Building a vertically integrated genomic learning health system: The biobank at the Colorado Center for Personalized Medicine

Abstract

Precision medicine initiatives across the globe have led to a revolution of repositories linking large-scale genomic data with electronic health records, enabling genomic analyses across the entire phenome. Many of these initiatives focus solely on research insights, leading to limited direct benefit to patients. We describe the biobank at the Colorado Center for Personalized Medicine (CCPM Biobank) that was jointly developed by the University of Colorado Anschutz Medical Campus and UCHealth to serve as a unique, dual-purpose research and clinical resource accelerating personalized medicine. This living resource currently has more than 200,000 participants with ongoing recruitment. We highlight the clinical, laboratory, regulatory, and HIPAA-compliant informatics infrastructure along with our stakeholder engagement, consent, recontact, and participant engagement strategies. We characterize aspects of genetic and geographic diversity unique to the Rocky Mountain region, the primary catchment area for CCPM Biobank participants. We leverage linked health and demographic information of the CCPM Biobank participant population to demonstrate the utility of the CCPM Biobank to replicate complex trait associations in the first 33,674 genotyped individuals across multiple disease domains. Finally, we describe our current efforts toward return of clinical genetic test results, including high-impact pathogenic variants and pharmacogenetic information, and our broader goals as the CCPM Biobank continues to grow. Bringing clinical and research interests together fosters unique clinical and translational questions that can be addressed from the large EHR-linked CCPM Biobank resource within a HIPAA- and CLIA-certified environment.

Keywords: biobanking; electronic health records; learning health system; pharmacogenomics; precision medicine.

Figure 1

Cumulative enrollment and sample collection of CCPM Biobank participants over time Enrollment shown in blue and sample collection in red.

Figure 2

Density of recruitment of CCPM Biobank participants Density across (A) the USA and (B) Colorado shown, at the level of the first three digits of zip codes.

Figure 3

CCPM Biobank participant proportional phecode use by domain and specific phecode as derived from the EHR across participants in the entire CCPM Biobank Domain shown at left and phecode at right.

Figure 4

Ancestry and population structure estimates in the CCPM Biobank (A) Counts of closely related pairs of individuals as determined by PONDEROSA. (B) Principal components analysis of CCPM Biobank participants (gray points) overlaid with individuals from a global reference panel (colored points). (C) Admixture estimates for CCPM Biobank participants from the five largest EHR race/ethnicity categories. 1000 Genomes and Human Genome Diversity Project (HGDP) data are provided for reference.

Figure 5

Replication of known associations in the CCPM Biobank across a range of traits, comparing CCPM Biobank findings with REGENIE to those found in the GWAS catalog. Error bars represent the 95% confidence interval for the odds ratio For all, the risk-increasing allele is compared, and with multiple reporters, the largest dataset in the GWAS catalog was used for reference. OMIM identifiers for nearest genes are as follows: APOE (MIM: 107741), SMAD3 (MIM: 603109), TERT (MIM: 187270), HCG22 (MIM: 613918), PTCSC2 (MIM: N/A), HLA-DRA (MIM: 142860), FTO (MIM: 610966), HCP5 (MIM: 604676), HLA-DRB1 (MIM: 142857), HLA-DQB1 (MIM: 604305), and TCF7L2 (MIM: 602228).