Article

Whole-genome deep learning analysis reveals causal role of noncoding mutations in autism

2018-05-11

Abstract excerpt

We address the challenge of detecting the contribution of noncoding mutations to disease with a deep-learning-based framework that predicts specific regulatory effects and deleterious disease impact of genetic variants. Applying this framework to 1,790 Autism Spectrum Disorder (ASD) simplex families reveals autism disease causality of noncoding mutations by demonstrating that ASD probands harbor transcriptional (T...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 913b333a-6779-5943-88da-d30558e7ad73
DOI: 10.1101/319681

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Whole-genome deep learning analysis reveals causal role of noncoding mutations in autismDOI 10.1101/319681Read

Massively parallel characterization of non-coding de novo mutations in autism spectrum disorder.Semantic proximity Read

Genome-wide de novo risk score implicates promoter variation in autism spectrum disorder.Semantic proximity Read

Coding and noncoding de novo variation converge on developmental trajectories of cortical layer 5–6 neurons in autism spectrum disorderSemantic proximity Read

Non-coding Regulatory Variants in ASD (Autism Spectrum Disorders) Disrupt CTCF Domains and Shape Cell-Type–Specific Neurodevelopmental Landscapes Revealed by Single-Cell Analyses and Cortical OrganoidsSemantic proximity Read

Mapping the regulatory effects of common and rare non-coding variants across cellular and developmental contexts in the brain and heartSemantic proximity Read

Large-scale exome sequencing study implicates both developmental and functional changes in the neurobiology of autismSemantic proximity Read

Deciphering the Non-Coding Genome in Autism Spectrum Disorders (ASD): A Study of De Novo and Rare Inherited Variants through Targeted Sequencing in Regulatory RegionsSemantic proximity Read

ASiDentify (ASiD): a machine learning model to predict new autism spectrum disorder risk genes.Semantic proximity Read

Challenges in screening for de novo noncoding variants contributing to genetically complex phenotypes.Semantic proximity Read

De Novo Synonymous Mutations in Regulatory Elements Contribute to the Genetic Etiology of Autism and Schizophrenia.Semantic proximity Read

Dissecting Autism Genetic Risk Using Single-cell RNA-seq DataSemantic proximity Read

An analytical framework for whole-genome sequence association studies and its implications for autism spectrum disorder.Semantic proximity Read