DATA-DRIVEN COMPUTATIONAL APPROACHES TO UNRAVEL AGE/SEX BIASES & CROSS-SPECIES ANALOGS OF COMPLEX TRAITS AND DISEASES

Cellular mechanisms and genetic underpinnings of most complex diseases and traits are not well understood. Most diseases also vary in their incidence and presentation in people of different ages and sexes, yet it is still largely unclear how age and sex influence normal tissue physiology and disease at the molecular level. Additionally, while we need research organisms to experimentally study many aspects of human disease etiology, choosing the best genes and conditions in a model organism for such studies is difficult due to our incomplete knowledge of functional and phenotypic conservation across species. The goal of my research is to address these challenges towards gaining a systematic understanding of the genetic etiology of complex diseases and traits. I have worked towards this goal by developing computational frameworks capable of leveraging massive amounts of publicly-available genomic data with prior knowledge using network analysis and machine learning. These approaches have shed light on the genomic signatures, pathways, and interactions that characterize the age/sex biases and cross-species analogs of complex diseases and traits. I make all the code to reproduce these approaches available by github and have provided tools to make the results searchable by scientists investigating these important biological factors. Collectively, this research will help build infrastructure for advancing biomedical research into the era of precision medicine.