Integrating electronic health records and polygenic risk to identify genetically unrelated comorbidities of schizophrenia that might be modifiable

Background Schizophrenia patients have substantial comorbidity contributing to reduced life expectancy of 10-20 years. Identifying modifiable comorbidities could improve rates of premature mortality. Conditions that frequently co-occur but lack shared genetic risk with schizophrenia are more likely to be products of treatment, behavior, or environmental factors and therefore enriched for potentially modifiable associations. Methods Phenome-wide comorbidity was calculated from electronic health records (EHR) of 250,000 patients across two independent healthcare institutions (Vanderbilt University Medical Center and Mass General Brigham) and association with schizophrenia polygenic risk scores (PRS) across the same phenotypes in linked biobanks. Results Schizophrenia comorbidity was significantly correlated across institutions (r=0.85) and the 77 identified comorbidities were consistent with prior literature. Overall, comorbidity and PRS associations were significantly correlated (r=0.55, p=1.29x10-118). However, directly testing for the absence of genetic effects identified 36 comorbidities having significantly equivalent schizophrenia PRS distributions between cases and controls. This set included phenotypes known to be consequences of antipsychotic medications(e.g., “movement disorders”) or of the disease such as reduced hygiene (e.g., diseases of the nail) validating the approach. It also highlighted phenotypes with less clear causal relationships and minimal genetic effect such as tobacco use disorder and diabetes. Conclusions This work demonstrates the consistency and robustness of EHR-based schizophrenia comorbidities across independent institutions and with the existing literature. It identifies known and novel comorbidities with an absence of shared genetic risk indicating other causes that might be modifiable and where further study of causal pathways could improve outcomes for patients.