Association of CDH11 with Autism Spectrum Disorder Revealed by Matched-gene Co-expression Analysis and Mouse Behavioral Studies

AbstractA large number of putative risk genes for autism spectrum disorder (ASD) have been reported. The functions of most of these susceptibility genes in developing brains remain unknown, and causal relationships between their variation and autism traits have not been established. The aim of this study was to predict putative risk genes at the whole-genome level based on the analysis of gene co-expression with a group of high-confidence ASD risk genes (hcASDs). The results showed that three gene features – gene size, mRNA abundance, and guanine-cytosine content – affect the genome-wide co-expression profiles of hcASDs. To circumvent the interference of these features in gene co-expression analysis, we developed a method to determine whether a gene is significantly co-expressed with hcASDs by statistically comparing the co-expression profile of this gene with hcASDs to that of this gene with permuted gene sets of feature-matched genes. This method is referred to as "matched-gene co-expression analysis" (MGCA). With MGCA, we demonstrated the convergence in developmental expression profiles of hcASDs and improved the efficacy of risk gene prediction. The results of analysis of two recently-reported ASD candidate genes, CDH11 and CDH9, suggested the involvement of CDH11, but not CDH9, in ASD. Consistent with this prediction, behavioral studies showed that Cdh11-null mice, but not Cdh9-null mice, have multiple autism-like behavioral alterations. This study highlights the power of MGCA in revealing ASD-associated genes and the potential role of CDH11 in ASD.

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Identification of CDH11 as an ASD risk gene by matched-gene co-expression analysis and mouse behavioral studies

10.1101/2020.02.04.931121 ◽

2020 ◽

Cited By ~ 2

Author(s):

Nan Wu ◽

Yue Wang ◽

Yi-Hsuan Pan ◽

Xiao-Bing Yuan

Keyword(s):

Expression Analysis ◽

Expression Profiles ◽

Autism Spectrum ◽

Molecular Pathways ◽

Behavioral Alterations ◽

High Confidence ◽

Risk Genes ◽

Risk Gene ◽

Behavioral Studies ◽

Mrna Length

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 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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Functional characterization of FABP3, 5 and 7 gene variants identified in schizophrenia and autism spectrum disorder and mouse behavioral studies

Human Molecular Genetics ◽

10.1093/hmg/ddu369 ◽

2014 ◽

Vol 23 (24) ◽

pp. 6495-6511 ◽

Cited By ~ 41

Author(s):

Chie Shimamoto ◽

Tetsuo Ohnishi ◽

Motoko Maekawa ◽

Akiko Watanabe ◽

Hisako Ohba ◽

...

Keyword(s):

Autism Spectrum Disorder ◽

Functional Characterization ◽

Autism Spectrum ◽

Spectrum Disorder ◽

Gene Variants ◽

Behavioral Studies

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Identification of risk genes for autism spectrum disorder through copy number variation analysis in Austrian families

Neurogenetics ◽

10.1007/s10048-014-0394-0 ◽

2014 ◽

Vol 15 (2) ◽

pp. 117-127 ◽

Cited By ~ 54

Author(s):

Gerald Egger ◽

Katharina M. Roetzer ◽

Abdul Noor ◽

Anath C. Lionel ◽

Huda Mahmood ◽

...

Keyword(s):

Autism Spectrum Disorder ◽

Copy Number Variation ◽

Copy Number ◽

Autism Spectrum ◽

Spectrum Disorder ◽

Variation Analysis ◽

Risk Genes ◽

Copy Number Variation Analysis ◽

Number Variation

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A co-segregating microduplication of chromosome 15q11.2 pinpoints two risk genes for autism spectrum disorder

American Journal of Medical Genetics Part B Neuropsychiatric Genetics ◽

10.1002/ajmg.b.31055 ◽

2009 ◽

Vol 9999B ◽

pp. n/a-n/a ◽

Cited By ~ 15

Author(s):

Bert van der Zwaag ◽

Wouter G. Staal ◽

Ron Hochstenbach ◽

Martin Poot ◽

Henk A. Spierenburg ◽

...

Keyword(s):

Autism Spectrum Disorder ◽

Autism Spectrum ◽

Spectrum Disorder ◽

Risk Genes

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An Epigenetically Distinct Subset of Children With Autism Spectrum Disorder Resulting From Differences in Blood Cell Composition

Frontiers in Neurology ◽

10.3389/fneur.2021.612817 ◽

2021 ◽

Vol 12 ◽

Author(s):

Maryam Jangjoo ◽

Sarah J. Goodman ◽

Sanaa Choufani ◽

Brett Trost ◽

Stephen W. Scherer ◽

...

Keyword(s):

Autism Spectrum Disorder ◽

Blood Cell ◽

Autism Spectrum ◽

Differential Methylation ◽

Repetitive Behaviors ◽

Spectrum Disorder ◽

Risk Genes ◽

Immune Systems ◽

Cell Composition ◽

Children With Asd

Background: Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder that often involves impaired cognition, communication difficulties and restrictive, repetitive behaviors. ASD is extremely heterogeneous both clinically and etiologically, which represents one of the greatest challenges in studying the molecular underpinnings of ASD. While hundreds of ASD-associated genes have been identified that confer varying degrees of risk, no single gene variant accounts for >1% of ASD cases. Notably, a large number of ASD-risk genes function as epigenetic regulators, indicating potential epigenetic dysregulation in ASD. As such, we compared genome-wide DNA methylation (DNAm) in the blood of children with ASD (n = 265) to samples from age- and sex-matched, neurotypical controls (n = 122) using the Illumina Infinium HumanMethylation450 arrays.Results: While DNAm patterns did not distinctly separate ASD cases from controls, our analysis identified an epigenetically unique subset of ASD cases (n = 32); these individuals exhibited significant differential methylation from both controls than the remaining ASD cases. The CpG sites at which this subset was differentially methylated mapped to known ASD risk genes that encode proteins of the nervous and immune systems. Moreover, the observed DNAm differences were attributable to altered blood cell composition, i.e., lower granulocyte proportion and granulocyte-to-lymphocyte ratio in the ASD subset, as compared to the remaining ASD cases and controls. This ASD subset did not differ from the rest of the ASD cases in the frequency or type of high-risk genomic variants.Conclusion: Within our ASD cohort, we identified a subset of individuals that exhibit differential methylation from both controls and the remaining ASD group tightly associated with shifts in immune cell type proportions. This is an important feature that should be assessed in all epigenetic studies of blood cells in ASD. This finding also builds on past reports of changes in the immune systems of children with ASD, supporting the potential role of altered immunological mechanisms in the complex pathophysiology of ASD. The discovery of significant molecular and immunological features in subgroups of individuals with ASD may allow clinicians to better stratify patients, facilitating personalized interventions and improved outcomes.

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Exonic Mosaic Mutations Contribute Risk for Autism Spectrum Disorder

10.1101/083428 ◽

2017 ◽

Cited By ~ 2

Author(s):

Deidre R. Krupp ◽

Rebecca A. Barnard ◽

Yannis Duffourd ◽

Sara A. Evans ◽

Ryan M. Mulqueen ◽

...

Keyword(s):

Autism Spectrum Disorder ◽

De Novo ◽

Large Family ◽

Recurrence Risk ◽

Autism Spectrum ◽

Spectrum Disorder ◽

Sequencing Data ◽

Full Cohort ◽

Risk Genes ◽

Family Based

AbstractGenetic risk factors for autism spectrum disorder (ASD) have yet to be fully elucidated. Postzygotic mosaic mutations (PMMs) have been implicated in several neurodevelopmental disorders and overgrowth syndromes. We systematically evaluated PMMs by leveraging whole-exome sequencing data on a large family-based ASD cohort, the Simons Simplex Collection. We found evidence that 11% of published single nucleotide variant (SNV) de novo mutations are potentially PMMs. We then developed a robust SNV PMM calling approach that leverages complementary callers, logistic regression modeling, and additional heuristics. Using this approach, we recalled SNVs and found that 22% of de novo mutations likely occur as PMMs in children. Unexpectedly, we found a significant burden of synonymous PMMs in probands that are predicted to alter splicing. We found no evidence of missense PMM burden in the full cohort. However, we did observe increased signal for missense PMMs in families without germline mutations in probands, which strengthens in genes intolerant to mutations. We also determined that 7-11% of parental mosaics are transmitted to children. Parental mosaic mutations make up 6.8% of all mutations newly germline in children, which has important implications for recurrence risk. PMMs intersect previously implicated high confidence and other ASD candidate risk genes, further suggesting that this class of mutations contribute to ASD risk. We also identified PMMs in novel candidate risk genes involved with chromatin remodeling or neurodevelopment. We estimate that PMMs contribute risk to 4-8% of simplex ASD cases. Overall, these findings argue for future studies of PMMs in ASD and related-disorders.

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