1932

Abstract

Polygenic risk scores (PRS) estimate an individual's genetic likelihood of complex traits and diseases by aggregating information across multiple genetic variants identified from genome-wide association studies. PRS can predict a broad spectrum of diseases and have therefore been widely used in research settings. Some work has investigated their potential applications as biomarkers in preventative medicine, but significant work is still needed to definitively establish and communicate absolute risk to patients for genetic and modifiable risk factors across demographic groups. However, the biggest limitation of PRS currently is that they show poor generalizability across diverse ancestries and cohorts. Major efforts are underway through methodological development and data generation initiatives to improve their generalizability. This review aims to comprehensively discuss current progress on the development of PRS, the factors that affect their generalizability, and promising areas for improving their accuracy, portability, and implementation.

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An erratum has been published for this article:
Erratum: Challenges and Opportunities for Developing More Generalizable Polygenic Risk Scores
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2022-08-10
2024-04-13
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Literature Cited

  1. 1.
    Uffelmann E, Huang QQ, Munung NS, de Vries J, Okada Y et al. 2021. Genome-wide association studies. Nat. Rev. Methods Primers 1:59
    [Google Scholar]
  2. 2.
    Visscher PM, Wray NR, Zhang Q, Sklar P, McCarthy MI et al. 2017. 10 years of GWAS discovery: biology, function, and translation. Am. J. Hum. Genet. 101:15–22
    [Google Scholar]
  3. 3.
    Fisher RA. 1919. The correlation between relatives on the supposition of Mendelian inheritance. Trans. R. Soc. Edinb. 52:2399–433
    [Google Scholar]
  4. 4.
    Wray NR, Kemper KE, Hayes BJ, Goddard ME, Visscher PM. 2019. Complex trait prediction from genome data: contrasting EBV in livestock to PRS in humans: genomic prediction. Genetics 211:41131–41
    [Google Scholar]
  5. 5.
    Meuwissen TH, Hayes BJ, Goddard ME. 2001. Prediction of total genetic value using genome-wide dense marker maps. Genetics 157:41819–29
    [Google Scholar]
  6. 6.
    Lande R, Thompson R. 1990. Efficiency of marker-assisted selection in the improvement of quantitative traits. Genetics 124:3743–56
    [Google Scholar]
  7. 7.
    Wientjes YCJ, Bijma P, Calus MPL. 2020. Optimizing genomic reference populations to improve crossbred performance. Genet. Sel. Evol. 52:65
    [Google Scholar]
  8. 8.
    Visscher PM, Hill WG, Wray NR. 2008. Heritability in the genomics era—concepts and misconceptions. Nat. Rev. Genet. 9:4255–66
    [Google Scholar]
  9. 9.
    Yang J, Zeng J, Goddard ME, Wray NR, Visscher PM. 2017. Concepts, estimation and interpretation of SNP-based heritability. Nat. Genet. 49:91304–10
    [Google Scholar]
  10. 10.
    Wray NR, Yang J, Hayes BJ, Price AL, Goddard ME, Visscher PM 2013. Pitfalls of predicting complex traits from SNPs. Nat. Rev. Genet. 14:7507–15
    [Google Scholar]
  11. 11.
    Bulik-Sullivan B, Finucane HK, Anttila V, Gusev A, Day FR et al. 2015. An atlas of genetic correlations across human diseases and traits. Nat. Genet. 47:111236–41
    [Google Scholar]
  12. 12.
    Yang J, Benyamin B, McEvoy BP, Gordon S, Henders AK et al. 2010. Common SNPs explain a large proportion of the heritability for human height. Nat. Genet. 42:7565–69
    [Google Scholar]
  13. 13.
    Tropf FC, Lee SH, Verweij RM, Stulp G, van der Most PJ et al. 2017. Hidden heritability due to heterogeneity across seven populations. Nat. Hum. Behav. 1:10757–65
    [Google Scholar]
  14. 14.
    Ge T, Chen C-Y, Neale BM, Sabuncu MR, Smoller JW. 2017. Phenome-wide heritability analysis of the UK Biobank. PLOS Genet 13:4e1006711
    [Google Scholar]
  15. 15.
    Martin AR, Kanai M, Kamatani Y, Okada Y, Neale BM, Daly MJ 2019. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat. Genet. 51:4584–91
    [Google Scholar]
  16. 16.
    Martin AR, Gignoux CR, Walters RK, Wojcik GL, Neale BM et al. 2017. Human demographic history impacts genetic risk prediction across diverse populations. Am. J. Hum. Genet. 100:4635–49
    [Google Scholar]
  17. 17.
    Zhu H, Zhou X. 2020. Statistical methods for SNP heritability estimation and partition: a review. Comput. Struct. Biotechnol. J. 18:1557–68
    [Google Scholar]
  18. 18.
    Kichaev G, Yang W-Y, Lindstrom S, Hormozdiari F, Eskin E et al. 2014. Integrating functional data to prioritize causal variants in statistical fine-mapping studies. PLOS Genet 10:10e1004722
    [Google Scholar]
  19. 19.
    Gusev A, Lee SH, Trynka G, Finucane H, Vilhjálmsson BJ et al. 2014. Partitioning heritability of regulatory and cell-type-specific variants across 11 common diseases. Am. J. Hum. Genet. 95:5535–52
    [Google Scholar]
  20. 20.
    Finucane HK, Bulik-Sullivan B, Gusev A, Trynka G, Reshef Y et al. 2015. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47:111228–35
    [Google Scholar]
  21. 21.
    Lee SH, Yang J, Goddard ME, Visscher PM, Wray NR. 2012. Estimation of pleiotropy between complex diseases using SNP-derived genomic relationships and restricted maximum likelihood. Bioinformatics 28:192540–42
    [Google Scholar]
  22. 22.
    Lambert SA, Gil L, Jupp S, Ritchie SC, Xu Y et al. 2021. The Polygenic Score Catalog as an open database for reproducibility and systematic evaluation. Nat. Genet. 53:4420–25
    [Google Scholar]
  23. 23.
    Int. Schizophr. Consort 2009. Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature 460:7256748–52
    [Google Scholar]
  24. 24.
    Kooperberg C, LeBlanc M, Obenchain V. 2010. Risk prediction using genome-wide association studies. Genet. Epidemiol. 34:7643–52
    [Google Scholar]
  25. 25.
    Speed D, Balding DJ. 2014. MultiBLUP: improved SNP-based prediction for complex traits. Genome Res 24:91550–57
    [Google Scholar]
  26. 26.
    Albiñana C, Grove J, McGrath JJ, Agerbo E, Wray NR et al. 2021. Leveraging both individual-level genetic data and GWAS summary statistics increases polygenic prediction. Am. J. Hum. Genet. 108:61001–11
    [Google Scholar]
  27. 27.
    Shi J, Park J-H, Duan J, Berndt ST, Moy W et al. 2016. Winner's curse correction and variable thresholding improve performance of polygenic risk modeling based on genome-wide association study summary-level data. PLOS Genet 12:12e1006493
    [Google Scholar]
  28. 28.
    Läll K, Mägi R, Morris A, Metspalu A, Fischer K. 2017. Personalized risk prediction for type 2 diabetes: the potential of genetic risk scores. Genet. Med. 19:3322–29
    [Google Scholar]
  29. 29.
    Vilhjálmsson BJ, Yang J, Finucane HK, Gusev A, Lindström S et al. 2015. Modeling linkage disequilibrium increases accuracy of polygenic risk scores. Am. J. Hum. Genet. 97:4576–92
    [Google Scholar]
  30. 30.
    Robinson MR, Kleinman A, Graff M, Vinkhuyzen AAE, Couper D et al. 2017. Genetic evidence of assortative mating in humans. Nat. Hum. Behav. 1:16
    [Google Scholar]
  31. 31.
    Mak TSH, Porsch RM, Choi SW, Zhou X, Sham PC. 2017. Polygenic scores via penalized regression on summary statistics. Genet. Epidemiol. 41:6469–80
    [Google Scholar]
  32. 32.
    Lloyd-Jones LR, Zeng J, Sidorenko J, Yengo L, Moser G et al. 2019. Improved polygenic prediction by Bayesian multiple regression on summary statistics. Nat. Commun. 10:5086
    [Google Scholar]
  33. 33.
    Ge T, Chen C-Y, Ni Y, Feng Y-CA, Smoller JW. 2019. Polygenic prediction via Bayesian regression and continuous shrinkage priors. Nat. Commun. 10:1776
    [Google Scholar]
  34. 34.
    Privé F, Arbel J, Vilhjálmsson BJ. 2020. LDpred2: better, faster, stronger. Bioinformatics 36:22–235424–31
    [Google Scholar]
  35. 35.
    Ni G, Zeng J, Revez JA, Wang Y, Zheng Z et al. 2021. A comparison of ten polygenic score methods for psychiatric disorders applied across multiple cohorts. Biol. Psychiatry 90:9611–20
    [Google Scholar]
  36. 36.
    Pain O, Glanville KP, Hagenaars SP, Selzam S, Fürtjes AE et al. 2021. Evaluation of polygenic prediction methodology within a reference-standardized framework. PLOS Genet 17:5e1009021
    [Google Scholar]
  37. 37.
    Kulm S, Marderstein A, Mezey J, Elemento O. 2021. A systematic framework for assessing the clinical impact of polygenic risk scores. medRxiv 10.1101/2020.04.06.20055574. https://doi.org/10.1101/2020.04.06.20055574
    [Crossref]
  38. 38.
    Ma Y, Zhou X. 2021. Genetic prediction of complex traits with polygenic scores: a statistical review. Trends Genet 37:11995–1011
    [Google Scholar]
  39. 39.
    Maier RM, Zhu Z, Lee SH, Trzaskowski M, Ruderfer DM et al. 2018. Improving genetic prediction by leveraging genetic correlations among human diseases and traits. Nat. Commun. 9:989
    [Google Scholar]
  40. 40.
    Turley P, Walters RK, Maghzian O, Okbay A, Lee JJ et al. 2018. Multi-trait analysis of genome-wide association summary statistics using MTAG. Nat. Genet. 50:2229–37
    [Google Scholar]
  41. 41.
    Hu Y, Lu Q, Liu W, Zhang Y, Li M, Zhao H. 2017. Joint modeling of genetically correlated diseases and functional annotations increases accuracy of polygenic risk prediction. PLOS Genet 13:6e1006836
    [Google Scholar]
  42. 42.
    Maier RM, Zhu Z, Lee SH, Trzaskowski M, Ruderfer DM et al. 2018. Improving genetic prediction by leveraging genetic correlations among human diseases and traits. Nat. Commun. 9:989
    [Google Scholar]
  43. 43.
    Márquez-Luna C, Loh P-R South Asian Type 2 Diabetes (SAT2D) Consort., SIGMA Type 2 Diabetes Consort., Price AL. 2017. Multiethnic polygenic risk scores improve risk prediction in diverse populations. Genet. Epidemiol. 41:8811–23
    [Google Scholar]
  44. 44.
    Weissbrod O, Kanai M, Shi H, Gazal S, Peyrot W et al. 2021. Leveraging fine-mapping and non-European training data to improve trans-ethnic polygenic risk scores. medRxiv 10.1101/2021.01.19.21249483. https://doi.org/10.1101/2021.01.19.21249483
  45. 45.
    Majara L, Kalungi A, Koen N, Zar H, Stein DJ et al. 2021. Low generalizability of polygenic scores in African populations due to genetic and environmental diversity. bioRxiv 10.1101/2021.01.12.426453. https://doi.org/10.1101/2021.01.12.426453
    [Crossref]
  46. 46.
    Ruan Y, Lin Y-F, Feng Y-CA, Chen C-Y, Lam M et al. 2021. Improving polygenic prediction in ancestrally diverse populations. medRxiv 10.1101/2020.12.27.20248738. https://doi.org/10.1101/2020.12.27.20248738
    [Crossref]
  47. 47.
    Shi H, Gazal S, Kanai M, Koch EM, Schoech AP et al. 2021. Population-specific causal disease effect sizes in functionally important regions impacted by selection. Nat. Commun. 12:1098
    [Google Scholar]
  48. 48.
    Brown BC, Asian Genetic Epidemiol. Netw. Type 2 Diabetes Consort., Ye CJ, Price AL, Zaitlen N. 2016. Transethnic genetic-correlation estimates from summary statistics. Am. J. Hum. Genet. 99:176–88
    [Google Scholar]
  49. 49.
    Márquez-Luna C, Gazal S, Loh P-R, Kim SS, Furlotte N et al. 2021. Incorporating functional priors improves polygenic prediction accuracy in UK Biobank and 23andMe data sets. Nat. Commun. 12:6502
    [Google Scholar]
  50. 50.
    Hu Y, Lu Q, Powles R, Yao X, Yang C et al. 2017. Leveraging functional annotations in genetic risk prediction for human complex diseases. PLOS Comput. Biol. 13:6e1005589
    [Google Scholar]
  51. 51.
    Gazal S, Finucane HK, Furlotte NA, Loh P-R, Palamara PF et al. 2017. Linkage disequilibrium-dependent architecture of human complex traits shows action of negative selection. Nat. Genet. 49:101421–27
    [Google Scholar]
  52. 52.
    Amariuta T, Ishigaki K, Sugishita H, Ohta T, Koido M et al. 2020. Improving the trans-ancestry portability of polygenic risk scores by prioritizing variants in predicted cell-type-specific regulatory elements. Nat. Genet. 52:121346–54
    [Google Scholar]
  53. 53.
    Sivakumaran S, Agakov F, Theodoratou E, Prendergast JG, Zgaga L et al. 2011. Abundant pleiotropy in human complex diseases and traits. Am. J. Hum. Genet. 89:5607–18
    [Google Scholar]
  54. 54.
    Watanabe K, Stringer S, Frei O, Umićević Mirkov M, de Leeuw C et al. 2019. A global overview of pleiotropy and genetic architecture in complex traits. Nat. Genet. 51:1339–48
    [Google Scholar]
  55. 55.
    Udler MS, Kim J, von Grotthuss M, Bonàs-Guarch S, Cole JB et al. 2018. Type 2 diabetes genetic loci informed by multi-trait associations point to disease mechanisms and subtypes: a soft clustering analysis. PLOS Med 15:9e1002654
    [Google Scholar]
  56. 56.
    Daetwyler HD, Villanueva B, Woolliams JA. 2008. Accuracy of predicting the genetic risk of disease using a genome-wide approach. PLOS ONE 3:10e3395
    [Google Scholar]
  57. 57.
    Turley P, Meyer MN, Wang N, Cesarini D, Hammonds E et al. 2021. Problems with using polygenic scores to select embryos. N. Engl. J. Med. 385:78–86
    [Google Scholar]
  58. 58.
    Lencz T, Backenroth D, Granot-Hershkovitz E, Green A, Gettler K et al. 2021. Utility of polygenic embryo screening for disease depends on the selection strategy. eLife 10:e64716
    [Google Scholar]
  59. 59.
    Lee SH, Goddard ME, Wray NR, Visscher PM. 2012. A better coefficient of determination for genetic profile analysis. Genet. Epidemiol. 36:3214–24
    [Google Scholar]
  60. 60.
    Choi SW, Mak TS-H, O'Reilly PF 2020. Tutorial: a guide to performing polygenic risk score analyses. Nat. Protoc. 15:92759–72
    [Google Scholar]
  61. 61.
    Pain O, Gillett AC, Austin JC, Folkersen L, Lewis CM. 2022. A tool for translating polygenic scores onto the absolute scale using summary statistics. Eur. J. Hum. Genet. https://doi.org/10.1038/s41431-021-01028-z
    [Crossref] [Google Scholar]
  62. 62.
    Pal Choudhury P, Maas P, Wilcox A, Wheeler W, Brook M et al. 2020. iCARE: an R package to build, validate and apply absolute risk models. PLOS ONE 15:2e0228198
    [Google Scholar]
  63. 63.
    Lewis ACF, Green RC, Vassy JL. 2021. Polygenic risk scores in the clinic: translating risk into action. Human Genet. Genom. Adv. 2:4100047
    [Google Scholar]
  64. 64.
    Schultz LM, Merikangas AK, Ruparel K, Jacquemont S, Glahn DC et al. 2022. Stability of polygenic scores across discovery genome-wide association studies. Hum. Genet. Genom. Adv. 3:2100091
    [Google Scholar]
  65. 65.
    Ding Y, Hou K, Burch KS, Lapinska S, Privé F et al. 2021. Large uncertainty in individual PRS estimation impacts PRS-based risk stratification. Nat. Genet. 54:30–39
    [Google Scholar]
  66. 66.
    Lambert SA, Gil L, Jupp S, Ritchie SC, Xu Y et al. 2021. The Polygenic Score Catalog as an open database for reproducibility and systematic evaluation. Nat. Genet. 53:4420–25
    [Google Scholar]
  67. 67.
    Wand H, Lambert SA, Tamburro C, Iacocca MA, O'Sullivan JW et al. 2021. Improving reporting standards for polygenic scores in risk prediction studies. Nature 591:7849211–19
    [Google Scholar]
  68. 68.
    Martin AR, Gignoux CR, Walters RK, Wojcik GL, Neale BM et al. 2017. Human demographic history impacts genetic risk prediction across diverse populations. Am. J. Hum. Genet. 100:4635–49
    [Google Scholar]
  69. 69.
    Scutari M, Mackay I, Balding D. 2016. Using genetic distance to infer the accuracy of genomic prediction. PLOS Genet 12:9e1006288
    [Google Scholar]
  70. 70.
    Martin AR, Kanai M, Kamatani Y, Okada Y, Neale BM, Daly MJ 2019. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat. Genet. 51:4584–91
    [Google Scholar]
  71. 71.
    Duncan L, Shen H, Gelaye B, Meijsen J, Ressler K et al. 2019. Analysis of polygenic risk score usage and performance in diverse human populations. Nat. Commun. 10:3328
    [Google Scholar]
  72. 72.
    Wang Y, Guo J, Ni G, Yang J, Visscher PM, Yengo L. 2020. Theoretical and empirical quantification of the accuracy of polygenic scores in ancestry divergent populations. Nat. Commun. 11:3865
    [Google Scholar]
  73. 73.
    Privé F, Aschard H, Carmi S, Folkersen L, Hoggart C et al. 2022. 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. 109:112–23
    [Google Scholar]
  74. 74.
    Wray NR. 2005. Allele frequencies and the r2 measure of linkage disequilibrium: impact on design and interpretation of association studies. Twin Res. Hum. Genet. 8:287–94
    [Google Scholar]
  75. 75.
    VanLiere JM, Rosenberg NA. 2008. Mathematical properties of the r2 measure of linkage disequilibrium. Theor. Popul. Biol. 74:1130–37
    [Google Scholar]
  76. 76.
    Kim MS, Patel KP, Teng AK, Berens AJ, Lachance J. 2018. Genetic disease risks can be misestimated across global populations. Genome Biol 19:179
    [Google Scholar]
  77. 77.
    Martin AR, Teferra S, Möller M, Hoal EG, Daly MJ. 2018. The critical needs and challenges for genetic architecture studies in Africa. Curr. Opin. Genet. Dev. 53:113–20
    [Google Scholar]
  78. 78.
    Guo J, Bakshi A, Wang Y, Jiang L, Yengo L et al. 2021. Quantifying genetic heterogeneity between continental populations for human height and body mass index. Sci. Rep. 11:5240
    [Google Scholar]
  79. 79.
    Shi H, Burch KS, Johnson R, Freund MK, Kichaev G et al. 2020. Localizing components of shared transethnic genetic architecture of complex traits from GWAS summary data. Am. J. Hum. Genet. 106:6805–17
    [Google Scholar]
  80. 80.
    Kerminen S, Martin AR, Koskela J, Ruotsalainen SE, Havulinna AS et al. 2019. Geographic variation and bias in the polygenic scores of complex diseases and traits in Finland. Am. J. Hum. Genet. 104:61169–81
    [Google Scholar]
  81. 81.
    Sakaue S, Hirata J, Kanai M, Suzuki K, Akiyama M et al. 2020. Dimensionality reduction reveals fine-scale structure in the Japanese population with consequences for polygenic risk prediction. Nat. Commun. 11:1569
    [Google Scholar]
  82. 82.
    Berg JJ, Harpak A, Sinnott-Armstrong N, Joergensen AM, Mostafavi H et al. 2019. Reduced signal for polygenic adaptation of height in UK Biobank. eLife 8:e39725
    [Google Scholar]
  83. 83.
    Sohail M, Maier RM, Ganna A, Bloemendal A, Martin AR et al. 2019. Polygenic adaptation on height is overestimated due to uncorrected stratification in genome-wide association studies. eLife 8:e39702
    [Google Scholar]
  84. 84.
    Cavazos TB, Witte JS. 2021. Inclusion of variants discovered from diverse populations improves polygenic risk score transferability. Hum. Genet. Genom. Adv. 2:1100017
    [Google Scholar]
  85. 85.
    Bitarello BD, Mathieson I. 2020. Polygenic scores for height in admixed populations. G3 10:114027–36
    [Google Scholar]
  86. 86.
    Atkinson EG, Maihofer AX, Kanai M, Martin AR, Karczewski KJ et al. 2021. Tractor uses local ancestry to enable the inclusion of admixed individuals in GWAS and to boost power. Nat. Genet. 53:2195–204
    [Google Scholar]
  87. 87.
    Marnetto D, Pärna K, Läll K, Molinaro L, Montinaro F et al. 2020. Ancestry deconvolution and partial polygenic score can improve susceptibility predictions in recently admixed individuals. Nat. Commun. 11:1628
    [Google Scholar]
  88. 88.
    Coram MA, Fang H, Candille SI, Assimes TL, Tang H. 2017. Leveraging multi-ethnic evidence for risk assessment of quantitative traits in minority populations. Am. J. Hum. Genet. 101:2218–26
    [Google Scholar]
  89. 89.
    Marnetto D, Pärna K, Läll K, Molinaro L, Montinaro F et al. 2020. Ancestry deconvolution and partial polygenic score can improve susceptibility predictions in recently admixed individuals. Nat. Commun. 11:1628
    [Google Scholar]
  90. 90.
    Bigdeli TB, Genovese G, Georgakopoulos P, Meyers JL, Peterson RE et al. 2019. Contributions of common genetic variants to risk of schizophrenia among individuals of African and Latino ancestry. Mol. Psychiatry 25:2455–67
    [Google Scholar]
  91. 91.
    Brandes N, Weissbrod O, Linial M. 2021. Open problems in human trait genetics. arXiv:2108.06684 [q-bio.PE]
  92. 92.
    Howe LJ, Nivard MG, Morris TT, Hansen AF. 2021. Within-sibship GWAS improve estimates of direct genetic effects. bioRxiv 10.1101/2021.03.05.433935. https://doi.org/10.1101/2021.03.05.433935
    [Crossref]
  93. 93.
    Young AI, Benonisdottir S, Przeworski M, Kong A. 2019. Deconstructing the sources of genotype-phenotype associations in humans. Science 365:64601396–400
    [Google Scholar]
  94. 94.
    Mostafavi H, Harpak A, Agarwal I, Conley D, Pritchard JK, Przeworski M. 2020. Variable prediction accuracy of polygenic scores within an ancestry group. eLife 9:e48376
    [Google Scholar]
  95. 95.
    Kong A, Thorleifsson G, Frigge ML, Vilhjalmsson BJ, Young AI et al. 2018. The nature of nurture: effects of parental genotypes. Science 359:6374424–28
    [Google Scholar]
  96. 96.
    Selzam S, Ritchie SJ, Pingault J-B, Reynolds CA, O'Reilly PF, Plomin R 2019. Comparing within- and between-family polygenic score prediction. Am. J. Hum. Genet. 105:2351–63
    [Google Scholar]
  97. 97.
    Weiner DJ, Wigdor EM, Ripke S, Walters RK, Kosmicki JA et al. 2017. Polygenic transmission disequilibrium confirms that common and rare variation act additively to create risk for autism spectrum disorders. Nat. Genet. 49:7978–85
    [Google Scholar]
  98. 98.
    Hujoel MLA, Loh P-R, Neale BM, Price AL 2021. Incorporating family history of disease improves polygenic risk scores in diverse populations. bioRxiv 10.1101/2021.04.15.439975. https://doi.org/10.1101/2021.04.15.439975
    [Crossref]
  99. 99.
    Tada H, Melander O, Louie JZ, Catanese JJ, Rowland CM et al. 2016. Risk prediction by genetic risk scores for coronary heart disease is independent of self-reported family history. Eur. Heart J. 37:6561–67
    [Google Scholar]
  100. 100.
    Fry A, Littlejohns TJ, Sudlow C, Doherty N, Adamska L et al. 2017. Comparison of sociodemographic and health-related characteristics of UK Biobank participants with those of the general population. Am. J. Epidemiol. 186:91026–34
    [Google Scholar]
  101. 101.
    Freeman GH. 1973. Statistical methods for the analysis of genotype-environment interactions. Heredity 31:3339–54
    [Google Scholar]
  102. 102.
    Hunter DJ. 2005. Gene-environment interactions in human diseases. Nat. Rev. Genet. 6:4287–98
    [Google Scholar]
  103. 103.
    Kilpeläinen TO, Qi L, Brage S, Sharp SJ, Sonestedt E et al. 2011. Physical activity attenuates the influence of FTO variants on obesity risk: a meta-analysis of 218,166 adults and 19,268 children. PLOS Med 8:11e1001116
    [Google Scholar]
  104. 104.
    Young AI, Wauthier F, Donnelly P. 2016. Multiple novel gene-by-environment interactions modify the effect of FTO variants on body mass index. Nat. Commun. 7:12724
    [Google Scholar]
  105. 105.
    Mullins N, Power RA, Fisher HL, Hanscombe KB, Euesden J et al. 2016. Polygenic interactions with environmental adversity in the aetiology of major depressive disorder. Psychol. Med. 46:4759–70
    [Google Scholar]
  106. 106.
    Mas S, Boloc D, Rodríguez N, Mezquida G, Amoretti S et al. 2020. Examining gene-environment interactions using aggregate scores in a first-episode psychosis cohort. Schizophr. Bull. 46:41019–25
    [Google Scholar]
  107. 107.
    Werme J, van der Sluis S, Posthuma D, de Leeuw CA. 2021. Genome-wide gene-environment interactions in neuroticism: an exploratory study across 25 environments. Transl. Psychiatry 11:180
    [Google Scholar]
  108. 108.
    Barcellos SH, Carvalho LS, Turley P. 2018. Education can reduce health differences related to genetic risk of obesity. PNAS 115:42E9765–72
    [Google Scholar]
  109. 109.
    Tyrrell J, Wood AR, Ames RM, Yaghootkar H, Beaumont RN et al. 2017. Gene-obesogenic environment interactions in the UK Biobank study. Int. J. Epidemiol. 46:2559–75
    [Google Scholar]
  110. 110.
    Park SK, Tao Y, Meeker JD, Harlow SD, Mukherjee B. 2014. Environmental risk score as a new tool to examine multi-pollutants in epidemiologic research: an example from the NHANES study using serum lipid levels. PLOS ONE 9:6e98632
    [Google Scholar]
  111. 111.
    Park SK, Zhao Z, Mukherjee B. 2017. Construction of environmental risk score beyond standard linear models using machine learning methods: application to metal mixtures, oxidative stress and cardiovascular disease in NHANES. Environ. Health 16:1102
    [Google Scholar]
  112. 112.
    Lakhani CM, Tierney BT, Manrai AK, Yang J, Visscher PM, Patel CJ. 2019. Repurposing large health insurance claims data to estimate genetic and environmental contributions in 560 phenotypes. Nat. Genet. 51:2327–34
    [Google Scholar]
  113. 113.
    He Y, Lakhani CM, Rasooly D, Manrai AK, Tzoulaki I, Patel CJ. 2021. Comparisons of polyexposure, polygenic, and clinical risk scores in risk prediction of type 2 diabetes. Diabetes Care 44:4935–43
    [Google Scholar]
  114. 114.
    Lewis CM, Vassos E. 2020. Polygenic risk scores: from research tools to clinical instruments. Genome Med 12:44
    [Google Scholar]
  115. 115.
    Khan SS, Cooper R, Greenland P. 2020. Do polygenic risk scores improve patient selection for prevention of coronary artery disease?. JAMA 323:7614–15
    [Google Scholar]
  116. 116.
    Torkamani A, Wineinger NE, Topol EJ. 2018. The personal and clinical utility of polygenic risk scores. Nat. Rev. Genet. 19:9581–90
    [Google Scholar]
  117. 117.
    Shah PD. 2021. Polygenic risk scores for breast cancer—Can they deliver on the promise of precision medicine?. JAMA Netw. Open 4:8e2119333
    [Google Scholar]
  118. 118.
    Sugrue LP, Desikan RS. 2019. What are polygenic scores and why are they important?. JAMA 321:181820–21
    [Google Scholar]
  119. 119.
    Lloyd-Jones DM, Braun LT, Ndumele CE, Smith SC, Sperling LS et al. 2019. Use of risk assessment tools to guide decision-making in the primary prevention of atherosclerotic cardiovascular disease: a special report from the American Heart Association and American College of Cardiology. Circulation 139:25e1162–77
    [Google Scholar]
  120. 120.
    Inouye M, Abraham G, Nelson CP, Wood AM, Sweeting MJ et al. 2018. Genomic risk prediction of coronary artery disease in 480,000 adults: implications for primary prevention. J. Am. Coll. Cardiol. 72:161883–93
    [Google Scholar]
  121. 121.
    Mars N, Widén E, Kerminen S, Meretoja T, Pirinen M et al. 2020. The role of polygenic risk and susceptibility genes in breast cancer over the course of life. Nat. Commun. 11:6383
    [Google Scholar]
  122. 122.
    Dennis JK, Sealock JM, Straub P, Lee YH, Hucks D et al. 2021. Clinical laboratory test-wide association scan of polygenic scores identifies biomarkers of complex disease. Genome Med 13:6
    [Google Scholar]
  123. 123.
    Feng Y-CA, Ge T, Cordioli M, Ganna A, Smoller JW et al. 2020. Findings and insights from the genetic investigation of age of first reported occurrence for complex disorders in the UK Biobank and FinnGen. bioRxiv 10.1101/2020.11.20.20234302. https://doi.org/10.1101/2020.11.20.20234302
    [Crossref]
  124. 124.
    Gao C, Polley EC, Hart SN, Huang H, Hu C et al. 2021. Risk of breast cancer among carriers of pathogenic variants in breast cancer predisposition genes varies by polygenic risk score. J. Clin. Oncol. 39:232564–73
    [Google Scholar]
  125. 125.
    Esserman LJ, WISDOM Study Athena Investig. 2017. The WISDOM Study: breaking the deadlock in the breast cancer screening debate. npj Breast Cancer 3:34
    [Google Scholar]
  126. 126.
    Mavaddat N, Michailidou K, Dennis J, Lush M, Fachal L et al. 2019. Polygenic risk scores for prediction of breast cancer and breast cancer subtypes. Am. J. Hum. Genet. 104:121–34
    [Google Scholar]
  127. 127.
    Maas P, Barrdahl M, Joshi AD, Auer PL, Gaudet MM et al. 2016. Breast cancer risk from modifiable and nonmodifiable risk factors among white women in the United States. JAMA Oncol 2:101295–302
    [Google Scholar]
  128. 128.
    Padilla-Martínez F, Collin F, Kwasniewski M, Kretowski A 2020. Systematic review of polygenic risk scores for type 1 and type 2 diabetes. Int. J. Mol. Sci. 21:51703
    [Google Scholar]
  129. 129.
    Udler MS, McCarthy MI, Florez JC, Mahajan A. 2019. Genetic risk scores for diabetes diagnosis and precision medicine. Endocr. Rev. 40:61500–20
    [Google Scholar]
  130. 130.
    Dikilitas O, Schaid DJ, Kosel ML, Carroll RJ, Chute CG et al. 2020. Predictive utility of polygenic risk scores for coronary heart disease in three major racial and ethnic groups. Am. J. Hum. Genet. 106:5707–16
    [Google Scholar]
  131. 131.
    Khera AV, Chaffin M, Aragam KG, Haas ME, Roselli C et al. 2018. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat. Genet. 50:91219–24
    [Google Scholar]
  132. 132.
    Lewis ACF, Green RC. 2021. Polygenic risk scores in the clinic: new perspectives needed on familiar ethical issues. Genome Med 13:14
    [Google Scholar]
  133. 133.
    Landi I, Kaji DA, Cotter L, Van Vleck T, Belbin G et al. 2021. Prognostic value of polygenic risk scores for adults with psychosis. Nat. Med. 27:1576–81
    [Google Scholar]
  134. 134.
    Ruderfer DM, Charney AW, Readhead B, Kidd BA, Kähler AK et al. 2016. Polygenic overlap between schizophrenia risk and antipsychotic response: a genomic medicine approach. Lancet Psychiatry 3:4350–57
    [Google Scholar]
  135. 135.
    Karavani E, Zuk O, Zeevi D, Barzilai N, Stefanis NC et al. 2019. Screening human embryos for polygenic traits has limited utility. Cell 179:61424–35.e8
    [Google Scholar]
  136. 136.
    Lee A, Mavaddat N, Wilcox AN, Cunningham AP, Carver T et al. 2019. BOADICEA: a comprehensive breast cancer risk prediction model incorporating genetic and nongenetic risk factors. Genet. Med. 21:81708–18
    [Google Scholar]
  137. 137.
    Pashayan N, Antoniou AC, Ivanus U, Esserman LJ, Easton DF et al. 2020. Personalized early detection and prevention of breast cancer: ENVISION consensus statement. Nat. Rev. Clin. Oncol. 17:11687–705
    [Google Scholar]
  138. 138.
    Gagnon J, Lévesque E, Clin. Advis. Comm. Breast Cancer Screen. Prev., Borduas F, Chiquette J et al. 2016. Recommendations on breast cancer screening and prevention in the context of implementing risk stratification: impending changes to current policies. Curr. Oncol. 23:6e615–25
    [Google Scholar]
  139. 139.
    Richards S, Aziz N, Bale S, Bick D, Das S et al. 2015. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet. Med. 17:5405–24
    [Google Scholar]
  140. 140.
    Mosley JD, Gupta DK, Tan J, Yao J, Wells QS et al. 2020. Predictive accuracy of a polygenic risk score compared with a clinical risk score for incident coronary heart disease. JAMA 323:7627–35
    [Google Scholar]
  141. 141.
    Elliott J, Bodinier B, Bond TA, Chadeau-Hyam M, Evangelou E et al. 2020. Predictive accuracy of a polygenic risk score-enhanced prediction model versus a clinical risk score for coronary artery disease. JAMA 323:7636–45
    [Google Scholar]
  142. 142.
    Widén E, Junna N, Ruotsalainen S, Surakka I, Mars N et al. 2020. Communicating polygenic and non-genetic risk for atherosclerotic cardiovascular disease—an observational follow-up study. medRxiv 10.1101/2020.09.18.20197137. https://doi.org/10.1101/2020.09.18.20197137
    [Crossref]
  143. 143.
    Kullo IJ, Jouni H, Austin EE, Brown S-A, Kruisselbrink TM et al. 2016. Incorporating a genetic risk score into coronary heart disease risk estimates: effect on low-density lipoprotein cholesterol levels (the MI-GENES Clinical Trial). Circulation 133:121181–88
    [Google Scholar]
  144. 144.
    Nauffal V, Morrill VN, Jurgens SJ, Choi SH, Hall AW et al. 2021. Monogenic and polygenic contributions to QTc prolongation in the population. medRxiv 10.1101/2021.06.18.21258578. https://doi.org/10.1101/2021.06.18.21258578
    [Crossref]
  145. 145.
    Carver T, Hartley S, Lee A, Cunningham AP. 2021. CanRisk Tool—a web interface for the prediction of breast and ovarian cancer risk and the likelihood of carrying genetic pathogenic variants. Cancer Epidemiol 30:3469–73
    [Google Scholar]
  146. 146.
    Brigden T, Sanderson S, Janus J, de Villiers CB, Moorthie S et al. 2021. Implementing polygenic scores for cardiovascular disease into NHS health checks Tech. Rep. phg Foundation Cambridge, UK:
  147. 147.
    Folkersen L, Pain O, Ingason A, Werge T, Lewis CM, Austin J. 2020. Impute.me: an open-source, non-profit tool for using data from direct-to-consumer genetic testing to calculate and interpret polygenic risk scores. Front. Genet. 11:578
    [Google Scholar]
  148. 148.
    Multhaup ML, Kita R, Krock B, Eriksson N, Fontanillas P et al. 2019. The science behind 23andMe's type 2 diabetes report. White Pap23–19 23 and Me Sunnyvale, CA:
    [Google Scholar]
  149. 149.
    Hughes E, Tshiaba P, Wagner S, Judkins T, Rosenthal E et al. 2021. Integrating clinical and polygenic factors to predict breast cancer risk in women undergoing genetic testing. JCO Precis. Oncol. 5:307–16
    [Google Scholar]
  150. 150.
    Riveros-Mckay F, Weale ME, Moore R, Selzam S, Krapohl E et al. 2021. Integrated polygenic tool substantially enhances coronary artery disease prediction. Circ. Genom. Precis. Med. 14:2e003304
    [Google Scholar]
  151. 151.
    Genom. plc 2019. Polygenic risk scores White Pap., Genomics plc Oxford, UK: https://www.genomicsplc.com/wp-content/uploads/2020/11/Genomics-plc-PRS-details_White-Paper-April-2019.pdf
  152. 152.
    Silarova B, Sharp S, Usher-Smith JA, Lucas J, Payne RA et al. 2019. Effect of communicating phenotypic and genetic risk of coronary heart disease alongside web-based lifestyle advice: the INFORM randomised controlled trial. Heart 105:13982–89
    [Google Scholar]
  153. 153.
    ISPG 2021. Advisory on the use of polygenic risk scores to screen embryos for adult mental health conditions Statement, ISPG https://ispg.net/ethics-statement/
  154. 154.
    Obermeyer Z, Powers B, Vogeli C, Mullainathan S. 2019. Dissecting racial bias in an algorithm used to manage the health of populations. Science 366:6464447–53
    [Google Scholar]
  155. 155.
    Privé F, Vilhjálmsson BJ, Aschard H, Blum MGB. 2019. Making the most of clumping and thresholding for polygenic scores. Am. J. Hum. Genet. 105:61213–21
    [Google Scholar]
  156. 156.
    Zeng J, Xue A, Jiang L, Lloyd-Jones LR, Wu Y et al. 2021. Widespread signatures of natural selection across human complex traits and functional genomic categories. Nat. Commun. 12:1164
    [Google Scholar]
  157. 157.
    Chun S, Imakaev M, Hui D, Patsopoulos NA, Neale BM et al. 2020. Non-parametric polygenic risk prediction via partitioned GWAS summary statistics. Am. J. Hum. Genet. 107:146–59
    [Google Scholar]
  158. 158.
    Yang S, Zhou X. 2020. Accurate and scalable construction of polygenic scores in large biobank data sets. Am. J. Hum. Genet. 106:5679–93
    [Google Scholar]
  159. 159.
    Zhou G, Zhao H. 2021. A fast and robust Bayesian nonparametric method for prediction of complex traits using summary statistics. PLOS Genet 17:7e1009697
    [Google Scholar]
  160. 160.
    Newcombe PJ, Nelson CP, Samani NJ, Dudbridge F. 2019. A flexible and parallelizable approach to genome-wide polygenic risk scores. Genet. Epidemiol. 43:7730–41
    [Google Scholar]
  161. 161.
    Chung W, Chen J, Turman C, Lindstrom S, Zhu Z et al. 2019. Efficient cross-trait penalized regression increases prediction accuracy in large cohorts using secondary phenotypes. Nat. Commun. 10:569
    [Google Scholar]
  162. 162.
    Chen T-H, Chatterjee N, Landi MT, Shi J. 2021. A penalized regression framework for building polygenic risk models based on summary statistics from genome-wide association studies and incorporating external information. J. Am. Stat. Assoc. 116:533133–43
    [Google Scholar]
  163. 163.
    Ballard JL, O'Connor LJ 2021. Shared components of heritability across genetically correlated traits. bioRxiv 10.1101/2021.11.25.470021. https://doi.org/10.1101/2021.11.25.470021
    [Crossref]
  164. 164.
    Cai M, Xiao J, Zhang S, Wan X, Zhao H et al. 2021. A unified framework for cross-population trait prediction by leveraging the genetic correlation of polygenic traits. Am. J. Hum. Genet. 108:4632–55
    [Google Scholar]
  165. 165.
    Kelemen M, Vigorito E, Anderson CA, Wallace C. 2021. ShaPRS: leveraging shared genetic effects across traits or ancestries improves accuracy of polygenic scores. medRxiv 10.1101/2021.12.10.21267272
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