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.
