CoChat
Sign Up
Cited by 1,360Genetic Associations and EpidemiologyGenomics and Rare DiseasesHealth, Environment, Cognitive Aging

Polygenic risk scores: from research tools to clinical instruments

Open access
Lewis, Cathryn M., Vassos, Evangelos·2020·Genome Medicine
DOI 10.1186/s13073-020-00742-5PMC PMC7236300
Open this paper in CoChat — free accountOpen PDF

Abstract

Genome-wide association studies have shown unequivocally that common complex disorders have a polygenic genetic architecture and have enabled researchers to identify genetic variants associated with diseases. These variants can be combined into a polygenic risk score that captures part of an individual’s susceptibility to diseases. Polygenic risk scores have been widely applied in research studies, confirming the association between the scores and disease status, but their clinical utility has yet to be established. Polygenic risk scores may be used to estimate an individual’s lifetime genetic risk of disease, but the current discriminative ability is low in the general population. Clinical implementation of polygenic risk score (PRS) may be useful in cohorts where there is a higher prior probability of disease, for example, in early stages of diseases to assist in diagnosis or to inform treatment choices. Important considerations are the weaker evidence base in application to non-European ancestry and the challenges in translating an individual’s PRS from a percentile of a normal distribution to a lifetime disease risk. In this review, we consider how PRS may be informative at different points in the disease trajectory giving examples of progress in the field and discussing obstacles that need to be addressed before clinical implementation.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1186/s13073-020-00742-5

Cites

17 of 17 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Germline variants in BRCA1 and BRCA2 have predictive value in treatment response and survival.

    “and have a predictive value in terms of treatment response and survival.185,188”

    From Artificial Intelligence and Early Detection of Pancreatic Cancer: 2020 Summative Review (2021) · cites this paper as [185]

    The Disease risk prediction > Medical conditions section discusses BRCA1 and BRCA2 in predicting breast cancer susceptibility, not treatment response or survival. The PRS in treatment choice section discusses treatment response generally but does not link either outcome to BRCA1 or BRCA2. Searches for BRCA-related treatment response and survival found no passage addressing the attributed claim; page numbers were not available in our copy. (LLM, conf=0.76)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 71

  1. [3]Mavaddat N Michailidou K Dennis J Lush M Fachal L Lee A Tyrer JP Chen TH Wang Q Bolla MK Polygenic risk scores for prediction of breast cancer and breast cancer subtypes Am J Hum Genet 2019 104 21 34 30554720 PMC6323553
  2. [4]Wray NR Ripke S Mattheisen M Trzaskowski M Byrne EM Abdellaoui A Adams MJ Agerbo E Air TM Andlauer TMF Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression Nat Genet 2018 50 668 681 29700475 PMC5934326
  3. [6]Lewis CM Hagenaars SP Progressing polygenic medicine in psychiatry through electronic health records JAMA Psychiatry 2019 76 470 472 30698626
  4. [7]Zheutlin AB, Dennis J, Karlsson Linner R, Moscati A, Restrepo N, Straub P, Ruderfer D, Castro VM, Chen CY, Ge T, et al. Penetrance and pleiotropy of polygenic risk scores for schizophrenia in 106,160 patients across four health care systems. Am J Psychiatry. 2019;176(10):846–55. PMC6961974 31416338
  5. [8]Swets JA Indices of discrimination or diagnostic accuracy: their ROCs and implied models Psychol Bull 1986 99 100 117 3704032
  6. [9]Aschard H A perspective on interaction effects in genetic association studies Genet Epidemiol 2016 40 678 688 27390122 PMC5132101
  7. [11]Choi SW, O’Reilly PF. PRSice-2: Polygenic Risk Score software for biobank-scale data. Gigascience. 2019;8(7). 10.1093/gigascience/giz082. PMC6629542 31307061 · DOI
  8. [12]Chang CC Chow CC Tellier LC Vattikuti S Purcell SM Lee JJ Second-generation PLINK: rising to the challenge of larger and richer datasets Gigascience 2015 4 7 25722852 PMC4342193
  9. [13]Vilhjalmsson BJ Yang J Finucane HK Gusev A Lindstrom S Ripke S Genovese G Loh PR Bhatia G Do R Modeling linkage disequilibrium increases accuracy of polygenic risk scores Am J Hum Genet 2015 97 576 592 26430803 PMC4596916
  10. [14]Lloyd-Jones LR Zeng J Sidorenko J Yengo L Moser G Kemper KE Wang H Zheng Z Magi R Esko T Improved polygenic prediction by Bayesian multiple regression on summary statistics Nat Commun 2019 10 5086 31704910 PMC6841727
  11. [15]Lee SH Goddard ME Wray NR Visscher PM A better coefficient of determination for genetic profile analysis Genet Epidemiol 2012 36 214 224 22714935
  12. [17]Mars NJ, Koskela JT, Ripatti P, Kiiskinen TTJ, Havulinna AS, Lindbohm JV, Ahola-Olli A, Kurki M, Karjalainen J, Palta P, et al: Polygenic and clinical risk scores and their impact on age at onset of cardiometabolic diseases and common cancers. Nat Med 2020;26(4):549–557. doi: 10.1038/s41591-020-0800-0. Epub 2020 Apr 7.. 32273609 · DOI
  13. [18]Hu Y Lu Q Liu W Zhang Y Li M Zhao H Joint modeling of genetically correlated diseases and functional annotations increases accuracy of polygenic risk prediction PLoS Genet 2017 13 e1006836 28598966 PMC5482506
  14. [19]Hu Y Lu Q Powles R Yao X Yang C Fang F Xu X Zhao H Leveraging functional annotations in genetic risk prediction for human complex diseases PLoS Comput Biol 2017 13 e1005589 28594818 PMC5481142
  15. [20]Kong A Thorleifsson G Frigge ML Vilhjalmsson BJ Young AI Thorgeirsson TE Benonisdottir S Oddsson A Halldorsson BV Masson G The nature of nurture: effects of parental genotypes Science 2018 359 424 428 29371463
  16. [24]Vassos E Di Forti M Coleman J Iyegbe C Prata D Euesden J O’Reilly P Curtis C Kolliakou A Patel H An examination of polygenic score risk prediction in individuals with first-episode psychosis Biol Psychiatry 2017 81 470 477 27765268
  17. [25]Kim MS Patel KP Teng AK Berens AJ Lachance J Genetic disease risks can be misestimated across global populations Genome Biol 2018 19 179 30424772 PMC6234640
  18. [27]Adeyemo AA Zaghloul NA Chen G Doumatey AP Leitch CC Hostelley TL Nesmith JE Zhou J Bentley AR Shriner D ZRANB3 is an African-specific type 2 diabetes locus associated with beta-cell mass and insulin response Nat Commun 2019 10 3195 31324766 PMC6642147
  19. [28]Suzuki K Akiyama M Ishigaki K Kanai M Hosoe J Shojima N Hozawa A Kadota A Kuriki K Naito M Identification of 28 new susceptibility loci for type 2 diabetes in the Japanese population Nat Genet 2019 51 379 386 30718926
  20. [30]Marquez-Luna C Loh PR South Asian Type 2 Diabetes C, Consortium STD Price AL Multiethnic polygenic risk scores improve risk prediction in diverse populations Genet Epidemiol 2017 41 811 823 29110330 PMC5726434
  21. [31]Coram MA Fang H Candille SI Assimes TL Tang H Leveraging multi-ethnic evidence for risk assessment of quantitative traits in minority populations Am J Hum Genet 2017 101 218 226 28757202 PMC5544393
  22. [32]Grinde KE Qi Q Thornton TA Liu S Shadyab AH Chan KHK Reiner AP Sofer T Generalizing polygenic risk scores from Europeans to Hispanics/Latinos Genet Epidemiol 2019 43 50 62 30368908 PMC6330129
  23. [33]Wojcik GL Graff M Nishimura KK Tao R Haessler J Gignoux CR Highland HM Patel YM Sorokin EP Avery CL Genetic analyses of diverse populations improves discovery for complex traits Nature 2019 570 514 518 31217584 PMC6785182
  24. [34]Sugrue LP Desikan RS What are polygenic scores and why are they important? JAMA 2019 321 1820 1821 30958510
  25. [35]Torkamani A Wineinger NE Topol EJ The personal and clinical utility of polygenic risk scores Nat Rev Genet 2018 19 581 590 29789686
  26. [36]Wilson PW D’Agostino RB Levy D Belanger AM Silbershatz H Kannel WB Prediction of coronary heart disease using risk factor categories Circulation 1998 97 1837 1847 9603539
  27. [37]Assmann G Cullen P Schulte H Simple scoring scheme for calculating the risk of acute coronary events based on the 10-year follow-up of the prospective cardiovascular Munster (PROCAM) study Circulation 2002 105 310 315 11804985
  28. [39]Wilson PW Meigs JB Sullivan L Fox CS Nathan DM D’Agostino RB Sr Prediction of incident diabetes mellitus in middle-aged adults: the Framingham Offspring Study Arch Intern Med 2007 167 1068 1074 17533210
  29. [40]Damen JA Hooft L Schuit E Debray TP Collins GS Tzoulaki I Lassale CM Siontis GC Chiocchia V Roberts C Prediction models for cardiovascular disease risk in the general population: systematic review BMJ 2016 353 i2416 27184143 PMC4868251
  30. [41]Carver RB Castera J Gericke N Evangelista NA El-Hani CN Young adults’ belief in genetic determinism, and knowledge and attitudes towards modern genetics and genomics: the PUGGS Questionnaire PLoS One 2017 12 e0169808 28114357 PMC5256916
  31. [42]Berg JS, Agrawal PB, Bailey DB Jr, Beggs AH, Brenner SE, Brower AM, Cakici JA, Ceyhan-Birsoy O, Chan K, Chen F, et al. Newborn sequencing in genomic medicine and public health. Pediatrics. 2017;139(2). 10.1542/peds.2016-2252. Epub 2017 Jan 17. PMC5260149 28096516 · DOI
  32. [43]Hollands GJ French DP Griffin SJ Prevost AT Sutton S King S Marteau TM The impact of communicating genetic risks of disease on risk-reducing health behaviour: systematic review with meta-analysis BMJ 2016 352 i1102 26979548 PMC4793156
  33. [44]Silarova B Sharp S Usher-Smith JA Lucas J Payne RA Shefer G Moore C Girling C Lawrence K Tolkien Z Effect of communicating phenotypic and genetic risk of coronary heart disease alongside web-based lifestyle advice: the INFORM Randomised Controlled Trial Heart 2019 105 982 989 30928969 PMC6582721
  34. [45]Austin J The effect of genetic test-based risk information on behavioral outcomes: a critical examination of failed trials and a call to action Am J Med Genet A 2015 167A 2913 2915 26284465 PMC4715431
  35. [46]Martin AR Daly MJ Robinson EB Hyman SE Neale BM Predicting polygenic risk of psychiatric disorders Biol Psychiatry 2019 86 97 109 30737014 PMC6599546
  36. [47]Lambert SA, Abraham G, Inouye M. Towards clinical utility of polygenic risk scores. Hum Mol Genet. 2019;28(R2):R133-42. 31363735
  37. [48]Aung N, Vargas JD, Yang C, Cabrera CP, Warren HR, Fung K, Tzanis E, Barnes MR, Rotter JI, Taylor KD, et al. Genome-wide analysis of left ventricular image-derived phenotypes identifies fourteen loci associated with cardiac morphogenesis and heart failure development. Circulation. 2019;140(16):1318-30. PMC6791514 31554410
  38. [49]Haas ME Aragam KG Emdin CA Bick AG International Consortium for Blood P Hemani G Davey Smith G Kathiresan S Genetic association of albuminuria with cardiometabolic disease and blood pressure Am J Hum Genet 2018 103 461 473 30220432 PMC6174360
  39. [50]Antoniou AC Cunningham AP Peto J Evans DG Lalloo F Narod SA Risch HA Eyfjord JE Hopper JL Southey MC The BOADICEA model of genetic susceptibility to breast and ovarian cancers: updates and extensions Br J Cancer 2008 98 2015 PMC2361716 18349832
  40. [51]Dai J Lv J Zhu M Wang Y Qin N Ma H He YQ Zhang R Tan W Fan J Identification of risk loci and a polygenic risk score for lung cancer: a large-scale prospective cohort study in Chinese populations Lancet Respir Med 2019 7 881 891 31326317 PMC7015703
  41. [52]Oram RA Redondo MJ New insights on the genetics of type 1 diabetes Curr Opin Endocrinol Diabetes Obes 2019 26 181 187 31219823
  42. [53]Onengut-Gumuscu S Chen WM Robertson CC Bonnie JK Farber E Zhu Z Oksenberg JR Brant SR Bridges SL Jr Edberg JC Type 1 diabetes risk in African-ancestry participants and utility of an ancestry-specific genetic risk score Diabetes Care 2019 42 406 415 30659077 PMC6385701
  43. [54]Khera AV Chaffin M Wade KH Zahid S Brancale J Xia R Distefano M Senol-Cosar O Haas ME Bick A Polygenic prediction of weight and obesity trajectories from birth to adulthood Cell 2019 177 587 596 31002795 PMC6661115
  44. [56]Cai N Revez JA Adams MJ Andlauer TFM Breen G Byrne EM Clarke TK Forstner AJ Grabe HJ Hamilton SP Minimal phenotyping yields genome-wide association signals of low specificity for major depression Nat Genet 2020 52 437 447 32231276 PMC7906795
  45. [58]Sullivan PF Kendler KS Neale MC Schizophrenia as a complex trait: evidence from a meta-analysis of twin studies Arch Gen Psychiatry 2003 60 1187 1192 14662550
  46. [59]Lichtenstein P Yip BH Bjork C Pawitan Y Cannon TD Sullivan PF Hultman CM Common genetic determinants of schizophrenia and bipolar disorder in Swedish families: a population-based study Lancet 2009 373 234 239 19150704 PMC3879718
  47. [60]Sullivan PF Neale MC Kendler KS Genetic epidemiology of major depression: review and meta-analysis Am J Psychiatry 2000 157 1552 1562 11007705
  48. [62]Peyrot WJ Van der Auwera S Milaneschi Y Dolan CV Madden PAF Sullivan PF Strohmaier J Ripke S Rietschel M Nivard MG Does childhood trauma moderate polygenic risk for depression? A meta-analysis of 5765 subjects from the psychiatric genomics consortium Biol Psychiatry 2018 84 138 147 29129318 PMC5862738
  49. [63]Khera AV Emdin CA Drake I Natarajan P Bick AG Cook NR Chasman DI Baber U Mehran R Rader DJ Genetic risk, adherence to a healthy lifestyle, and coronary disease N Engl J Med 2016 375 2349 2358 27959714 PMC5338864
  50. [64]Lourida I, Hannon E, Littlejohns TJ, Langa KM, Hypponen E, Kuzma E, Llewellyn DJ. Association of lifestyle and genetic risk with incidence of dementia. JAMA. 2019;322(5):430-37. PMC6628594 31302669
  51. [65]Gillett AC Vassos E Lewis CM Transforming summary statistics from logistic regression to the liability scale: application to genetic and environmental risk scores Hum Hered 2018 83 210 224 30865946
  52. [66]Natarajan P Young R Stitziel NO Padmanabhan S Baber U Mehran R Sartori S Fuster V Reilly DF Butterworth A Polygenic risk score identifies subgroup with higher burden of atherosclerosis and greater relative benefit from statin therapy in the primary prevention setting Circulation 2017 135 2091 2101 28223407 PMC5484076
  53. [68]Schunkert H Samani NJ Statin treatment: can genetics sharpen the focus? Lancet 2015 385 2227 2229 25748613
  54. [69]Tadros R Tan HL Investigators E-N El Mathari S Kors JA Postema PG Lahrouchi N Beekman L Radivojkov-Blagojevic M Amin AS Predicting cardiac electrical response to sodium-channel blockade and Brugada syndrome using polygenic risk scores Eur Heart J 2019 40 3097 3107 31504448 PMC6769824
  55. [70]Garcia-Gonzalez J Tansey KE Hauser J Henigsberg N Maier W Mors O Placentino A Rietschel M Souery D Zagar T Pharmacogenetics of antidepressant response: a polygenic approach Prog Neuro-Psychopharmacol Biol Psychiatry 2017 75 128 134 28159590
  56. [71]Ward J Graham N Strawbridge RJ Ferguson A Jenkins G Chen W Hodgson K Frye M Weinshilboum R Uher R Polygenic risk scores for major depressive disorder and neuroticism as predictors of antidepressant response: meta-analysis of three treatment cohorts PLoS One 2018 13 e0203896 30240446 PMC6150505
  57. [72]Zhang JP Robinson D Yu J Gallego J Fleischhacker WW Kahn RS Crespo-Facorro B Vazquez-Bourgon J Kane JM Malhotra AK Lencz T Schizophrenia polygenic risk score as a predictor of antipsychotic efficacy in first-episode psychosis Am J Psychiatry 2019 176 21 28 30392411 PMC6461047
  58. [73]Ramanuj P Ferenchick EK Pincus HA Depression in primary care: part 2-management BMJ 2019 365 l835 30962249
  59. [75]Gibson G On the utilization of polygenic risk scores for therapeutic targeting PLoS Genet 2019 15 e1008060 31022172 PMC6483161
  60. [76]Gibson G Going to the negative: genomics for optimized medical prescription Nat Rev Genet 2019 20 1 2 30348998
  61. [78]Oetjens MT Kelly MA Sturm AC Martin CL Ledbetter DH Quantifying the polygenic contribution to variable expressivity in eleven rare genetic disorders Nat Commun 2019 10 4897 31653860 PMC6814771
  62. [79]Kuchenbaecker KB, McGuffog L, Barrowdale D, Lee A, Soucy P, Dennis J, Domchek SM, Robson M, Spurdle AB, Ramus SJ, et al. Evaluation of polygenic risk scores for breast and ovarian cancer risk prediction in BRCA1 and BRCA2 mutation carriers. J Natl Cancer Inst. 2017;109(7). 10.1093/jnci/djw302. PMC5408990 28376175 · DOI
  63. [80]Lee A Mavaddat N Wilcox AN Cunningham AP Carver T Hartley S Babb de Villiers C Izquierdo A Simard J Schmidt MK BOADICEA: a comprehensive breast cancer risk prediction model incorporating genetic and nongenetic risk factors Genet Med 2019 21 1708 1718 30643217 PMC6687499
  64. [81]Bergen SE Ploner A Howrigan D Group CNVA, the Schizophrenia Working Group of the Psychiatric Genomics C O’Donovan MC Smoller JW Sullivan PF Sebat J Neale B Kendler KS Joint contributions of rare copy number variants and common SNPs to risk for schizophrenia Am J Psychiatry 2019 176 29 35 30392412 PMC6408268
  65. [82]Tansey KE Rees E Linden DE Ripke S Chambert KD Moran JL McCarroll SA Holmans P Kirov G Walters J Common alleles contribute to schizophrenia in CNV carriers Mol Psychiatry 2016 21 1085 1089 26390827 PMC4960448
  66. [83]Vassos E Collier DA Holden S Patch C Rujescu D St Clair D Lewis CM Penetrance for copy number variants associated with schizophrenia Hum Mol Genet 2010 19 3477 3481 20587603
  67. [84]Cleynen I, Engchuan W, Hestand MS, Heung T, Holleman AM, Johnston HR, Monfeuga T, DM MD-MG, Gur RE, Morrow BE, et al. Genetic contributors to risk of schizophrenia in the presence of a 22q11.2 deletion. Mol Psychiatry. 2020. 10.1038/s41380-020-0654-3. [Epub ahead of print]. PMC7396297 32015465 · DOI
  68. [85]Nelson SC Bowen DJ Fullerton SM Third-party genetic interpretation tools: a mixed-methods study of consumer motivation and behavior Am J Hum Genet 2019 105 122 131 31204012 PMC6612532
  69. [86]Bolli A, Di Domenico P, Bottà G. Software as a service for the genomic prediction of complex diseases. bioRxiv. 2019:763722. 10.1101/763722. · DOI
  70. [88]Dudbridge F Pashayan N Yang J Predictive accuracy of combined genetic and environmental risk scores Genet Epidemiol 2018 42 4 19 29178508 PMC5847122
  71. [89]Pet DB Holm IA Williams JL Myers MF Novak LL Brothers KB Wiesner GL Clayton EW Physicians’ perspectives on receiving unsolicited genomic results Genet Med 2019 21 311 318 29904163 PMC6294706

Cited by

Sorted by citations

Authors
Lewis, C. M., & Vassos, E.
Affiliations
King's College London (GB)
Corresponding author
Lewis, Cathryn M.
ORCID
Venue
Genome Medicine
Publisher
Springer Science and Business Media LLC
Published
2020-05-18
Volume
12
Issue
1
Pages
44-44
ISSN
1756-994X (ISSN-L)
DOI
10.1186/s13073-020-00742-5
PMC
PMC7236300
OpenAlex
W3028329270
Cited by
1,360
References
17
Citations by year
2026: 173 · 2025: 264 · 2024: 285 · 2023: 264 · 2022: 212 · 2021: 131 · 2020: 28 · 2018: 1
FWCI
22.73
Type
journal article
Language
en
Open access
Licence
Locations
  • Genome Medicine (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Research Portal (King's College London) (publishedVersion, other-oa)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • South London and Maudsley NHS Foundation Trust
  • National Institute for Health and Care Research
  • Department of Health and Social Care
  • King's College London
  • Medical Research Council (MR/N015746/1, MR/N015746, N015746/1)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
  • Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Health, Environment, Cognitive Aging
Keywords
polygenic risk score, clinical implementation, genetic susceptibility, complex diseases, non-European ancestry, early diagnosis, treatment decision-making, polygenic architecture
MeSH terms
Direct-To-Consumer Screening and Testing, Humans, Research Design, Risk Factors, Genetic Variation, Genetic Predisposition to Disease, Multifactorial Inheritance

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review