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Genotype to phenotype relationships in autism spectrum disorders

Nature Neuroscience volume 18, pages 191–198 (2015)Cite this article

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Abstract

Autism spectrum disorders (ASDs) are characterized by phenotypic and genetic heterogeneity. Our analysis of functional networks perturbed in ASD suggests that both truncating and nontruncating de novo mutations contribute to autism, with a bias against truncating mutations in early embryonic development. We find that functional mutations are preferentially observed in genes likely to be haploinsufficient. Multiple cell types and brain areas are affected, but the impact of ASD mutations appears to be strongest in cortical interneurons, pyramidal neurons and the medium spiny neurons of the striatum, implicating cortical and corticostriatal brain circuits. In females, truncating ASD mutations on average affect genes with 50–100% higher brain expression than in males. Our results also suggest that truncating de novo mutations play a smaller role in the etiology of high-functioning ASD cases. Overall, we find that stronger functional insults usually lead to more severe intellectual, social and behavioral ASD phenotypes.

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Figure 1: The network implicated by NETBAG+ based on ASD-associated de novo SNVs and CNVs from recent studies (network is comprised of 159 genes, P = 0.036).
Figure 2: Temporal gene expression profiles in the human brain across developmental stages for implicated gene sets.
Figure 3: Cell-type expression biases for network mutations and recurrent truncating mutations.
Figure 4: Average numbers of de novo mutations per individual for probands with different IQs.
Figure 5: Temporal gene expression profiles in the human brain across developmental stages for genes affected in subsets of probands with different phenotypic scores.

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Acknowledgements

We would like to sincerely thank M. Wigler, M. State, D. Geschwind, A. Packer, G. Fischbach and all of the members of the Vitkup laboratory for helpful discussions. This work was supported by a grant from the Simons Foundation SFARI# 308962 to D.V. and US National Centers for Biomedical Computing (MAGNet) grant U54CA121852 to Columbia University. S.R.G. was supported in part by US NIGMS training grant T32 GM082797. S.J.S. was supported by a Howard Hughes Medical Institute International Student Research Fellowship.

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Author notes

  1. Jonathan Chang and Sarah R Gilman: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Biomedical Informatics, Columbia University, New York, New York, USA

    Jonathan Chang, Sarah R Gilman, Andrew H Chiang & Dennis Vitkup

  2. Department of Systems Biology, Center for Computational Biology and Bioinformatics, Columbia University, New York, New York, USA

    Jonathan Chang, Sarah R Gilman, Andrew H Chiang & Dennis Vitkup

  3. Department of Psychiatry, University of California, San Francisco, California, USA

    Stephan J Sanders

  4. Department of Psychiatry, Department of Genetics, Yale University, New Haven, Connecticut, USA

    Stephan J Sanders

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  1. Jonathan Chang
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Contributions

J.C., S.R.G. and A.H.C. performed computational analysis and interpreted the results. S.J.S. contributed data, interpreted the results and contributed to the functional analysis. D.V. designed the study, supervised the project and interpreted the results. J.C., S.R.G., A.H.C. and D.V. wrote the manuscript.

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Correspondence to Dennis Vitkup.

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The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Hierarchical clustering of the implicated network.

Average linkage hierarchical clustering was used to divide the implicated network into functional clusters. The inverses of the phenotypic network likelihood scores were used as the clustering metric. In this way, gene pairs strongly connected in the phenotypic network were considered to be closer in distance. Clusters were assigned the following functions using DAVID: green represents the neuronal signaling and cytoskeleton cluster, red represents the chromatin modification and regulation cluster, blue represents the channel activity cluster, and cyan represents the postsynaptic density cluster. Grey nodes represent genes that are not members of any considered cluster.

Supplementary Figure 2 Temporal gene expression profiles in the human brain across developmental stages for genes in the implicating network (red) and random gene sets selected to match network genes based on various criteria.

Human expression data were obtained from the HBT database. Random sets shown in the figure are matched by connectivity in the phenotypic network (cyan), by protein sequence length (purple), and by protein length and connectivity (green). The vertical dashed line separates prenatal and postnatal developmental stages. Error bars represent s.e.m.

Supplementary Figure 3 Non-normalized (left) and normalized (right) temporal brain expression profiles for individual genes in the functional clusters of the implicated network.

Human expression data were obtained from the HBT database. The individual panels in the figure represent profiles for: (a) postsynaptic density cluster genes, (b) chromatin modification and regulation cluster genes, (c) signaling and cytoskeleton cluster genes, and (d) channel activity cluster genes. Non-normalized profiles are shown in the left column. Every gene profile shown in the right column was normalized by dividing by the gene’s average expression value across all developmental stages. The colored lines in the right column show the average gene profile in each functional cluster. Vertical dashed lines separate prenatal and postnatal developmental stages. Error bars represent s.e.m.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–3 and Supplementary Tables 3–9 (PDF 1677 kb)

Supplementary Methods Checklist (PDF 113 kb)

Supplementary Table 1

Genetic SNV and CNV events from the Simons Simplex Collection used as input for NETBAG. (XLSX 95 kb)

Supplementary Table 2

Implicated ASD network genes and associated prioritization annotations (average log2 brain expression and truncating SNV status). (XLSX 49 kb)

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Chang, J., Gilman, S., Chiang, A. et al. Genotype to phenotype relationships in autism spectrum disorders. Nat Neurosci 18, 191–198 (2015). https://doi.org/10.1038/nn.3907

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