The heterogeneous pathogenesis of ASD appears to be driven by genetic and environmental interactions, which also plays a vital role in predisposing individuals to ASD with different commitment levels. ASD has a strong and complex genetic component, with multiple familial inheritance patterns and an estimate of up to 1000 genes potentially implicated. Over the past decade, genomic technologies have enabled rapid progress in the identification of risk genes for ASD. In addition, correlating regulation of these genes and other epigenetic regulators like small non-coding RNA and phenomena such as DNA methylation with brain development trajectories enable understanding of downstream atypical behaviour manifestations.
We wish to explore specific candidate genes even as we work on obtaining large DNA sets from subjects across the city and possibly from across the country. We will explore relationships between genomic data and environmental adversities that are being noticed in our database besides relating them with trajectories of brain development. Computational models using advanced machine learning algorithms will be explored for better understanding and characterization of these relationships.