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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. 3 — a,b, Statistical importance of each factor measured by percentage variance explained in the phenotype matrix (a) and factor prediction strength (b). c, Top phenotype loadings for the top five SoftImpute factors. d,e, Estimates of heritability (d) (n = 337,126 for all factors) and genetic correlation (e) of the top five SoftImpute factors with MDD-relevant traits (n values for all MDD-relevant traits can be found in Supplementary Table 1). f, Logistic regression GWAS Manhattan plot of GPpsy conditioning on SoftImpute factor 1; the red line shows the genome-wide significance threshold. g, LocusZoom plot of the significant GWAS locus in gene NEGR1. All error bars indicate 95% CI.