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Pharmacogenomic genotypes define genetic ancestry in patients and enable population-specific genomic implementation

The Pharmacogenomics Journal volume 20, pages 126–135 (2020)Cite this article

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Abstract

The importance of genetic ancestry characterization is increasing in genomic implementation efforts, and clinical pharmacogenomic guidelines are being published that include population-specific recommendations. Our aim was to test the ability of focused clinical pharmacogenomic SNP panels to estimate individual genetic ancestry (IGA) and implement population-specific pharmacogenomic clinical decision-support (CDS) tools. Principle components and STRUCTURE were utilized to assess differences in genetic composition and estimate IGA among 1572 individuals from 1000 Genomes, two independent cohorts of Caucasians and African Americans (AAs), plus a real-world validation population of patients undergoing pharmacogenomic genotyping. We found that clinical pharmacogenomic SNP panels accurately estimate IGA compared to genome-wide genotyping and identify AAs with ≥70 African ancestry (sensitivity >82%, specificity >80%, PPV >95%, NPV >47%). We also validated a new AA-specific warfarin dosing algorithm for patients with ≥70% African ancestry and implemented it at our institution as a novel CDS tool. Consideration of IGA to develop an institutional CDS tool was accomplished to enable population-specific pharmacogenomic guidance at the point-of-care. These capabilities were immediately applied for guidance of warfarin dosing in AAs versus Caucasians, but also provide a real-world model that can be extended to other populations and drugs as actionable genomic evidence accumulates.

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Acknowledgements

This research was supported by an NIH/National Heart, Lung, and Blood Institute/Ruth L. Kirschstein National Research Service Award (NRSA) Individual Postdoctoral Fellowship 1F32HL123311-01A1 (WH), KL2 TR 002387 (WH); NIH K23 GM 100288-01A1 (PHO), NIH/National Heart, Lung, and Blood Institute grant 5 U01 HL105198-09 (PHO and MJR); NIH U54 MD010723 (DOM and MAP); The William F. O’Connor Foundation, and The University of Chicago Comprehensive Cancer Center support grant. We would also like to thank Andrew Skol, Ph.D. for his invaluable guidance in data analysis.

Author information

Author notes

  1. Edward Leung

    Present address: University of Southern California, Keck School of Medicine, Department of Pathology and Laboratory Medicine, Los Angeles, CA, USA

  2. These authors contributed equally: Minoli A. Perera, Peter H. O’Donnell

Authors and Affiliations

  1. University of Chicago, Department of Medicine, Section of Genetic Medicine, Section of Cardiology, Chicago, IL, USA

    Wenndy Hernandez & Barbara E. Stranger

  2. University of Chicago, Center for Personalized Therapeutics, Chicago, IL, USA

    Keith Danahey, Xun Pei, Mark J. Ratain & Peter H. O’Donnell

  3. University of Chicago, Center for Research Informatics, Chicago, IL, USA

    Keith Danahey & Samuel L. Volchenboum

  4. University of Chicago, Department of Pathology, UChicago Advanced Technology Clinical Pharmacogenomics Laboratory, Chicago, IL, USA

    Xun Pei, Kiang-Teck J. Yeo & Edward Leung

  5. University of Chicago, Department of Medicine, Chicago, IL, USA

    Mark J. Ratain, David O. Meltzer & Peter H. O’Donnell

  6. University of Chicago, Committee on Clinical Pharmacology and Pharmacogenomics, Chicago, IL, USA

    Mark J. Ratain & Peter H. O’Donnell

  7. University of Chicago, Institute of Genomics and Systems Biology, and Center for Data Intensive Science, Chicago, IL, USA

    Barbara E. Stranger

  8. Northwestern University, Department of Pharmacology, Chicago, IL, USA

    Minoli A. Perera

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Contributions

WH and PHO wrote the manuscript; WH, KD, XP, KJY, EL, MAP, and PHO collected data; WH, SLV, MJR, DOM, BES, MAP, and PHO designed research; WH, KD, BES, MAP, and PHO interpreted data; WH, KD, MAP, and PHO performed research and analyzed data.

Corresponding author

Correspondence to Peter H. O’Donnell.

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Conflict of interest

KD, MJR, and PHO are co-inventors on a pending patent application for a Genomic Prescribing System. MJR receives royalties related to UGT1A1 genotyping, but no royalties were received from the genotyping performed in this work. The other authors declare that they have no conflict of interest.

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Hernandez, W., Danahey, K., Pei, X. et al. Pharmacogenomic genotypes define genetic ancestry in patients and enable population-specific genomic implementation. Pharmacogenomics J 20, 126–135 (2020). https://doi.org/10.1038/s41397-019-0095-z

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