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

David R. Crosslin, Peggy D. Robertson, David S. Carrell, Adam S. Gordon, David S. Hanna, Amber Burt, Stephanie M. Fullerton, Aaron Scrol, James Ralston, Kathleen Leppig, Andrea Hartzler, Eric Baldwin, Mariza De Andrade, Iftikhar Jan Kullo, Gerard Tromp, Kimberly F. Doheny, Marylyn D. Ritchie, Paul K. Crane, Deborah A. Nickerson, Eric B. LarsonGail P. Jarvik

Research output: Contribution to journalArticle

6 Citations (Scopus)

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.

Original languageEnglish (US)
Article number67
JournalGenome Medicine
Volume7
Issue number1
DOIs
StatePublished - Jul 3 2015

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Electronic Health Records
Genomics
Pharmacogenetics
Genes
Clinical Decision Support Systems
Genetic Databases
Malignant Hyperthermia
Health
Computational Biology
Virulence
Pharmacogenomic Variants
Cardiac Arrhythmias
Cholesterol
Phenotype
Costs and Cost Analysis
DNA
Research
Pharmaceutical Preparations

ASJC Scopus subject areas

  • Genetics(clinical)
  • Genetics
  • Molecular Biology
  • Molecular Medicine

Cite this

Crosslin, D. R., Robertson, P. D., Carrell, D. S., Gordon, A. S., Hanna, D. S., Burt, A., ... Jarvik, G. P. (2015). Prospective participant selection and ranking to maximize actionable pharmacogenetic variants and discovery in the eMERGE Network. Genome Medicine, 7(1), [67]. https://doi.org/10.1186/s13073-015-0181-z

Prospective participant selection and ranking to maximize actionable pharmacogenetic variants and discovery in the eMERGE Network. / Crosslin, David R.; Robertson, Peggy D.; Carrell, David S.; Gordon, Adam S.; Hanna, David S.; Burt, Amber; Fullerton, Stephanie M.; Scrol, Aaron; Ralston, James; Leppig, Kathleen; Hartzler, Andrea; Baldwin, Eric; De Andrade, Mariza; Kullo, Iftikhar Jan; Tromp, Gerard; Doheny, Kimberly F.; Ritchie, Marylyn D.; Crane, Paul K.; Nickerson, Deborah A.; Larson, Eric B.; Jarvik, Gail P.

In: Genome Medicine, Vol. 7, No. 1, 67, 03.07.2015.

Research output: Contribution to journalArticle

Crosslin, DR, Robertson, PD, Carrell, DS, Gordon, AS, Hanna, DS, Burt, A, Fullerton, SM, Scrol, A, Ralston, J, Leppig, K, Hartzler, A, Baldwin, E, De Andrade, M, Kullo, IJ, Tromp, G, Doheny, KF, Ritchie, MD, Crane, PK, Nickerson, DA, Larson, EB & Jarvik, GP 2015, 'Prospective participant selection and ranking to maximize actionable pharmacogenetic variants and discovery in the eMERGE Network', Genome Medicine, vol. 7, no. 1, 67. https://doi.org/10.1186/s13073-015-0181-z
Crosslin, David R. ; Robertson, Peggy D. ; Carrell, David S. ; Gordon, Adam S. ; Hanna, David S. ; Burt, Amber ; Fullerton, Stephanie M. ; Scrol, Aaron ; Ralston, James ; Leppig, Kathleen ; Hartzler, Andrea ; Baldwin, Eric ; De Andrade, Mariza ; Kullo, Iftikhar Jan ; Tromp, Gerard ; Doheny, Kimberly F. ; Ritchie, Marylyn D. ; Crane, Paul K. ; Nickerson, Deborah A. ; Larson, Eric B. ; Jarvik, Gail P. / Prospective participant selection and ranking to maximize actionable pharmacogenetic variants and discovery in the eMERGE Network. In: Genome Medicine. 2015 ; Vol. 7, No. 1.
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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.",
author = "Crosslin, {David R.} and Robertson, {Peggy D.} and Carrell, {David S.} and Gordon, {Adam S.} and Hanna, {David S.} and Amber Burt and Fullerton, {Stephanie M.} and Aaron Scrol and James Ralston and Kathleen Leppig and Andrea Hartzler and Eric Baldwin and {De Andrade}, Mariza and Kullo, {Iftikhar Jan} and Gerard Tromp and Doheny, {Kimberly F.} and Ritchie, {Marylyn D.} and Crane, {Paul K.} and Nickerson, {Deborah A.} and Larson, {Eric B.} and Jarvik, {Gail P.}",
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T1 - Prospective participant selection and ranking to maximize actionable pharmacogenetic variants and discovery in the eMERGE Network

AU - Crosslin, David R.

AU - Robertson, Peggy D.

AU - Carrell, David S.

AU - Gordon, Adam S.

AU - Hanna, David S.

AU - Burt, Amber

AU - Fullerton, Stephanie M.

AU - Scrol, Aaron

AU - Ralston, James

AU - Leppig, Kathleen

AU - Hartzler, Andrea

AU - Baldwin, Eric

AU - De Andrade, Mariza

AU - Kullo, Iftikhar Jan

AU - Tromp, Gerard

AU - Doheny, Kimberly F.

AU - Ritchie, Marylyn D.

AU - Crane, Paul K.

AU - Nickerson, Deborah A.

AU - Larson, Eric B.

AU - Jarvik, Gail P.

PY - 2015/7/3

Y1 - 2015/7/3

N2 - 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.

AB - 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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