Genetic analysis of circulating metabolic traits in 619,372 individuals
7 affiliations
- 1Institute of Computer Science, University of Tartu, Tartu, Estonia
- 2Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
- 3Institute of Mathematics and Statistics, University of Tartu, Tartu, Estonia
- 4Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- 5University Center for Primary Care and Public Health, Unisanté, University of Lausanne, Lausanne, Switzerland
- 6Institute of Computer Science, University of Tartu, Tartu, Estonia. [email protected]
- 7Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia. [email protected]
Abstract
Interpreting genetic associations with complex traits can be greatly improved by detailed understanding of the molecular consequences of these variants. However, although genome-wide association studies (GWAS) for common complex diseases routinely profile 1M+ individuals, studies of molecular phenotypes have lagged behind. We performed a GWAS meta-analysis for 249 circulating metabolic traits in the Estonian Biobank and the UK Biobank in up to 619,372 individuals, identifying 88,604 significant locus-metabolite associations and 8,774 independent lead variants, including 987 lead variants with a minor allele frequency less than 1%. We demonstrate how common and low-frequency associations converge on shared genes and pathways, bridging the gap between rare-variant burden testing and common-variant GWAS. We used Mendelian randomisation (MR) to explore putative causal links between metabolic traits, coronary artery disease and type 2 diabetes (T2D). Surprisingly, up to 85% of the tested metabolite-disease pairs had statistically significant genome-wide MR estimates, likely reflecting complex indirect effects driven by horisontal pleiotropy. To avoid these pleiotropic effects, we used cis -MR to test the phenotypic impact of inhibiting specific drug targets. We found that although plasma levels of branched-chain amino acids (BCAAs) have been associated with T2D in both observational and genome-wide MR studies, inhibiting the BCAA catabolism pathway to lower BCAA levels is unlikely to reduce T2D risk. Our publicly available results provide a valuable novel resource for GWAS interpretation and drug target prioritisation.
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BibTeX
@article{tambets2026genetic,
title = {{Genetic analysis of circulating metabolic traits in 619,372 individuals}},
author = {Ralf Tambets and Mihkel Jesse and Jaanika Kronberg and Adriaan van der Graaf and Erik Abner and Urmo Võsa and Ida Rahu and Nele Taba and Anastassia Kolde and Dzvenymyra Yarish and Sariyya Abdullayeva and Anastasiia Alekseienko and Andres Veidenberg and Mari Nelis and Georgi Hudjashov and Mait Metspalu and Reedik Mägi and Andres Metspalu and Lili A. Milani and Krista Fischer and Zoltán Kutalik and Tõnu Esko and Kaur Alasoo and Palta Priit},
journal = {Nature},
year = {2026},
month = may,
doi = {10.1038/s41586-026-10532-5},
url = {https://doi.org/10.1038/s41586-026-10532-5},
note = {CC BY},
}
RIS
TY - JOUR
TI - Genetic analysis of circulating metabolic traits in 619,372 individuals
AU - Ralf Tambets
AU - Mihkel Jesse
AU - Jaanika Kronberg
AU - Adriaan van der Graaf
AU - Erik Abner
AU - Urmo Võsa
AU - Ida Rahu
AU - Nele Taba
AU - Anastassia Kolde
AU - Dzvenymyra Yarish
AU - Sariyya Abdullayeva
AU - Anastasiia Alekseienko
AU - Andres Veidenberg
AU - Mari Nelis
AU - Georgi Hudjashov
AU - Mait Metspalu
AU - Reedik Mägi
AU - Andres Metspalu
AU - Lili A. Milani
AU - Krista Fischer
AU - Zoltán Kutalik
AU - Tõnu Esko
AU - Kaur Alasoo
AU - Palta Priit
PY - 2026
DA - 2026/05/20
JO - Nature
DO - 10.1038/s41586-026-10532-5
UR - https://doi.org/10.1038/s41586-026-10532-5
AB - Interpreting genetic associations with complex traits can be greatly improved by detailed understanding of the molecular consequences of these variants. However, although genome-wide association studies (GWAS) for common complex diseases routinely profile 1M+ individuals, studies of molecular phenotypes have lagged behind. We performed a GWAS meta-analysis for 249 circulating metabolic traits in the Estonian Biobank and the UK Biobank in up to 619,372 individuals, identifying 88,604 significant locus-metabolite associations and 8,774 independent lead variants, including 987 lead variants with a minor allele frequency less than 1%. We demonstrate how common and low-frequency associations converge on shared genes and pathways, bridging the gap between rare-variant burden testing and common-variant GWAS. We used Mendelian randomisation (MR) to explore putative causal links between metabolic traits, coronary artery disease and type 2 diabetes (T2D). Surprisingly, up to 85% of the tested metabolite-disease pairs had statistically significant genome-wide MR estimates, likely reflecting complex indirect effects driven by horisontal pleiotropy. To avoid these pleiotropic effects, we used cis -MR to test the phenotypic impact of inhibiting specific drug targets. We found that although plasma levels of branched-chain amino acids (BCAAs) have been associated with T2D in both observational and genome-wide MR studies, inhibiting the BCAA catabolism pathway to lower BCAA levels is unlikely to reduce T2D risk. Our publicly available results provide a valuable novel resource for GWAS interpretation and drug target prioritisation.
SN - 0028-0836
SN - 1476-4687
ER -