Evidence map›Paper›PMID 33032641›Full record

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.

Siwei Chen, Jiebiao Wang, Ercument Cicek, Kathryn Roeder, Haiyuan Yu, Bernie Devlin

Open access · goldAbstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 1 pooled it
4.0field-weighted citation impact, top 6% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

19 citing papers in PubMed, 1 synthesis or guideline pooled it, 31 citations in OpenAlex.

  1. Pooled it
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  18. Ankyrins and neurological disease.Current opinion in neurobiology · 2021
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 4 institutions in 2 countries.

Siwei ChenDepartment of Computational Biology, Cornell University, Ithaca, NY, 14853, USA.ORCID 0000-0003-1942-7381
Jiebiao WangDepartment of Biostatistics, University of Pittsburgh School of Public Health, Pittsburgh, PA, 15213, USA.ORCID 0000-0003-3828-4400
Ercument CicekDepartment of Computer Engineering, Bilkent University, 06800, Ankara, Turkey.ORCID 0000-0001-8613-6619
Kathryn RoederComputational Biology Department, Carnegie Mellon University, Pittsburgh, PA, 15213, USA. roeder@andrew.cmu.edu.ORCID 0000-0002-8869-6254
Haiyuan YuDepartment of Computational Biology, Cornell University, Ithaca, NY, 14853, USA. haiyuan.yu@cornell.edu.ORCID 0000-0001-7597-6049
Bernie DevlinDepartment of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15213, USA. devlinbj@upmc.edu.ORCID 0000-0003-2524-4290
Cornell University · USUniversity of Pittsburgh · USBilkent University · TRCarnegie Mellon University · US

Funding

Identification and Functional Validation of Human Infertility AllelesR01HD082568 · NICHD · CORNELL UNIVERSITY · PI SCHIMENTI, JOHN C · 2015 to 2024
$6.0M
Genetics Association in Schizophrenia and Other DisordersR37MH057881 · NIMH · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI DEVLIN, BERNIE · 2013 to 2022
$5.4M
New Generation DelPhi: large systems and beyond electrostaticsR01GM093937 · NIGMS · CLEMSON UNIVERSITY · PI ALEXOV, EMIL GEORGIEV · 2010 to 2023
$4.3M
High-throughput functional characterization of human enhancersUM1HG009393 · NHGRI · CORNELL UNIVERSITY · PI LIS, JOHN T, YU, HAIYUAN · 2017 to 2021
$4.1M
Methods for single-cell CRISPR screens and multiomic data: constructing powerful well-calibrated tests, circumventing unmeasured confounding, and accounting for denoising and imputationR01MH123184 · NIMH · CARNEGIE-MELLON UNIVERSITY · PI KATHRYN M ROEDER · 2020 to 2026
$3.8M
Integrated high-throughput functional assays to identify ulcerative colitis causal risk variantsR01DK115398 · NIDDK · CORNELL UNIVERSITY · PI KLEIN, ROBERT J., YU, HAIYUAN · 2018 to 2022
$3.3M
Quantifying molecular consequences of human missense variants with large-scale interactome perturbation studiesR01GM125639 · NIGMS · CORNELL UNIVERSITY · PI ALEXOV, EMIL GEORGIEV, CLARK, ANDREW G · 2018 to 2021
$2.6M
Whole Genome Sequencing to Discover Familial Myeloma Risk GenesR01CA167824 · NCI · WEILL MEDICAL COLL OF CORNELL UNIV · PI LIPKIN, STEVEN M, OFFIT, KENNETH · 2012 to 2016
$1.9M
Using Protein Interactome Networks to Understand the Functional Role of Coding DNR01GM104424 · NIGMS · WEILL MEDICAL COLL OF CORNELL UNIV · PI LIPKIN, STEVEN M, YU, HAIYUAN · 2013 to 2016
$1.7M
Towards a comprehensive multiscale 3D human interactome networkR01GM124559 · NIGMS · CORNELL UNIVERSITY · PI YU, HAIYUAN · 2017 to 2020
$1.5M
NCI NIH HHS R01 CA167824NHGRI NIH HHS UM1 HG009393NICHD NIH HHS R01 HD082568NIDDK NIH HHS R01 DK115398NIGMS NIH HHS R01 GM093937NIGMS NIH HHS R01 GM104424NIGMS NIH HHS R01 GM124559NIGMS NIH HHS R01 GM125639NIMH NIH HHS R01 MH123184NIMH NIH HHS R37 MH057881
6 · The paper itself

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.

Indexed as

Gene Expression RegulationGenetic Predisposition to DiseaseAutistic DisorderHumansMutation, MissenseNeuronsProtein Interaction MapsRisk FactorsTranscriptomeAutism spectrum disorderCell-type-specific transcriptomeDe novo missense variationProtein–protein interaction

Identifiers

PMID33032641
PMCPMC7545940
OpenAlexW3092290783

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

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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.