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. 2022 Sep 28;23(Suppl 3):398. doi: 10.1186/s12859-022-04947-w

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© The Author(s) 2022

Open AccessThis 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/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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Fig. 1 — Schematic diagram describing the pipeline of this study which used S-PrediXcan to predict genes that are highly associated with total brain volume (TBV) and intracranial volume (ICV). S-PrediXcan integrates two inputs, one of them was trained PrediXcan elastic-net prediction models which derived from GTEx genotyping and transcriptome data of 13 brain tissues. The other inputs were GWAS summary statistics data of our interested traits: (1) TBV from UKB and (2) ICV from ENIGMA2. The first S-PrediXcan analysis on UKB data yielded predicted genes that are highly associated with TBV. The second S-PrediXcan analysis aimed to perform a targeted study on a similar trait (ICV) using the GWAS summary data from an independent cohort (ENIGMA2) to determine which TBV-associated genes are also significantly associated with ICV