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Genome Med. 2015 Jul 3;7(1):67. doi: 10.1186/s13073-015-0181-z. eCollection 2015.

Prospective participant selection and ranking to maximize actionable pharmacogenetic variants and discovery in the eMERGE Network.

Author information

1
Department of Medicine, Division of Medical Genetics, University of Washington, 1705 NE Pacific Street, Seattle, 98195 WA USA ; Department of Genome Sciences, University of Washington, 3720 15th Avenue NE, Seattle, 98195 WA USA.
2
Department of Genome Sciences, University of Washington, 3720 15th Avenue NE, Seattle, 98195 WA USA.
3
Group Health Research Institute, Group Health Cooperative, 1730 Minor Avenue, Seattle, 98101 WA USA.
4
Department of Pathology, University of Washington, 1959 NE Pacific Street, Seattle, 98195 WA USA.
5
Department of Medicine, Division of Medical Genetics, University of Washington, 1705 NE Pacific Street, Seattle, 98195 WA USA.
6
Department of Bioethics and Humanities, University of Washington, 1959 NE Pacific Street, Seattle, 98195 WA USA.
7
Division of Biomedical Statistics and Informatics, Mayo Clinic, 200 First Street SW, Rochester, 55905 MN USA.
8
Division of Cardiovascular Diseases, Mayo Clinic, 200 First Street SW, Rochester, 55905 MN USA.
9
The Sigfried and Janet Weis Center for Research, Geisinger Health System, 100 North Academy Avenue, Danville, 17882 PA USA.
10
Center for Inherited Disease Research, Johns Hopkins University School of Medicine, 333 Cassell Drive, Baltimore, 21224 MD USA.
11
Center for Systems Genomics, Department of Biochemistry and Molecular Biology, Pennsylvania State University, 512A Wartik Laboratory, University Park, 16802 PA USA ; Biomedical and Translational Informatics, Geisinger Health System, 100 North Academy Avenue, Danville, 17882 PA USA.
12
Division of General Internal Medicine, University of Washington, 325 Ninth Avenue, Seattle, 981014 WA USA.

Abstract

BACKGROUND:

In an effort to return actionable results from variant data to electronic health records (EHRs), participants in the Electronic Medical Records and Genomics (eMERGE) Network are being sequenced with the targeted Pharmacogenomics Research Network sequence platform (PGRNseq). This cost-effective, highly-scalable, and highly-accurate platform was created to explore rare variation in 84 key pharmacogenetic genes with strong drug phenotype associations.

METHODS:

To return Clinical Laboratory Improvement Amendments (CLIA) results to our participants at the Group Health Cooperative, we sequenced the DNA of 900 participants (61 % female) with non-CLIA biobanked samples. We then selected 450 of those to be re-consented, to redraw blood, and ultimately to validate CLIA variants in anticipation of returning the results to the participant and EHR. These 450 were selected using an algorithm we designed to harness data from self-reported race, diagnosis and procedure codes, medical notes, laboratory results, and variant-level bioinformatics to ensure selection of an informative sample. We annotated the multi-sample variant call format by a combination of SeattleSeq and SnpEff tools, with additional custom variables including evidence from ClinVar, OMIM, HGMD, and prior clinical associations.

RESULTS:

We focused our analyses on 27 actionable genes, largely driven by the Clinical Pharmacogenetics Implementation Consortium. We derived a ranking system based on the total number of coding variants per participant (75.2±14.7), and the number of coding variants with high or moderate impact (11.5±3.9). Notably, we identified 11 stop-gained (1 %) and 519 missense (20 %) variants out of a total of 1785 in these 27 genes. Finally, we prioritized variants to be returned to the EHR with prior clinical evidence of pathogenicity or annotated as stop-gain for the following genes: CACNA1S and RYR1 (malignant hyperthermia); SCN5A, KCNH2, and RYR2 (arrhythmia); and LDLR (high cholesterol).

CONCLUSIONS:

The incorporation of genetics into the EHR for clinical decision support is a complex undertaking for many reasons including lack of prior consent for return of results, lack of biospecimens collected in a CLIA environment, and EHR integration. Our study design accounts for these hurdles and is an example of a pilot system that can be utilized before expanding to an entire health system.

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