http://www.sciencedaily.com/releases/20 ... 134627.htm
Prediction of Autism by Translation and Immune/Inflammation Coexpressed Genes in Toddlers From Pediatric Community Practices.
Pramparo T1, Pierce K1, Lombardo MV2, Carter Barnes C1, Marinero S1, Ahrens-Barbeau C1, Murray SS3, Lopez L1, Xu R4, Courchesne E1.
The identification of genomic signatures that aid early identification of individuals at risk for autism spectrum disorder (ASD) in the toddler period remains a major challenge because of the genetic and phenotypic heterogeneity of the disorder. Generally, ASD is not diagnosed before the fourth to fifth birthday.
To apply a functional genomic approach to identify a biologically relevant signature with promising performance in the diagnostic classification of infants and toddlers with ASD.
DESIGN, SETTING, AND PARTICIPANTS:
Proof-of-principle study of leukocyte RNA expression levels from 2 independent cohorts of children aged 1 to 4 years (142 discovery participants and 73 replication participants) using Illumina microarrays. Coexpression analysis of differentially expressed genes between Discovery ASD and control toddlers were used to define gene modules and eigengenes used in a diagnostic classification analysis. Independent validation of the classifier performance was tested on the replication cohort. Pathway enrichment and protein-protein interaction analyses were used to confirm biological relevance of the functional networks in the classifier. Participant recruitment occurred in general pediatric clinics and community settings. Male infants and toddlers (age range, 1-4 years) were enrolled in the study. Recruitment criteria followed the 1-Year Well-Baby Check-Up Approach. Diagnostic judgment followed DSM-IV-TR and Autism Diagnostic Observation Schedule criteria for autism. Participants with ASD were compared with control groups composed of typically developing toddlers as well as toddlers with global developmental or language delay.
MAIN OUTCOMES AND MEASURES:
Logistic regression and receiver operating characteristic curve analysis were used in a classification test to establish the accuracy, specificity, and sensitivity of the module-based classifier.
Our signature of differentially coexpressed genes was enriched in translation and immune/inflammation functions and produced 83% accuracy. In an independent test with approximately half of the sample and a different microarray, the diagnostic classification of ASD vs control samples was 75% accurate. Consistent with its ASD specificity, our signature did not distinguish toddlers with global developmental or language delay from typically developing toddlers (62% accuracy).
CONCLUSIONS AND RELEVANCE:
This proof-of-principle study demonstrated that genomic biomarkers with very good sensitivity and specificity for boys with ASD in general pediatric settings can be identified. It also showed that a blood-based clinical test for at-risk male infants and toddlers could be refined and routinely implemented in pediatric diagnostic settings.