Association of Polygenic Risk Scores for Multiple Cancers in a Phenome-wide Study: Results from The Michigan Genomics Initiative

Abstract

Health systems are stewards of patient electronic health record (EHR) data with extraordinarily rich depth and breadth, reflecting thousands of diagnoses and exposures.Measures of genomic variation integrated with EHRs offer a potential strategy to accurately stratify patients for risk profiling and discover new relationships between diagnoses and genomes. The objective of this study was to evaluate whether Polygenic Risk Scores (PRS) for common cancers are associated with multiple phenotypes in a Phenome-wide Association Study (PheWAS) conducted in 28,260 unrelated, genotyped patients of recent European ancestry who consented to participate in the Michigan Genomics Initiative, a longitudinal biorepository effort within Michigan Medicine. PRS for 12 cancer traits were calculated using summary statistics from the NHGRI-EBI catalog.A total of 1,711 synthetic case-control studies was used for PheWAS analyses. There were 13,490 (47.7%) patients with at least one cancer diagnosis in this study sample.PRSs exhibited strong association for several cancer traits they were designed for including female breast cancer, prostate cancer, melanoma, basal cell carcinoma, squamous cell carcinoma and thyroid cancer. Phenome-wide significant associations were observed between PRS and many non-cancer diagnoses. To differentiate PRS associations driven by the primary trait from associations arising through shared genetic risk profiles, the idea of "exclusion PRS PheWAS" was introduced. This approach led to phenome-wide significant associations between a lower risk for hypothyroidism in patients with high thyroid cancer PRS and a higher risk for actinic keratosis in patients with high squamous cell carcinoma PRS after removing all cases of the primary cancer trait. Further analysis of temporal order of the diagnoses improved our understanding of . CC-BY-NC-ND 4.0 International license peer-reviewed) is the author/funder. It is made available under a

The emerging landscape of health research based on biobanks linked to electronic health records: Existing resources, statistical challenges, and potential opportunities

“…Compared to population‐based biobanks, academic medical center‐based biobanks tend to see more patients with rare or complicated diseases due to the availability of specialty care and, thus, are often useful for investigating rare conditions. For example, MGI is enriched for analyses of some cancer types, most notably melanoma of the skin, since Michigan Medicine is known for its skin cancer treatment and care. In all cases, the data generating mechanisms have the potential to induce selection and participation biases into the analysis.…”

“…Within pre‐existing biobanks, researchers seek to sample patients for inclusion in a particular study. A common study design involves phenotype‐specific case‐control sampling, where all observed cases for a particular phenotype are selected and some subset of (possibly matched) controls for that phenotype are sampled from the biobank . Cases are often defined as subjects receiving a particular diagnosis code a prespecified number of times, for example, twice.…”

“…The software KING (Kinship‐based Inference for GWAS) uses genotype data to determine pairwise kinship between patients . We might then define the study sample restricted to unrelated patients and apply methods that rely on independence between patients (eg, Firth‐corrected logistic regression in Fritsche et al). Statistical modeling approaches such as mixed modeling (eg, SAIGE) can also be used to account for residual correlations between individuals …”

“…Similarly, heuristic approaches have been suggested to identify a maximal independent set of uncorrelated phenotypes among pairwise correlations between pairs of phenotypes . A popular method for identifying phenotypes is to aggregate ICD codes into a set of phenotype codes called “phecodes.” For example, using 1578 phecodes in MGI, we identified a maximal set of 981 phenotypes with no pairwise Pearson correlation above 0.1.…”

“…In Section 3, we discuss general statistical issues related to EHR‐linked biobank research, including study design, sampling strategy, phenotype identification, and missing data. We illustrate some of these issues using data from two biobanks: the Michigan Genomics Initiative (MGI) and the UK Biobank (UKB) . In Section 4, we mention potential opportunities and promising future directions for expanded and principled biobank‐based research through a discussion of novel and emerging uses of EHR data, the creation of improved analytic infrastructure, and the integration of EHR with external data sources.…”