The twin goals of this study are to use large-scale genomic methods to identify biomarkers of autism spectrum disorders (ASD) in cell lines derived from autistic individuals, and to gain a better understanding of the biology of these disorders. The central hypothesis driving this analytical approach is that blood cells from individuals with ASD will reflect molecular defects or genetic control elements that are relevant to autism. This hypothesis and approach is supported by of our preliminary findings: 1) cell lines derived from the blood of three identical twin pairs that differ in severity of ASD show a different profile of expressed genes; 2) the shared highly differentially expressed genes are significantly enriched in pathways critical to the development and function of the nervous system; 3) the levels of expression of certain genes appear to be related to the severity of the disorder when compared to the levels of expression of the same genes in cell lines from respective non-affected siblings; 4) candidate genes from preliminary microarray studies have associated quantitative trait loci containing reported autism susceptibility genes or loci. Thus, this differential expression profile which is observed in easily accessible blood-derived cells may be reflective of aberrant gene expression in the autistic vs. normal brain. This study will utilize DNA microarrays to: 1) identify differentially expressed genes in lymphoblastoid cell lines from individuals with ASD in comparison to unaffected individuals; 2) determine whether subgroups of ASD segregated according to phenotypic expression of ASD using existing diagnostic instruments, such as the ADI-R, can be differentiated through gene expression profiling; 3) build a classifier for ASD based on various class prediction algorithms, including k-nearest neighbors, centroid classification, and neural networks; 4) analyze signaling or metabolic pathways affected in the experimental subgroups; 5) map and identify genetic determinants that are responsible for differentially expressed genes using existing genetic data in the Autism Genetics Resource Exchange genotype database. At present, diagnosis of ASD relies primarily upon sometimes biased behavioral observations by clinicians or therapists and parent/teacher questionnaires. Reliable biomarkers would greatly facilitate the early and definitive detection of these disorders, thereby permitting early intervention and therapy. ? ? ?
