Evidence map›Paper›PMID 36863698›Full record

ArticleAmerican journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics2023

De novo mutations disturb early brain development more frequently than common variants in schizophrenia.

Toshiyuki Itai, Peilin Jia, Yulin Dai, Jingchun Chen, Xiangning Chen, Zhongming Zhao

Open access · bronzeAbstract read
In one paragraph

Article in American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.3field-weighted citation impact, top 32% 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

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. Article
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 3 institutions in 1 country.

Toshiyuki ItaiCenter for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA.ORCID 0000-0002-9774-0464
Peilin JiaCenter for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA.ORCID 0000-0003-4523-4153
Yulin DaiCenter for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA.ORCID 0000-0003-1874-7893
Jingchun ChenNevada Institute of Personalized Medicine, University of Nevada Las Vegas, Las Vegas, Nevada, USA.ORCID 0000-0001-9408-0117
Xiangning ChenCenter for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA.ORCID 0000-0001-9575-9447
Zhongming ZhaoCenter for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA.ORCID 0000-0002-3477-0914
The University of Texas Health Science Center at Houston · USThe University of Texas Health Science Center · USUniversity of Nevada, Las Vegas · US

Funding

Tracking and EvaluationU54GM104944 · NIGMS · UNIVERSITY OF NEVADA LAS VEGAS · PI WARD, TONY JOHN · 2013 to 2023
$39.6M
The Genome Analysis and Sequencing Pipeline (GASP)P20GM121325 · NIGMS · UNIVERSITY OF NEVADA LAS VEGAS · PI GUHA, PRASUN · 2018 to 2022
$11.5M
Transforming dbGaP genetic and genomic data to FAIR-ready by artificial intelligence and machine learning algorithmsR01LM012806 · NLM · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI Zhongming Zhao · 2017 to 2026
$3.7M
Understanding the genetic architecture of schizophrenia in Chinese populationR01MH101054 · NIMH · VIRGINIA COMMONWEALTH UNIVERSITY · PI CHEN, XIANGNING · 2013 to 2015
$602k
NIGMS NIH HHS P20 GM121325NIGMS NIH HHS U54 GM104944NIH HHS R01LM012806NIH HHS R01MH101054NIMH NIH HHS R01 MH101054NLM NIH HHS R01 LM012806
6 · The paper itself

Abstract

Investigating functional, temporal, and cell-type expression features of mutations is important for understanding a complex disease. Here, we collected and analyzed common variants and de novo mutations (DNMs) in schizophrenia (SCZ). We collected 2,636 missense and loss-of-function (LoF) DNMs in 2,263 genes across 3,477 SCZ patients (SCZ-DNMs). We curated three gene lists: (a) SCZ-neuroGenes (159 genes), which are intolerant to LoF and missense DNMs and are neurologically important, (b) SCZ-moduleGenes (52 genes), which were derived from network analyses of SCZ-DNMs, and (c) SCZ-commonGenes (120 genes) from a recent GWAS as reference. To compare temporal gene expression, we used the BrainSpan dataset. We defined a fetal effect score (FES) to quantify the involvement of each gene in prenatal brain development. We further employed the specificity indexes (SIs) to evaluate cell-type expression specificity from single-cell expression data in cerebral cortices of humans and mice. Compared with SCZ-commonGenes, SCZ-neuroGenes and SCZ-moduleGenes were highly expressed in the prenatal stage, had higher FESs, and had higher SIs in fetal replicating cells and undifferentiated cell types. Our results suggested that gene expression patterns in specific cell types in early fetal stages might have impacts on the risk of SCZ during adulthood.

Indexed as

BrainMutationSchizophreniaAnimalsFetusHumansLoss of Function MutationMiceMutation, MissenseNeuronsOrgan Specificitycell-type-specific enrichment analysiscommon variantde novo mutationprenatal and postnatal comparisonschizophreniasingle-cell RNA-sequencing

Identifiers

PMID36863698
PMCPMC11270591
OpenAlexW4322762507

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.