Article

A framework for the interpretation of de novo mutation in human disease

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Abstract

Spontaneously arising (de novo) mutations have an important role in medical genetics. For diseases with extensive locus heterogeneity, such as autism spectrum disorders (ASDs), the signal from de novo mutations is distributed across many genes, making it difficult to distinguish disease-relevant mutations from background variation. Here we provide a statistical framework for the analysis of excesses in de novo mutation per gene and gene set by calibrating a model of de novo mutation. We applied this framework to de novo mutations collected from 1,078 ASD family trios, and, whereas we affirmed a significant role for loss-of-function mutations, we found no excess of de novo loss-of-function mutations in cases with IQ above 100, suggesting that the role of de novo mutations in ASDs might reside in fundamental neurodevelopmental processes. We also used our model to identify 1,000 genes that are significantly lacking in functional coding variation in non-ASD samples and are enriched for de novo loss-of-function mutations identified in ASD cases.

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Acknowledgements

All data from published studies are available in the respective publications. All newly generated data and computational tools used in this paper will be available online as downloadable material. We have also constructed a website to query genes that provides information on constraint and the de novo mutations found in the specified gene across published studies of de novo mutation. We would like to thank E. Daly and M. Chess for their contributions to data analysis and the construction of the website, respectively. We acknowledge the following resources and families who contributed to them: the National Institute of Mental Health (NIMH) repository (U24MH068457); the Autism Genetic Resource Exchange (AGRE) Consortium, a program of Autism Speaks (1U24MH081810 to C.M. Lajonchere); The Autism Simplex Collection (TASC) (grant from Autism Speaks); the Simons Foundation Autism Research Initiative (SFARI) Simplex Collection (grant from the Simons Foundation); and The Autism Consortium (grant from the Autism Consortium). This work was directly supported by US National Institutes of Health (NIH) grants R01MH089208 (M.J.D.), R01MH089025 (J.D.B.), R01MH089004 (G.D.S.), R01MH089175 (R.A.G.) and R01MH089482 (J.S.S.) and was supported in part by US NIH grants P50HD055751 (E.H.C.), R01MH057881 (B.D.) and R01MH061009 (J.S.S.). We acknowledge partial support from grants U54HG003273 (R.A.G.) and U54HG003067 (E. Lander). We thank T. Lehner (NIMH), A. Felsenfeld (National Human Genome Research Institute) and P. Bender (NIMH) for their support and contribution to the project. E.B., J.D.B., B.D., M.J.D., R.A.G., K. Roeder, A.S., G.D.S. and J.S.S. are lead investigators in the ARRA Autism Sequencing Collaboration (AASC). We would also like to thank the NHLBI GO Exome Sequencing Project (ESP) and its ongoing studies that produced and provided exome variant calls on the web: the Lung GO Sequencing Project (HL-102923), the Women's Health Initiative (WHI) Sequencing Project (HL-102924), the Broad GO Sequencing Project (HL-102925), the Seattle GO Sequencing Project (HL-102926) and the Heart GO Sequencing Project (HL-103010).

Author information

Affiliations

  1. Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA.

    • Kaitlin E Samocha
    • , Elise B Robinson
    • , Jack A Kosmicki
    • , Andrew Kirby
    • , Daniel G MacArthur
    • , Shaun M Purcell
    • , Benjamin M Neale
    •  & Mark J Daly
  2. Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA.

    • Kaitlin E Samocha
    • , Elise B Robinson
    • , Christine Stevens
    • , Andrew Kirby
    • , Daniel G MacArthur
    • , Stacey B Gabriel
    • , Shaun M Purcell
    • , Benjamin M Neale
    •  & Mark J Daly
  3. Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA.

    • Kaitlin E Samocha
    • , Elise B Robinson
    • , Christine Stevens
    • , Benjamin M Neale
    •  & Mark J Daly
  4. Program in Genetics and Genomics, Biological and Biomedical Sciences, Harvard Medical School, Boston, Massachusetts, USA.

    • Kaitlin E Samocha
  5. Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA.

    • Stephan J Sanders
  6. Department of Genetics, Yale University School of Medicine, New Haven, Connecticut, USA.

    • Stephan J Sanders
  7. Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas, USA.

    • Aniko Sabo
    • , Eric Boerwinkle
    •  & Richard A Gibbs
  8. Psychiatric and Neurodevelopmental Genetics Unit, Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA.

    • Lauren M McGrath
    • , Shaun M Purcell
    •  & Aarno Palotie
  9. Center for Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA.

    • Jack A Kosmicki
    •  & Dennis P Wall
  10. Department of Pathology, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA.

    • Jack A Kosmicki
    •  & Dennis P Wall
  11. Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland.

    • Karola Rehnström
    •  & Aarno Palotie
  12. Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge, UK.

    • Karola Rehnström
    •  & Aarno Palotie
  13. Department of Genetics, Harvard Medical School, Boston, Massachusetts, USA.

    • Swapan Mallick
  14. Synapdx, Lexington, Massachusetts, USA.

    • Mark DePristo
  15. Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, USA.

    • Shaun M Purcell
    •  & Joseph D Buxbaum
  16. Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, New York, USA.

    • Shaun M Purcell
    •  & Joseph D Buxbaum
  17. Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA.

    • Shaun M Purcell
    •  & Joseph D Buxbaum
  18. Human Genetics Center, University of Texas Health Science Center at Houston, Houston, Texas, USA.

    • Eric Boerwinkle
  19. Seaver Autism Center for Research and Treatment, Icahn School of Medicine at Mount Sinai, New York, New York, USA.

    • Joseph D Buxbaum
  20. Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA.

    • Joseph D Buxbaum
  21. Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA.

    • Joseph D Buxbaum
  22. Department of Psychiatry, University of Illinois at Chicago, Chicago, Illinois, USA.

    • Edwin H Cook Jr
  23. Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.

    • Gerard D Schellenberg
  24. Center for Molecular Neuroscience, Vanderbilt University, Nashville, Tennessee, USA.

    • James S Sutcliffe
  25. Department of Psychiatry, University of Pittsburgh Medical School, Pittsburgh, Pennsylvania, USA.

    • Bernie Devlin
  26. Department of Statistics, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA.

    • Kathryn Roeder
  27. Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA.

    • Kathryn Roeder

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Contributions

K.E.S., B.M.N. and M.J.D. conceived and designed the mutational model and constraint methods. K.E.S. and E.B.R. executed the analyses. K.E.S., E.B.R., L.M.M., J.A.K., S.M., A.K., D.P.W., D.G.M., S.M.P., J.D.B., B.D. and K. Roeder contributed to analysis concepts and methods. K.E.S., S.J.S., C.S., A.S., K. Rehnström, S.B.G., M.D., A.P., E.B., J.D.B., E.H.C., R.A.G., G.D.S., J.S.S., B.D., K. Roeder, B.M.N. and M.J.D. contributed autism sequencing, evaluation and manuscript comments. K.E.S., E.B.R., B.M.N. and M.J.D. performed the primary writing.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Mark J Daly.

Integrated supplementary information

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–3, Supplementary Tables 3–9 and Supplementary Note.

Excel files

  1. 1.

    Gene-specific probabilities of mutation.

    The per-gene probabilities of mutation are listed for each gene (transcript specified) by mutation type. Probabilities of mutation are given per chromosome and have been transformed by log10. “NA” is listed when there is no probability of mutation due usually to low coverage.

  2. 2.

    Top 1,003 constrained genes.

    The gene-specific information listed includes transcript and identifier, chromosome, transcription start position, number of coding bases, probabilities of a synonymous and missense mutation (given per chromosome), the number of observed and expected synonymous and missense variants, the signed Z scores for the deviation for both synonymous and missense variants, and the ratio of missing missense variation (“ratio_missing”).