Liu, Jinling MS

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.