Whole-genome deep-learning analysis identifies contribution of noncoding mutations to autism risk

Abstract

We address the challenge of detecting the contribution of noncoding mutations to disease with a deep-learning-based framework that predicts the specific regulatory effects and the deleterious impact of genetic variants. Applying this framework to 1,790 autism spectrum disorder (ASD) simplex families reveals a role in disease for noncoding mutations—ASD probands harbor both transcriptional- and post-transcriptional-regulation-disrupting de novo mutations of significantly higher functional impact than those in unaffected siblings. Further analysis suggests involvement of noncoding mutations in synaptic transmission and neuronal development and, taken together with previous studies, reveals a convergent genetic landscape of coding and noncoding mutations in ASD. We demonstrate that sequences carrying prioritized mutations identified in probands possess allele-specific regulatory activity, and we highlight a link between noncoding mutations and heterogeneity in the IQ of ASD probands. Our predictive genomics framework illuminates the role of noncoding mutations in ASD and prioritizes mutations with high impact for further study, and is broadly applicable to complex human diseases.

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Fig. 1: The increased effect burden of noncoding regulatory mutations in ASD.
Fig. 2: Analysis of the effects of noncoding mutations converges on brain-specific signals and neurodevelopmental processes.
Fig. 3: Allele-specific transcriptional activity of ASD noncoding mutations.

Data availability

ASD WGS data can be obtained from the Simons Foundation Autism Research Initiative (SFARI). All variant predicted scores have been made available as supplementary material and an interactive web interface is available at https://hb.flatironinstitute.org/asdbrowser/.

Code availability

The code used in this study is available from https://hb.flatironinstitute.org/asdbrowser/help.

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Acknowledgements

We are grateful to the families participating in the SFARI SSC. This work is supported by NIH grants R01HG005998, U54HL117798 and R01GM071966, HHS grant HHSN272201000054C and Simons Foundation grant 395506 to O.G.T.; NIH grants 1UM1HG008901, NS034389, NS081706 and NS097404 and Simons Foundation grant SFARI 240432 to R.B.D.; and STARR Cancer Consortium Award I10-0056 to C.Y.P. and R.B.D. O.G.T. is a senior fellow of the Genetic Networks program of the Canadian Institute for Advanced Research (CIFAR). R.B.D. is an Investigator of the Howard Hughes Medical Institute. The authors acknowledge all members of the Troyanskaya and Darnell laboratory for helpful discussions. We also thank the SFARI, Simons Foundation and Flatiron Institute, in particular N. Volfovsky and M. Benedetti. We are pleased to acknowledge that a substantial portion of the work in this paper was performed at the TIGRESS high-performance computer center at Princeton University, which is jointly supported by the Princeton Institute for Computational Science and Engineering and the Princeton University Office of Information Technology’s Research Computing department. O.G.T. is a CIFAR fellow.

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Authors

Contributions

J.Z., C.Y.P., C.L.T., R.B.D. and O.G.T. conceived and designed the study. J.Z. and C.Y.P. developed the computational methods and performed the analyses. J.Z. developed the DNA model and C.Y.P. developed the RNA model. C.L.T. designed and performed luciferase assay experiments. Y.Y., C.S., J.J.F. and Y.T. designed and performed the minigene splicing assay and RBP experiments. A.K.W., J.F. and K.Y. developed the web interface. A.P. contributed ideas and insights. J.Z., C.Y.P., C.L.T., R.B.D. and O.G.T. wrote the manuscript.

Corresponding authors

Correspondence to Robert B. Darnell or Olga G. Troyanskaya.

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The authors declare no competing interests.

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Supplementary information

Supplementary Information

Supplementary Note and Supplementary Figures 1–16

Reporting Summary

Supplementary Table 1

All de novo mutations identified from the WGS cohort with predicted disease impact scores

Supplementary Table 2

Genomic variant set analysis of mutational burden for transcriptional and post-transcriptional disruptions

Supplementary Table 3

NDEA significance levels of proband excess for all genes

Supplementary Table 4

NDEA significance levels of proband excess for all gene sets

Supplementary Table 5

Cluster-specific gene set enrichment for top NDEA significant genes

Supplementary Table 6

Genomic sequences tested in luciferase assays (plasmid backbone pGL4.23)

Supplementary Table 7

List of chromatin profiles used in this study

Supplementary Table 8

List of RBP profiles used in this study

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Zhou, J., Park, C.Y., Theesfeld, C.L. et al. Whole-genome deep-learning analysis identifies contribution of noncoding mutations to autism risk. Nat Genet 51, 973–980 (2019). https://doi.org/10.1038/s41588-019-0420-0

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