Whole-genome deep-learning analysis identifies contribution of noncoding mutations to autism risk.
Nat Genet
; 51(6): 973-980, 2019 06.
Article
em En
| MEDLINE
| ID: mdl-31133750
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.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Genoma Humano
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Predisposição Genética para Doença
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RNA não Traduzido
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Genômica
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Transtorno do Espectro Autista
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Aprendizado Profundo
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Mutação
Idioma:
En
Ano de publicação:
2019
Tipo de documento:
Article
País de afiliação:
Estados Unidos