. R. et al. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat. Genet. 51, 584–591 (2019).
Mavaddat, N. et al. Polygenic risk scores for prediction of breast cancer and breast cancer subtypes. Am. J. Hum. Genet. 104, 21–34 (2019).
Kuchenbaecker, K. B. et al. Risks of breast, ovarian, and contralateral breast cancer for BRCA1 and BRCA2 mutation carriers. J. Am. Med. Assoc. 317, 2402–2416 (2017).
Antoniou, A. et al. Average risks of breast and ovarian cancer associated with BRCA1 or BRCA2 mutations detected in case series unselected for family history: a combined analysis of 22 studies. Am. J. Hum. Genet. 72, 1117–1130 (2003).
International Mismatch Repair Consortium. Variation in the risk of colorectal cancer in families with Lynch syndrome: a retrospective cohort study. Lancet Oncol. 22, 1014–1022 (2021).
Kuchenbaecker, K. B. et al. Evaluation of polygenic risk scores for breast and ovarian cancer risk prediction in BRCA1 and BRCA2 mutation carriers. J. Natl Cancer Inst. 109, djw302 (2017).
Barnes, D. R. et al. Polygenic risk scores and breast and epithelial ovarian cancer risks for carriers of BRCA1 and BRCA2 pathogenic variants. Genet. Med. 22, 1653–1666 (2020).
Barnes, D. R. et al. Breast and prostate cancer risks for male BRCA1 and BRCA2 pathogenic variant carriers using polygenic risk scores. J. Natl Cancer Inst. 114, 109–122 (2022).
Fahed, A. C. et al. Polygenic background modifies penetrance of monogenic variants for tier 1 genomic conditions. Nat. Commun. 11, 3635 (2020).
Jenkins, M. A. et al. Assessment of a polygenic risk score for colorectal cancer to predict risk of lynch syndrome colorectal Cancer. JNCI Cancer Spectr. 5, pkab022 (2021).
Garcia-Closas, M., Gunsoy, N. B. & Chatterjee, N. Combined associations of genetic and environmental risk factors: implications for prevention of breast cancer. J. Natl Cancer Inst. 106, dju305 (2014).
Milne, R. L. et al. Assessing interactions between the associations of common genetic susceptibility variants, reproductive history and body mass index with breast cancer risk in the breast cancer association consortium: a combined case–control study. Breast Cancer Res. 12, R110 (2010).
Nickels, S. et al. Evidence of gene–environment interactions between common breast cancer susceptibility loci and established environmental risk factors. PLoS Genet. 9, e1003284 (2013).
Rudolph, A. et al. Investigation of gene–environment interactions between 47 newly identified breast cancer susceptibility loci and environmental risk factors. Int. J. Cancer 136, E685–E696 (2015).
Rudolph, A. et al. Joint associations of a polygenic risk score and environmental risk factors for breast cancer in the Breast Cancer Association Consortium. Int. J. Epidemiol. 47, 526–536 (2018).
Vachon, C. M. et al. Joint association of mammographic density adjusted for age and body mass index and polygenic risk score with breast cancer risk. Breast Cancer Res. 21, 68 (2019).
Kapoor, P. M. et al. Assessment of interactions between 205 breast cancer susceptibility loci and 13 established risk factors in relation to breast cancer risk, in the Breast Cancer Association Consortium. Int. J. Epidemiol. 49, 216–232 (2020).
Wang, X. et al. Genome-wide interaction analysis of menopausal hormone therapy use and breast cancer risk among 62,370 women. Sci. Rep. 12, 6199 (2022).
Archambault, A. N. et al. Risk stratification for early-onset colorectal cancer using a combination of genetic and environmental risk scores: an international multi-center study. J. Natl Cancer Inst. 114, 528–539 (2022).
Usher-Smith, J. A., Walter, F. M., Emery, J. D., Win, A. K. & Griffin, S. J. Risk prediction models for colorectal cancer: a systematic review. Cancer Prev. Res. 9, 13–26 (2016).
Walker, J. G. et al. The use of a risk assessment and decision support tool (CRISP) compared with usual care in general practice to increase risk-stratified colorectal cancer screening: study protocol for a randomised controlled trial. Trials 19, 397 (2018).
Pal Choudhury, P. et al. iCARE: an R package to build, validate and apply absolute risk models. PLoS ONE 15, e0228198 (2020).
Lee, A. et al. Comprehensive epithelial tubo-ovarian cancer risk prediction model incorporating genetic and epidemiological risk factors. J. Med. Genet. 59, 632–643 (2021).
Harrison, H. et al. The current state of genetic risk models for the development of kidney cancer: a review and validation. BJU Int. 130, 550–561 (2022).
Nyberg, T. et al. CanRisk-prostate: a comprehensive, externally validated risk model for the prediction of future prostate cancer. J. Clin. Oncol. 41, 1092–1104 (2022).
Saunders, C. L. et al. External validation of risk prediction models incorporating common genetic variants for incident colorectal cancer using UK Biobank. Cancer Prev. Res. 13, 509–520 (2020).
Hurson, A. N. et al. Prospective evaluation of a breast-cancer risk model integrating classical risk factors and polygenic risk in 15 cohorts from six countries. Int. J. Epidemiol. 50, 1897–1911 (2022).
Pal Choudhury, P. et al. Comparative validation of the BOADICEA and Tyrer–Cuzick breast cancer risk models incorporating classical risk factors and polygenic risk in a population-based prospective cohort of women of European ancestry. Breast Cancer Res. 23, 22 (2021).
Sud, A., Turnbull, C. & Houlston, R. Will polygenic risk scores for cancer ever be clinically useful? npj Precis. Oncol. 5, 40 (2021).
Clayton, D. G. Prediction and interaction in complex disease genetics: experience in type 1 diabetes. PLoS Genet. 5, e1000540 (2009).
Archer, S. et al. Personalised risk prediction in hereditary breast and ovarian cancer: a protocol for a multi-centre randomised controlled trial. Cancers 14, 2716 (2022).
PRiMo Trial. Using polygenic risk modification to improve breast cancer prevention. https://www.petermac.org/research/clinical-research/clinical-research-by-centre/familial-cancer-research-centre/primo-trial (2023).
Thomas, M. et al. Genome-wide modeling of polygenic risk score in colorectal cancer risk. Am. J. Hum. Genet. 107, 432–444 (2020).
National Institute for Health and Care Excellence. Familial breast cancer classification care and managing breast cancer and related risks in people with a family history of breast cancer (CG164) (2013).
Moyer, V. A. Screening for prostate cancer: U.S. Preventive Services Task Force recommendation statement. Ann. Intern. Med. 157, 120–134 (2012).
Rouge-Bugat, M.-E. et al. MyPeBS International randomized study comparing personalised, risk-stratified to standard breast cancer screening in women aged 40–70: focus on recruitment strategy in France. La Presse Médicale Open 3, 100022 (2022).
Esserman, L. J., Study, W. & Athena, I. The WISDOM study: breaking the deadlock in the breast cancer screening debate. npj Breast Cancer 3, 34 (2017).
Saya, S. et al. The impact of a comprehensive risk prediction model for colorectal cancer on a population screening program. JNCI Cancer Spectr. 4, pkaa062 (2020).
Matthias, M. S. & Imperiale, T. F. A risk prediction tool for colorectal cancer screening: a qualitative study of patient and provider facilitators and barriers. BMC Fam. Pract. 21, 43 (2020).
Gaba, F. et al. Population study of ovarian cancer risk prediction for targeted screening and prevention. Cancers 12, 1241 (2020).
Gaba, F. et al. Unselected population genetic testing for personalised ovarian cancer risk prediction: a qualitative study using semi-structured interviews. Diagnostics 12, 1028 (2022).
Nordstrom, T. et al. Prostate cancer screening using a combination of risk-prediction, MRI, and targeted prostate biopsies (STHLM3-MRI): a prospective, population-based, randomised, open-label, non-inferiority trial. Lancet Oncol. 22, 1240–1249 (2021).
Linder, J. E. et al. Returning integrated genomic risk and clinical recommendations: the eMERGE study. Genet. Med. 25, 100006 (2023).
Brooks, J. D. et al. Personalized risk assessment for prevention and early detection of breast cancer: integration and implementation (PERSPECTIVE I&I). J. Pers. Med. 11, 511 (2021).
Hao, L. et al. Development of a clinical polygenic risk score assay and reporting workflow. Nat. Med. 28, 1006–1013 (2022).
Clifton, L., Collister, J. A., Liu, X., Littlejohns, T. J. & Hunter, D. J. Assessing agreement between different polygenic risk scores in the UK Biobank. Sci. Rep. 12, 12812 (2022).
Wand, H. et al. Improving reporting standards for polygenic scores in risk prediction studies. Nature 591, 211–219 (2021).
Dixon, P., Keeney, E., Taylor, J. C., Wordsworth, S. & Martin, R. M. Can polygenic risk scores contribute to cost-effective cancer screening? A systematic review. Genet. Med. 24, 1604–1617 (2022).
Hollands, G. J. et al. The impact of communicating genetic risks of disease on risk-reducing health behaviour: systematic review with meta-analysis. Br. Med. J. 352, i1102 (2016).
Khan, Z. et al. Polygenic risk for skin autoimmunity impacts immune checkpoint blockade in bladder cancer. Proc. Natl Acad. Sci. USA 117, 12288–12294 (2020).
Khan, Z. et al. Genetic variation associated with thyroid autoimmunity shapes the systemic immune response to PD-1 checkpoint blockade. Nat. Commun. 12, 3355 (2021).
Shahamatdar, S. et al. Germline features associated with immune infiltration in solid tumors. Cell Rep. 30, 2900–2908.e4 (2020).
Mills, M. C. & Rahal, C. The GWAS Diversity Monitor tracks diversity by disease in real time. Nat. Genet. 52, 242–243 (2020).
Petrovski, S. & Goldstein, D. B. Unequal representation of genetic variation across ancestry groups creates healthcare inequality in the application of precision medicine. Genome Biol. 17, 157 (2016).
Popejoy, A. B. & Fullerton, S. M. Genomics is failing on diversity. Nature 538, 161–164 (2016).
Scutari, M., Mackay, I. & Balding, D. Using genetic distance to infer the accuracy of genomic prediction. PLoS Genet. 12, e1006288 (2016).
Duncan, L. et al. Analysis of polygenic risk score usage and performance in diverse human populations. Nat. Commun. 10, 3328 (2019).
Prive, F. et al. Portability of 245 polygenic scores when derived from the UK Biobank and applied to 9 ancestry groups from the same cohort. Am. J. Hum. Genet. 109, 12–23 (2022). This study shows a low portability of PGS for 245 traits derived from the UK Biobank samples of UK ancestry to nine other ancestries and demonstrates a canonical relation between genetic distance and predictive performance for most traits.
Tanigawa, Y. et al. Significant sparse polygenic risk scores across 813 traits in UK Biobank. PLoS Genet. 18, e1010105 (2022).
Kim, M. S., Patel, K. P., Teng, A. K., Berens, A. J. & Lachance, J. Genetic disease risks can be misestimated across global populations. Genome Biol. 19, 179 (2018).
Ntzani, E. E., Liberopoulos, G., Manolio, T. A. & Ioannidis, J. P. Consistency of genome-wide associations across major ancestral groups. Hum. Genet. 131, 1057–1071 (2012).
Martin, A. R. et al. Human demographic history impacts genetic risk prediction across diverse populations. Am. J. Hum. Genet. 100, 635–649 (2017).
Wang, Y. et al. Theoretical and empirical quantification of the accuracy of polygenic scores in ancestry divergent populations. Nat. Commun. 11, 3865 (2020).
Jing, L., Su, L. & Ring, B. Z. Ethnic background and genetic variation in the evaluation of cancer risk: a systematic review. PLoS ONE 9, e97522 (2014).
Gurdasani, D., Barroso, I., Zeggini, E. & Sandhu, M. S. Genomics of disease risk in globally diverse populations. Nat. Rev. Genet. 20, 520–535 (2019).
Pulit, S. L., Voight, B. F. & de Bakker, P. I. Multiethnic genetic association studies improve power for locus discovery. PLoS ONE 5, e12600 (2010).
Taliun, D. et al. Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program. Nature 590, 290–299 (2021).
Cavazos, T. B. & Witte, J. S. Inclusion of variants discovered from diverse populations improves polygenic risk score transferability. HGG Adv. 2, 100017 (2021).
Peterson, R. E. et al. Genome-wide association studies in ancestrally diverse populations: opportunities, methods, pitfalls, and recommendations. Cell 179, 589–603 (2019).
Wen, W. et al. Prediction of breast cancer risk based on common genetic variants in women of East Asian ancestry. Breast Cancer Res. 18, 124 (2016).
Ho, W. K. et al. European polygenic risk score for prediction of breast cancer shows similar performance in Asian women. Nat. Commun. 11, 3833 (2020).
Nagai, A. et al. Overview of the BioBank Japan Project: study design and profile. J. Epidemiol. 27, S2–S8 (2017).
Chen, Z. et al. China Kadoorie Biobank of 0.5 million people: survey methods, baseline characteristics and long-term follow-up. Int. J. Epidemiol. 40, 1652–1666 (2011).
Ramsay, M. Growing genomic research on the African continent: the H3Africa Consortium. S. Afr. Med. J. 105, 1016–1017 (2015).
Mapes, B. M. et al. Diversity and inclusion for the All of Us research program: a scoping review. PLoS ONE 15, e0234962 (2020).
Weissbrod, O. et al. Leveraging fine-mapping and multipopulation training data to improve cross-population polygenic risk scores. Nat. Genet. 54, 450–458 (2022).This paper reports a method for improving cross-population polygenic scores by incorporating functionally informed fine-mapping to estimate causal effects together with the incorporation of non-European data into the training data.
Conti, D. V. et al. Trans-ancestry genome-wide association meta-analysis of prostate cancer identifies new susceptibility loci and informs genetic risk prediction. Nat. Genet. 53, 65–75 (2021).
Graham, S. E. et al. The power of genetic diversity in genome-wide association studies of lipids. Nature 600, 675–679 (2021).
Weissbrod, O. et al. Functionally informed fine-mapping and polygenic localization of complex trait heritability. Nat. Genet. 52, 1355–1363 (2020).
Choi, J., Jia, G., Wen, W., Long, J. & Zheng, W. Evaluating polygenic risk scores in assessing risk of nine solid and hematologic cancers in European descendants. Int. J. Cancer 147, 3416–3423 (2020).
Hung, R. J. et al. Assessing lung cancer absolute risk trajectory based on a polygenic risk model. Cancer Res. 81, 1607–1615 (2021).
Jia, G. et al. Evaluating the utility of polygenic risk scores in identifying high-risk individuals for eight common cancers. JNCI Cancer Spectr. 4, pkaa021 (2020).
Steinberg, J. et al. Independent evaluation of melanoma polygenic risk scores in UK and Australian prospective cohorts. Br. J. Dermatol. 186, 823–834 (2022).
Sharma, S., Tapper, W. J., Collins, A. & Hamady, Z. Z. R. Predicting pancreatic cancer in the UK Biobank Cohort using polygenic risk scores and diabetes mellitus. Gastroenterology 162, 1665–1674.e2 (2022).
R Core Team. R: A Language and Environment for Statistical Computing https://www.R-project.org/ (R Foundation for Statistical Computing, 2021).
Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag, 2016).
Gallagher, S. et al. Association of a polygenic risk score with breast cancer among women carriers of high- and moderate-risk breast cancer genes. JAMA Netw. Open 3, e208501 (2020).
Gao, C. et al. Risk of breast cancer among carriers of pathogenic variants in breast cancer predisposition genes varies by polygenic risk score. J. Clin. Oncol. 39, 2564–2573 (2021).
Schumacher, F. R. et al. Association analyses of more than 140,000 men identify 63 new prostate cancer susceptibility loci. Nat. Genet. 50, 928–936 (2018).
Acknowledgements
This work was supported by the Cancer Research UK grant no. PPRPGM-Nov20\100002, the Gray Foundation and the PERSPECTIVE I&I Project, which the Government of Canada funds through Genome Canada (#13529) and the Canadian Institutes of Health Research (#155865). S.K. is supported by a UK Research and Innovation Future Leaders Fellowship (#MR/T043202/1).
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Related links
CanRisk: https://www.canrisk.org/
Consortium of Investigators of Modifiers of BRCA1/2: https://cimba.ccge.medschl.cam.ac.uk/
The Polygenic Score Catalogue: https://www.pgscatalog.org/
Glossary
- 313-SNP PGS
-
A polygenic score with 313 SNPs for breast cancer risk developed by Mavaddat et al.36 using data from a large GWAS with independent validation using data from the UK Biobank.
- Common risk alleles
-
Alleles that are associated with disease risk that have a frequency of at least 1% in the population.
- Genetic architecture
-
The range of risk alleles for a given phenotype as characterized by the risk allele frequencies and their associated risks.
- High-penetrance variants
-
Inherited genetic variants in which carriers of a particular allele are highly likely to develop the disease or trait associated with that variant.
- Linkage studies
-
A type of study designed to identify disease-associated genes, making use of the phenomenon of meiotic linkage (that is, the fact that previously unmapped genes for a disease and nearby genetic markers with known chromosomal position are frequently inherited together in families with a high incidence of the disease).
- Lynch syndrome
-
Also known as hereditary nonpolyposis colorectal cancer, which is an autosomal dominant condition owing to inherited mutations in DNA mismatch repair genes (most commonly MLH1, MSH2, MSH6 or PMS2) that confer high (>50%) lifetime risk of colorectal, endometrial and other cancers.
- Narrow-sense heritability
-
The proportion of variation in a disease or trait that is explained by adding up the average effects of all risk alleles.
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Yang, X., Kar, S., Antoniou, A.C. et al. Polygenic scores in cancer. Nat Rev Cancer 23, 619–630 (2023). https://doi.org/10.1038/s41568-023-00599-x
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DOI: https://doi.org/10.1038/s41568-023-00599-x
