Review Article | Published:

The personal and clinical utility of polygenic risk scores

Nature Reviews Geneticsvolume 19pages581590 (2018) | Download Citation


Initial expectations for genome-wide association studies were high, as such studies promised to rapidly transform personalized medicine with individualized disease risk predictions, prevention strategies and treatments. Early findings, however, revealed a more complex genetic architecture than was anticipated for most common diseases — complexity that seemed to limit the immediate utility of these findings. As a result, the practice of utilizing the DNA of an individual to predict disease has been judged to provide little to no useful information. Nevertheless, recent efforts have begun to demonstrate the utility of polygenic risk profiling to identify groups of individuals who could benefit from the knowledge of their probabilistic susceptibility to disease. In this context, we review the evidence supporting the personal and clinical utility of polygenic risk profiling.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Additional information

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.



  1. 1.

    Natarajan, P. et al. Polygenic risk score identifies subgroup with higher burden of atherosclerosis and greater relative benefit from statin therapy in the primary prevention setting. Circulation 135, 2091–2101 (2017).

  2. 2.

    Maas, P. et al. Breast cancer risk from modifiable and nonmodifiable risk factors among white women in the United States. JAMA Oncol. 2, 1295–1302 (2016).This study clearly lays out the utility of a breast cancer PRS for risk-based rather than age-based recommendations for breast cancer screening mammography.

  3. 3.

    Seibert, T. M. et al. Polygenic hazard score to guide screening for aggressive prostate cancer: development and validation in large scale cohorts. BMJ 360, j5757 (2018).

  4. 4.

    Desikan, R. S. et al. Genetic assessment of age-associated Alzheimer disease risk: development and validation of a polygenic hazard score. PLoS Med. 14, e1002258 (2017).

  5. 5.

    Khera, A. V. et al. Genetic risk, adherence to a healthy lifestyle, and coronary disease. N. Engl. J. Med. 375, 2349–2358 (2016).

  6. 6.

    Paquette, M. et al. Polygenic risk score predicts prevalence of cardiovascular disease in patients with familial hypercholesterolemia. J. Clin. Lipidol 11, 725–732.e5 (2017).

  7. 7.

    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). This study clearly lays out the case for the combined testing of monogenic and polygenic disease risk factors.

  8. 8.

    Lecarpentier, J. et al. Prediction of breast and prostate cancer risks in male BRCA1 and BRCA2 mutation carriers using polygenic risk scores. J. Clin. Oncol. 35, 2240–2250 (2017).

  9. 9.

    Witte, J. S., Visscher, P. M. & Wray, N. R. The contribution of genetic variants to disease depends on the ruler. Nat. Rev. Genet. 15, 765–776 (2014).This reference provides a detailed breakdown of various measures and interpretations of heritability.

  10. 10.

    Manolio, T. A. et al. Finding the missing heritability of complex diseases. Nature 461, 747–753 (2009).

  11. 11.

    Wray, N. R., Yang, J., Goddard, M. E. & Visscher, P. M. The genetic interpretation of area under the ROC curve in genomic profiling. PLoS Genet. 6, e1000864 (2010).

  12. 12.

    Cook, N. R. Use and misuse of the receiver operating characteristic curve in risk prediction. Circulation 115, 928–935 (2007).

  13. 13.

    Anglian Breast Cancer Study Group. Prevalence and penetrance of BRCA1 and BRCA2 mutations in a population-based series of breast cancer cases. Br. J. Cancer 83, 1301–1308 (2000).

  14. 14.

    Peto, J. et al. Prevalence of BRCA1 and BRCA2 gene mutations in patients with early-onset breast cancer. J. Natl Cancer Inst. 91, 943–949 (1999).

  15. 15.

    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).

  16. 16.

    Timpson, N. J., Greenwood, C. M. T., Soranzo, N., Lawson, D. J. & Richards, J. B. Genetic architecture: the shape of the genetic contribution to human traits and disease. Nat. Rev. Genet. 19, 110–124 (2018).

  17. 17.

    Chatterjee, N. et al. Projecting the performance of risk prediction based on polygenic analyses of genome-wide association studies. Nat. Genet. 45, 400–405 (2013).

  18. 18.

    Badano, J. L. & Katsanis, N. Beyond Mendel: an evolving view of human genetic disease transmission. Nat. Rev. Genet. 3, 779–789 (2002).

  19. 19.

    Katsanis, N. The continuum of causality in human genetic disorders. Genome Biol. 17, 233 (2016).

  20. 20.

    Hartiala, J. et al. The genetic architecture of coronary artery disease: current knowledge and future opportunities. Curr. Atheroscler Rep. 19, 6 (2017).

  21. 21.

    Amos, C. I. et al. The OncoArray Consortium: a network for understanding the genetic architecture of common cancers. Cancer Epidemiol. Biomarkers Prev. 26, 126–135 (2017).

  22. 22.

    Ridge, P. G. et al. Assessment of the genetic variance of late-onset Alzheimer’s disease. Neurobiol. Aging 41, 200 e13–200.e20 (2016).

  23. 23.

    Fuchsberger, C. et al. The genetic architecture of type 2 diabetes. Nature 536, 41–47 (2016).A very large-scale, comprehensive GWAS for type 2 diabetes mellitus that finds no evidence for low-frequency variants of moderate effect size despite being powered to detect such associations.

  24. 24.

    Shi, H., Kichaev, G. & Pasaniuc, B. Contrasting the genetic architecture of 30 complex traits from summary association data. Am. J. Hum. Genet. 99, 139–153 (2016).

  25. 25.

    Yang, J. et al. Common SNPs explain a large proportion of the heritability for human height. Nat. Genet. 42, 565–569 (2010).

  26. 26.

    Zhu, Z. et al. Dominance genetic variation contributes little to the missing heritability for human complex traits. Am. J. Hum. Genet. 96, 377–385 (2015).

  27. 27.

    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 and implications for the future. Preprint at bioRxiv, 175406 (2017).

  28. 28.

    Speed, D. et al. Reevaluation of SNP heritability in complex human traits. Nat. Genet. 49, 986–992 (2017).

  29. 29.

    Yang, J., Zeng, J., Goddard, M. E., Wray, N. R. & Visscher, P. M. Concepts, estimation and interpretation of SNP-based heritability. Nat. Genet. 49, 1304–1310 (2017).

  30. 30.

    Evans, L. et al. Comparison of methods that use whole genome data to estimate the heritability and genetic architecture of complex traits. Preprint at bioRxiv, 115527 (2017).

  31. 31.

    Stahl, E. A. et al. Bayesian inference analyses of the polygenic architecture of rheumatoid arthritis. Nat. Genet. 44, 483–489 (2012).

  32. 32.

    Browning, S. R. & Browning, B. L. Population structure can inflate SNP-based heritability estimates. Am. J. Hum. Genet. 89, 191–193 (2011).

  33. 33.

    Krishna Kumar, S., Feldman, M. W., Rehkopf, D. H. & Tuljapurkar, S. Limitations of GCTA as a solution to the missing heritability problem. Proc. Natl Acad. Sci. USA 113, E61–E70 (2016).

  34. 34.

    Yang, J., Lee, S. H., Wray, N. R., Goddard, M. E. & Visscher, P. M. GCTA-GREML accounts for linkage disequilibrium when estimating genetic variance from genome-wide SNPs. Proc. Natl Acad. Sci. USA 113, E4579–E4580 (2016).

  35. 35.

    Bhatia, G. et al. Subtle stratification confounds estimates of heritability from rare variants. Preprint at bioRxiv, 048181 (2016).

  36. 36.

    Barton, N. H., Etheridge, A. M. & Veber, A. The infinitesimal model: definition, derivation, and implications. Theor. Popul. Biol. 118, 50–73 (2017).

  37. 37.

    Boyle, E. A., Li, Y. I. & Pritchard, J. K. An expanded view of complex traits: from polygenic to omnigenic. Cell 169, 1177–1186 (2017).This reference lays out the theoretical basis for the omnigenic model of inheritance.

  38. 38.

    Nikpay, M. et al. A comprehensive 1,000 Genomes-based genome-wide association meta-analysis of coronary artery disease. Nat. Genet. 47, 1121–1130 (2015).A very large-scale, comprehensive GWAS for coronary artery disease that finds no evidence for low-frequency variants of moderate effect size, despite being powered to detect such associations.

  39. 39.

    Howson, J. M. M. et al. Fifteen new risk loci for coronary artery disease highlight arterial-wall-specific mechanisms. Nat. Genet. 49, 1113–1119 (2017).

  40. 40.

    Michailidou, K. et al. Association analysis identifies 65 new breast cancer risk loci. Nature 551, 92–94 (2017).

  41. 41.

    Easton, D. F. et al. Gene-panel sequencing and the prediction of breast-cancer risk. N. Engl. J. Med. 372, 2243–2257 (2015).

  42. 42.

    Mancuso, N. et al. The contribution of rare variation to prostate cancer heritability. Nat. Genet. 48, 30–35 (2016).

  43. 43.

    Al Olama, A. A. et al. A meta-analysis of 87,040 individuals identifies 23 new susceptibility loci for prostate cancer. Nat. Genet. 46, 1103–1109 (2014).

  44. 44.

    Fletcher, O. & Houlston, R. S. Architecture of inherited susceptibility to common cancer. Nat. Rev. Cancer 10, 353–361 (2010).

  45. 45.

    Gatz, M. et al. Role of genes and environments for explaining Alzheimer disease. Arch. Gen. Psychiatry 63, 168–174 (2006).

  46. 46.

    Sims, R. et al. Rare coding variants in PLCG2, ABI3, and TREM2 implicate microglial-mediated innate immunity in Alzheimer’s disease. Nat. Genet. 49, 1373–1384 (2017).

  47. 47.

    Van Cauwenberghe, C., Van Broeckhoven, C. & Sleegers, K. The genetic landscape of Alzheimer disease: clinical implications and perspectives. Genet. Med. 18, 421–430 (2016).

  48. 48.

    Lander, E. S. The new genomics: global views of biology. Science 274, 536–539 (1996).

  49. 49.

    Reich, D. E. & Lander, E. S. On the allelic spectrum of human disease. Trends Genet. 17, 502–510 (2001).

  50. 50.

    Chakravarti, A. Population genetics — making sense out of sequence. Nat. Genet. 21, 56–60 (1999).

  51. 51.

    Risch, N. & Merikangas, K. The future of genetic studies of complex human diseases. Science 273, 1516–1517 (1996).

  52. 52.

    Visscher, P. M., Brown, M. A., McCarthy, M. I. & Yang, J. Five years of GWAS discovery. Am. J. Hum. Genet. 90, 7–24 (2012).

  53. 53.

    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).This reference provides a detailed overview of recommended approaches to developing PRS models and translating them to clinically useful measures of risk.

  54. 54.

    Dudbridge, F. Power and predictive accuracy of polygenic risk scores. PLoS Genet. 9, e1003348 (2013).

  55. 55.

    Vilhjalmsson, B. J. et al. Modeling linkage disequilibrium increases accuracy of polygenic risk scores. Am. J. Hum. Genet. 97, 576–592 (2015).

  56. 56.

    Collins, F. S. & Varmus, H. A new initiative on precision medicine. N. Engl. J. Med. 372, 793–795 (2015).

  57. 57.

    US Preventive Services Task Force. Statin use for the primary prevention of cardiovascular disease in adults: US Preventive Services Task Force recommendation statement. JAMA 316, 1997–2007 (2016).

  58. 58.

    Macedo, A. F. et al. Unintended effects of statins from observational studies in the general population: systematic review and meta-analysis. BMC Med. 12, 51 (2014).

  59. 59.

    Sattar, N. et al. Statins and risk of incident diabetes: a collaborative meta-analysis of randomised statin trials. Lancet 375, 735–742 (2010).

  60. 60.

    Redberg, R. F. & Katz, M. H. Statins for primary prevention: the debate is intense, but the data are weak. JAMA 316, 1979–1981 (2016).

  61. 61.

    Greenland, P. & Bonow, R. O. Interpretation and use of another statin guideline. JAMA 316, 1977–1979 (2016).

  62. 62.

    Cook, N. R. & Ridker, P. M. Calibration of the pooled cohort equations for atherosclerotic cardiovascular disease: an update. Ann. Intern. Med. 165, 786–794 (2016).

  63. 63.

    Rana, J. S. et al. Accuracy of the atherosclerotic cardiovascular risk equation in a large contemporary, multiethnic population. J. Am. Coll. Cardiol. 67, 2118–2130 (2016).

  64. 64.

    Mega, J. L. et al. Genetic risk, coronary heart disease events, and the clinical benefit of statin therapy: an analysis of primary and secondary prevention trials. Lancet 385, 2264–2271 (2015).A landmark study demonstrating the utility of PRSs for the prioritization of statin therapy.

  65. 65.

    Abraham, G. et al. Genomic prediction of coronary heart disease. Eur. Heart J. 37, 3267–3278 (2016).

  66. 66.

    Tada, H. et al. Risk prediction by genetic risk scores for coronary heart disease is independent of self-reported family history. Eur. Heart J. 37, 561–567 (2016).

  67. 67.

    Tikkanen, E., Havulinna, A. S., Palotie, A., Salomaa, V. & Ripatti, S. Genetic risk prediction and a 2-stage risk screening strategy for coronary heart disease. Arterioscler. Thromb. Vasc. Biol. 33, 2261–2266 (2013).

  68. 68.

    Ripatti, S. et al. A multilocus genetic risk score for coronary heart disease: case-control and prospective cohort analyses. Lancet 376, 1393–1400 (2010).

  69. 69.

    Kullo, I. J. et al. Incorporating a genetic risk score into coronary heart disease risk estimates: effect on low-density lipoprotein cholesterol levels (the MI-GENES clinical trial). Circulation 133, 1181–1188 (2016).

  70. 70.

    Umans-Eckenhausen, M. A., Defesche, J. C., van Dam, M. J. & Kastelein, J. J. Long-term compliance with lipid-lowering medication after genetic screening for familial hypercholesterolemia. Arch. Intern. Med. 163, 65–68 (2003).

  71. 71.

    Khera, A. V. et al. Genome-wide polygenic score to identify a monogenic risk-equivalent for coronary disease. Preprint at bioRxiv, 218388 (2017).

  72. 72.

    Siu, A. L. & US Preventive Services Task Force. Screening for breast cancer: US Preventive Services Task Force recommendation statement. Ann. Intern. Med. 164, 279–296 (2016).

  73. 73.

    Mavaddat, N. et al. Prediction of breast cancer risk based on profiling with common genetic variants. J. Natl Cancer Inst. 107, djv036 (2015).

  74. 74.

    Hsu, L. et al. A model to determine colorectal cancer risk using common genetic susceptibility loci. Gastroenterology 148, 1330–1339.e14 (2015).

  75. 75.

    Bibbins-Domingo, K., Grossman, D. C. & Curry, S. J. The US Preventive Services Task Force 2017 draft recommendation statement on screening for prostate cancer: an invitation to review and comment. JAMA 317, 1949–1950 (2017).

  76. 76.

    Hamdy, F. C. et al. 10-year outcomes after monitoring, surgery, or radiotherapy for localized prostate cancer. N. Engl. J. Med. 375, 1415–1424 (2016).

  77. 77.

    Pashayan, N. et al. Implications of polygenic risk-stratified screening for prostate cancer on overdiagnosis. Genet. Med. 17, 789–795 (2015).

  78. 78.

    Eeles, R. et al. The genetic epidemiology of prostate cancer and its clinical implications. Nat. Rev. Urol. 11, 18–31 (2014).

  79. 79.

    Tosoian, J. J. et al. Active surveillance program for prostate cancer: an update of the Johns Hopkins experience. J. Clin. Oncol. 29, 2185–2190 (2011).

  80. 80.

    Morganstein, J. The Handbook of Health Behavior Change edited by Kristin A. Reikert, Judith K. Ockene and Lori Pbert. Psychiatry 79, 95–96 (2016).

  81. 81.

    Wray, N. R. et al. Pitfalls of predicting complex traits from SNPs. Nat. Rev. Genet. 14, 507–515 (2013).

  82. 82.

    Martin, A. R. et al. Human demographic history impacts genetic risk prediction across diverse populations. Am. J. Hum. Genet. 100, 635–649 (2017).This analysis highlights the lack of transferability of PRS populations of dissimilar ancestry.

  83. 83.

    Gaudet, M. M. et al. Identification of a BRCA2-specific modifier locus at 6p24 related to breast cancer risk. PLoS Genet. 9, e1003173 (2013).

  84. 84.

    Couch, F. J. et al. Genome-wide association study in BRCA1 mutation carriers identifies novel loci associated with breast and ovarian cancer risk. PLoS Genet. 9, e1003212 (2013).

  85. 85.

    Wang, J. et al. Polygenic versus monogenic causes of hypercholesterolemia ascertained clinically. Arterioscler. Thromb. Vasc. Biol. 36, 2439–2445 (2016).

  86. 86.

    Green, R. C. et al. Disclosure of APOE genotype for risk of Alzheimer’s disease. N. Engl. J. Med. 361, 245–254 (2009).

  87. 87.

    Collins, R. E., Wright, A. J. & Marteau, T. M. Impact of communicating personalized genetic risk information on perceived control over the risk: a systematic review. Genet. Med. 13, 273–277 (2011).

  88. 88.

    Bloss, C. S., Schork, N. J. & Topol, E. J. Effect of direct-to-consumer genomewide profiling to assess disease risk. N. Engl. J. Med. 364, 524–534 (2011).

  89. 89.

    Visscher, P. M. et al. 10 years of GWAS discovery: biology, function, and translation. Am. J. Hum. Genet. 101, 5–22 (2017).

  90. 90.

    Wellcome Trust Case Control, C. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447, 661–678 (2007).

  91. 91.

    Easton, D. F. et al. Genome-wide association study identifies novel breast cancer susceptibility loci. Nature 447, 1087–1093 (2007).

  92. 92.

    Yeager, M. et al. Genome-wide association study of prostate cancer identifies a second risk locus at 8q24. Nat. Genet. 39, 645–649 (2007).

  93. 93.

    Gudmundsson, J. et al. Genome-wide association study identifies a second prostate cancer susceptibility variant at 8q24. Nat. Genet. 39, 631–637 (2007).

  94. 94.

    Zanke, B. W. et al. Genome-wide association scan identifies a colorectal cancer susceptibility locus on chromosome 8q24. Nat. Genet. 39, 989–994 (2007).

  95. 95.

    Coon, K. D. et al. A high-density whole-genome association study reveals that APOE is the major susceptibility gene for sporadic late-onset Alzheimer’s disease. J. Clin. Psychiatry 68, 613–618 (2007).

  96. 96.

    Caulfield, T. & McGuire, A. L. Direct-to-consumer genetic testing: perceptions, problems, and policy responses. Annu. Rev. Med. 63, 23–33 (2012).

  97. 97.

    Gutierrez, A. 23andMe, Inc. 11/22/13. U.S. Food and Drug Administration (2013).

  98. 98.

    DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium. et al. Genome-wide trans-ancestry meta-analysis provides insight into the genetic architecture of type 2 diabetes susceptibility. Nat. Genet. 46, 234–244 (2014).

  99. 99.

    Molteni, M. Ancestry’s genetic testing kits are heading for your stocking this year. Wired (2017).

  100. 100.

    Regalado, A. 2017 was the year consumer DNA testing blew up. MIT Technol. Rev. (2018).

Download references


This work is supported by The Scripps Translational Science, a National Institutes of Health-National Center for Advancing Translational Sciences (NIH-NCATS) Clinical and Translational Science Award (CTSA; 5 UL1 TR001114). Further support is from U54GM114833 and the Foundation Leducq.

Reviewer information

Nature Reviews Genetics thanks N. Chatterjee, P. Kraft and the other, anonymous reviewer(s) for their contribution to the peer review of this work.

Author information


  1. The Scripps Translational Science Institute, The Scripps Research Institute, La Jolla, CA, USA

    • Ali Torkamani
    • , Nathan E. Wineinger
    •  & Eric J. Topol
  2. Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA

    • Ali Torkamani
    •  & Nathan E. Wineinger
  3. Department of Molecular Medicine, The Scripps Research Institute, La Jolla, CA, USA

    • Eric J. Topol


  1. Search for Ali Torkamani in:

  2. Search for Nathan E. Wineinger in:

  3. Search for Eric J. Topol in:


The authors contributed equally to all aspects of this article.

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to Ali Torkamani.


Polygenic risk scores

(PRSs). A weighted sum of the number of risk alleles carried by an individual, where the risk alleles and their weights are defined by the loci and their measured effects as detected by genome wide association studies.

Genetic architecture

The underlying genetic basis of a trait or disease. The combination of the number, type, frequency, relationship between and magnitude of effect of genetic variants contributing to a trait.


The proportion of total variation between individuals within a population that is due to genetic factors.

Genome-wide association studies

(GWAS). A genetic study designed to rapidly scan for statistical links between a genome-wide set of known genetic variants and a disease or other phenotype of interest.


One of two or more alternative forms of a genetic variation.

Absolute risk

Absolute risk is the unqualified probability, or risk, that a certain event will occur; it ranges from 0–100%.


A term used to describe diseases with one contributing gene, that is, familial risk is driven by high-risk variants, which is in contrast to polygenic disease, where several genetic factors contribute to the disease.

Minor allele frequency

(MAF). The frequency at which the second most frequent allele occurs in a population.


A technique for the inference of unobserved genotypes based on their statistical relationship with observed genotypes.

Relative risk

Relative risk is the probability, or risk, that a certain event will occur in comparison to the event rate in a reference group; often expressed as the ratio of absolute risk between two groups, thus a value of 1.0 means no difference in risk.

About this article

Publication history



Further reading