Genomics and Applied Biology 2026, Vol.17, No.3, 138-153 http://bioscipublisher.com/index.php/gab 152 Broesch T., Crittenden A., Beheim B., Blackwell A., Bunce J., Colleran H., Hagel K., Kline M., Mcelreath R., Nelson R., Pisor A., Prall S., Pretelli I., Purzycki B., Quinn E., Ross C., Scelza B., Starkweather K., Stieglitz J., and Mulder M., 2020, Navigating cross-cultural research: Methodological and ethical considerations, Proceedings of the Royal Society B, 287(1935): 20201245. https://doi.org/10.1098/rspb.2020.1245 Cai M., Xiao J., Zhang S., Wan X., Zhao H., Chen G., and Yang C., 2021, A unified framework for cross-population trait prediction by leveraging the genetic correlation of polygenic traits, American Journal of Human Genetics, 108(4): 632-655. https://doi.org/10.1016/j.ajhg.2021.03.002 Chapman C., 2022, Ethical, legal, and social implications of genetic risk prediction for multifactorial disease, Journal of Community Genetics, 14(5): 441-452. https://doi.org/10.1007/s12687-022-00625-9 Ding Y., Hou K., Xu Z., Pimplaskar A., Petter E., Boulier K., Privé F., Vilhjálmsson B., Loohuis L., and Pasaniuc B., 2023, Polygenic scoring accuracy varies across the genetic ancestry continuum, Nature, 618(7966): 774-781. https://doi.org/10.1038/s41586-023-06079-4 Du Z., He J., and Jiao W., 2025, Plant graph-based pangenomics: Techniques, applications, and challenges, aBIOTECH, 2025: 1-16. https://doi.org/10.1007/s42994-025-00206-7 Duncan L., Shen H., Gelaye B., Meijsen J., Ressler K., Feldman M., Peterson R., and Domingue B., 2019, Analysis of polygenic risk score usage and performance in diverse human populations, Nature Communications, 10(1): 3328. https://doi.org/10.1038/s41467-019-11112-0 Fang X.J., 2026, Genome-wide relationship matrix-based heritability estimation: statistical interpretation, comparability, and practical diagnostics in the GCTA-GREML framework, Computational Molecular Biology, 16(1): 11-20. Fang X.J., and Wu W.R., 2026, Evolution of statistical genetic paradigms: from linkage analysis and candidate gene strategies to GWAS, Molecular Plant Breeding, 24(9): 2817-2829. Gorjanc G., Gaynor R., and Hickey J., 2017, Optimal cross selection for long-term genetic gain, Theoretical and Applied Genetics, 131(9): 1953-1966. https://doi.org/10.1007/s00122-018-3125-3 Gunn S., Wang X., Posner D., Cho K., Huffman J., Gaziano M., Wilson P., Sun Y., Peloso G., and Lunetta K., 2024, Comparison of methods for building polygenic scores for diverse populations, Human Genetics and Genomics Advances, 6: 100355. https://doi.org/10.1016/j.xhgg.2024.100355 Hickey J., Chiurugwi T., Mackay I., and Powell W., 2017, Genomic prediction unifies animal and plant breeding programs to form platforms for biological discovery, Nature Genetics, 49(9): 1297-1303. https://doi.org/10.1038/ng.3920 Jayasinghe D., Eshetie S., Beckmann K., Benyamin B., and Lee S., 2024, Advancements and limitations in polygenic risk score methods for genomic prediction: A scoping review, Human Genetics, 143(12): 1401-1431. https://doi.org/10.1007/s00439-024-02716-8 Jung H.U., Jung H., Baek E., Kang J., Kwon S., You J., Lim J., and Oh B., 2025, Assessment of polygenic risk score performance in East Asian populations for ten common diseases, Communications Biology, 8(1): 374. https://doi.org/10.1038/s42003-025-07767-9 Kachuri L., Chatterjee N., Hirbo J., Schaid D., Martin I., Kullo I., Kenny E., Pasaniuc B., Witte J., and Ge T., 2024, Principles and methods for transferring polygenic risk scores across global populations, Nature Reviews Genetics, 25(1): 8-25. https://doi.org/10.1038/s41576-023-00637-2 Kullo I., 2024, Promoting equity in polygenic risk assessment through global collaboration, Nature Genetics, 56(9): 1780-1787. https://doi.org/10.1038/s41588-024-01843-2 Lennon N., Kottyan L., Kachulis C., Abul-Husn N., Arias J., Belbin G., Below J., Berndt S., Chung W., Cimino J., and Kenny E., 2024, Selection, optimization and validation of ten chronic disease polygenic risk scores for clinical implementation in diverse US populations, Nature Medicine, 30(2): 480-487. https://doi.org/10.1038/s41591-024-02796-z Lewis A., and Green R., 2021, Polygenic risk scores in the clinic: New perspectives on familiar ethical issues, Genome Medicine, 13(1): 14. https://doi.org/10.1186/s13073-021-00829-7 Lewis A., Chisholm R., Connolly J., Esplin E., Glessner J., Gordon A., Green R., Hakonarson H., Harr M., Holm I., Jarvik G., Karlson B., Kenny E., Kottyan L., Lennon N., Linder J., Luo Y., Martin L., Perez E., Puckelwartz M., Rasmussen-Torvik L., Sabatello M., Sharp R., Smoller J., Sterling R., Terek S., Wei W., and Fullerton S., 2024, Managing differential performance of polygenic risk scores across groups: Real-world experience of the eMERGE Network, American Journal of Human Genetics, 111(6): 999-1005. https://doi.org/10.1016/j.ajhg.2024.04.005 Martin A., Kanai M., Kamatani Y., Okada Y., Neale B.M., and Daly M., 2019, Clinical use of current polygenic risk scores may exacerbate health disparities, Nature Genetics, 51(4): 584-591. https://doi.org/10.1038/s41588-019-0379-x Murray G., Lin T., Austin J., Mcgrath J., Hickie I., and Wray N., 2020, Could polygenic risk scores be useful in psychiatry? A review, JAMA Psychiatry, 78(2): 210-219. https://doi.org/10.1001/jamapsychiatry.2020.3042
RkJQdWJsaXNoZXIy MjQ4ODYzNA==