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Loss-of-function variants in ADCY3 increase risk of obesity and type 2 diabetes

Nature Genetics volume 50, pages 172–174 (2018)Cite this article

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Abstract

We have identified a variant in ADCY3 (encoding adenylate cyclase 3) associated with markedly increased risk of obesity and type 2 diabetes in the Greenlandic population. The variant disrupts a splice acceptor site, and carriers have decreased ADCY3 RNA expression. Additionally, we observe an enrichment of rare ADCY3 loss-of-function variants among individuals with type 2 diabetes in trans-ancestry cohorts. These findings provide new information on disease etiology relevant for future treatment strategies.

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Fig. 1: ADCY3 isoforms, observed loss-of-function variants and functional consequences based on RNA sequencing of leukocytes from 17 Greenlandic individuals.

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Acknowledgements

We gratefully acknowledge the participants in the Greenlandic health surveys. We thank J. Flannick (Broad Institute) for help with obtaining the AMP-T2D exome sequencing data. The Novo Nordisk Foundation Center for Basic Metabolic Research is an independent Research Center at the University of Copenhagen partially funded by an unrestricted donation from the Novo Nordisk Foundation (http://www.metabol.ku.dk). This project was also funded by the Danish Council for Independent Research (Sapere Aude DFF-Ung Eliteforsker grant 11-120909 from FSS to I.M.; DFF-4090-00244 from FNU to I.M.; Sapere Aude DFF-Forskningsleder 6108-00038B from FNU to R.A.; DFF-4181-00383 to A.A.), the Steno Diabetes Center Copenhagen (http://www.steno.dk), Simon Fougner Hartmanns Familiefond to N.G., the Lundbeck Foundation (R215-2015-4174) to A.A., the Novo Nordisk Foundation (NNF15OC0017918 to N.G.; NNF16OC0019986 to N.G.; NNFCC0018486 to T.H.) and the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement 638173) to R.A. The Greenlandic health surveys (IHIT and B99) were supported by Karen Elise Jensen’s Foundation, the Department of Health in Greenland, NunaFonden, the Medical Research Council of Denmark, the Medical Research Council of Greenland and the Commission for Scientific Research in Greenland.

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Author notes

  1. Niels Grarup and Ida Moltke contributed equally to this work.

Authors and Affiliations

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

    Niels Grarup, Mette K. Andersen, Timo Kern, Yuvaraj Mahendran, Oluf Pedersen & Torben Hansen

  2. Bioinformatics Centre, Department of Biology, University of Copenhagen, Copenhagen, Denmark

    Ida Moltke, Maria Dalby, Kristoffer Vitting-Seerup, Emil Jørsboe, Robin Andersson & Anders Albrechtsen

  3. Biotech Research and Innovation Centre (BRIC), University of Copenhagen, Copenhagen, Denmark

    Kristoffer Vitting-Seerup

  4. National Institute of Public Health, University of Southern Denmark, Copenhagen, Denmark

    Christina V. L. Larsen, Inger K. Dahl-Petersen, Peter Bjerregaard & Marit E. Jørgensen

  5. Greenland Centre for Health Research, University of Greenland, Nuuk, Greenland

    Christina V. L. Larsen, Peter Bjerregaard & Marit E. Jørgensen

  6. Department of Human Genetics, Wellcome Trust Sanger Institute, Hinxton, UK

    Arthur Gilly, Daniel Suveges & Eleftheria Zeggini

  7. Department of Nutrition and Dietetics, Harokopio University of Athens, Athens, Greece

    George Dedoussis

  8. Steno Diabetes Center Copenhagen, Gentofte, Denmark

    Marit E. Jørgensen

  9. Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark

    Torben Hansen

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  1. Niels Grarup
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Contributions

T.H. and A.A. conceived and headed the project. I.M. and A.A. designed the statistical setup for the association testing, while T.H., N.G., M.K.A. and O.P. designed the experimental setup for the DNA extraction, genotyping and sequencing. M.E.J. and P.B. were principal investigators of the population studies in Greenland and, together with C.V.L.L. and I.K.D.-P., provided the Greenlandic samples, collected and defined the phenotypes, and provided context for these samples. I.M., N.G., E.J. and A.A. performed the association analyses. T.K. and Y.M. designed the experimental setup for RNA extraction and sequencing. A.G., D.S., G.D. and E.Z. performed the loss-of-function analysis in the Greek cohorts. K.V.-S., M.D. and R.A. performed the RNA sequence analysis. N.G., I.M., M.K.A., A.A. and T.H. wrote the majority of the manuscript with input from all authors. All authors approved the final version of the manuscript.

Corresponding authors

Correspondence to Marit E. Jørgensen, Anders Albrechtsen or Torben Hansen.

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Integrated supplementary information

Supplementary Figure 1

Procedure used for identifying and selecting loss-of-function variants to study further.

Supplementary Figure 2 RNA expression of ADCY3 stratified according to c.2433–1G>A genotype

a, Sashimi plot of ADCY3 exons 13–15, displaying raw mapped RNA sequencing reads, pooled within individuals wild type (WT) (7 GG carriers), heterozygous (HE) (6 GA carriers) and homozygous (HO) (4 AA carriers) for the ADCY3 c.2433–1G>A variant from leukocytes and one adipose tissue sample. RNA sequencing densities are aligned to the transcript isoforms shown in Fig. 1. Read depth is displayed for each track (top left). Junction reads are visualized as arcs connecting the pair of exons at junction borders together with the number of junction reads. The plot was simplified by removing junction-spanning reads only observed once or connecting to features outside of exons 13–15 from the visualization. b, Splicing patterns within ADCY3, exons 13–15, quantified as percentage spliced in (PSI) for each ADCY3 c.2433–1G>A genotype group. c, Transcripts per million (TPM) normalized isoform expression for each of the three predicted ADCY3 isoforms, within each of the ADCY3 c.2433–1G>A genotype groups. The lower and upper hinges of the boxes in b and c correspond to the first and third quartiles of data, respectively, while the middle line is the median. The whiskers extend to the largest and smallest data points no further than 1.5 times the interquartile range.

Supplementary Figure 3 QQ plot for gene-based burden tests for association with type 2 diabetes in trans-ancestry cohorts

A QQ plot of the P values from gene-based burden tests performed using loss-of-function variants (MAF < 5%) from the Accelerating Medicines Partnership Type 2 Diabetes Knowledge Portal (http://www.type2diabetesgenetics.org/). The analysis was performed as described in the Methods. The inflation factor lambda is the median test statistics divided by the expected median. No systematic inflation of our test statistics is observed.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–3 and Supplementary Tables 2–6.

Life Sciences Reporting Summary.

Supplementary Table 1

All loss-of-function variants present in at least one copy in the Greenlandic exome sequencing data.

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Grarup, N., Moltke, I., Andersen, M.K. et al. Loss-of-function variants in ADCY3 increase risk of obesity and type 2 diabetes. Nat Genet 50, 172–174 (2018). https://doi.org/10.1038/s41588-017-0022-7

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