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Cross-ancestry genomic research: time to close the gap

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References

  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.

    CAS  Article  Google Scholar 

  2. Shapiro MD, Tavori H, Fazio S. PCSK9: from basic science discoveries to clinical trials. Circ Res. 2018;122:1420–38.

    CAS  Article  Google Scholar 

  3. Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature. 2014;511:421–7.

    Article  Google Scholar 

  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.

    CAS  Article  Google Scholar 

  5. Peterson RE, Kuchenbaecker K, Walters RK, Chen C-Y, Popejoy AB, Periyasamy S, et al. Genome-wide association studies in ancestrally diverse populations: opportunities, methods, pitfalls, and recommendations. Cell. 2019;179:589–603.

    CAS  Article  Google Scholar 

  6. Duncan L, Shen H, Gelaye B, Meijsen J, Ressler K, Feldman M, et al. Analysis of polygenic risk score usage and performance in diverse human populations. Nat Commun. 2019;10:3328.

    CAS  Article  Google Scholar 

  7. Martin AR, Kanai M, Kamatani Y, Okada Y, Neale BM, Daly MJ. Current clinical use of polygenic scores will risk exacerbating health disparities. Nat Genet. 2019;51:584–91.

    CAS  Article  Google Scholar 

  8. Martin AR, Gignoux CR, Walters RK, Wojcik GL, Neale BM, Gravel S, et al. Human demographic history impacts genetic risk prediction across diverse populations. Am J Hum Genet. 2017;100:635–49.

    CAS  Article  Google Scholar 

  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.

    CAS  Article  Google Scholar 

  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.

    CAS  Article  Google Scholar 

  11. Seldin MF, Pasaniuc B, Price AL. New approaches to disease mapping in admixed populations. Nat Rev Genet. 2011;12:523–8.

    CAS  Article  Google Scholar 

  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.

    CAS  Article  Google Scholar 

  13. Turley P, Martin AR, Goldman G, Li H, Walters RK, Jala JB, et al. Multi-ancestry meta-analysis yields novel genetic discoveries and ancestry-specific associations. bioRxiv. 2021.

  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.

    CAS  Article  Google Scholar 

  15. Luo Y, Li X, Wang X, Gazal S, Mercader JM, 23andMe Research Team, et al. Estimating heritability and its enrichment in tissue-specific gene sets in admixed populations. Hum Mol Genet. 2021;30:1521–34.

    CAS  Article  Google Scholar 

  16. Zhang J, Schumacher FR. Evaluating the estimation of genetic correlation and heritability using summary statistics. Mol Genet Genomics. 2021;296:1221–34.

    CAS  Article  Google Scholar 

  17. Hoggart C, Choi S, Preuss M, O’Reilly P. BridgePRS: a powerful trans-ancestry polygenic risk score method. Europe PMC. 2022.

  18. Ruan Y, Lin Y-F, Feng Y-CA, Chen C-Y, Lam M, Guo Z, et al. Improving polygenic prediction in ancestrally diverse populations. medRxiv. 2021.

  19. Weissbrod O, Kanai M, Shi H, Gazal S, Peyrot WJ, Khera AV, et al. Leveraging fine-mapping and multipopulation training data to improve cross-population polygenic risk scores. Nat Genet. 2022;54:450–8.

    CAS  Article  Google Scholar 

  20. Zhang H, Zhan J, Jin J, Zhang J, Ahearn TU, Yu Z, et al. Novel methods for multi-ancestry polygenic prediction and their evaluations in 3.7 million individuals of diverse ancestry. bioRxiv. 2022.

  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.

    Article  Google Scholar 

  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-specific regulatory elements. Nat Genet. 2020;52:1346–54.

    CAS  Article  Google Scholar 

  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.

    Article  Google Scholar 

  24. Atutornu J, Milne R, Costa A, Patch C, Middleton A. Towards equitable and trustworthy genomics research. EBioMedicine. 2022;76:103879.

    Article  Google Scholar 

  25. Atkinson E, Choquet H, Khor CC, Wonkam A. Improving equity in human genomics research. Commun Biol. 2022;5:281.

    Article  Google Scholar 

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Funding

This work is supported by the California Tobacco-Related Disease Research Program (28IR-0070 to AAP and T29KT0526 to SSR and SBB), NIDA (DP1DA054394 to SSR), by the Department of Veterans Affairs via NIDA (R21DA050160 and 1IK2CX002095-01A1 to JLMO), the National Institutes of Mental Health (K01 MH121659 to EGA), the National Institute of General Medical Sciences (GM139534-01 to GET), and the U.S. Department of Veterans Affairs National Center for Posttraumatic Stress Disorder. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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SSR conceived the idea. SSR and EGA wrote the first draft of the manuscript. All authors edited and approved the final version of the manuscript.

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Correspondence to Sandra Sanchez-Roige.

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Atkinson, E.G., Bianchi, S.B., Ye, G.Y. et al. Cross-ancestry genomic research: time to close the gap. Neuropsychopharmacol. (2022). https://doi.org/10.1038/s41386-022-01365-7

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