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

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...

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Veröffentlicht in:	Nature genetics 2019-06, Vol.51 (6), p.973-980
Hauptverfasser:	Zhou, Jian, Park, Christopher Y., Theesfeld, Chandra L., Wong, Aaron K., Yuan, Yuan, Scheckel, Claudia, Fak, John J., Funk, Julien, Yao, Kevin, Tajima, Yoko, Packer, Alan, Darnell, Robert B., Troyanskaya, Olga G.
Format:	Artikel
Sprache:	eng
Schlagworte:	631/114/2164 631/208/212 631/208/366/1373 Agriculture Algorithms Alleles Analysis Animal Genetics and Genomics Autism Autism Spectrum Disorder - diagnosis Autism Spectrum Disorder - genetics Bioinformatics Biomedical and Life Sciences Biomedicine Cancer Research Computational Biology - methods Consortia Deep Learning Disease Disruption DNA sequencing Gene Expression Gene Expression Regulation Gene Function Gene mutations Gene regulation Genes, Reporter Genetic aspects Genetic Association Studies Genetic diversity Genetic Predisposition to Disease Genetic variance Genome, Human Genomes Genomics Genomics - methods Health aspects Heterogeneity Human Genetics Humans Mutation Neurogenesis Non-coding RNA Nucleotide sequencing Ontology Phenotype Post-transcription Proteins Risk factors RNA Processing, Post-Transcriptional RNA, Untranslated Studies Synaptic transmission Synaptogenesis Transcription factors Transcription, Genetic
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