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. 2023 Nov 20;55(12):2082–2093. doi: 10.1038/s41588-023-01559-9

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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. 4 — a, Description of the evaluated input choices for MTAG and their resulting GWAS summary statistics. The MTAG effective sample size refers to the power-equivalent sample size of the MTAG GWAS versus the single-trait GWAS¹⁹, calculated as n_effect = n_single ⨯ (χ²_MTAG − 1)/(χ²_single − 1), where the χ² terms are the average GWAS chi-squared values. b,c, Manhattan plots for the MTAG models with the fewest (MTAG.FamilyHistory) (b) and most (MTAG.AllDep+Envs) (c) GWAS hits. −log₁₀(P) values shown on the y axis were before adjustment for multiple testing; red lines show the genome-wide significance threshold of P < 5 × 10⁻⁸. d, SNP-based heritability estimates on the observed and liability scales for observed, imputed, and MTAG GWASs on LifetimeMDD as well as reference phenotypes (n_effect is shown in Supplementary Table 5). e, Estimated genetic correlations for observed, imputed and MTAG analyses of LifetimeMDD and reference phenotypes. All error bars indicate 95% CI.