ArticleNature genetics2019
Whole-genome deep-learning analysis identifies contribution of noncoding mutations to autism risk.
Article in Nature genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 158 papers, 3 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
158 citing papers in PubMed, 3 syntheses or guidelines pooled it, 325 citations in OpenAlex.
- Comprehensive multi-omics integration identifies differentially active enhancers during human brain development with clinical relevance.Genome medicine · 2021Pooled it
- Systematic Review and Bioinformatic Analysis of microRNA Expression in Autism Spectrum Disorder Identifies Pathways Associated With Cancer, Metabolism, Cell Signaling, and Cell Adhesion.Frontiers in psychiatry · 2021Pooled it
- Common genetic risk variants identified in the SPARK cohort support DDHD2 as a candidate risk gene for autism.Translational psychiatry · 2020Pooled it
- Genomic analyses implicate noncoding de novo variants in congenital heart disease.Nature genetics · 2020Trial
- Leveraging genomic deep learning models for the prediction of noncoding variant effects.Nature genetics · 2026Review
- Spatiotemporal AI-based modeling of gene regulation and variant effects in the developing human brain using Seal.Nature neuroscience · 2026Article
- Article
- A Critical Synthesis of Machine Learning in Autism Spectrum Disorder Genomic Research: From Transcriptomics to Microbiome.Medeniyet medical journal · 2026Article
- De novo structural variants in autism spectrum disorder disrupt distal regulatory interactions of neuronal genes.Genome research · 2026Article
- A token-pruning framework enables efficient representation of the human genome for RNA modification analysis.Bioinformatics (Oxford, England) · 2026Article
- Mitochondrial Dysfunction in Autism and Attention-Deficit/Hyperactivity Disorder: Evidence from Genetic, Biochemical, and Neuroimaging Approaches.Antioxidants (Basel, Switzerland) · 2026Review
- Article
- First Reported Co-Occurrence of Bardet-Biedl Syndrome Type 10 and Autism Spectrum Disorder: A Case Report and Clinical Review.Molecular syndromology · 2026Article
- Mapping functional non-coding variation in individual human genomes through haplotyping, multiomics, and deep learning.Nature communications · 2026Article
- Variant-resolved prediction of context-specific isoform variation with a graph-based attention model.Cell genomics · 2026Article
- Deep learning for psychiatric genomics: from tools to applications.Current opinion in genetics & development · 2026Review
- So Fragile, So Human: Noncoding DNA Regions Orchestrating Gene Expression Involved in Neurodevelopmental Disorders and in Human Brain Evolution.International journal of molecular sciences · 2026Review
- HumanBase: an interactive AI platform for human biology.Nature methods · 2026Article
- Inferring genotype-phenotype maps using attention models.PNAS nexus · 2026Article
- Silencer variants are key drivers of gene up-regulation in Alzheimer's disease.Science advances · 2026Article
98 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors at 3 institutions in 2 countries.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.