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. 2024 Mar 6;628(8006):130–138. doi: 10.1038/s41586-024-07148-y

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© The Author(s) 2024

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/.

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Extended Data Fig. 3 — The forest plots in panels a and b show examples of two lead SNPs in which glucose associations were significant in the fasted cohorts (top; n = 68,559) and non-significant in the non-fasted cohorts (bottom; n = 58,112). The associations were analyzed by inverse variance weighted GWAS meta-analysis. These associations were absent in the UK Biobank. Effect sizes (betas and 95% confidence intervals), effect allele frequencies (EAF) and p-values are indicated for each cohort. Cohort acronyms can be found in Supplementary Table 1. Panels c and d show regional association plots of the MTNR1B (c) and GLIS3 (d) loci in the fasted (left) and non-fasted (center) cohorts and in UK Biobank (right). SNPs with p < 0.1 are shown. 500-kb flanking regions around each lead SNP are shown. The linkage disequilibrium values (r²) are based on the 1000Genomes European population.