PROJECT SUMMARY/ABSTRACT Thoracic aortic aneurysm (TAA) is a degenerative aortopathy that affects children and adults and predisposes to sudden thoracic aortic dissection. Patients with TAA require lifelong cardiac care intended to prevent progressive dilation or dissection. Cardiac decision-making requires projection of future risk, but the status quo for establishing prognosis is not personalized and lacks precision. The basis for rapid progression versus stability or improvement over time is not understood nor predictable using current approaches. Novel methods to classify risk for disease progression upon diagnosis are urgently needed. Our long-term objective is to leverage molecular and image analysis tools to create novel methods to classify risk of aortopathy progression precisely and identify mechanisms amenable to future therapeutic targeting. Our first aim is to identify genetic variants that are associated with increased or decreased longitudinal rate of aortic dilation, hypothesizing that single nucleotide polymorphisms (SNPs) regulate inter-individual variability in TAA progression. We will perform whole genome sequencing (WGS) in study participants who are receiving longitudinal pediatric aortopathy care. Enrollment will occur at two pediatric cardiac centers with subspecialty aortopathy programs: Indiana University Health’s Riley Hospital and Children’s Healthcare of Atlanta. We will test SNPs for their association with rates of aortic dilation using mixed model analysis of serial aortic diameter measurements. In our second aim, we will leverage our unique aortopathy tissue biobank to identify novel candidate SNPs in transcriptional regulatory elements that contribute to TAA pathogenesis. We will perform deep RNA sequencing of flash frozen human TAA tissues and controls and integrate these data with WGS to identify genes with differential allele-specific expression in TAA. We will directly test the impacts of candidate SNPs in 3’-untranslated regions and enhancers on allelic transcription using our high-throughput reporter assays in cultured human aortic smooth muscle cells. For our third aim, in collaboration with biomedical engineers at Purdue University, we have pioneered a novel automated algorithm that tracks aortic root kinematics across cardiac cycles and extracts morphological and functional metrics using clinical echocardiography. We will establish a large normative data set of novel metrics in young healthy subjects and compare metrics between TAA cases and matched controls. We hypothesize that abnormal metrics portend increased rate of aortic dilation at follow-up, helping us predict clinical outcomes. Upon completion, these aims will elucidate a genetic basis for transcriptional dysregulation in TAA, identify SNPs that modify TAA progression, and advance echocardiographic phenotyping of the aortic root. Creation of a novel classifier to predict risk for TAA progression, without significantly modifying existing clinical ...