The DOI matches its Crossref record; 55 of the 55 references it cites were found in a registry or an index.
Whole-genome deep-learning analysis identifies contribution of noncoding mutations to autism risk
Open accessAbstract
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. Application of a deep-learning-based framework shows that individuals with autism spectrum disorder (ASD) harbor regulation-disrupting mutations of higher functional impact than those in unaffected siblings and identifies a convergent genetic landscape of coding and noncoding de novo mutations in ASD.
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Chromatin accessibility dynamics in a model of human forebrain development
Trevino, A. E., Sinnott-Armstrong, N., Andersen, J., Yoon, S.-J., Huber, N., Pritchard, J. K., Chang, H. Y., Greenleaf, W. J., & Pașca, S. P. (2020) · Science · doi:10.1126/science.aay1645 · Cited by 251
Metadata
- Authors
- Zhou, J., Park, C. Y., Theesfeld, C. L., Wong, A. K., Yuan, Y., Scheckel, C., Fak, J. J., Funk, J., Yao, K., Tajima, Y., Packer, A., Darnell, R. B., & Troyanskaya, O. G.
- Venue
- Nature Genetics
- Published
- 2019-05-27
- Volume
- 51
- Issue
- 6
- Pages
- 973-980
- ISSN
- 1061-4036 (ISSN-L)
- 1546-1718
- OpenAlex
- W2946936654
- Cited by
- 284
- References
- 55
- FWCI
- 23.66
- Type
- journal article
- Language
- en
- Open access
- green
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- Zhou, J., Park, C. Y., Theesfeld, C. L., Wong, A. K., Yuan, Y., Scheckel, C., Fak, J. J., Funk, J., Yao, K., Tajima, Y., Packer, A., Darnell, R. B., & Troyanskaya, O. G. (2019). Whole-genome deep-learning analysis identifies contribution of noncoding mutations to autism risk. Nature Genetics. https://doi.org/10.1038/s41588-019-0420-0
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