Genomic Landscapes in Large Scale Integrated JRA Studies (Project Summary)
A better understanding of the complexity of both genetic and environmental contributions to the disease risk would greatly facilitate correct diagnosis and treatment of juvenile rheumatoid arthritis (JRA). This would be particularly valuable if it lead to predictions that could be made to distinguish patients whose course of disease leads to severe joint erosion. At present this cannot be predicted in the early stages of the JRA onset.
Studies currently in progress, which include a contract to identify the genetic components of JRA and a P-01 grant study to identify gene expression patterns to use as new markers of disease, offer potential large-scale datasets of genetic, gene expression and clinical information, which will allow new and powerful insights into biological pathways and processes that underlie JRA.
Our general aim is to develop novel computational methods for large-scale data mining and classification as well as build carefully validated databases of JRA gene expression and relevant polymorphisms in order to facilitate multidimensional mining with the ability to leverage the use of a priori knowledge of relevant gene-gene interactions and pathways.
In particular, an integrated database of clinical, gene expression and polymorphism profiles for a large population of JRA patients is being built. Large-scale classification algorithms that are suitable for the analysis of the diverse genomic and clinical data are being developed in collaboration with mathematicians from the Division of Biomedical Informatics , Jarek Meller, PhD, and Michael Wagner, PhD. Special emphasis is given to dimension reduction and the development of statistical significance measures, two key computational challenges arising from the type of data involved. Finally once the database and computational tools are complete these tools may be used to classify JRA subtype and clinical outcomes.
