Project Summary/Abstract Language disorder is a neurodevelopmental condition that occurs in ~7% of children starting school (Tomblin et al., 1997; Norbury et al., 2016). It has high comorbidity with autism spectrum disorder (ASD) (Ellis Weismer, 2013) and other impairments (Mueller and Tomblin, 2012; Snowling et al., 2020), which makes it challenging to identify its unique genomic and synaptic mechanisms. The focus of this proposal is to apply computational genomics and behavioral neuroscience to study language disorder in the context of learned sound association, the process by which the auditory nervous system associates a sound with a certain outcome (Stanley, 2023; Stanley et al., 2024). In Aim 1, I will use genome-wide association studies (GWAS) and post-GWAS analysis methods to conduct a large-scale analysis of the variants, genes, and pathways involved in language disorder and identify shared and distinct genetic factors between language disorder and ASD using human genetic data from the Genetics of Language (GenLang) consortium, the Simons Foundation Powering Autism Research (SPARK) dataset, the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort, and the United Kingdom (UK) Biobank. In Aim 2, I will conduct behavioral sound association experiments with wildtype (WT) and mutant mouse models using an interactive virtual reality (VR) environment, where mice will be trained to associate auditory cues with rewarding/aversive stimuli. Neuronal activity will be measured using fiber photometry recordings in the tail of the striatum. The comparison of human genetic data from Aim 1 with mouse physiology and behavioral data in Aim 2 will identify the timing and role for language disorder/ASD-linked genes in sound association learning and its disruption in neurodevelopmental disease.