Blood-Based Transcriptomic Mega-Analyses Comparing Individuals with Autism Spectrum Disorder and Unaffected Comparison Subjects

Background:  Microarray technology has been applied in the study of Autism Spectrum Disorders (ASDs) for more than a decade. Blood-based studies offered a promising source of non-invasive biomarker signal and shed light on differences in immune cell functions.  However, the collective body of blood-based ASD microarray literature suffers from some inconsistencies. 

Objectives:  In order to summarize the existing literature, we performed a mega-analysis of all available microarray whole blood and lymphocyte studies that comparing ASD cases (n = 647) and unaffected, non-family member comparison subjects (n = 468).

Methods:  We modeled covariate effects and adjusted regression models per gene to remove non-significant covariates, providing best-estimates of transcripts dysregulated in ASD.  We also performed permutation-based gene set and co-expression network analyses. Our results provide the best available characterization of dysregulated gene transcripts and emergent functions in ASD blood samples. 

Results:  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 explore evidence that distinct subgroups of ASD cases may contribute to observed effects for different gene sets and network modules.  We also explore evidence for sex-differences in the transcriptomic signature of ASD; more studies of female samples will be required to confirm these findings. Finally, we constructed generalizable machine-learning classifiers using the blood-based microarray data. 

Conclusions:  We contrast our results with respect to blood- and brain-based RNA and protein biomarker (e.g. cytokines, growth factors) studies and we discuss the ways that blood transcriptomic signals implicate some pathways known to play causal roles in syndromic ASDs.