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. 2023 Jul 7;14:4023. doi: 10.1038/s41467-023-38930-7

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© The Author(s) 2023

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 1 — A To evaluate the different PGSs, we performed various splits of the available data. Firstly, we held out test sets of 20% of individuals in each ancestry group. From the remaining 80%, we constructed three types of training sets: a single-ancestry set consisting only of European-ancestry individuals (purple block), a single-ancestry set consisting of non-European-ancestry individuals (yellow block), and a dual-ancestry set consisting of both European-ancestry and non-European-ancestry individuals (blue block). For each training set, we used another 20% of the data to select the regularisation parameter in the LASSO. B For the dual-ancestry training set, we used an importance weighted LASSO, assigning higher weights to individuals in the minority-ancestry group. See Methods for full details.