A multifaceted approach for analyzing complex phenotypic data in rodent models of autism

Autism (MIM 209850) is a multifactorial disorder with a broad clinical presentation. A number of high-confidence ASD risk genes are known; however, the contribution of non-genetic environmental factors towards ASD remains largely uncertain. Here, we present a bioinformatics resource of genetic and induced models of ASD developed using a shared annotation platform. Using this data, we depict the intricate trends in the research approaches to analyze rodent models of ASD. We identify the top 30 most frequently studied phenotypes extracted from rodent models of ASD based on 787 publications. As expected, many of these include animal model equivalents of the core phenotypes associated with ASD,such as impairments in social behavior and repetitive behavior, as well as several comorbid features of ASD including anxiety, seizures, and motor-control deficits. These phenotypes have also been studied in models based on a broad range of environmental inducers present in the database, of which gestational exposure to valproic acid (VPA) and maternal immune activation models comprising lipopolysaccharide (LPS) and poly I:C are the most studied. In our unique dataset of rescue models, we identify 24 pharmaceutical agents tested on established models derived from various ASD genes and CNV loci for their efficacy in mitigating symptoms relevant for ASD. As a case study, we analyze a large collection of Shank3 mouse models providing a high-resolution view of the in vivo role of this high-confidence ASD gene, which is the gateway towards understanding and dissecting the heterogeneous phenotypes seen in single-gene models of ASD. The trends described in this study could be useful for researchers to compare ASD models and to establish a complete profile for all relevant animal models in ASD research.