Blood transcriptomic comparison of individuals with and without autism spectrum disorder: A combined-samples mega-analysis

Daniel S. Tylee, Jonathan L. Hess, Thomas P. Quinn, Rahul Barve, Hailiang Huang, Yanli Zhang-James, Jeffrey Chang, Boryana Stamova, Frank R Sharp, Irva Hertz-Picciotto, Stephen V. Faraone, Sek Won Kong, Stephen J. Glatt

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

Blood-based microarray studies comparing individuals affected with autism spectrum disorder (ASD) and typically developing individuals help characterize differences in circulating immune cell functions and offer potential biomarker signal. We sought to combine the subject-level data from previously published studies by mega-analysis to increase the statistical power. We identified studies that compared ex vivo blood or lymphocytes from ASD-affected individuals and unrelated comparison subjects using Affymetrix or Illumina array platforms. Raw microarray data and clinical meta-data were obtained from seven studies, totaling 626 affected and 447 comparison subjects. Microarray data were processed using uniform methods. Covariate-controlled mixed-effect linear models were used to identify gene transcripts and co-expression network modules that were significantly associated with diagnostic status. Permutation-based gene-set analysis was used to identify functionally related sets of genes that were over- and under-expressed among ASD samples. Our results were consistent with diminished interferon-, EGF-, PDGF-, PI3K-AKT-mTOR-, and RAS-MAPK-signaling cascades, and increased ribosomal translation and NK-cell related activity in ASD. We explored evidence for sex-differences in the ASD-related transcriptomic signature. We also demonstrated that machine-learning classifiers using blood transcriptome data perform with moderate accuracy when data are combined across studies. Comparing our results with those from blood-based studies of protein biomarkers (e.g., cytokines and trophic factors), we propose that ASD may feature decoupling between certain circulating signaling proteins (higher in ASD samples) and the transcriptional cascades which they typically elicit within circulating immune cells (lower in ASD samples). These findings provide insight into ASD-related transcriptional differences in circulating immune cells.

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Biomarkers
Genes
Autism Spectrum Disorder
Phosphatidylinositol 3-Kinases
Transcriptome
Epidermal Growth Factor
Sex Characteristics
Natural Killer Cells
Interferons
Linear Models
Proteins
Lymphocytes
Cytokines
Machine Learning

Keywords

  • Gene expression
  • Immune system
  • Machine learning
  • Microarray

ASJC Scopus subject areas

  • Genetics(clinical)
  • Cellular and Molecular Neuroscience
  • Psychiatry and Mental health

Cite this

Blood transcriptomic comparison of individuals with and without autism spectrum disorder : A combined-samples mega-analysis. / Tylee, Daniel S.; Hess, Jonathan L.; Quinn, Thomas P.; Barve, Rahul; Huang, Hailiang; Zhang-James, Yanli; Chang, Jeffrey; Stamova, Boryana; Sharp, Frank R; Hertz-Picciotto, Irva; Faraone, Stephen V.; Kong, Sek Won; Glatt, Stephen J.

In: American Journal of Medical Genetics, Part B: Neuropsychiatric Genetics, 2016.

Research output: Contribution to journalArticle

Tylee, Daniel S. ; Hess, Jonathan L. ; Quinn, Thomas P. ; Barve, Rahul ; Huang, Hailiang ; Zhang-James, Yanli ; Chang, Jeffrey ; Stamova, Boryana ; Sharp, Frank R ; Hertz-Picciotto, Irva ; Faraone, Stephen V. ; Kong, Sek Won ; Glatt, Stephen J. / Blood transcriptomic comparison of individuals with and without autism spectrum disorder : A combined-samples mega-analysis. In: American Journal of Medical Genetics, Part B: Neuropsychiatric Genetics. 2016.
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abstract = "Blood-based microarray studies comparing individuals affected with autism spectrum disorder (ASD) and typically developing individuals help characterize differences in circulating immune cell functions and offer potential biomarker signal. We sought to combine the subject-level data from previously published studies by mega-analysis to increase the statistical power. We identified studies that compared ex vivo blood or lymphocytes from ASD-affected individuals and unrelated comparison subjects using Affymetrix or Illumina array platforms. Raw microarray data and clinical meta-data were obtained from seven studies, totaling 626 affected and 447 comparison subjects. Microarray data were processed using uniform methods. Covariate-controlled mixed-effect linear models were used to identify gene transcripts and co-expression network modules that were significantly associated with diagnostic status. Permutation-based gene-set analysis was used to identify functionally related sets of genes that were over- and under-expressed among ASD samples. Our results were consistent with diminished interferon-, EGF-, PDGF-, PI3K-AKT-mTOR-, and RAS-MAPK-signaling cascades, and increased ribosomal translation and NK-cell related activity in ASD. We explored evidence for sex-differences in the ASD-related transcriptomic signature. We also demonstrated that machine-learning classifiers using blood transcriptome data perform with moderate accuracy when data are combined across studies. Comparing our results with those from blood-based studies of protein biomarkers (e.g., cytokines and trophic factors), we propose that ASD may feature decoupling between certain circulating signaling proteins (higher in ASD samples) and the transcriptional cascades which they typically elicit within circulating immune cells (lower in ASD samples). These findings provide insight into ASD-related transcriptional differences in circulating immune cells.",
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AU - Tylee, Daniel S.

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AU - Quinn, Thomas P.

AU - Barve, Rahul

AU - Huang, Hailiang

AU - Zhang-James, Yanli

AU - Chang, Jeffrey

AU - Stamova, Boryana

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AU - Glatt, Stephen J.

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AB - Blood-based microarray studies comparing individuals affected with autism spectrum disorder (ASD) and typically developing individuals help characterize differences in circulating immune cell functions and offer potential biomarker signal. We sought to combine the subject-level data from previously published studies by mega-analysis to increase the statistical power. We identified studies that compared ex vivo blood or lymphocytes from ASD-affected individuals and unrelated comparison subjects using Affymetrix or Illumina array platforms. Raw microarray data and clinical meta-data were obtained from seven studies, totaling 626 affected and 447 comparison subjects. Microarray data were processed using uniform methods. Covariate-controlled mixed-effect linear models were used to identify gene transcripts and co-expression network modules that were significantly associated with diagnostic status. Permutation-based gene-set analysis was used to identify functionally related sets of genes that were over- and under-expressed among ASD samples. Our results were consistent with diminished interferon-, EGF-, PDGF-, PI3K-AKT-mTOR-, and RAS-MAPK-signaling cascades, and increased ribosomal translation and NK-cell related activity in ASD. We explored evidence for sex-differences in the ASD-related transcriptomic signature. We also demonstrated that machine-learning classifiers using blood transcriptome data perform with moderate accuracy when data are combined across studies. Comparing our results with those from blood-based studies of protein biomarkers (e.g., cytokines and trophic factors), we propose that ASD may feature decoupling between certain circulating signaling proteins (higher in ASD samples) and the transcriptional cascades which they typically elicit within circulating immune cells (lower in ASD samples). These findings provide insight into ASD-related transcriptional differences in circulating immune cells.

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