Evidence map›Paper›PMID 40373085›Full record

ArticlePloS one2025

SOX7: Autism associated gene identified by analysis of multi-Omics data.

Samantha Gonzales, Jane Zizhen Zhao, Na Young Choi, Prabha Acharya, Sehoon Jeong, Xuexia Wang, Moo-Yeal Lee

Abstract read
In one paragraph

Article in PloS one, 2025. 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
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Samantha GonzalesDepartment of Biostatistics, Florida International University, Miami, Florida, United States of America.
Jane Zizhen ZhaoDepartment of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America.
Na Young ChoiDepartment of Biomedical Engineering, University of North Texas, Denton, Texas, United States of America.
Prabha AcharyaDepartment of Biomedical Engineering, University of North Texas, Denton, Texas, United States of America.
Sehoon JeongDepartment of Artificial Intelligence and Data Science, Sejong University, Seoul, South Korea.
Xuexia WangDepartment of Biostatistics, Florida International University, Miami, Florida, United States of America.ORCID 0000-0002-0613-2608
Moo-Yeal LeeDepartment of Biomedical Engineering, University of North Texas, Denton, Texas, United States of America.ORCID 0000-0001-6922-609X

Funding

Modeling diabetes using an integrated plate systemUH3DK119982 · NIDDK · CINCINNATI CHILDRENS HOSP MED CTR · PI LEE, MOO-YEAL, WELLS, JAMES M · 2020 to 2022
$5.4M
Pillar and perfusion well plate platforms for reproducible organoid culture from iPSCR44TR003491 · NCATS · BIOPRINTING LABORATORIES, INC. · PI JOSHI, PRANAV, LEE, MOO-YEAL · 2020 to 2021
$838k
FIU-Diversity Center for Genomic Research (FIU-DCGR)UG3HG013615 · NHGRI · FLORIDA INTERNATIONAL UNIVERSITY · PI BLACK, STEPHEN M, WANG, XUEXIA · 2024 to 2024
$817k
NCATS NIH HHS R44 TR003491NHGRI NIH HHS UG3 HG013615NIDDK NIH HHS UH3 DK119982
6 · The paper itself

Abstract

Genome-wide association studies and next generation sequencing data analyses based on DNA information have identified thousands of mutations associated with autism spectrum disorder (ASD). However, more than 99% of identified mutations are non-coding. Thus, it is unclear which of these mutations might be functional and thus potentially causal variants. Transcriptomic profiling using total RNA-sequencing has been one of the most utilized approaches to link protein levels to genetic information at the molecular level. The transcriptome captures molecular genomic complexity that the DNA sequence solely does not. Some mutations alter a gene's DNA sequence but do not necessarily change expression and/or protein function. To date, few common variants reliably associated with the diagnosis status of ASD despite consistently high estimates of heritability. In addition, reliable biomarkers used to diagnose ASD or molecular mechanisms to define the severity of ASD do not exist. Therefore, it is necessary to integrate DNA and RNA testing together to identify true causal genes and propose useful biomarkers for ASD. We performed gene-based association studies with adaptive test using genome-wide association studies' (GWAS) summary statistics with two large GWAS datasets (ASD 2019 data: 18,382 ASD cases and 27,969 controls [discovery data]; ASD 2017 data: 6,197 ASD cases and 7,377 controls [replication data]) which were obtained from the Psychiatric Genomics Consortium (PGC). In addition, we investigated differential expression between ASD cases and controls for genes identified in gene-based GWAS with two RNA-seq datasets (GSE211154: 20 cases and 19 controls; GSE30573: 3 cases and 3 controls). We identified 5 genes significantly associated with ASD in ASD 2019 data (KIZ-AS1, p = 8.67 × 10-10; KIZ, p = 1.16 × 10-9; XRN2, p = 7.73 × 10-9; SOX7, p = 2.22 × 10-7; LOC101929229 also known as PINX1-DT, p = 2.14 × 10-6). Among these 5 genes, gene SOX7 (p = 0.00087) and LOC101929229 (p = 0.009) were replicated in ASD 2017 data. KIZ-AS1 (p = 0.059) and KIZ (p = 0.06) were close to the boundary of replication in ASD 2017 data. Genes SOX7 (p = 0.036 in all samples; p = 0.044 in white samples) indicated significant expression differences between cases and controls in the GSE211154 RNA-seq data. Furthermore, gene SOX7 was upregulated in cases than in controls in the GSE30573 RNA-seq data (p = 0.0017; Benjamini-Hochberg adjusted p = 0.0085). SOX7 encodes a member of the SOX (SRY-related HMG-box) family of transcription factors pivotally contributing to determining of the cell fate and identity in many lineages. The encoded protein may act as a transcriptional regulator after forming a protein complex with other proteins leading to autism. Gene SOX7 in the transcription factor family could be associated with ASD. This finding may provide new diagnostic and therapeutic strategies for ASD.

Indexed as

Autism Spectrum DisorderAutistic DisorderSOXF Transcription FactorsFemaleGene Expression ProfilingGenetic Predisposition to DiseaseGenome-Wide Association StudyGenomicsHumansMultiomicsMutationPolymorphism, Single NucleotideTranscriptomeSOXF Transcription Factors

Identifiers

PMID40373085
PMCPMC12080844

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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.