Evidence map›Paper›PMID 37790478›Full record

ArticleResearch square2023

SOX7: Novel Autistic Gene Identified by Analysis of Multi-Omics Data.

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

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In one paragraph

Article in Research square, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
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0citing papers in PubMed
–field-weighted citation impact
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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Samantha GonzalesFlorida International University.
Jane Zizhen ZhaoMiami Dade College Kendall Campus and School for Advanced Studies.
Na Young ChoiUniversity of North Texas.
Prabha AcharyaUniversity of North Texas.
Sehoon JeongInje University.
Moo-Yeal LeeUniversity of North Texas.

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
NCATS NIH HHS R44 TR003491NIDDK NIH HHS UH3 DK119982
6 · The paper itself

Abstract

Background: Despite thousands of variants identified by genome-wide association studies (GWAS) to be associated with autism spectrum disorder (ASD), it is unclear which mutations are causal because most are noncoding. Consequently, reliable diagnostic biomarkers are lacking. RNA-seq analysis captures biomolecular complexity that GWAS cannot by considering transcriptomic patterns. Therefore, integrating DNA and RNA testing may reveal causal genes and useful biomarkers for ASD. Methods: We performed gene-based association studies using an adaptive test method with GWAS summary statistics from two large Psychiatric Genomics Consortium (PGC) datasets (ASD2019: 18,382 cases and 27,969 controls; ASD2017: 6,197 cases and 7,377 controls). We also investigated differential expression for genes identified with the adaptive test using an RNA-seq dataset (GSE30573: 3 cases and 3 controls) and DESeq2. Results: We identified 5 genes significantly associated with ASD in ASD2019 (KIZ-AS1, p = 8.67×10 Limitations: The limitation of the gene-based analysis is the reliance on a reference population for estimating linkage disequilibrium between variants. The similarity of this reference population to the population of study is crucial to the accuracy of many gene-based analyses, including those performed in this study. As a result, the extent of our findings is limited to European populations, as this was our reference of choice. Future work includes a tighter integration of DNA and RNA information as well as extensions to non-European populations that have been under-researched. Conclusions: These findings suggest that SOX7 and its related SOX family genes encode transcription factors that are critical to the downregulation of the canonical Wnt/

Indexed as

autism spectrum disorder (ASD)gene-based association testGenome-wide association studies (GWAS)RNA-seq data analysis

Identifiers

PMID37790478
PMCPMC10543249

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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.