Cross-ancestry genomic research: time to close the gap
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This disparity is problematic because genomic discoveries may not be transferable across populations, even though it is assumed that they share the underlying biological mechanisms.
“This disparity is problematic because genomic discoveries may not be transferable across populations, even though it is assumed that they share the underlying biological mechanisms37”
From Shared genetic architectures of educational attainment in East Asian and European populations (2024) · cites this paper as [37]
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Cites
Clinical use of current polygenic risk scores may exacerbate health disparities
Martin, A. R., Kanai, M., Kamatani, Y., Okada, Y., Neale, B. M., & Daly, M. J. (2019) · Nature Genetics · doi:10.1038/s41588-019-0379-x · Cited by 2,831 · see citing works
Human Demographic History Impacts Genetic Risk Prediction across Diverse Populations
Martin, A. R., Gignoux, C. R., Walters, R. K., Wojcik, G. L., Neale, B. M., Gravel, S., Daly, M. J., Bustamante, C. D., & Kenny, E. E. (2017) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2017.03.004 · Cited by 1,391 · see citing works
Analysis of polygenic risk score usage and performance in diverse human populations
Duncan, L., Shen, H., Gelaye, B., Meijsen, J., Ressler, K., Feldman, M., Peterson, R., & Domingue, B. (2019) · Nature Communications · doi:10.1038/s41467-019-11112-0 · Cited by 1,038 · see citing works
Peterson, R. E., Kuchenbaecker, K., Walters, R. K., Chen, C.-Y., Popejoy, A. B., Periyasamy, S., Lam, M., Iyegbe, C., Strawbridge, R. J., Brick, L., et al. (2019) · Cell · doi:10.1016/j.cell.2019.08.051 · Cited by 797 · cited 2× · see citing works
“Beyond these efforts to generate more diverse data and enhance methods for its analysis, other challenges remain to increase equity in genomics research [5, 9, 10, 24].”
BackgroundImproving polygenic prediction in ancestrally diverse populations
Ruan, Y., Lin, Y.-F., Feng, Y.-C. A., Chen, C.-Y., Lam, M., Guo, Z., Initiatives, S. G. A., Ahn, Y. M., Akiyama, K., Arai, M., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01054-7 · Cited by 707 · see citing works
Multi-Ancestry Meta-Analysis yields novel genetic discoveries and ancestry-specific associations
Turley, P., Martin, A. R., Goldman, G., Li, H., Kanai, M., Walters, R. K., Jala, J. B., Lin, K., Millwood, I. Y., Carey, C. E., et al. (2021) · doi:10.1101/2021.04.23.441003 · Cited by 35 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 18
- [1]1. Visscher PM, Wray NR, Zhang Q, Sklar P, McCarthy MI, Brown MA, et al. 10 years of GWAS discovery: biology, function, and translation. Am J Hum Genet. 2017;101:5 -22.
- [2]2. Shapiro MD, Tavori H, Fazio S. PCSK9: from basic science discoveries to clinical trials. Circ Res. 2018;122:1420 -38.
- [3]3. Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature. 2014;511:421 -7.
- [4]4. Wray NR, Goddard ME, Visscher PM. Prediction of individual genetic risk to disease from genome-wide association studies. Genome Res. 2007;17:1520 -8.
- [9]9. Fatumo S, Chikowore T, Choudhury A, Ayub M, Martin AR, Kuchenbaecker K. A roadmap to increase diversity in genomic studies. Nat Med. 2022;28:243 -50.
- [10]10. Palk AC, Dalvie S, de Vries J, Martin AR, Stein DJ. Potential use of clinical polygenic risk scores in psychiatry -ethical implications and communicating high polygenic risk. Philos Ethics Humanit Med. 2019;14:4.
- [11]11. Seldin MF, Pasaniuc B, Price AL. New approaches to disease mapping in admixed populations. Nat Rev Genet. 2011;12:523 -8.
- [12]12. Atkinson EG, Maihofer AX, Kanai M, Martin AR, Karczewski KJ, Santoro ML, et al. Tractor uses local ancestry to enable the inclusion of admixed individuals in GWAS and to boost power. Nat Genet. 2021;53:195 -204.
- [14]14. Zhong Y, De T, Alarcon C, Park CS, Lec B, Perera MA. Discovery of novel hepatocyte eQTLs in African Americans. PLoS Genet. 2020;16:e1008662.
- [15]15. Luo Y, Li X, Wang X, Gazal S, Mercader JM, 23andMe Research Team, et al. Estimating heritability and its enrichment in tissue-speci fi c gene sets in admixed populations. Hum Mol Genet. 2021;30:1521 -34.
- [16]16. Zhang J, Schumacher FR. Evaluating the estimation of genetic correlation and heritability using summary statistics. Mol Genet Genomics. 2021;296:1221 -34.
- [19]19. Weissbrod O, Kanai M, Shi H, Gazal S, Peyrot WJ, Khera AV, et al. Leveraging fi nemapping and multipopulation training data to improve cross-population polygenic risk scores. Nat Genet. 2022;54:450 -8.
- [20]20. Zhang H, Zhan J, Jin J, Zhang J, Ahearn TU, Yu Z, et al. Novel methods for multiancestry polygenic prediction and their evaluations in 3.7 million individuals of diverse ancestry. bioRxiv. 2022.
- [21]21. Liang Y, Pividori M, Manichaikul A, Palmer AA, Cox NJ, Wheeler HE, et al. Polygenic transcriptome risk scores (PTRS) can improve portability of polygenic risk scores across ancestries. Genome Biol. 2022;23:23.
- [22]22. Amariuta T, Ishigaki K, Sugishita H, Ohta T, Koido M, Dey KK, et al. Improving the trans-ancestry portability of polygenic risk scores by prioritizing variants in predicted cell-type-speci fi c regulatory elements. Nat Genet. 2020;52: 1346 -54.
- [23]23. McAllister K, Mechanic LE, Amos C, Aschard H, Blair IA, Chatterjee N, et al. Current challenges and new opportunities for gene-environment interaction studies of complex diseases. Am J Epidemiol. 2017;186:753 -61.
- [24]24. Atutornu J, Milne R, Costa A, Patch C, Middleton A. Towards equitable and trustworthy genomics research. EBioMedicine. 2022;76:103879.
- [25]25. Atkinson E, Choquet H, Khor CC, Wonkam A. Improving equity in human genomics research. Commun Biol. 2022;5:281.
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Smith, L. A., Cahill, J. A., Lee, J., & Graim, K. S. (2025) · Nature Communications · doi:10.1038/s41467-025-57216-8 · Cited by 32 · see citing works
Oliva, A., Kaphle, A., Reguant, R., Sng, L. M. F., Twine, N. A., Malakar, Y., Wickramarachchi, A., Keller, M., Ranbaduge, T., Chan, E. K. F., et al. (2024) · GigaScience · doi:10.1093/gigascience/giae021 · Cited by 41 · see citing works
“Gener ativ e methods can be used to create datasets that act as pr oxies for underr epr esented populations, going some way to address the known Eurocentric bias in genomic studies [ 102 ].”
Psychiatric genetics in the diverse landscape of Latin American populations
Bruxel, E. M., Rovaris, D. L., Belangero, S. I., Chavarría‐Soley, G., Barboza, A. B. C., Martínez‐Magaña, J. J., Nagamatsu, S. T., Nievergelt, C. M., Núñez-Ríos, D. L., Ota, V., et al. (2025) · Nature Genetics · doi:10.1038/s41588-025-02127-z · Cited by 21 · see citing works
“Diversifying psychiatric genetics research is imperative not only because of scientific reasons to unravel disease biology but also because of ethical concerns to avoid the exacerbation of global health disparities 140 .”
Self-citationSubstance use and common contributors to morbidity: A genetics perspective
Sanchez‐Roige, S., Kember, R. L., & Arpana Agrawal, A. (2022) · EBioMedicine · doi:10.1016/j.ebiom.2022.104212 · Cited by 23 · see citing works
Self-citationShared genetic architectures of educational attainment in East Asian and European populations
Chen, T.-T., Kim, J., Lam, M., Chuang, Y.-F., Chiu, Y.-L., Lin, S.-C., Jung, S.-H., Kim, B., Kim, S., Cho, C., et al. (2024) · Nature Human Behaviour · doi:10.1038/s41562-023-01781-9 · Cited by 18 · see citing works
“This disparity is problematic because genomic Article https://doi.org/10.1038/s41562-023-01781-9 discoveries may not be transferable across populations, even though it is assumed that they share the underlying biological mechanisms 37 , and our study helps to fill this gap.”
InfluentialMulti-trait GWAS for diverse ancestries: mapping the knowledge gap
Troubat, L., Fettahoglu, D., Henches, L., Aschard, H., & Julienne, H. (2024) · BMC Genomics · doi:10.1186/s12864-024-10293-3 · Cited by 18 · see citing works
“The genetic community is increasingly aware of this issue and strongly recommend to include more individuals from non-European ancestries in GWAS studies [2,7,9–12].”
BackgroundCheng, X., Du, F., Long, X., & Huang, J.-Z. (2023) · Genes · doi:10.3390/genes14101859 · Cited by 18 · see citing works
“For now, the majority of GWASs are Eurocentric, which introduces bias when analyzing other ances-tries [42].”
BackgroundBridging the gap: returning genetic results to indigenous communities in Latin America
Arango-Isaza, E., Aninao, M. J., Campbell, R., Martínez, F. I., Shimizu, K. K., & Barbieri, C. (2023) · Frontiers in Genetics · doi:10.3389/fgene.2023.1304974 · Cited by 10 · see citing works
Xavier, R. M., Shanavas, Y., Britt, B. M., & George, W. T. (2023) · PLoS ONE · doi:10.1371/journal.pone.0284356 · Cited by 6 · see citing works
Genome-wide association studies of lifetime and frequency of cannabis use in 131,895 individuals
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Self-citationRaghavan, R. P., Alexander, K. T., Sadasivan, S., Parmar, C. D., & Kathirvel, M. (2026) · Journal of Personalized Medicine · doi:10.3390/jpm16010029 · Cited by 2 · see citing works
“Population bias and generalizability: A major limitation of current datasets is the overrepresentation of individuals of European ancestry [84].”
Smith, L. A., Cahill, J. A., & Graim, K. S. (2023) · Research Square · doi:10.21203/rs.3.rs-3168446/v1 · Cited by 2
A comprehensive overview of the biological foundations of ADHD
Folego-Temoteo, I., Lima, Y. C., Grevet, E. H., Vidor, M. V., Tavares, M. E., da Silva, B. S., Bau, C. H. D., & Rovaris, D. L. (2026) · Bioscience Reports · doi:10.1042/bsr20254061 · Cited by 1 · see citing works
Shah, N., Tan, T., Mauer, J., Lin, Y.-S., Maihofer, A. X., Zai, C. C. H., PGC-PTSD ancestry working group, Atkinson, E. G., Santoro, M., Nievergelt, C. M., et al. (2026) · Bioinformatics · doi:10.1093/bioinformatics/btag124
“The inclusion of a broad range of populations in GWAS is crucial for several reasons (Atkinson et al. 2022).”
Self-citationAustin-Zimmerman, I., Thorpe, H., Meredith, J. J., Khokhar, J., Ge, T., di Forti, M., Agrawal, A., Johnson, E., & Sanchez-Roige, S. (2025) · Psychological Medicine · doi:10.1017/s0033291725102717
Self-citationShah, N. N., Tan, T.-T., Honorato-Mauer, J., Lin, Y.-S., Maihofer, A., Zai, C., Santoro, M. L., Nievergelt, C., & Atkinson, E. G. (2025) · bioRxiv · doi:10.1101/2025.09.02.673402
Self-citation
- Authors
- Atkinson, E. G., Bianchi, S. B., Ye, G. Y., Martínez-Magaña, J. J., Tietz, G. E., Montalvo-Ortiz, J. L., Giusti-Rodriguez, P., Palmer, A. A., & Sanchez-Roige, S.
- Affiliations
- Baylor College of Medicine (US)
- University of California San Diego (US)
- ORCID
- Venue
- Neuropsychopharmacology
- Published
- 2022-06-23
- Volume
- 47
- Issue
- 10
- Pages
- 1737-1738
- ISSN
- 0893-133X (ISSN-L)
- 1740-634X
- PMC
- PMC9372026
- OpenAlex
- W4283316558
- Cited by
- 19
- References
- 0
- Citations by year
- 2026: 4 · 2025: 5 · 2024: 5 · 2023: 4 · 2022: 1
- FWCI
- 3.92
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Neuropsychopharmacology (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- U.S. Department of Veterans Affairs (IK2 CX002095)
- National Institute of Mental Health (K01MH121659)
- National Institute on Drug Abuse (R21DA050160, DP1DA054394)
- National Institute of General Medical Sciences (T32GM139534)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Race, Genetics, and Society
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Forensic and Genetic Research
- Keywords
- genome-wide association study, ancestral diversity, health disparities, underrepresented groups, translational genomics
- MeSH terms
- Genomics
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