Genome-wide expression profiling data analysis to study autism genetic models
University of California, Los Angeles
Weatherstone Predoctoral Fellowship
Previous studies indicate that autism spectrum disorders (ASDs) can be conceptualized as the result of multiple rare and common variants that act in combination to shape different aspects of cognition and behavior. It has been proposed that autism spectrum disorders are caused by combinations of multiple genetic variants, or mutations that affect many essential genes to shape different aspects of cognition and behavior. This study aims to test that model in an attempt to explain possible risk factors of autism. Using gene expression data from more than 1000 blood samples from patients with autism and their unaffected family members, gene signatures will be examined in order to classify disease subgroups. This is the first time such a large data set has been used for a comprehensive gene expression study in ASD. The project has the potential to advance the understanding of autism and contribute to disease diagnosis and treatment. The study will first focus on whether there are consistent gene expression changes between cases and controls, which can help us find biomarkers for ASDs diagnosis. It will then focus on the expression pattern for each individual to see whether there are genes that behave significantly differently in certain cases, which will likely promote the realization of personalized medicine.