A genetic map of human metabolism across the allele frequency spectrum

Genetic studies of human metabolism have been limited in scale and allelic breadth. Here we provide a data-driven map of the genetic regulation of circulating small molecules and lipoprotein characteristics (249 traits) measured using proton nuclear magnetic resonance spectroscopy across the allele frequency spectrum in ~450,000 individuals. Trans-ancestral meta-analyses identify 29,824 locus-metabolite associations mapping to 753 regions with effects largely consistent between men and women and large ancestral groups represented in UK Biobank. We observe and classify extreme genetic pleiotropy, identify regulators of lipid metabolism, and assign effector genes at >100 loci through rare-to-common allelic series. We propose roles for genes less established in metabolic control (for example, SIDT2), genes characterized by phenotypic heterogeneity (for example, APOA1) and genes with specific disease relevance (for example, VEGFA). Our study demonstrates the value of broad, large-scale metabolomic phenotyping to identify and characterize regulators of human metabolism.

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Publication Details

Journal
Nature Genetics
Published
2025-10-01
DOI
https://doi.org/10.1038/s41588-025-02355-3
Citations
1
Primary Topic
Metabolomics and Mass Spectrometry Studies
Type
article
Field-Weighted Citation Impact
0.81
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A genetic map of human metabolism across the allele frequency spectrum

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A genetic map of human metabolism across the allele frequency spectrum

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Abstract

Genetic studies of human metabolism have been limited in scale and allelic breadth. Here we provide a data-driven map of the genetic regulation of circulating small molecules and lipoprotein characteristics (249 traits) measured using proton nuclear magnetic resonance spectroscopy across the allele frequency spectrum in ~450,000 individuals. Trans-ancestral meta-analyses identify 29,824 locus-metabolite associations mapping to 753 regions with effects largely consistent between men and women and large ancestral groups represented in UK Biobank. We observe and classify extreme genetic pleiotropy, identify regulators of lipid metabolism, and assign effector genes at >100 loci through rare-to-common allelic series. We propose roles for genes less established in metabolic control (for example, SIDT2), genes characterized by phenotypic heterogeneity (for example, APOA1) and genes with specific disease relevance (for example, VEGFA). Our study demonstrates the value of broad, large-scale metabolomic phenotyping to identify and characterize regulators of human metabolism.

Nature GeneticsVol. 57(10)
Innsbruck Medical University (AT), Queen Mary University of London (GB), Universität Innsbruck (AT), German Centre for Cardiovascular Research (DE), Berlin Institute of Health at Charité - Universitätsmedizin Berlin (DE), Imperial College London (GB), Charité - Universitätsmedizin Berlin (DE)
Openalex Percentile: Top 23%
Metabolomics and Mass Spectrometry Studies
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