UCLA

Bioinformatics and computational methods play an important role in advancing medical research with their ability to leverage large datasets, including data from electronic health records (EHR) linked biobanks. Precision medicine can benefit from leveraging a more comprehensive picture of a patient’s genotypes and phenotypic presentation for targeted interventions and treatment planning. In this work, I discuss the applications of bioinformatics methods in the UCLA ATLAS biobank, in evaluating craniofacial traits and their risk factors: specifically, head and neck cancer and tobacco use disorder. First, I describe phenome-wide and lab-wide association analysis pipelines that leverage the breadth of the available information in the biobank, and the results of preliminary investigations of the phenome-wide and laboratory-wide associations of a genetic predisposition to tobacco use disorder. Next, I present the results of an evaluation of the predictive performance of a tobacco use polygenic score across different genetic ancestry groups and further discuss the differences in disease presentations in tobacco use-predisposed individuals with and without a history of the associated tobacco use behavior. Next, I employ these pipelines and statistical methods in the examination of the interplay of serum bilirubin, tobacco use, head and neck, and lung cancer. I present the results of this project, examining the effect of environmental and genetic factors on serum bilirubin and associations with head and neck cancer and lung cancer. Lastly, I propose a research project to examine the germline risk factors for oropharyngeal cancer and discuss the future directions of this work.