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Research > Portfolio > Funded Research

Lam Tsoi, Ph.D., Research Assistant Professor, Dermatology, University of Michigan, Ann Arbor, MI, USA

Discovery Grant
2018

Deciphering the Shared Genetic Components of Psoriasis Comorbidities by Integrating Genetics and Health Records

Psoriasis
Active

Layman's Statement: Comorbidities including cardiovascular disorders, immune-mediated disease, etc., cause psoriatic patients to have worse health outcomes, and make clinical management more complex. Identifying comorbidities and revealing the genetic components they share with psoriasis is therefore critical for understanding their common biological mechanisms, and allowing us to improve healthcare for these patients. This proposal aims to integrate traditional epidemiological investigations on medical records with genetic association studies: we will first utilize >3 million medical records in the University of Michigan Health System and the Michigan Genomics Initiative (MGI) to reveal psoriasis comorbidities using epidemiological model (Aim 1); we will then perform cross-disease genetic association studies between our psoriasis genetic cohorts (11,024 and 16,336 genotyped cases and controls, respectively) and the MGI data, consisting of >40,000 genotyped and phenotyped patients, for each comorbidity identified (Aim 2); finally, we will use machinelearning to model the risk of comorbidity for individual psoriatic patients (Aim 3). This project integrates basic science findings with clinical data, and will facilitate future implementation of precision health care in psoriasis.


Grant Abstract: Psoriasis has a complex genetic architecture with high heritability. Studies from the last decade have significantly advanced our understanding of the disease genetics, through the identification of >80 susceptibility loci. Many of genes participating in IL17/TNFa signaling pathways map to the psoriasis associated regions, and they can be targets of existing drugs for different autoimmune disorders. Interestingly, there is substantial overlap in diseaseassociated regions between psoriasis and its comorbidities, including multiple sclerosis, diabetes, and inflammatory bowel disease. Nevertheless until now, most epidemiological investigations (to identify comorbidities) and genetic studies (to reveal disease associated regions) for psoriasis have been conducted separately, and yet there is no study evaluating the genetic determinants of psoriasis and its comorbidities collectively and systematically. We hypothesize that by integrating genetics and health record information we can identify the shared associated signals between psoriasis and its comorbidities. In this proposal, we aim to first utilize the Electronic Health Record (EHR) data from the University of Michigan Health System, consisting of >3 million patient records, to reveal novel psoriasis comorbidities using epidemiological model. We will then compare and meta-analyze our psoriasis genetic cohorts (11,024 and 16,336 genotyped cases and controls, respectively) with the genetic association studies of other complex comorbidities available in the Michigan Genomics Initiative (MGI), consisting of >40,000 genotyped and phenotyped individuals. Finally, we will model the risk of comorbidity among psoriatic patients. The shared genetic components identified in this proposal will reveal common pathological mechanisms, and our results will also facilitate the risk assessment of getting other comorbidities among psoriatic patients. The findings from this discovery work will have great translational impact on individualized medicine for psoriasis.