Project Summary/Abstract Individuals with Down syndrome (DS) tend to have many cooccurring conditions across the life span, such as Alzheimer’s disease, autism and congenital heart defects (CHD). Very strikingly, DS is a strong risk factor for CHD. Compared to the general population, children with DS have a 2000-fold increased risk of developing atrioventricular septal defects (AVSD), a type of CHD. Here, we propose to systematically apply and evaluate an individualized Bayesian inference (IBI) framework to the harmonized clinical and whole genome sequencing (WGS) data by the Kids First (KF) and INCLUDE programs to identify significant variants underlying CHD in patients without and with DS, and to understand why there is an increased risk for DS patients to develop CHD. Compared to the population-based genome wide association studies (GWAS), IBI considers the inter-individual genomic heterogeneity and infers personalized significant variants for advancing precision medicine; IBI is also capable of detecting rare or low-frequency variants by focusing on each individual’s genome that may have been missed by the parallel GWAS analysis in the same cohort. Specific Aim 1: Apply and validate the IBI framework in an integrated KF and INCLUDE cohort and further identify significant genomic variants underlying CHD in DS patients. Specific Aim 2: Build and deploy in CAVATICA a standardized novel workflow of IBI to share with the KF and INCLUDE community for identifying significant genomic variants of pediatric conditions. If successful, this project will produce a novel, validated, standardized and shareable workflow of IBI for inferring significant variants of diseases in an individual-specific manner, which has a great potential in advancing personalized medicine for conditions that affect DS individuals and the general population. The publication of this impactful IBI workflow on CAVATIVA may also attract new users and significantly increase utilization of KF and INCLUDE data. Moreover, our efforts may lead to new insights on probable genomic causes that underlie the high prevalence of CHD in DS individuals, and further inform the design of personalized prevention or treatment strategies for these diseases.

Project Narrative Individuals with Down syndrome (DS) often have co-occurring medical conditions across their life span, and children with DS have a greatly increased risk of congenital heart defects (CHD). To improve our understanding of this much increased risk, we propose to apply our novel tool of individualized Bayesian inference (IBI) algorithm to Kids First/INCLUDE genomic/phenotypic data and search for the significant genomic variants that are potentially responsible for this in an individualized manner. These individualized significant variants would inform the design of personalized prevention or treatment strategies for the co-occurring conditions in DS individuals.