ArticleMolecular autism2020
De novo missense variants disrupting protein-protein interactions affect risk for autism through gene co-expression and protein networks in neuronal cell types.
Article in Molecular autism, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it, 31 citations in OpenAlex.
- Genomic insights and advanced machine learning: characterizing autism spectrum disorder biomarkers and genetic interactions.Metabolic brain disease · 2024Pooled it
- Genetic Heterogeneity in Autism Spectrum Disorder: Diagnostic Yield, Recurrent Genes, and Rare Variant-Phenotype Associations from Whole-Exome Sequencing.International journal of molecular sciences · 2026Article
- A comprehensive in silico investigation into the deleterious nonsynonymous single nucleotide polymorphisms of the human transcription factor EB (TFEB) gene and their predicted association with cancer.Journal, genetic engineering & biotechnology · 2026Article
- Impact of Genetic Variants Associated with Neurodevelopmental Disorders on the WAVE Regulatory Complex.Journal of chemical information and modeling · 2025Article
- Common characteristics of variants linked to autism spectrum disorder in the WAVE regulatory complex.Frontiers in computational neuroscience · 2025Article
- ACCURATE ESTIMATION OF RARE CELL-TYPE FRACTIONS FROM TISSUE OMICS DATA VIA HIERARCHICAL DECONVOLUTION.The annals of applied statistics · 2024Article
- Neurogenetic underpinnings of nicotine use severity: Integrating the brain transcriptomes and GWAS variants via network approaches.Psychiatry research · 2024Article
- Statistical methods for assessing the effects of de novo variants on birth defects.Human genomics · 2024Review
- The genetic landscape of autism spectrum disorder in the Middle Eastern population.Frontiers in genetics · 2024Article
- Transcriptional co-activators: emerging roles in signaling pathways and potential therapeutic targets for diseases.Signal transduction and targeted therapy · 2023Review
- Whole Genome Analysis of Dizygotic Twins With Autism Reveals Prevalent Transposon Insertion Within Neuronal Regulatory Elements: Potential Implications for Disease Etiology and Clinical Assessment.Journal of autism and developmental disorders · 2023Article
- A Comprehensive Study of De Novo Mutations on the Protein-Protein Interaction Interfaces Provides New Insights into Developmental Delay.Biomolecules · 2022Article
- Prioritizing de novo autism risk variants with calibrated gene- and variant-scoring models.Human genetics · 2022Article
- Characterization of cell-cell communication in autistic brains with single-cell transcriptomes.Journal of neurodevelopmental disorders · 2022Article
- Implications of disease-related mutations at protein-protein interfaces.Current opinion in structural biology · 2022Review
- MAGI-MS: multiple seed-centric module discovery.Bioinformatics advances · 2022Article
- A missense variant rs2585405 in clock gene PER1 is associated with the increased risk of noise-induced hearing loss in a Chinese occupational population.BMC medical genomics · 2021Article
- Ankyrins and neurological disease.Current opinion in neurobiology · 2021Review
- Article
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Authors and funding
6 authors at 4 institutions in 2 countries.
Funding
Abstract
backgroundWhole-exome sequencing studies have been useful for identifying genes that, when mutated, affect risk for autism spectrum disorder (ASD). Nonetheless, the association signal primarily arises from de novo protein-truncating variants, as opposed to the more common missense variants. Despite their commonness in humans, determining which missense variants affect phenotypes and how remains a challenge. We investigate the functional relevance of de novo missense variants, specifically whether they are likely to disrupt protein interactions, and nominate novel genes in risk for ASD through integrated genomic, transcriptomic, and proteomic analyses.
methodsUtilizing our previous interactome perturbation predictor, we identify a set of missense variants that are likely disruptive to protein-protein interactions. For genes encoding the disrupted interactions, we evaluate their expression patterns across developing brains and within specific cell types, using both bulk and inferred cell-type-specific brain transcriptomes. Connecting all disrupted pairs of proteins, we construct an "ASD disrupted network." Finally, we integrate protein interactions and cell-type-specific co-expression networks together with published association data to implicate novel genes in ASD risk in a cell-type-specific manner.
resultsExtending earlier work, we show that de novo missense variants that disrupt protein interactions are enriched in individuals with ASD, often affecting hub proteins and disrupting hub interactions. Genes encoding disrupted complementary interactors tend to be risk genes, and an interaction network built from these proteins is enriched for ASD proteins. Consistent with other studies, genes identified by disrupted protein interactions are expressed early in development and in excitatory and inhibitory neuronal lineages. Using inferred gene co-expression for three neuronal cell types-excitatory, inhibitory, and neural progenitor-we implicate several hundred genes in risk (FDR [Formula: see text]0.05), ~ 60% novel, with characteristics of genuine ASD genes. Across cell types, these genes affect neuronal morphogenesis and neuronal communication, while neural progenitor cells show strong enrichment for development of the limbic system. LIMITATIONS: Some analyses use the imperfect guilt-by-association principle; results are statistical, not functional.
conclusionsDisrupted protein interactions identify gene sets involved in risk for ASD. Their gene expression during brain development and within cell types highlights how they relate to ASD.
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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.