Using Bayesian networks to examine endophenotypes of Autism Spectrum Disorders and their relationship to possible risk factors and genetic polymorphisms.
Many children suffer from Autistic Spectrum Disorders (ASD) manifesting in several cardinal features and cormobidities, which complicates both the diagnosis and prognosis. The outcome of this condition may be improved if diagnosed during the early developmental stages of a child. We aim to use a Bayesian approach to examine the inter-relationships between the clinical features which will be the basis for classification into different endophenotypes; investigate the association between each endop henotype and comorbidities as well as other neuro-developmental disorders, environmental and genetic factors. To achieve these objectives, we will use data from children already identified with ASD in Kenya, Tanzania and Oxford to construct Bayesian Network models. These models will help identify the interdependency between features of autism, to potentially elucidate the causal pathways.
Interagency Autism Coordinating Committee (IACC)
Autism Research Database (AFD)
