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Pathogenic variants in actionable MODY genes are associated with type 2 diabetes

Nature Metabolism volume 2, pages 1126–1134 (2020)Cite this article

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Abstract

Genome-wide association studies have identified 240 independent loci associated with type 2 diabetes (T2D) risk, but this knowledge has not advanced precision medicine. In contrast, the genetic diagnosis of monogenic forms of diabetes (including maturity-onset diabetes of the young (MODY)) are textbook cases of genomic medicine. Recent studies trying to bridge the gap between monogenic diabetes and T2D have been inconclusive. Here, we show a significant burden of pathogenic variants in genes linked with monogenic diabetes among people with common T2D, particularly in actionable MODY genes, thus implying that there should be a substantial change in care for carriers with T2D. We show that, among 74,629 individuals, this burden is probably driven by the pathogenic variants found in GCK, and to a lesser extent in HNF4A, KCNJ11, HNF1B and ABCC8. The carriers with T2D are leaner, which evidences a functional metabolic effect of these mutations. Pathogenic variants in actionable MODY genes are more frequent than was previously expected in common T2D. These results open avenues for future interventions assessing the clinical interest of these pathogenic mutations in precision medicine.

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Fig. 1: Association between T2D risk and P/LP variants in each actionable MODY gene.

Data availability

All relevant data have been included in the manuscript and/or in its supplementary tables and figures. Given the sensitivity and risk of re-identification, all clinical data linked with NGS data for this study is available only upon request from the corresponding authors. We used the following web links for publicly available datasets: (1) T2D Knowledge Portal. http://www.type2diabetesgenetics.org/gene/geneInfo/XXX, where XXX is the gene name; (2) Genome Aggregation Database (gnomAD). https://gnomad.broadinstitute.org/; (3) dbNSFP. https://sites.google.com/site/jpopgen/dbNSFP; (4) dbSNP. https://www.ncbi.nlm.nih.gov/snp/. Source data are provided with this paper.

Code availability

Code to perform analyses related to bioinformatics and biostatistics in this manuscript are available at https://github.com/umr1283/MODY_GENES (https://doi.org/10.5281/zenodo.4005715).

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Acknowledgements

We are grateful to all individuals included in the different cohort studies. We thank O. Sand and I. Rabearivelo for their contribution to the first computer analyses. This research has been conducted using the UK Biobank Application Number 40436. We thank the Genome Aggregation Database (gnomAD) and the groups that provided exome and genome variant data to this resource. A full list of contributing groups can be found at https://gnomad.broadinstitute.org/about. We would like to thank the Type 2 Diabetes Knowledge Portal and the groups that provided data to this resource. This work was supported by grants from the French National Research Agency (ANR-10-LABX-46 (European Genomics Institute for Diabetes) and ANR-10-EQPX-07-01 (LIGAN-PM)), from the European Research Council (ERC GEPIDIAB – 294785, to P.F.; ERC Reg-Seq – 715575, to A. Bonnefond), and from the National Center for Precision Diabetic Medicine – PreciDIAB, which is jointly supported by the French National Agency for Research (ANR-18-IBHU-0001), by the European Union (FEDER), by the Hauts-de-France Regional Council and by the European Metropolis of Lille (MEL). Funding was also provided to the Renown Institute for Health Innovation by Renown Health and the Renown Health Foundation.

Author information

Author notes

  1. Loïc Yengo

    Present address: Institute for Molecular Bioscience, the University of Queensland, St Lucia, Australia

  2. These authors contributed equally: Mathilde Boissel, Alexandre Bolze, Emmanuelle Durand.

Authors and Affiliations

  1. Inserm UMR1283, CNRS UMR8199, European Genomic Institute for Diabetes (EGID), Université de Lille, Institut Pasteur de Lille, Lille University Hospital, Lille, France

    Amélie Bonnefond, Mathilde Boissel, Emmanuelle Durand, Bénédicte Toussaint, Emmanuel Vaillant, Stefan Gaget, Franck De Graeve, Aurélie Dechaume, Frédéric Allegaert, David Le Guilcher, Loïc Yengo, Véronique Dhennin, Martine Vaxillaire, Mickaël Canouil & Philippe Froguel

  2. Department of Metabolism, Imperial College London, London, UK

    Amélie Bonnefond & Philippe Froguel

  3. Helix, San Mateo, CA, USA

    Alexandre Bolze, James T. Lu, Elizabeth T. Cirulli & Nicole L. Washington

  4. Fleurbaix Laventie Association, Laventie, France

    Jean-Michel Borys

  5. Desert Research Institute, Reno, NV, USA

    Gai Elhanan & Joseph J. Grzymski

  6. Renown Institute of Health Innovation, Reno, NV, USA

    Gai Elhanan & Joseph J. Grzymski

  7. Department of Diabetology Endocrinology Nutrition, Hôpital Bichat, DHU FIRE, Assistance Publique Hôpitaux de Paris, Paris, France

    Ronan Roussel

  8. Inserm U1138, Centre de Recherche des Cordeliers, Paris, France

    Ronan Roussel & Michel Marre

  9. UFR de Médecine, University Paris Diderot, Sorbonne Paris Cité, Paris, France

    Ronan Roussel

  10. Inserm U1018, Institut Gustave Roussy, Center for Research in Epidemiology and Population Health, Villejuif, France

    Beverley Balkau

  11. University Paris-Saclay, University Paris-Sud, Villejuif, France

    Beverley Balkau

  12. CMC Ambroise Paré, Neuilly-sur-Seine, France

    Michel Marre

  13. CERITD (Centre d’Étude et de Recherche pour l’Intensification du Traitement du Diabète), Evry, France

    Sylvia Franc & Guillaume Charpentier

  14. Department of Diabetes, Sud-Francilien Hospital, University Paris-Sud, Orsay, Corbeil-Essonnes, France

    Sylvia Franc

Authors
  1. Amélie Bonnefond
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  2. Mathilde Boissel
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Contributions

A. Bonnefond and P.F. conceived the idea of the study, supervised the analyses and performed data interpretation; E.D., B.T. and E.V. made libraries and performed NGS, with help from A.D. and V.D.; S.G., J.-M.B., J.T.L., E.T.C., G.E., R.R., B.B., M.M., S.F., G.C., M.V., N.L.W., J.J.G and P.F. managed the collection of samples with clinical data; F.A. prepared samples; F.D.G. and D.L.G. performed computer analyses; A. Bonnefond, M.B. and A. Bolze performed data curation and statistical analysis, with help from L.Y. and M.C.; A. Bonnefond wrote the first draft of the paper, with help from P.F.; all authors critically reviewed the paper and approved the report for submission.

Corresponding authors

Correspondence to Amélie Bonnefond or Philippe Froguel.

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Competing interests

R.R. is an advisory panel member for AstraZeneca, AbbVie, Sanofi, Merck Sharp & Dohme, Eli Lilly, Janssen, Novo Nordisk, Diabnext, Vaiomer and Physiogenex; is a speaker for Bayer and Servier; and has received research funding and provided research support to Danone Research, Amgen, Sanofi and Novo Nordisk. A. Bolze, E.T.C., J.L. and N.L.W. are employees and shareholders of Helix. No other conflicts were reported.

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Extended data

Extended Data Fig. 1 Mean rate of rare coding variants of potential interest and samples which were filtered out by each QC across the 33 genes linked with monogenic diabetes, in the RaDiO study.

The figure shows the rate of excluded samples (on the left) or variants (on the right) per QC.

Extended Data Fig. 2 Association between P/LP variants in actionable MODY genes and age of T2D diagnosis, in the 2,178 participants with T2D from the RaDiO study.

Association analyses were performed using a linear regression adjusted for age, sex, BMI and genetic ancestry. β, mean effect; SD, standard deviation; SE, standard error.

Extended Data Fig. 3 Association between P/LP variants in actionable MODY genes and insulin intake, in the 2,178 participants with T2D from the RaDiO study.

Association analyses were performed using a logistic regression adjusted for age, sex, BMI and genetic ancestry. CI, confidence interval; NA, not available; OR, odds ratio.

Extended Data Fig. 4 Association between P/LP variants in actionable MODY genes and metformin intake, in the 2,178 participants with T2D from the RaDiO study.

Association analyses were performed using a logistic regression adjusted for age, sex, BMI and genetic ancestry. CI, confidence interval; NA, not available; OR, odds ratio.

Extended Data Fig. 5 Association between P/LP variants in actionable MODY genes and family history of T2D, in the 2,178 participants with T2D from the RaDiO study.

Association analyses were performed using a logistic regression adjusted for age, sex, BMI and genetic ancestry. CI, confidence interval; OR, odds ratio.

Extended Data Fig. 6 Study design reporting entry criteria for cases and controls in the RaDiO study, the UK Biobank, the HNP study and the AMP T2D knowledge portal.

The figure shows the study design for each population study. Cases are highlighted in orange, controls are highlighted in green and exclusions are highlighted in yellow.

Extended Data Fig. 7

Bootstrapping with varying sample sizes and (a.) controls with fasting glucose < 6.1 mmol/l or (b.) controls with fasting glucose < 7.0 mmol/l, in the RaDiO study. Average: 90 sets of bootstrap.

Supplementary information

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Bonnefond, A., Boissel, M., Bolze, A. et al. Pathogenic variants in actionable MODY genes are associated with type 2 diabetes. Nat Metab 2, 1126–1134 (2020). https://doi.org/10.1038/s42255-020-00294-3

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