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. 2024 Sep 3;27(10):2021–2032. doi: 10.1038/s41593-024-01742-z

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© The Author(s) 2024

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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

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Extended Data Fig. 9 — a-b, Scatterplots showing the Pearson’s correlation (R, two-sided) between chronological age (x-axis) and DNA methylation age (DNAmAge; y-axis) as estimated using the CorticalClock (a) and Horvath’s multi-tissue clock (b). c, Scatterplots showing log normalized gene expression, corrected for covariates, across aging for genes showing an interactive effect between aging and disease status; SLC25A37 in Astro_FB (left), OXCT1 in Exc_L4-6_2 (middle), and AC007402.1 in OPC (right). Error bands represent the 95% confidence interval. d, Plot depicting the number of genes associated with both age and psychiatric disease. Size of the circle is proportional to the number of overlapping genes and color indicates the percentage of genes regulated in the same (common) direction across respective cell types.