Polygenic risk scores (PRS) are poised to improve biomedical outcomes via precision medicine. However, the major ethical and scientific challenge surrounding clinical implementation of PRS is that those available today are several times more accurate in individuals of European ancestry than other ancestries. This disparity is an inescapable consequence of Eurocentric biases in genome-wide association studies, thus highlighting that—unlike clinical biomarkers and prescription drugs, which may individually work better in some populations but do not ubiquitously perform far better in European populations—clinical uses of PRS today would systematically afford greater improvement for European-descent populations. Early diversifying efforts show promise in leveling this vast imbalance, even when non-European sample sizes are considerably smaller than the largest studies to date. To realize the full and equitable potential of PRS, greater diversity must be prioritized in genetic studies, and summary statistics must be publically disseminated to ensure that health disparities are not increased for those individuals already most underserved.
Subscribe to Journal
Get full journal access for 1 year
only $4.92 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
Knowles, J. W. & Ashley, E. A. Cardiovascular disease: the rise of the genetic risk score. PLoS Med. 15, e1002546–e1002547 (2018).
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).
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).
Sharp, S. A. et al. Development and standardization of an improved type 1 diabetes genetic risk score for use in newborn screening and incident diagnosis. Diabetes Care 42, 200–207 (2019).
Khera, A. V. et al. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat. Genet. 50, 1219–1224 (2018).
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).
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).
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).
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).
Frieser, M. J., Wilson, S. & Vrieze, S. Behavioral impact of return of genetic test results for complex disease: systematic review and meta-analysis. Health Psychol. 37, 1134–1144 (2018).
Khera, A. V. et al. Genetic risk, adherence to a healthy lifestyle, and coronary disease. N. Engl. J. Med. 375, 2349–2358 (2016).
Khera, A. V. & Kathiresan, S. Genetics of coronary artery disease: discovery, biology and clinical translation. Nat. Rev. Genet. 18, 331–344 (2017).
Martin, A. R. et al. Human demographic history impacts genetic risk prediction across diverse populations. Am. J. Hum. Genet. 100, 635–649 (2017).
Scutari, M., Mackay, I. & Balding, D. Using genetic distance to infer the accuracy of genomic prediction. PLoS Genet. 12, e1006288 (2016).
Vilhjálmsson, B. J. et al. Modeling linkage disequilibrium increases accuracy of polygenic risk scores. Am. J. Hum. Genet. 97, 576–592 (2015).
Ware, E. B. et al. Heterogeneity in polygenic scores for common human traits. Preprint at https://www.biorxiv.org/content/10.1101/106062v1 (2017).
Curtis, D. Polygenic risk score for schizophrenia is more strongly associated with ancestry than with schizophrenia. Psychiatr. Genet. 28, 85–89 (2018).
Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427 (2014).
Belsky, D. W. et al. Development and evaluation of a genetic risk score for obesity. Biodemography Soc. Biol. 59, 85–100 (2013).
Domingue, B. W., Belsky, D., Conley, D., Harris, K. M. & Boardman, J. D. Polygenic influence on educational attainment: new evidence from The National Longitudinal Study of Adolescent to Adult Health. AERA Open 1, 1–13 (2015).
Lee, J. J. et al. Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nat. Genet. 50, 1112–1121 (2018).
Vassos, E. et al. An examination of polygenic score risk prediction in individuals with first-episode psychosis. Biol. Psychiatry 81, 470–477 (2017).
Akiyama, M. et al. Genome-wide association study identifies 112 new loci for body mass index in the Japanese population. Nat. Genet. 49, 1458–1467 (2017).
Li, Z. et al. Genome-wide association analysis identifies 30 new susceptibility loci for schizophrenia. Nat. Genet. 49, 1576–1583 (2017).
Need, A. C. & Goldstein, D. B. Next generation disparities in human genomics: concerns and remedies. Trends Genet. 25, 489–494 (2009).
Popejoy, A. B. & Fullerton, S. M. Genomics is failing on diversity. Nature 538, 161–164 (2016).
Morales, J. et al. A standardized framework for representation of ancestry data in genomics studies, with application to the NHGRI-EBI GWAS Catalog. Genome Biol. 19, 21 (2018).
Rosenberg, N. A. et al. Genome-wide association studies in diverse populations. Nat. Rev. Genet. 11, 356–366 (2010).
Sham, P. C., Cherny, S. S., Purcell, S. & Hewitt, J. K. Power of linkage versus association analysis of quantitative traits, by use of variance-components models, for sibship data. Am. J. Hum. Genet. 66, 1616–1630 (2000).
1000 Genomes Project Consortium. et al. A global reference for human genetic variation. Nature 526, 68–74 (2015).
Williams, A. L. et al. Sequence variants in SLC16A11 are a common risk factor for type 2 diabetes in Mexico. Nature 506, 97–101 (2014).
Estrada, K. et al. Association of a low-frequency variant in HNF1A with type 2 diabetes in a Latino population. JAMA 311, 2305–2314 (2014).
Haiman, C. A. et al. Genome-wide association study of prostate cancer in men of African ancestry identifies a susceptibility locus at 17q21. Nat. Genet. 43, 570–573 (2011).
Genovese, G. et al. Association of trypanolytic ApoL1 variants with kidney disease in African Americans. Science 329, 841–845 (2010).
Liu, J. Z. et al. Association analyses identify 38 susceptibility loci for inflammatory bowel disease and highlight shared genetic risk across populations. Nat. Genet. 47, 979–986 (2015).
Carlson, C. S. et al. Generalization and dilution of association results from European GWAS in populations of non-European ancestry: the PAGE study. PLoS Biol. 11, e1001661 (2013).
Easton, D. F. et al. Genome-wide association study identifies novel breast cancer susceptibility loci. Nature 447, 1087–1093 (2007).
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).
Waters, K. M. et al. Consistent association of type 2 diabetes risk variants found in europeans in diverse racial and ethnic groups. PLoS Genet. 6, e1001078–e1001079 (2010).
Lam, M. et al. Comparative genetic architectures of schizophrenia in East Asian and European populations. Preprint at https://www.biorxiv.org/content/10.1101/445874v2 (2018).
McCarthy, S. et al. A reference panel of 64,976 haplotypes for genotype imputation. Nat. Genet. 48, 1279–1283 (2016).
Huang, H. et al. Fine-mapping inflammatory bowel disease loci to single-variant resolution. Nature 547, 173–178 (2017).
Sohail, M. et al. Signals of polygenic adaptation on height have been overestimated due to uncorrected population structure in genome-wide association studies. Preprint at https://www.biorxiv.org/content/10.1101/355057v3 (2018).
Berg, J. J. et al. Reduced signal for polygenic adaptation of height in UK Biobank. Preprint at https://www.biorxiv.org/content/10.1101/354951v4 (2018).
Kerminen, S. et al. Geographic variation and bias in polygenic scores of complex diseases and traits in Finland. Preprint at https://www.biorxiv.org/content/10.1101/485441v1 (2018).
Novembre, J. & Barton, N. H. Tread lightly interpreting polygenic tests of selection. Genetics 208, 1351–1355 (2018).
Henn, B. M., Botigué, L. R., Bustamante, C. D., Clark, A. G. & Gravel, S. Estimating the mutation load in human genomes. Nat. Rev. Genet. 16, 333–343 (2015).
Brown, B. C., Asian Genetic Epidemiology Network Type 2 Diabetes Consortium, Ye, C. J., Price, A. L. & Zaitlen, N. Transethnic genetic-correlation estimates from summary statistics. Am. J. Hum. Genet. 99, 76–88 (2016).
Galinsky, K. J. et al. Estimating cross-population genetic correlations of causal effect sizes. Genet. Epidemiol. 43, 180–188 (2019).
Li, D., Zhao, H. & Gelernter, J. Strong protective effect of the aldehyde dehydrogenase gene (ALDH2) 504lys (*2) allele against alcoholism and alcohol-induced medical diseases in Asians. Hum. Genet. 131, 725–737 (2012).
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).
Paré, G., Mao, S. & Deng, W. Q. A machine-learning heuristic to improve gene score prediction of polygenic traits. Sci. Rep. 7, 12665 (2017).
Martin, A. R. et al. An unexpectedly complex architecture for skin pigmentation in Africans. Cell 171, 1340–1353.e14 (2017).
Duncan, L. E. et al. Largest GWAS of PTSD (N=20 070) yields genetic overlap with schizophrenia and sex differences in heritability. Mol. Psychiatry 23, 666–673 (2018).
H3Africa Consortium. et al. Enabling the genomic revolution in Africa. Science 344, 1346–1348 (2014).
Hindorff, L. A. et al. Prioritizing diversity in human genomics research. Nat. Rev. Genet. 19, 175–185 (2018).
Kanai, M. et al. Genetic analysis of quantitative traits in the Japanese population links cell types to complex human diseases. Nat. Genet. 50, 390–400 (2018).
Howrigan, D. Details and Considerations of the UK Biobank GWAS. http://www.nealelab.is/blog/2017/9/11/details-and-considerations-of-the-uk-biobank-gwas (accessed 9 November 2017)
Fry, A. et al. Comparison of sociodemographic and health-related characteristics of UK Biobank participants with those of the general population. Am. J. Epidemiol. 186, 1026–1034 (2017).
Liu, S. et al. Genomic analyses from non-invasive prenatal testing reveal genetic associations, patterns of viral infections, and Chinese population history. Cell 175, 347–359.e14 (2018).
Wray, N. R. et al. Pitfalls of predicting complex traits from SNPs. Nat. Rev. Genet. 14, 507–515 (2013).
Wray, N. R. et al. Research review: polygenic methods and their application to psychiatric traits. J. Child Psychol. Psychiatry 55, 1068–1087 (2014).
Torkamani, A., Wineinger, N. E. & Topol, E. J. The personal and clinical utility of polygenic risk scores. Nat. Rev. Genet. 19, 581–590 (2018).
Manrai, A. K., Patel, C. J. & Ioannidis, J. P. A. In the era of precision medicine and big data, who is normal? JAMA 319, 1981–1982 (2018).
Plenge, R. M., Scolnick, E. M. & Altshuler, D. Validating therapeutic targets through human genetics. Nat. Rev. Drug Discov. 12, 581–594 (2013).
Carroll, M. D., Kit, B. K., Lacher, D. A., Shero, S. T. & Mussolino, M. E. Trends in lipids and lipoproteins in US adults, 1988–2010. JAMA 308, 1545–1554 (2012).
Rappoport, N. et al. Comparing ethnicity-specific reference intervals for clinical laboratory tests from EHR data. J. Appl. Lab. Med. 3, 366–377 (2018).
Lim, E., Miyamura, J. & Chen, J. J. Racial/ethnic-specific reference intervals for common laboratory tests: a comparison among Asians, Blacks, Hispanics, and White. Hawaii J. Med. Public Health 74, 302–310 (2015).
Hero, J. O., Zaslavsky, A. M. & Blendon, R. J. The United States leads other nations in differences by income in perceptions of health and health care. Health Aff. (Millwood) 36, 1032–1040 (2017).
Williams, D. R., Priest, N. & Anderson, N. B. Understanding associations among race, socioeconomic status, and health: Patterns and prospects. Health Psychol. 35, 407–411 (2016).
Gilly, A. et al. Very low depth whole genome sequencing in complex trait association studies. Bioinformatics https://doi.org/10.1093/bioinformatics/bty1032 (2018).
Pasaniuc, B. et al. Extremely low-coverage sequencing and imputation increases power for genome-wide association studies. Nat. Genet. 44, 631–635 (2012).
Martin, A. R., Teferra, S., Möller, M., Hoal, E. G. & Daly, M. J. The critical needs and challenges for genetic architecture studies in Africa. Curr. Opin. Genet. Dev. 53, 113–120 (2018).
Coles, E. & Mensah, G. A. Geography of genetics and genomics research funding in Africa. Glob. Heart 12, 173–176 (2017).
Mulder, N. J. et al. Development of bioinformatics infrastructure for genomics research. Glob. Heart 12, 91–98 (2017).
MacArthur, J. et al. The new NHGRI-EBI Catalog of published genome-wide association studies (GWAS Catalog). Nucleic Acids Res. 45, D896–D901 (2017).
We thank A. Khera for helpful discussions. We also thank M. Kubo, Y. Murakami, M. Akiyama and K. Ishigaki for their support in the BBJ Project analysis. We are grateful to S. Gazal for help in calculating LD scores. This work was supported by funding from the National Institutes of Health (K99MH117229 to A.R.M.). UKBB analyses were conducted via application 31063. The BBJ Project was supported by the Tailor-Made Medical Treatment Program of the Ministry of Education, Culture, Sports, Science, and Technology (MEXT) and the Japan Agency for Medical Research and Development (AMED). M.K. was supported by a Nakajima Foundation Fellowship and the Masason Foundation.
The authors declare no competing interests.
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Martin, A.R., Kanai, M., Kamatani, Y. et al. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat Genet 51, 584–591 (2019). https://doi.org/10.1038/s41588-019-0379-x
Human Genomics (2021)
BMC Medical Research Methodology (2021)
Genome Medicine (2021)
Genome Medicine (2021)
Inflammation and Regeneration (2021)