Abstract

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder with a strong genetic basis. Yet, only a small fraction of potentially causal genes—about 65 genes out of an estimated several hundred—are known with strong genetic evidence from sequencing studies. We developed a complementary machine-learning approach based on a human brain-specific gene network to present a genome-wide prediction of autism risk genes, including hundreds of candidates for which there is minimal or no prior genetic evidence. Our approach was validated in a large independent case–control sequencing study. Leveraging these genome-wide predictions and the brain-specific network, we demonstrated that the large set of ASD genes converges on a smaller number of key pathways and developmental stages of the brain. Finally, we identified likely pathogenic genes within frequent autism-associated copy-number variants and proposed genes and pathways that are likely mediators of ASD across multiple copy-number variants. All predictions and functional insights are available at http://asd.princeton.edu.

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Acknowledgements

We are grateful to all of the families at the participating Simons Simplex Collection (SSC) sites, as well as the SSC principal investigators. We thank all members of the Troyanskaya lab for valuable discussions. We thank J. Spiro and other members of the Simons Foundation for constant feedback on the work and manuscript. This work was primarily supported by US National Institutes of Health (NIH) grants R01 GM071966 and R01 HG005998 to O.G.T. V.Y. was supported in part by US NIH grant T32 HG003284. This work was supported in part by US NIH grant P50 GM071508. O.G.T. is a senior fellow of the Genetic Networks program of the Canadian Institute for Advanced Research (CIFAR).

Author information

Author notes

    • Arjun Krishnan
    •  & Ran Zhang

    These authors contributed equally to this work.

Affiliations

  1. Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, USA.

    • Arjun Krishnan
    • , Chandra L Theesfeld
    • , Alicja Tadych
    •  & Olga G Troyanskaya
  2. Department of Molecular Biology, Princeton University, Princeton, New Jersey, USA.

    • Ran Zhang
  3. Department of Computer Science, Princeton University, Princeton, New Jersey, USA.

    • Victoria Yao
    •  & Olga G Troyanskaya
  4. Simons Foundation, New York, New York, USA.

    • Aaron K Wong
    • , Natalia Volfovsky
    • , Alan Packer
    •  & Alex Lash
  5. Flatiron Institute, Simons Foundation, New York, New York, USA.

    • Olga G Troyanskaya

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Contributions

A.K., R.Z., A.L. and O.G.T. conceived and designed the research. A.K. and R.Z. performed computational analyses with contributions from A.L., V.Y., A.K.W. and C.L.T. N.V. provided data. A.T. developed the web interface with contributions from A.K.W., A.K. and R.Z. A.K., R.Z., A.L., A.P. and O.G.T. wrote the manuscript with inputs from V.Y. and C.L.T., and all authors contributed to revisions. A.K. and R.Z. are co-first authors and are listed alphabetically.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Alex Lash or Olga G Troyanskaya.

Integrated supplementary information

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–15

  2. 2.

    Supplementary Methods Checklist

Excel files

  1. 1.

    Supplementary Table 1: Training gold standard.

    Our training gold standard consisted of known ASD-associated genes (with varying levels of evidence E1-4) as positives and non-mental-health-related genes as negatives. The positives are listed along with their evidence level and source database.

  2. 2.

    Supplementary Table 2: Top 20 biological processes enriched in our SVM model for predicting ASD-genes.

    We analyzed our ASD-gene prediction model to identify which biological processes and pathways contribute the most in associating a gene with ASD in the brain-specific network. The table contains the top 20 statistically enriched Gene Ontology biological processes among genes that are most highly “weighted” by the model, i.e., associated with the highest feature weights in our SVM model. The most informative genes in our ASD network-based model are strongly enriched for neurological processes, providing insight into the general underlying processes that may be driving our predictions.

  3. 3.

    Supplementary Table 3: Genome-wide prediction of ASD-associated genes.

    The predicted ASD-association ranking of all genes in the genome is listed along with detailed information on their gold standard status, prediction score, prediction probability, prediction P and Q values, and membership in ASD-related gene sets. The file also contains the evaluation of the genome-wide ranking controlling for gene length and neuronal functional annotations, and literature support for select top-ranked genes not used in our positive training standard.

  4. 4.

    Supplementary Table 4: Targets of de novo mutations identified by exome sequencing of the Simon Simplex Collection.

    Genes harboring de novo likely-gene-disrupting (LGD; also known as loss-of-function) or synonymous (SYN) mutations identified in autistic children (probands; prb) and unaffected sibling (sib) are listed separately.

  5. 5.

    Supplementary Table 5: ASD-association of brain developmental gene-expression signatures.

    All signatures that are significantly enriched among the top-ranked ASD genes are listed here along with the number of genes in each signature and their enrichment scores.

  6. 6.

    Supplementary Table 6: ASD-associated functional modules in the brain-specific network.

    The nine modules of top-ranked ASD genes each tightly connected in the brain-specific network are presented here with information about their module/cluster membership, connectivity within each cluster, and enriched biological processes in each cluster.

  7. 7.

    Supplementary Table 7: Prioritization of genes within ASD-associated CNVs.

    The table contains the complete ASD ranking of genes within each of eight autism-associated CNVs along with details on previous genetic or functional evidence for the connection of individual CNV-genes to ASD.

  8. 8.

    Supplementary Table 8: Functional analysis of ASD-associated CNVs.

    Results from the functional analysis of top-ranked genes in the eight ASD-associated CNVs are presented here, with details on the specific ‘intermediate’ genes and processes that connect the CNV genes to the molecular phenotype of autism. The table also contains literature support for select intermediate genes.

  9. 9.

    Supplementary Table 9: Detailed functional, developmental, and CNV information for our top-decile genes.

    Top 2,500 ASD candidate genes along with their functional module memberships, spatiotemporal developmental gene-expression patterns, and CNV membership.

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DOI

https://doi.org/10.1038/nn.4353

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