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. 2023 Apr 10;24:192. doi: 10.1186/s12864-023-09295-4

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© The Author(s) 2023

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 — Comparison of variants associated with 24 traits from 3 multi-trait GWAS methods. Multivariate (mtGWAS), meta-analyses with complete dataset (metaGWAS¹), and meta-analyses including samples with missing trait records (metaGWAS²) were based on array-derived genotypes. A Proportions of significantly associated variants discovered across chromosomes, groups of traits and multi-trait methods. B Overlaps between the associated variants revealed by each of the methods. Sum across all four trait-groups. C QQ plot between -log10(P) of variants (N = 41) associated in both mtGWAS and metaGWAS². The line denotes a correlation of 1. D QTL detected by different methods across all trait groups