Expression analysis of a novel mRNA variant of the schizophrenia risk gene ZNF804A

AbstractIn the study of autism spectrum disorder (ASD) by gene co-expression analysis (GCA), we found that four gene features, including gene size, mRNA length, mRNA abundance, and guanine-cytosine content, profoundly affect gene co-expression profiles. To circumvent the potential interference of these confounding factors on GCA, we developed the “matched-gene co-expression analysis” (MGCA) to investigate gene co-expression relationships. This method demonstrated the convergent expression profile of high confidence ASD risk genes and effectively revealed convergent molecular pathways of ASD risk genes. Application of MGCA to two ASD candidate genes CDH11 and CDH9 showed association of CDH11, but not CDH9, with ASD. Mouse behavioral studies showed that Cdh11-null mice, but not Cdh9-null mice, have multiple autistic-like behavioral alterations. This study confirmed that CDH11 is an important ASD risk gene and demonstrated the importance of considering matched gene features in the analysis of gene co-expression.

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Identification of novel methylation quantitative trait loci (mqtls) and functional characterization using CRISPR/CAS9 and gene expression analysis prioritizes Plec as an oa risk gene

Osteoarthritis and Cartilage ◽

10.1016/j.joca.2018.02.044 ◽

2018 ◽

Vol 26 ◽

pp. S13-S14

Author(s):

S.J. Rice ◽

A.K. Sorial ◽

G. Aubourg ◽

C. Shepherd ◽

M. Tselepi ◽

...

Keyword(s):

Gene Expression ◽

Quantitative Trait Loci ◽

Expression Analysis ◽

Quantitative Trait ◽

Gene Expression Analysis ◽

Functional Characterization ◽

Risk Gene ◽

Trait Loci ◽

Methylation Quantitative Trait Loci

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Identification of CDH11 as an ASD Risk Gene by Matched-gene Co-expression Analysis and Mouse Behavioral Studies

10.21203/rs.3.rs-90298/v1 ◽

2020 ◽

Author(s):

Nan Wu ◽

Yue Wang ◽

Jing-Yan Jia ◽

Yi-Hsuan Pan ◽

Xiao-Bing Yuan

Keyword(s):

Expression Analysis ◽

Gene Knockout ◽

Expression Profiles ◽

Cell Types ◽

Developmental Expression ◽

Molecular Pathways ◽

Behavioral Tests ◽

Risk Genes ◽

Risk Gene ◽

Behavioral Studies

Abstract Background: Gene co-expression analysis (GCA) has emerged as an important tool to identify convergent molecular pathways of ASD risk genes. The aim of this study is to identify ASD-relevant genes at the whole-genome level using GCA with consideration of the effect of confounding factors on GCA, including the size, expression level, and guanine-cytosine content of genes. Methods: Pearson’s correlation coefficient was computed to indicate the co-expression of a gene pair based on the BrainSpan human brain transcriptome dataset. Whether a gene is significantly co-expressed with a group of high-confidence ASD risk genes (hcASDs) was determined by statistically comparing the co-expression of this gene with the hcASD gene set to that of this gene with permuted gene sets of matched gene features. This method is referred to as "matched-gene co-expression analysis" (MGCA). Gene ontology (GO) analysis and construction of integrated GO enrichment networks were performed to reveal convergent pathways of co-expressed genes. Behavioral tests were carried out in gene knockout mice. Results: Gene size, mRNA length, mRNA abundance, and guanine-cytosine content were found to affect co-expression profiles of ASD genes. Using the MGCA method, we confirmed the convergence in the developmental expression profiles of hcASDs. MGCA also effectively revealed convergent molecular pathways of ASD risk genes and determined that CDH11, but not CDH9, is associated with ASD. Mouse behavioral studies showed that Cdh11-null mice, but not Cdh9-null mice, have multiple autistic-like behavioral alterations.Limitations: The use of tissue-derived transcriptomes instead of single-cell transcriptomes may have detected coincident expression of some functionally irrelevant genes in different cell types. Some ASD risk genes may have been missed due to the highly stringent statistical standard of MGCA. Another limitation is the relatively small number of animals analyzed in behavioral tests. Conclusions: Results of this study revealed the importance of considering matched gene features in GCA. CDH11 was confirmed to be an important ASD risk gene and Cdh11-null mice were found to be a very useful animal model for investigation of ASD.

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