Figure - PMC

Skip to main content

An official website of the United States government

Here's how you know

Here's how you know

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

View full-text article in PMC

. 2023 Nov 28;14:7820. doi: 10.1038/s41467-023-43567-7

Search in PMC
Search in PubMed
View in NLM Catalog
Add to search

© 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 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/.

PMC Copyright notice

Fig. 2 — A The {1 × 18,640 } vector of unthresholded gene association statistics (Z-scores) derived by H-MAGMA analysis of the schizophrenia GWAS dataset was designated as the response variable, i.e., the dependent Y vector; and the {180 × 18,640} matrix of unthresholded gene association Z-scores for each of the MRI GWAS datasets was designated the predictor variable, i.e., the independent X matrix. The first PLS component (PLS1) defined the weighted functions of X and Y that were most strongly correlated overall weighted functions of the whole genome. The PLS1 weights for X (brain weights, w(X)_i, i = 1, 2, 3, …180) multiplied by X constituted a {1 × 18,640} vector of T scores (genes weighted by association with brain phenotypes); whereas, the PLS1 weights for Y (schizophrenia weights w(Y)_i) multiplied by Y constituted a {1 × 18,640} vector of U scores (genes weighted by association with schizophrenia). Thus genes with the highest absolute T and U scores can be regarded as the genes which contribute most strongly to the genetic covariation between schizophrenia and each regional brain phenotype^32,108. B Cortical surface maps of PLS1 weights for neurite density index (NDI), cortical thickness (CT) and surface area (SA). Cortical regions with higher PLS1 weights (shades of yellow) have stronger genetic covariation with schizophrenia: for SA, regions of insular and medial prefrontal cortex; for CT, visual, premotor and inferior parietal cortex; and for NDI, inferior frontal, inferior parietal, posterior cingulate and posterior opercular cortex. Scatterplots (Spearman’s correlations, ρ) illustrate the genetic relationships between schizophrenia (y-axis, Z-scores from H-MAGMA analysis of schizophrenia GWAS dataset) and brain surface area (x-axis, Z-scores from H-MAGMA analysis of MRI GWAS datasets) in two cortical regions, one with a low PLS1 weight (left, dark blue, ρ = 0.04), and one with a high PLS1 weight (right, yellow, ρ = 0.17). In both plots each point represents one of 18,640 genes. Spearman’s correlations were two-tailed. Source data are provided as a Source Data file.