Format

Send to

Choose Destination
Nat Genet. 2019 Jun;51(6):973-980. doi: 10.1038/s41588-019-0420-0. Epub 2019 May 27.

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

Author information

1
Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA.
2
Graduate Program in Quantitative and Computational Biology, Princeton University, Princeton, NJ, USA.
3
Flatiron Institute, Simons Foundation, New York, NY, USA.
4
Laboratory of Molecular Neuro-Oncology and Howard Hughes Medical Institute, The Rockefeller University, New York, NY, USA.
5
Gene Therapy Program, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
6
Institute of Neuropathology, University of Zurich, Zurich, Switzerland.
7
Simons Foundation, New York, NY, USA.
8
Laboratory of Molecular Neuro-Oncology and Howard Hughes Medical Institute, The Rockefeller University, New York, NY, USA. darnelr@rockefeller.edu.
9
Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA. ogt@cs.princeton.edu.
10
Flatiron Institute, Simons Foundation, New York, NY, USA. ogt@cs.princeton.edu.
11
Department of Computer Science, Princeton University, Princeton, NJ, USA. ogt@cs.princeton.edu.

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.

PMID:
31133750
DOI:
10.1038/s41588-019-0420-0

Supplemental Content

Full text links

Icon for Nature Publishing Group
Loading ...
Support Center