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Validation of lipid-related therapeutic targets for coronary heart disease prevention using human genetics

Author

Listed:
  • María Gordillo-Marañón

    (University College London)

  • Magdalena Zwierzyna

    (University College London
    UCL British Heart Foundation Research Accelerator)

  • Pimphen Charoen

    (University College London
    Mahidol University
    Mahidol University)

  • Fotios Drenos

    (University College London
    Brunel University London)

  • Sandesh Chopade

    (University College London
    UCL British Heart Foundation Research Accelerator)

  • Tina Shah

    (University College London
    UCL British Heart Foundation Research Accelerator)

  • Jorgen Engmann

    (University College London
    UCL British Heart Foundation Research Accelerator)

  • Nishi Chaturvedi

    (University College London
    University College London)

  • Olia Papacosta

    (University College London)

  • Goya Wannamethee

    (University College London)

  • Andrew Wong

    (University College London)

  • Reecha Sofat

    (University College London)

  • Mika Kivimaki

    (University College London)

  • Jackie F. Price

    (University of Edinburgh)

  • Alun D. Hughes

    (University College London
    UCL British Heart Foundation Research Accelerator
    University College London)

  • Tom R. Gaunt

    (MRC Integrative Epidemiology Unit at the University of Bristol
    University of Bristol
    University Hospitals Bristol National Health Service Foundation Trust and University of Bristol)

  • Deborah A. Lawlor

    (MRC Integrative Epidemiology Unit at the University of Bristol
    University of Bristol
    University Hospitals Bristol National Health Service Foundation Trust and University of Bristol)

  • Anna Gaulton

    (Wellcome Genome Campus, Hinxton)

  • Aroon D. Hingorani

    (University College London
    UCL British Heart Foundation Research Accelerator)

  • Amand F. Schmidt

    (University College London
    UCL British Heart Foundation Research Accelerator
    University Medical Center Utrecht, Heidelberglaan 100)

  • Chris Finan

    (University College London
    UCL British Heart Foundation Research Accelerator
    University Medical Center Utrecht, Heidelberglaan 100)

Abstract

Drug target Mendelian randomization (MR) studies use DNA sequence variants in or near a gene encoding a drug target, that alter the target’s expression or function, as a tool to anticipate the effect of drug action on the same target. Here we apply MR to prioritize drug targets for their causal relevance for coronary heart disease (CHD). The targets are further prioritized using independent replication, co-localization, protein expression profiles and data from the British National Formulary and clinicaltrials.gov. Out of the 341 drug targets identified through their association with blood lipids (HDL-C, LDL-C and triglycerides), we robustly prioritize 30 targets that might elicit beneficial effects in the prevention or treatment of CHD, including NPC1L1 and PCSK9, the targets of drugs used in CHD prevention. We discuss how this approach can be generalized to other targets, disease biomarkers and endpoints to help prioritize and validate targets during the drug development process.

Suggested Citation

  • María Gordillo-Marañón & Magdalena Zwierzyna & Pimphen Charoen & Fotios Drenos & Sandesh Chopade & Tina Shah & Jorgen Engmann & Nishi Chaturvedi & Olia Papacosta & Goya Wannamethee & Andrew Wong & Ree, 2021. "Validation of lipid-related therapeutic targets for coronary heart disease prevention using human genetics," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-25731-z
    DOI: 10.1038/s41467-021-25731-z
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    References listed on IDEAS

    as
    1. Claudia Giambartolomei & Damjan Vukcevic & Eric E Schadt & Lude Franke & Aroon D Hingorani & Chris Wallace & Vincent Plagnol, 2014. "Bayesian Test for Colocalisation between Pairs of Genetic Association Studies Using Summary Statistics," PLOS Genetics, Public Library of Science, vol. 10(5), pages 1-15, May.
    2. Frank Dudbridge, 2013. "Power and Predictive Accuracy of Polygenic Risk Scores," PLOS Genetics, Public Library of Science, vol. 9(3), pages 1-17, March.
    3. Amand F. Schmidt & Chris Finan & Maria Gordillo-Marañón & Folkert W. Asselbergs & Daniel F. Freitag & Riyaz S. Patel & Benoît Tyl & Sandesh Chopade & Rupert Faraway & Magdalena Zwierzyna & Aroon D. Hi, 2020. "Genetic drug target validation using Mendelian randomisation," Nature Communications, Nature, vol. 11(1), pages 1-12, December.
    4. Johannes Kettunen & Ayşe Demirkan & Peter Würtz & Harmen H.M. Draisma & Toomas Haller & Rajesh Rawal & Anika Vaarhorst & Antti J. Kangas & Leo-Pekka Lyytikäinen & Matti Pirinen & René Pool & Antti-Pek, 2016. "Genome-wide study for circulating metabolites identifies 62 loci and reveals novel systemic effects of LPA," Nature Communications, Nature, vol. 7(1), pages 1-9, September.
    5. Robin Andersson & Claudia Gebhard & Irene Miguel-Escalada & Ilka Hoof & Jette Bornholdt & Mette Boyd & Yun Chen & Xiaobei Zhao & Christian Schmidl & Takahiro Suzuki & Evgenia Ntini & Erik Arner & Eivi, 2014. "An atlas of active enhancers across human cell types and tissues," Nature, Nature, vol. 507(7493), pages 455-461, March.
    6. John D. Storey, 2002. "A direct approach to false discovery rates," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(3), pages 479-498, August.
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