Objectives: We have applied novel clustering methods to ADIR scores to identify distinct phenotypic subgroups of ASD. The purpose of this study was to identify gene expression profiles in lymphoblastoid cell lines that distinguish these subgroups from control individuals as well as to identify differentially affected pathways among the autistic groups.
Methods: DNA microarray analyses were conducted on over 115 lymphoblastoid cell lines from 3 distinct phenotypic groups of autistic individuals and age-matched, nonautistic controls from the NIMH Genetics repository. Datasets from these analyses were analyzed using Ingenuity Pathway Analysis and Pathway Studio 5 software.
Results: Comparison of gene expression profiles from control samples against all autistic samples reveals a set of genes for which gene expression level is associated with phenotypic severity. A 4-class analysis further reveals genes that separate the 3 phenotypes from each other as well as from controls. Statistical analyses of each subgroup vs. the control group identify differentially expressed genes unique to each subgroup as well as genes in common across subgroups. Bioinformatics analyses of the microarray data for each phenotypic subgroup similarly identify unique and common pathways and functions that are “enriched” in the respective gene datasets.
Conclusions: Gene expression profiles of lymphoblastoid cell lines from autistic case-controls not only discriminate between autistic and unaffected individuals but also correlate with phenotypic variants of ASD. These distinguishing genes can potentially be used to develop a diagnostic gene panel that can be used to screen for autism as well as reveal the variant of autism that is present.