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Polygenic risk of psychiatric disorders exhibits cross-trait associations in electronic health record data

By Rachel L Kember, Alison K Merikangas, Shefali S. Verma, Anurag Verma, Renae Judy, Regeneron Genetics Center, Scott M. Damrauer, Marylyn D. Ritchie, Daniel J Rader, Maja Bućan

Posted 29 Nov 2019
bioRxiv DOI: 10.1101/858027

Objective: Prediction of disease risk is a key component of precision medicine. Common, complex traits such as psychiatric disorders have a complex polygenic architecture making the identification of a single risk predictor difficult. Polygenic risk scores (PRS) denoting the sum of an individual's genetic liability for a disorder are a promising biomarker for psychiatric disorders, but require evaluation in a clinical setting. Methods: We develop PRS for six psychiatric disorders (schizophrenia, bipolar disorder, major depressive disorder, cross disorder, attention-deficit/hyperactivity disorder, anorexia nervosa) and 17 non-psychiatric traits in over 10,000 individuals from the Penn Medicine Biobank with accompanying electronic health records. We perform phenome-wide association analyses to test their association across disease categories. Results: Four of the six psychiatric PRS were associated with their primary phenotypes (odds ratios between 1.2-1.6). Individuals in the highest quintile of risk had between 1.4-2.9 times higher odds of the disorder than the remaining 80% of individuals. Cross-trait associations were identified both within the psychiatric domain and across trait domains. PRS for coronary artery disease and years of education were significantly associated with psychiatric disorders, largely driven by an association with tobacco use disorder. Conclusions: We demonstrate that the genetic architecture of common psychiatric disorders identified in a clinical setting confirms that which has been derived from large consortia. Even though the risk associated is low in this context, these results suggest that as identification of genetic markers proceeds, PRS is a promising approach for prediction of psychiatric disorders and associated conditions in clinical registries.

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