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Genome-wide discovery of genetic loci that uncouple excess adiposity from its comorbidities

Nature Metabolism volume 3, pages 228–243 (2021)Cite this article

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Abstract

Obesity is a major risk factor for cardiometabolic diseases. Nevertheless, a substantial proportion of individuals with obesity do not suffer cardiometabolic comorbidities. The mechanisms that uncouple adiposity from its cardiometabolic complications are not fully understood. Here, we identify 62 loci of which the same allele is significantly associated with both higher adiposity and lower cardiometabolic risk. Functional analyses show that the 62 loci are enriched for genes expressed in adipose tissue, and for regulatory variants that influence nearby genes that affect adipocyte differentiation. Genes prioritized in each locus support a key role of fat distribution (FAM13A, IRS1 and PPARG) and adipocyte function (ALDH2, CCDC92, DNAH10, ESR1, FAM13A, MTOR, PIK3R1 and VEGFB). Several additional mechanisms are involved as well, such as insulin–glucose signalling (ADCY5, ARAP1, CREBBP, FAM13A, MTOR, PEPD, RAC1 and SH2B3), energy expenditure and fatty acid oxidation (IGF2BP2), browning of white adipose tissue (CSK, VEGFA, VEGFB and SLC22A3) and inflammation (SH2B3, DAGLB and ADCY9). Some of these genes may represent therapeutic targets to reduce cardiometabolic risk linked to excess adiposity.

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Fig. 1: The 62 loci cluster in three groups, each with a specific association signature.
Fig. 2: Genetic risk scores and their associations in the UK Biobank population.
Fig. 3: Tissue and cell-type enrichment analyses.
Fig. 4: Overlap of the 62 loci with regulatory elements in human adipocytes.
Fig. 5: Prioritization of genes within each of the 62 loci.

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Data availability

Summary statistics for the cross-phenotype GWAS analyses are available at http://www.thelooslab.com/#data/ and the GWAS catalogue.

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Acknowledgements

R.J.F.L. is supported by the National Institutes of health (NIH; R01DK11011, R01DK107786 and R01DK124097). T.O.K. is supported by the Independent Research Fund Denmark (6110-00183) and the Novo Nordisk Foundation (NNF17OC0026848, NNF18CC0034900 and NNF20OC0063707). M.d.H. is a fellow of the Swedish Heart-Lung Foundation (20170872 and 20200781) and a Kjell and Märta Beijer Foundation researcher. M.d.H. is further supported by the Swedish Heart-Lung Foundation (20140543, 20170678, 20180706 and 20200602) and the Swedish Research Council (2015-03657 and 2019-01417). A.V.-P. is supported by NIH R01DK107786, an ERC Senior Investigator award (669879) and an MRC MDU Programme grant (PO 4050281695). Yuval Itan is supported by the NIH (R01DK123530). The work of A.R. and S.M. was supported by grants from the Independent Research Fund Denmark (Sapere Aude Advanced grant no. 12-125524), the Danish National Research Foundation (grant no. 141) to the Center for Functional Genomics and Tissue Plasticity and the Novo Nordisk Foundation. A.R. was further supported by a postdoctoral grant from the Danish Diabetes Academy supported by the Novo Nordisk Foundation. N.C. is supported by a grant from the Canadian Institutes of Health Research (fellowship MFE-158192).

Author information

Author notes

  1. These authors jointly supervised this work: Tuomas O. Kilpeläinen, Ruth J. F. Loos.

Authors and Affiliations

  1. Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark

    Lam O. Huang & Tuomas O. Kilpeläinen

  2. Steno Diabetes Center Copenhagen, Gentofte, Denmark

    Lam O. Huang

  3. Functional Genomics & Metabolism Research Unit, Department of Biochemistry and Molecular Biology, University of Southern Denmark, Odense, Denmark

    Alexander Rauch & Susanne Mandrup

  4. Molecular Endocrinology & Stem Cell Research Unit, Department of Endocrinology and Metabolism, Odense University Hospital and Steno Diabetes Center Odense and Department of Clinical Research, University of Southern Denmark, Odense, Denmark

    Alexander Rauch

  5. The Beijer Laboratory and Department of Immunology, Genetics and Pathology, Uppsala University and SciLifeLab, Uppsala, Sweden

    Eugenia Mazzaferro & Marcel den Hoed

  6. The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, NY, USA

    Michael Preuss, Cigdem S. Bayrak, Nathalie Chami, Zhe Wang, Ursula M. Schick, Nancy Yang, Yuval Itan & Ruth J. F. Loos

  7. The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, New York, NY, USA

    Michael Preuss, Nathalie Chami, Zhe Wang & Ruth J. F. Loos

  8. Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, UK

    Stefania Carobbio & Antonio Vidal-Puig

  9. Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, NY, USA

    Yuval Itan

  10. University of Cambridge Metabolic Research Laboratories, Wellcome-MRC Institute of Metabolic Science, Addenbrooke’s Hospital, Cambridge, UK

    Antonio Vidal-Puig

  11. Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, New York, NY, USA

    Tuomas O. Kilpeläinen & Ruth J. F. Loos

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  1. Lam O. Huang
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Contributions

R.J.F.L. and T.O.K. conceived the project, designed the experiments, supervised analyses and edited the manuscript. L.O.H. performed the final computational analyses for gene discovery, follow-up analyses and wrote the first draft of the manuscript and edited subsequent versions. U.M.S. and N.Y. performed the initial gene-discovery analyses. A.R. and S.M. led the human adipocyte analyses. C.S.B. and Y.I. performed IPA enrichment analysis and HGC biological distance calculations. E.M. performed the ToppGene and Endeavour screens, integrated results across all gene prioritization approaches and drafted the section on gene prioritization. M.d.H. supervised the integration of gene prioritization results, liaising with E.M. M.P. performed GRS analyses in the UKBB population. N.C. and Z.W. performed validation analyses in the UKBB population. S.C., A.V.-P., S.M., M.d.H., T.O.K. and R.J.F.L. examined the genes prioritized for their biological contribution. All the authors read and approved the manuscript.

Corresponding author

Correspondence to Ruth J. F. Loos.

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Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Metabolism thanks Jose Florez, Xueling Sim and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editors: Pooja Jha; Isabella Samuelson.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1

Overview of the different stages, approaches and results.

Extended Data Fig. 2 Tissue and cell type enrichment analyses for genes in cluster 1.

Tissues [A], cells [B] and physiological systems [C] significantly enriched for genes in 15 loci of cluster 1. Built-in iterative statistical computation using scoring, bias adjustment, and FDR estimation, based on the membership z-score. The p-value, called ‘nominal p-value’ is not corrected for multiple testing, whereas the FDR (shown in Tables), is corrected for multiple testing.

Extended Data Fig. 3 DNase-seq, H3K27ac ChIP-seq and RNA-seq data of hMSC-TERT4 cells during adipocyte differentiation.

(a) DNase-seq, H3K27ac ChIP-seq and RNA-seq data of hMSC-TERT4 cells during adipocyte differentiation. (a) DNase-seq and H3K27ac ChIP-seq read density (combined tracks from two independent experiments) in the NT5C2 locus and bar plot with expression level of NT5C2 from RNA-seq (n = 3 biologically independent samples). Bar plot with average and error bars representing standard deviation. DEseq2 derived Benjamin-Hochberg adjusted P-values below 0.01 when comparing to MSC are indicated. Lead SNP rs10883832 is indicated by red arrow. Black lines represent eQTLs that are in high LD with rs10883832. Black arrow shows rs11191548. DNase I hypersensitive sites are shaded. (b) PPARG locus as in (a). Lead SNP rs2881654 is indicated by red arrow. Black lines represent SNPs that are in high LD with rs2881654. (c) PIK3R1 locus as in (a). Lead SNP rs4976033 is indicated by red arrow.

Extended Data Fig. 4 Gene prioritization tools and genes prioritized.

61 unique candidate genes were prioritized by at least one of eight criteria used to integrate results from individual bioinformatics approaches.

Extended Data Fig. 5 A Sunburst plot showing the criteria by which each candidate gene was prioritized.

The root (inner circle) displays the chromosome on which the prioritized genes (middle circle) are located; the leaves (outer circle) display the criteria (color-coded) fulfilled by the gene. The plot shows that half of the candidate genes have been prioritized by multiple criteria. E-TG: Endeavour and ToppGene; IPA: ingenuity pathway analysis; HGC: human gene connectome. Criteria: 1 = DEPICT P ≤ 0.1; 2 = (eQTL or chromatin-chromatin interaction in relevant tissue) AND top 10% E-TG; 3 = eQTL in relevant tissue AND IPA ≥ 5 pathways, functions, networks; 4 = top 2.5% E-TG; 5 = top 10% E-TG AND IPA ≥ 5 pathways, functions, networks; 6 = Enhancer enrichment analysis; 7 = IPA ≥ 5 pathways, functions, networks; 8 HGC = average biological distance to PPARG and IRS1 < 10.

Extended Data Fig. 6

Number of genes prioritized by one or more of the eight criteria.

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Huang, L.O., Rauch, A., Mazzaferro, E. et al. Genome-wide discovery of genetic loci that uncouple excess adiposity from its comorbidities. Nat Metab 3, 228–243 (2021). https://doi.org/10.1038/s42255-021-00346-2

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