Methods and tools to integrate multi-omics datasets to understand preclinical autoimmune and immune-mediated diseases

ABSTRACT Autoimmune or immune-mediated diseases affect 10% of the world population, and most conditions have no cure. Existing treatment options are often broadly acting and have numerous side effects. Autoimmune diseases often have a preclinical phase where a subset of symptoms occur, but they usually do not yet meet the diagnostic

criteria for the disease stage. Many conditions may also have an early stage where the symptoms are milder. Early diagnosis at the preclinical phase or early disease stage is challenging as the symptoms are usually non- specific. Moreover, only a fraction of individuals will progress to disease stages, which poses challenges for

clinical interventions. Understanding the genetic basis for preclinical immune-mediated diseases and dissecting risk factors that underlie the progression from preclinical to disease stage is critical. In this application, we propose to develop a series of methods and software tools to better detect genetic associations with preclinical

to disease stage progressions, perform integrative genomic analysis to identify genetic signatures underlie the progression, and dissect genetic contribution to sex differences in autoimmune diseases. The methods and tools will be broadly applicable to a myriad of diseases. The proposed development received enthusiastic support

from researchers studying psoriasis, Sjogren's disease, lupus, vitiligo, etc. The proposed new tools can profoundly impact the understanding the genetic basis for preclinical diseases.