Exploring various polygenic risk scores for skin cancer in the phenomes of the Michigan genomics initiative and the UK Biobank with a visual catalog: PRSWeb.

Polygenic risk scores (PRS) are designed to serve as single summary measures that are easy to construct, condensing information from a large number of genetic variants associated with a disease. They have been used for stratification and prediction of disease risk. The primary focus of this paper is to demonstrate how we can combine PRS and electronic health records data to better understand the shared and unique genetic architecture and etiology of disease subtypes that may be both related and heterogeneous. PRS construction strategies often depend on the purpose of the study, the available data/summary estimates, and the underlying genetic architecture of a disease. We consider several choices for constructing a PRS using data obtained from various publicly-available sources including the UK Biobank and evaluate their abilities to predict not just the primary phenotype but also secondary phenotypes derived from electronic health records (EHR). This study was conducted using data from 30,702 unrelated, genotyped patients of recent European descent from the Michigan Genomics Initiative (MGI), a longitudinal biorepository effort within Michigan Medicine. We examine the three most common skin cancer subtypes in the USA: basal cell carcinoma, cutaneous squamous cell carcinoma, and melanoma. Using these PRS for various skin cancer subtypes, we conduct a phenome-wide association study (PheWAS) within the MGI data to evaluate PRS associations with secondary traits. PheWAS results are then replicated using population-based UK Biobank data and compared across various PRS construction methods. We develop an accompanying visual catalog called PRSweb that provides detailed PheWAS results and allows users to directly compare different PRS construction methods. Author summary In the study of genetically complex diseases, polygenic risk scores (PRS) synthesize information from multiple genetic risk factors to provide insight into a patient's inherited risk of developing a disease based on his/her genetic profile. These risk scores can be explored in conjunction with health and disease information available in electronic medical records. PRS may be associated with diseases that may be related to or precursors of the underlying disease of interest. In this paper, we demonstrate how PRS can be used in concert with the medical phenome to better understand the etiology of disease subtypes nested within a broad disease classification. This is done by examining the shared and distinct genetic risk factors across the related but heterogeneous disease subtypes and also through our comparison of the secondary associations across the phenome corresponding to the subtype specific PRS. We consider several PRS construction methods in our study. This framework of analysis is enabled by access to electronic health records and genetics data. Leveraging and harnessing the rich data resources of the Michigan Genomics Initiative, a biorepository effort at Michigan Medicine, and the large population-based UK Biobank study, we investigated the primary and secondary disease associations with PRS constructed for the three most common types of skin cancer: melanoma, basal cell carcinoma and cutaneous squamous cell carcinoma.