Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Letter
  • Published:

Prospective functional classification of all possible missense variants in PPARG

Nature Genetics volume 48, pages 1570–1575 (2016)Cite this article

Subjects

Abstract

Clinical exome sequencing routinely identifies missense variants in disease-related genes, but functional characterization is rarely undertaken, leading to diagnostic uncertainty1,2. For example, mutations in PPARG cause Mendelian lipodystrophy3,4 and increase risk of type 2 diabetes (T2D)5. Although approximately 1 in 500 people harbor missense variants in PPARG, most are of unknown consequence. To prospectively characterize PPARγ variants, we used highly parallel oligonucleotide synthesis to construct a library encoding all 9,595 possible single–amino acid substitutions. We developed a pooled functional assay in human macrophages, experimentally evaluated all protein variants, and used the experimental data to train a variant classifier by supervised machine learning. When applied to 55 new missense variants identified in population-based and clinical sequencing, the classifier annotated 6 variants as pathogenic; these were subsequently validated by single-variant assays. Saturation mutagenesis and prospective experimental characterization can support immediate diagnostic interpretation of newly discovered missense variants in disease-related genes.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Comprehensive functional testing of 9,595 PPARγ amino acid variants.
Figure 2: Integrating experimental function to construct a PPARγ classification table.
Figure 3: Experimental and clinical classification of new missense PPARG variants identified in sequenced individuals.
Figure 4: Ability of the PPARγ Arg212Trp variant to transactivate gene expression and bind DNA at endogenous enhancers.
Figure 5: Relationship of PPARγ function to T2D risk in the general population.

Similar content being viewed by others

Accession codes

Primary accessions

NCBI Reference Sequence

References

  1. Majewski, J., Schwartzentruber, J., Lalonde, E., Montpetit, A. & Jabado, N. What can exome sequencing do for you? J. Med. Genet. 48, 580–589 (2011).

    Article  CAS  Google Scholar 

  2. Gahl, W.A. et al. The National Institutes of Health Undiagnosed Diseases Program: insights into rare diseases. Genet. Med. 14, 51–59 (2012).

    Article  CAS  Google Scholar 

  3. Barroso, I. et al. Dominant negative mutations in human PPARγ associated with severe insulin resistance, diabetes mellitus and hypertension. Nature 402, 880–883 (1999).

    Article  CAS  Google Scholar 

  4. Jeninga, E.H., Gurnell, M. & Kalkhoven, E. Functional implications of genetic variation in human PPARγ. Trends Endocrinol. Metab. 20, 380–387 (2009).

    Article  CAS  Google Scholar 

  5. Majithia, A.R. et al. Rare variants in PPARG with decreased activity in adipocyte differentiation are associated with increased risk of type 2 diabetes. Proc. Natl. Acad. Sci. USA 111, 13127–13132 (2014).

    Article  CAS  Google Scholar 

  6. 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).

    Article  CAS  Google Scholar 

  7. Tennessen, J.A. et al. Evolution and functional impact of rare coding variation from deep sequencing of human exomes. Science 337, 64–69 (2012).

    Article  CAS  Google Scholar 

  8. McLaughlin, H.M. et al. A systematic approach to the reporting of medically relevant findings from whole genome sequencing. BMC Med. Genet. 15, 134 (2014).

    Article  Google Scholar 

  9. Manrai, A.K. et al. Genetic misdiagnoses and the potential for health disparities. N. Engl. J. Med. 375, 655–665 (2016).

    Article  Google Scholar 

  10. Altshuler, D. et al. The common PPARγ Pro12Ala polymorphism is associated with decreased risk of type 2 diabetes. Nat. Genet. 26, 76–80 (2000).

    Article  CAS  Google Scholar 

  11. Claussnitzer, M. et al. Leveraging cross-species transcription factor binding site patterns: from diabetes risk loci to disease mechanisms. Cell 156, 343–358 (2014).

    Article  CAS  Google Scholar 

  12. Tontonoz, P. & Spiegelman, B.M. Fat and beyond: the diverse biology of PPARγ. Annu. Rev. Biochem. 77, 289–312 (2008).

    Article  CAS  Google Scholar 

  13. Agostini, M. et al. Non–DNA binding, dominant-negative, human PPARγ mutations cause lipodystrophic insulin resistance. Cell Metab. 4, 303–311 (2006).

    Article  CAS  Google Scholar 

  14. Yu, S. et al. Adipocyte-specific gene expression and adipogenic steatosis in the mouse liver due to peroxisome proliferator–activated receptor γ1 (PPARγ1) overexpression. J. Biol. Chem. 278, 498–505 (2003).

    Article  CAS  Google Scholar 

  15. Tontonoz, P., Nagy, L., Alvarez, J.G., Thomazy, V.A. & Evans, R.M. PPARγ promotes monocyte/macrophage differentiation and uptake of oxidized LDL. Cell 93, 241–252 (1998).

    Article  CAS  Google Scholar 

  16. Barnes, M.R. & Gray, I.C. Bioinformatics for Geneticists (Wiley, 2003).

  17. Chandra, V. et al. Structure of the intact PPAR-γ–RXR-α nuclear receptor complex on DNA. Nature 456, 350–356 (2008).

    Article  Google Scholar 

  18. Garg, A. Acquired and inherited lipodystrophies. N. Engl. J. Med. 350, 1220–1234 (2004).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  20. Myocardial Infarction Genetics Consortium Investigators. et al. Inactivating mutations in NPC1L1 and protection from coronary heart disease. N. Engl. J. Med. 371, 2072–2082 (2014).

  21. González-Pérez, A. & López-Bigas, N. Improving the assessment of the outcome of nonsynonymous SNVs with a consensus deleteriousness score, Condel. Am. J. Hum. Genet. 88, 440–449 (2011).

    Article  Google Scholar 

  22. Fowler, D.M. et al. High-resolution mapping of protein sequence–function relationships. Nat. Methods 7, 741–746 (2010).

    Article  CAS  Google Scholar 

  23. Starita, L.M. et al. Massively parallel functional analysis of BRCA1 RING domain variants. Genetics 200, 413–422 (2015).

    Article  CAS  Google Scholar 

  24. Demir, T. et al. Familial partial lipodystrophy linked to a novel peroxisome proliferator activator receptor-γ (PPARG) mutation, H449L: a comparison of people with this mutation and those with classic codon 482 Lamin A/C (LMNA) mutations. Diabet. Med. 33, 1445–1450 (2016).

    Article  CAS  Google Scholar 

  25. Findlay, G.M., Boyle, E.A., Hause, R.J., Klein, J.C. & Shendure, J. Saturation editing of genomic regions by multiplex homology-directed repair. Nature 513, 120–123 (2014).

    Article  CAS  Google Scholar 

  26. Fowler, D.M. & Fields, S. Deep mutational scanning: a new style of protein science. Nat. Methods 11, 801–807 (2014).

    Article  CAS  Google Scholar 

  27. Melnikov, A., Rogov, P., Wang, L., Gnirke, A. & Mikkelsen, T.S. Comprehensive mutational scanning of a kinase in vivo reveals substrate-dependent fitness landscapes. Nucleic Acids Res. 42, e112 (2014).

    Article  Google Scholar 

  28. Brinkman, E.K., Chen, T., Amendola, M. & van Steensel, B. Easy quantitative assessment of genome editing by sequence trace decomposition. Nucleic Acids Res. 42, e168 (2014).

    Article  Google Scholar 

  29. Forman, B.M. et al. 15-deoxy-δ12,14-prostaglandin J2 is a ligand for the adipocyte determination factor PPARγ. Cell 83, 803–812 (1995).

    Article  CAS  Google Scholar 

  30. Ellard, S. et al. Improved genetic testing for monogenic diabetes using targeted next-generation sequencing. Diabetologia 56, 1958–1963 (2013).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

This work was supported by grants from the National Institute of Diabetes and Digestive and Kidney Diseases (1K08DK102877-01, to A.R.M.; 1R01DK097768-01, to D.A.), NIH/Harvard Catalyst (1KL2TR001100-01, to A.R.M.), the Broad Institute (SPARC award, to A.R.M. and T.M.), and the Wellcome Trust (095564, to K.C.; 107064, to D.B.S.).

We thank J. Doench, C. Zhu, D. O'Connell, G. Cowley, M. Sullender, D. MacArthur, E. Minkel, B. Bulik-Sullivan, and J. Avruch for helpful discussions, laboratory assistance, and manuscript review.

This paper is dedicated to the memory of Promila Nandi (30 April 1933–27 December 2013).

Author information

Author notes

  1. Sekar Kathiresan, Tarjei Mikkelsen & David Altshuler

    Present address: Department of Medicine, Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts, USA

  2. Tarjei Mikkelsen

    Present address: 10X Genomics, Inc., Pleasanton, California, USA,

  3. David Altshuler

    Present address: Vertex Pharmaceuticals, Boston, Massachusetts, USA,

  4. Ben Tsuda, Maura Agostini and Keerthana Gnanapradeepan: These authors contributed equally to this work.

  5. David B Savage and David Altshuler: These authors jointly directed this work.

Authors and Affiliations

  1. Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA

    Amit R Majithia, Ben Tsuda, Keerthana Gnanapradeepan, Robert Rice, Gina Peloso, Xiaolan Zhang, Nick Patterson, Marc Duby, Ted Sharpe, Sekar Kathiresan, Jose C Florez, Tarjei Mikkelsen & David Altshuler

  2. Department of Medicine, Diabetes Research Center, Diabetes Unit, Massachusetts General Hospital, Boston, Massachusetts, USA

    Amit R Majithia, Jose C Florez & David Altshuler

  3. Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts, USA

    Amit R Majithia, Sekar Kathiresan, Jose C Florez & David Altshuler

  4. Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA

    Amit R Majithia, Evan D Rosen, Sekar Kathiresan, Jose C Florez & David Altshuler

  5. University of Cambridge Metabolic Research Laboratories, Wellcome Trust–Medical Research Council Institute of Metabolic Science, Cambridge, UK

    Maura Agostini, Inês Barroso, Stephen O'Rahilly, Krishna Chatterjee & David B Savage

  6. Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, USA

    Gina Peloso

  7. Institute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter, UK

    Kashyap A Patel & Sian Ellard

  8. Molecular Cancer Research and Center for Molecular Medicine, University Medical Centre Utrecht, Utrecht, the Netherlands

    Marjoleine F Broekema & Eric Kalkhoven

  9. Division of Endocrinology and Metabolism, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA

    Evan D Rosen

  10. Department of Molecular Genetics, Royal Devon and Exeter National Health Service Foundation Trust, Exeter, UK

    Sian Ellard

Authors
  1. Amit R Majithia
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ben Tsuda
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Maura Agostini
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Keerthana Gnanapradeepan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Robert Rice
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Gina Peloso
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Kashyap A Patel
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Xiaolan Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Marjoleine F Broekema
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Nick Patterson
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Marc Duby
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Ted Sharpe
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Eric Kalkhoven
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Evan D Rosen
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Inês Barroso
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Sian Ellard
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Sekar Kathiresan
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Stephen O'Rahilly
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Krishna Chatterjee
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Jose C Florez
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Tarjei Mikkelsen
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. David B Savage
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. David Altshuler
    View author publications

    You can also search for this author in PubMed Google Scholar

Consortia

UK Monogenic Diabetes Consortium

Myocardial Infarction Genetics Consortium

UK Congenital Lipodystrophy Consortium

Contributions

A.R.M., T.M., and D.A. designed the study. A.R.M., B.T., M.A., and K.G. performed experiments with help from R.R., X.Z., M.F.B., and E.K. A.R.M. and N.P. analyzed the data with help from B.T., T.S., G.P., K.A.P., M.D., and T.M. I.B., S.E., S.K., S.O'R., K.C., and D.B.S. contributed clinical data and genotypes. A.R.M. and D.A. wrote the manuscript. D.B.S., S.O'R., K.C., E.D.R., and J.C.F. revised the manuscript.

Corresponding author

Correspondence to Amit R Majithia.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Additional information

A list of members and affiliations appears in the Supplementary Note.

A list of members and affiliations appears in the Supplementary Note.

A list of members and affiliations appears in the Supplementary Note.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–5, Supplementary Tables 1–4 and Supplementary Note. (PDF 2601 kb)

Source data

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Majithia, A., Tsuda, B., Agostini, M. et al. Prospective functional classification of all possible missense variants in PPARG. Nat Genet 48, 1570–1575 (2016). https://doi.org/10.1038/ng.3700

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/ng.3700

This article is cited by

Search

Advanced search

Quick links

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