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. 2023 Apr 17;29(4):950–962. doi: 10.1038/s41591-023-02268-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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Extended Data Fig. 2 — Two-sample Mendelian randomization was conducted using European PVS GWAS summary statistics (N = 38,598 (WM-PVS), N = 38,903 (BG-PVS) and N = 38,871 (HIP-PVS)), combined with summary statistics for lacunes (N = 1,715 cases / N = 15,096 controls) and WMH volume (N = 48,454) in MTAG, for the outcomes and European GWAS summary statistics for blood pressure traits (N = 757,601) to generate instruments for exposures (Supplementary Tables 1 and 29). Only significant associations after multiple testing correction (p < 1.19×10⁻³) in GSMR are shown. Each dot (or triangle if p < 1.19×10⁻³) represents the beta estimate from Mendelian randomization with lines representing the 95% confidence interval. Two-sided p-values of GSMR are reported. PVS indicates perivascular spaces; WM, white matter; BG, basal ganglia; HIP, hippocampus; MTAG, multi-trait analysis of genome-wide association summary statistics; DBP, diastolic blood pressure; PP, pulse pressure; SBP, systolic blood pressure; NS, not significant; GSMR, Generalised Summary-data-based Mendelian Randomisation; IVW, Inverse variance weighted; 2SMR, Two-SampleMR.