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Whole genome sequencing identifies high-impact variants in well-known pharmacogenomic genes

The Pharmacogenomics Journal volume 19, pages 127–135 (2019)Cite this article

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Abstract

More than 1100 genetic loci have been correlated with drug response outcomes but disproportionately few have been translated into clinical practice. One explanation for the low rate of clinical implementation is that the majority of associated variants may be in linkage disequilibrium (LD) with the causal variants, which are often elusive. This study aims to identify and characterize likely causal variants within well-established pharmacogenomic genes using next-generation sequencing data from the 1000 Genomes Project. We identified 69,319 genetic variations within 160 pharmacogenomic genes, of which 8207 variants are in strong LD (r2>0.8) with known pharmacogenomic variants. Of the latter, eight are coding or structural variants predicted to have high impact, with 19 additional missense variants that are predicted to have moderate impact. In conclusion, we identified putatively functional variants within known pharmacogenomics loci that could account for the association signals and represent the missing causative variants underlying drug response phenotypes.

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Acknowledgements

We would like to thank Dr. Alan R. Shuldiner and support from the Translational Pharmacogenomics Project. This manuscript was supported by NIH grants U01 HL65899, U01 HL105198, and K99 HL116651. Computations were performed on resources and with support provided by the Centre for Advanced Computing (CAC) at Queen’s University in Kingston, Ontario. The CAC is funded by the Canada Foundation for Innovation, the Government of Ontario, and Queen’s University. QLD receives funding from the Canadian Institutes of Health Research and Queen’s University.

Author contribution

All authors contributed to the writing of the manuscript. JC performed the data analyses and drafted the manuscript. QLD supervised data analyses and assisted in the writing of the manuscript. QLD and KGT designed the research project.

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Author notes

  1. These authors contributed equally: Kelan G. Tantisira, Qing Ling Duan

Authors and Affiliations

  1. Department of Biomedical and Molecular Sciences, Queen’s University, Kingston, ON, Canada

    Jihoon Choi & Qing Ling Duan

  2. School of Computing, Queen’s University, Kingston, ON, Canada

    Jihoon Choi & Qing Ling Duan

  3. Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA

    Kelan G. Tantisira

  4. Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA

    Kelan G. Tantisira

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  1. Jihoon Choi
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Correspondence to Qing Ling Duan.

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Choi, J., Tantisira, K.G. & Duan, Q.L. Whole genome sequencing identifies high-impact variants in well-known pharmacogenomic genes. Pharmacogenomics J 19, 127–135 (2019). https://doi.org/10.1038/s41397-018-0048-y

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