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. PRS 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. Further analysis of temporal order of the diagnoses improved our understanding of these secondary associations. This comprehensive PheWAS used PRS instead of a single variant.

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

“…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. 10,87 Cases are often defined as subjects receiving a particular diagnosis code a prespecified number of times, for example, twice. An advantage of case-control sampling is that it does not require additional longitudinal information and instead relies on dichotomized phenotypes, but it is heavily dependent on the "case" and "control" definitions.…”

“…The software KING (Kinship-based Inference for GWAS) uses genotype data to determine pairwise kinship between patients. 107 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 10 ). Statistical modeling approaches such as mixed modeling (eg, SAIGE) can also be used to account for residual correlations between individuals.…”

“…129 Similarly, heuristic approaches have been suggested to identify a maximal independent set of uncorrelated phenotypes among pairwise correlations between pairs of phenotypes. 10,177 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) 10,11 and the UK Biobank (UKB). 12,13 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.…”

Meta‐MultiSKAT: Multiple phenotype meta‐analysis for region‐based association test

“…We applied Meta-MultiSKAT to meta-analyze four white blood cell (WBC) subtype traits from the Michigan Genomics Initiative (MGI; Fritsche et al, 2018) study and the SardiNIA study (Sidore et al, 2015;Vacca et al, 2006). In addition to detecting the genes PRG2 [MIM: 605601] and RP11-872D17.8, that had significant association signals with WBCs within one of the studies, Meta-MultiSKAT further identified two additionally associated genes (IRF8 [MIM: 601565] and CCL24 [MIM: 602495]) that did not have any significant signals in either of the studies but were identified as significant only as a result of meta-analysis.…”