S4-multi: Enhancing polygenic score prediction in ancestrally diverse populations.
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| Title: | S4-multi: Enhancing polygenic score prediction in ancestrally diverse populations. |
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| Authors: | Baierl J; Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA., Tyrer JP; Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK., Lai PH; Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA., Gayther SA; Department of Medicine, UT Health, San Antonio, TX, USA., Hsiao YW; Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA., Jones M; Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA., Pharoah PDP; Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA., Peng PC; Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA. Electronic address: pei-chen.peng@cshs.org. |
| Source: | HGG advances [HGG Adv] 2026 Jan 15; Vol. 7 (1), pp. 100551. Date of Electronic Publication: 2025 Nov 27. |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Elsevier Inc Country of Publication: United States NLM ID: 101772885 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2666-2477 (Electronic) Linking ISSN: 26662477 NLM ISO Abbreviation: HGG Adv Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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