PROJECT SUMMARY/ABSTRACT Approximately 1 in 5 individuals with Down syndrome (DS) meet criteria for comorbid autism spectrum disorder (ASD), a tenfold increase in risk compared to the general population. Comorbid ASD is associated with delayed language, increased behavioral challenges, greater demands on caregivers, and higher costs of healthcare across the lifespan. Recent advances in precision medicine have the potential to substantially improve long-term outcomes among individuals with DS and comorbid conditions such as ASD. However, for this potential to be realized, reliable and valid measures are required. There is currently little scientific basis for the identification and measurement of ASD symptoms in DS. Without accurate measurement, clinical trials in DS cannot properly apply ASD inclusion criteria, stratify cohorts where necessary, or track response to treatment. Consequently, there is an urgent need for clinical trials to have reliable, valid ASD screening, diagnostic, and symptom monitoring tools in DS. To address this need, we propose to (1) evaluate the psychometric characteristics of ASD symptom measures in DS, and (2) characterize ASD symptom profiles in DS through deep phenotyping. Characterizing ASD symptoms and related developmental features in DS will further inform clinical trials by enabling them to stratify cohorts by comorbid ASD and monitor response to treatment across symptom profiles. These aims align with two priorities of the NIH INCLUDE Project: (a) increase the likelihood of clinical trial success through testing of clinical outcome assessment measures, and (b) define the presentation and course of co-occurring conditions in individuals with DS. In an effort to improve the efficiency, generalizability, and inclusiveness of future clinical trials, the proposed study will be conducted online. To accomplish these aims, we will leverage existing resources (NIH’s DS-Connect; Emory University’s DS360) to conduct a large-scale, nationwide study of ASD symptoms in 500 6- to 18-year-olds with DS. We will examine the reliability, validity, and variability of three well-known caregiver report-based ASD screening and symptom measures. We will leverage data from these ASD measures, along with additional deep phenotyping, to characterize the heterogeneity of the ASD phenotype in DS and identify symptom profiles. Finally, we propose an exploratory aim among a subsample (n = 25) at high or low ASD risk to examine the feasibility of tele-assessment methods for gathering direct, performance-based ASD evaluations. Data generated from this project will enhance clinical trial readiness by providing ASD measures in DS that can (a) screen for ASD risk to identify candidates for treatment and (b) stratify cohorts by ASD symptom profiles and monitor response to treatment across these profiles. Once validated, these ASD measures will provide a much-needed resource for future clinical trials to document outcomes in response to treatment. ...