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

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.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

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).

References

  1. 1.

    Mathers, C. D. & Loncar, D. Projections of global mortality and burden of disease from 2002 to 2030. PLoS Med. 3, e442 (2006).

    PubMed  PubMed Central  Google Scholar 

  2. 2.

    Abajobir, A. A. et al. Global, regional, and national under-5 mortality, adult mortality, age-specific mortality, and life expectancy, 1970–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet 390, 1084–1150 (2017).

    Google Scholar 

  3. 3.

    Dieleman, J. L. et al. US spending on personal health care and public health, 1996–2013. JAMA 316, 2627–2646 (2016).

    PubMed  PubMed Central  Google Scholar 

  4. 4.

    Willemsen, G. et al. The concordance and heritability of type 2 diabetes in 34,166 twin pairs from international twin registers: The discordant twin (DISCOTWIN) consortium. Twin Res. Hum. Genet. 18, 762–771 (2015).

    PubMed  Google Scholar 

  5. 5.

    Mahajan, A. et al. Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps. Nat. Genet. 50, 1505–1513 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. 6.

    Bonnefond, A. & Froguel, P. Rare and common genetic events in type 2 diabetes: what should biologists know? Cell Metab. 21, 357–368 (2015).

    CAS  PubMed  Google Scholar 

  7. 7.

    Vaxillaire, M. & Froguel, P. Monogenic diabetes: implementation of translational genomic research towards precision medicine. J. Diabetes 8, 782–795 (2016).

    PubMed  Google Scholar 

  8. 8.

    Babenko, A. P. et al. Activating mutations in the ABCC8 gene in neonatal diabetes mellitus. N. Engl. J. Med. 355, 456–466 (2006).

    CAS  PubMed  Google Scholar 

  9. 9.

    Pearson, E. R. et al. Switching from insulin to oral sulfonylureas in patients with diabetes due to Kir6.2 mutations. N. Engl. J. Med. 355, 467–477 (2006).

    CAS  PubMed  Google Scholar 

  10. 10.

    Shepherd, M., Shields, B., Ellard, S., Rubio-Cabezas, O. & Hattersley, A. T. A genetic diagnosis of HNF1A diabetes alters treatment and improves glycaemic control in the majority of insulin-treated patients. Diabet. Med. 26, 437–441 (2009).

    CAS  PubMed  Google Scholar 

  11. 11.

    Pearson, E. R. et al. Genetic cause of hyperglycaemia and response to treatment in diabetes. Lancet 362, 1275–1281 (2003).

    CAS  PubMed  Google Scholar 

  12. 12.

    Pearson, E. R. et al. Molecular genetics and phenotypic characteristics of MODY caused by hepatocyte nuclear factor 4α mutations in a large European collection. Diabetologia 48, 878–885 (2005).

    CAS  PubMed  Google Scholar 

  13. 13.

    Stride, A. et al. Cross-sectional and longitudinal studies suggest pharmacological treatment used in patients with glucokinase mutations does not alter glycaemia. Diabetologia 57, 54–56 (2014).

    CAS  PubMed  Google Scholar 

  14. 14.

    Garg, V. et al. GATA4 mutations cause human congenital heart defects and reveal an interaction with TBX5. Nature 424, 443–447 (2003).

    CAS  PubMed  Google Scholar 

  15. 15.

    Bockenhauer, D. & Jaureguiberry, G. HNF1B-associated clinical phenotypes: the kidney and beyond. Pediatr. Nephrol. 31, 707–714 (2016).

    PubMed  Google Scholar 

  16. 16.

    Kodo, K. et al. GATA6 mutations cause human cardiac outflow tract defects by disrupting semaphorin-plexin signaling. Proc. Natl Acad. Sci. USA 106, 13933–13938 (2009).

    CAS  PubMed  Google Scholar 

  17. 17.

    Naylor, R. N. et al. Cost-effectiveness of MODY genetic testing: translating genomic advances into practical health applications. Diabetes Care 37, 202–209 (2014).

    PubMed  Google Scholar 

  18. 18.

    GoodSmith, M. S., Skandari, M. R., Huang, E. S. & Naylor, R. N. The impact of biomarker screening and cascade genetic testing on the cost-effectiveness of MODY genetic testing. Diabetes Care 42, 2247–2255 (2019).

    CAS  PubMed  Google Scholar 

  19. 19.

    Fuchsberger, C. et al. The genetic architecture of type 2 diabetes. Nature 536, 41–47 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Flannick, J. et al. Exome sequencing of 20,791 cases of type 2 diabetes and 24,440 controls. Nature 570, 71–76 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Lek, M. et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285–291 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Sun, J., Zheng, Y. & Hsu, L. A unified mixed-effects model for rare-variant association in sequencing studies. Genet. Epidemiol. 37, 334–344 (2013).

    PubMed  PubMed Central  Google Scholar 

  23. 23.

    Bansal, V. et al. Spectrum of mutations in monogenic diabetes genes identified from high-throughput DNA sequencing of 6,888 individuals. BMC Med. 15, 213 (2017).

    PubMed  PubMed Central  Google Scholar 

  24. 24.

    Froguel, P. et al. Close linkage of glucokinase locus on chromosome 7p to early-onset non-insulin-dependent diabetes mellitus. Nature 356, 162–164 (1992).

    CAS  PubMed  Google Scholar 

  25. 25.

    Flannick, J. et al. Assessing the phenotypic effects in the general population of rare variants in genes for a dominant Mendelian form of diabetes. Nat. Genet. 45, 1380–1385 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Bonnefond, A. et al. Whole-exome sequencing and high throughput genotyping identified KCNJ11 as the thirteenth MODY gene. PLoS ONE 7, e37423 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Bonnefond, A. et al. GATA6 inactivating mutations are associated with heart defects and, inconsistently, with pancreatic agenesis and diabetes. Diabetologia 55, 2845–2847 (2012).

    CAS  PubMed  Google Scholar 

  28. 28.

    De Franco, E. et al. GATA6 mutations cause a broad phenotypic spectrum of diabetes from pancreatic agenesis to adult-onset diabetes without exocrine insufficiency. Diabetes 62, 993–997 (2013).

    PubMed  PubMed Central  Google Scholar 

  29. 29.

    Meur, G. et al. Insulin gene mutations resulting in early-onset diabetes: marked differences in clinical presentation, metabolic status, and pathogenic effect through endoplasmic reticulum retention. Diabetes 59, 653–661 (2010).

    CAS  PubMed  Google Scholar 

  30. 30.

    Castel, S. E. et al. Modified penetrance of coding variants by cis-regulatory variation contributes to disease risk. Nat. Genet. 50, 1327–1334 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Khera, A. V. et al. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat. Genet. 50, 1219–1224 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Baier, L. J. et al. ABCC8 R1420H loss-of-function variant in a southwest American Indian community: association with increased birth weight and doubled risk of type 2 diabetes. Diabetes 64, 4322–4332 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Balkau, B. [An epidemiologic survey from a network of French Health Examination Centres, (D.E.S.I.R.): epidemiologic data on the insulin resistance syndrome]. Rev. Epidemiol. Sante Publique 44, 373–375 (1996).

    CAS  PubMed  Google Scholar 

  34. 34.

    Sladek, R. et al. A genome-wide association study identifies novel risk loci for type 2 diabetes. Nature 445, 881–885 (2007).

    CAS  PubMed  Google Scholar 

  35. 35.

    Meyre, D. et al. Genome-wide association study for early-onset and morbid adult obesity identifies three new risk loci in European populations. Nat. Genet. 41, 157–159 (2009).

    CAS  PubMed  Google Scholar 

  36. 36.

    Romon, M. et al. Relationships between physical activity and plasma leptin levels in healthy children: the Fleurbaix–Laventie Ville Santé II Study. Int. J. Obes. Relat. Metab. Disord. 28, 1227–1232 (2004).

    CAS  PubMed  Google Scholar 

  37. 37.

    Bycroft, C. et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 562, 203–209 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. 38.

    Van Hout, C. V. et al. Whole exome sequencing and characterization of coding variation in 49,960 individuals in the UK Biobank. Preprint at bioRxiv https://doi.org/10.1101/572347 (2019).

  39. 39.

    Cirulli, E. T. et al. Genome-wide rare variant analysis for thousands of phenotypes in over 70,000 exomes from two cohorts. Nat. Commun. 11, 542 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. 41.

    McKenna, A. et al. The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. 42.

    Sherry, S. T. et al. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 29, 308–311 (2001).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Liu, X., Wu, C., Li, C. & Boerwinkle, E. dbNSFP v3.0: a one-stop database of functional predictions and annotations for human nonsynonymous and splice-site SNVs. Hum. Mutat. 37, 235–241 (2016).

    PubMed  PubMed Central  Google Scholar 

  44. 44.

    Regier, A. A. et al. Functional equivalence of genome sequencing analysis pipelines enables harmonized variant calling across human genetics projects. Nat. Commun. 9, 4038 (2018).

    PubMed  PubMed Central  Google Scholar 

  45. 45.

    McLaren, W. et al. The ensembl variant effect predictor. Genome Biol. 17, 122 (2016).

    PubMed  PubMed Central  Google Scholar 

  46. 46.

    Kendig, K. I. et al. Sentieon DNASeq variant calling workflow demonstrates strong computational performance and accuracy. Front. Genet. 10, 736 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. 47.

    Richards, S. et al. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet. Med. 17, 405–424 (2015).

    PubMed  PubMed Central  Google Scholar 

  48. 48.

    Adzhubei, I. A. et al. A method and server for predicting damaging missense mutations. Nat. Methods 7, 248–249 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. 49.

    Vaser, R., Adusumalli, S., Leng, S. N., Sikic, M. & Ng, P. C. SIFT missense predictions for genomes. Nat. Protoc. 11, 1–9 (2016).

    CAS  PubMed  Google Scholar 

  50. 50.

    Schwarz, J. M., Cooper, D. N., Schuelke, M. & Seelow, D. MutationTaster2: mutation prediction for the deep-sequencing age. Nat. Methods 11, 361–362 (2014).

    CAS  PubMed  Google Scholar 

  51. 51.

    Ellard, S., Colclough, K., Patel, K. A. & Hattersley, A. T. Prediction algorithms: pitfalls in interpreting genetic variants of autosomal dominant monogenic diabetes. J. Clin. Invest. 130, 14–16 (2020).

    PubMed  Google Scholar 

  52. 52.

    Abraham, G. & Inouye, M. Fast principal component analysis of large-scale genome-wide data. PLoS ONE 9, e93766 (2014).

    PubMed  PubMed Central  Google Scholar 

  53. 53.

    Siva, N. 1000 Genomes project. Nat. Biotechnol. 26, 256 (2008).

    PubMed  Google Scholar 

  54. 54.

    Alexander, D. H., Novembre, J. & Lange, K. Fast model-based estimation of ancestry in unrelated individuals. Genome Res. 19, 1655–1664 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. 55.

    Chen, H. et al. Efficient variant set mixed model association tests for continuous and binary traits in large-scale whole-genome sequencing studies. Am. J. Hum. Genet. 104, 260–274 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. 56.

    Moutsianas, L. et al. The power of gene-based rare variant methods to detect disease-associated variation and test hypotheses about complex disease. PLoS Genet. 11, e1005165 (2015).

    PubMed  PubMed Central  Google Scholar 

  57. 57.

    Lee, S., Abecasis, G. R., Boehnke, M. & Lin, X. Rare-variant association analysis: study designs and statistical tests. Am. J. Hum. Genet. 95, 5–23 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. 58.

    Balduzzi, S., Rücker, G. & Schwarzer, G. How to perform a meta-analysis with R: a practical tutorial. Evid. Based Ment. Health 22, 153–160 (2019).

    PubMed  Google Scholar 

Download references

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

Affiliations

Authors

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.

Ethics declarations

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.

Additional information

Peer review information Primary Handling Editor: Pooja Jha.

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

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

Supplementary Information

Supplementary Figures 1–3 and References for Supplementary Tables

Reporting Summary

Supplementary Tables 1–17

Source data

Source Data Fig. 1

Statistical Source Data

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

Search

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing