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. 2023 Jun 29;55(7):1243–1249. doi: 10.1038/s41588-023-01415-w

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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. 2 — a,b, SER (y axis, log scale) of SHAPEIT5 (blue) compared with Beagle v.5.4 (black) stratified by MAC (x axis) for the UKB WGS (a) and WES (b). The zoomed-in views show the relative reduction of SER using SHAPEIT5 compared with Beagle v.5.4 at rare variants. c,d, Imputation accuracy (Aggregate r2, y axis) for 1,000 white British samples genotyped with the Axiom array when using reference panels phased with either SHAPEIT5 (blue) or Beagle v.5.4 (black) WGS (c) or WES (d). In (c) the data were also imputed using the HRC reference panel (gray). The zoomed-in views show the increase of imputation accuracy at rare variants using the UKB dataset phased with SHAPEIT5 compared with Beagle v.5.4 as a reference panel.

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