Rare Genetic Variations in Takayasu's Arteritis: A Whole-Exome Sequencing Study

Takayasu's arteritis (TAK) is a rare inflammatory disease affecting large arteries, primarily the aorta and its major branches. There is an unmet need for better clinical management of TAK due to limited understanding of the disease biology. Substantial evidence has supported a genetic component contributing to the etiopathogenesis of TAK. Genetic association analysis is a powerful approach to understand the casual molecular alterations and disease heterogeneity. Previous studies using genome-wide association studies (GWAS) approach is limited by modest effect size and explaining only a minority of heritability. Due to evolutionary selection pressure, deleterious variants tend to be rare, particularly for early-onset debilitating diseases that potentially affect reproductive fitness. Thus, we hypothesized that TAK is a genetically complex disease driven in part by rare variants with relatively large effect size. The objective of this study is to discover novel rare genetic variants associated with TAK on gene-, pathway-, and protein interaction network-level by leveraging data from whole exome sequencing (WES) of 160 patients with TAK and 3000 general population controls.Our study will use state-of-the-art statistical methods and bioinformatic tools which allows for system-level investigation of disease biology. Gene-level collapsing analysis and multiple pathway-level analyses will be performed. Mantis-ml, a novel bioinformatic program based on machine-learning approach will be used to further prioritize potential causal genes in TAK. We will then evaluate and visualize top ranked genes in protein interaction networks and perform functional enrichment analysis using Metascape. Overall, we anticipate that this project will expand our understanding of the genetic landscape of TAK. This project will also be a critical step for my long-term goal toward improving personalized clinical management of rheumatic diseases based on advanced research in genomic medicine.