1. Epidemiology and Global Health
  2. Genetics and Genomics
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Improving statistical power in severe malaria genetic association studies by augmenting phenotypic precision

  1. James A Watson  Is a corresponding author
  2. Carolyne M Ndila
  3. Sophie Uyoga
  4. Alexander Macharia
  5. Gideon Nyutu
  6. Shebe Mohammed
  7. Caroline Ngetsa
  8. Neema Mturi
  9. Norbert Peshu
  10. Benjamin Tsofa
  11. Kirk Rockett
  12. Stije Leopold
  13. Hugh Kingston
  14. Elizabeth C George
  15. Kathryn Maitland
  16. Nicholas PJ Day
  17. Arjen M Dondorp
  18. Philip Bejon
  19. Thomas Williams
  20. Chris C Holmes
  21. Nicholas J White
  1. Mahidol Oxford Tropical Medicine Research Unit, Thailand
  2. KEMRI-Wellcome Trust Research Programme, Kenya
  3. Kenya Medical Research Institute-Wellcome Trust Research Programme, Kenya
  4. Wellcome Trust Centre for Human Genetics, United Kingdom
  5. Medical Research Council Clinical Trials Unit, United Kingdom
  6. Mahidol University, Thailand
  7. Kilifi KEMRI-Wellcome Trust Collaborative Research Programme, Kenya
  8. Department of Statistics, University of Oxford, United Kingdom
Research Article
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Cite this article as: eLife 2021;10:e69698 doi: 10.7554/eLife.69698

Abstract

Severe falciparum malaria has substantially affected human evolution. Genetic association studies of patients with clinically defined severe malaria and matched population controls have helped characterise human genetic susceptibility to severe malaria, but phenotypic imprecision compromises discovered associations. In areas of high malaria transmission the diagnosis of severe malaria in young children and, in particular, the distinction from bacterial sepsis, is imprecise. We developed a probabilistic diagnostic model of severe malaria using platelet and white count data. Under this model we re-analysed clinical and genetic data from 2,220 Kenyan children with clinically defined severe malaria and 3,940 population controls, adjusting for phenotype mis-labelling. Our model, validated by the distribution of sickle trait, estimated that approximately one third of cases did not have severe malaria. We propose a data-tilting approach for case-control studies with phenotype mis-labelling and show that this reduces false discovery rates and improves statistical power in genome-wide association studies.

Data availability

A curated minimal clinical dataset is currently available alongisde the code on the github repository. This will also be made available at publication via the KEMRI-Wellcome Harvard Dataverse(https://dataverse.harvard.edu/dataverse/kwtrp).Whole genome data are available from European Genome-Phenome Archive (dataset accession ID: EGAD00010001742).Requests for access to appropriately anonymized clinical data and directly typed genetic variants for the Kenyan severe malaria cohort can be made by application to the data access committee at the KEMRI-Wellcome Trust Research Programme by e-mail to mmunene@kemri-wellcome.org.The FEAST trial datasets are available from the principal investigator on reasonable request (k.maitland@imperial.ac.uk).Requests for access to appropriately anonymized clinical data from the AQ and AAV Vietnam study and the Asian paediatric cohort can be made via the Mahidol Oxford Tropical Medicine Research Unit data access committee by emailing the corresponding author JAW (jwatowatson@gmail.com) or Rita Chanviriyavuth (rita@tropmedres.ac).

The following previously published data sets were used

Article and author information

Author details

  1. James A Watson

    Malaria, Mahidol Oxford Tropical Medicine Research Unit, Bangkok, Thailand
    For correspondence
    jwatowatson@gmail.com
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5524-0325
  2. Carolyne M Ndila

    KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya
    Competing interests
    The authors declare that no competing interests exist.
  3. Sophie Uyoga

    Kenya Medical Research Institute-Wellcome Trust Research Programme, Kilifi, Kenya
    Competing interests
    The authors declare that no competing interests exist.
  4. Alexander Macharia

    Kenya Medical Research Institute-Wellcome Trust Research Programme, Kilifi, Kenya
    Competing interests
    The authors declare that no competing interests exist.
  5. Gideon Nyutu

    KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya
    Competing interests
    The authors declare that no competing interests exist.
  6. Shebe Mohammed

    KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya
    Competing interests
    The authors declare that no competing interests exist.
  7. Caroline Ngetsa

    KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya
    Competing interests
    The authors declare that no competing interests exist.
  8. Neema Mturi

    Kenya Medical Research Institute-Wellcome Trust Research Programme, Kilifi, Kenya
    Competing interests
    The authors declare that no competing interests exist.
  9. Norbert Peshu

    Kenya Medical Research Institute-Wellcome Trust Research Programme, Kilifi, Kenya
    Competing interests
    The authors declare that no competing interests exist.
  10. Benjamin Tsofa

    KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya
    Competing interests
    The authors declare that no competing interests exist.
  11. Kirk Rockett

    Wellcome Trust Centre for Human Genetics, Oxford, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  12. Stije Leopold

    Malaria, Mahidol Oxford Tropical Medicine Research Unit, Bangkok, Thailand
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0482-5689
  13. Hugh Kingston

    Malaria, Mahidol Oxford Tropical Medicine Research Unit, Bangkok, Thailand
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1869-8307
  14. Elizabeth C George

    Medical Research Council Clinical Trials Unit, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  15. Kathryn Maitland

    Kenya Medical Research Institute-Wellcome Trust Research Programme, Kilifi, Kenya
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0007-0645
  16. Nicholas PJ Day

    Mahidol Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2309-1171
  17. Arjen M Dondorp

    Mahidol Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5190-2395
  18. Philip Bejon

    Pathogen Vector Host Biology, Kilifi KEMRI-Wellcome Trust Collaborative Research Programme, Kilifi, Kenya
    Competing interests
    The authors declare that no competing interests exist.
  19. Thomas Williams

    Kenya Medical Research Institute-Wellcome Trust Research Programme, Kilifi, Kenya
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4456-2382
  20. Chris C Holmes

    Department of Statistics, University of Oxford, Oxford, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  21. Nicholas J White

    Malaria, Mahidol Oxford Tropical Medicine Research Unit, Bangkok, Thailand
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1897-1978

Funding

Wellcome Trust (209265/Z/17/Z)

  • Kathryn Maitland
  • Nicholas PJ Day

Wellcome Trust (202800/Z/16/Z)

  • Thomas Williams

Wellcome Trust (093956/Z/10/C)

  • Nicholas J White

Medical Research Council (MC\UU\12023/26)

  • Elizabeth C George

Wellcome Trust (WT077383/Z/05/Z)

  • Kirk Rockett

Medical Research Council (G0801439)

  • Elizabeth C George
  • Kathryn Maitland

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Human subjects: All clinical data are from published studies in which all participants or guardians gave fully informed consent. Access to the human genetic data was approved by the MalariaGen data access committee.

Reviewing Editor

  1. Jennifer Flegg, The University of Melbourne, Australia

Publication history

  1. Received: April 22, 2021
  2. Accepted: June 22, 2021
  3. Accepted Manuscript published: July 6, 2021 (version 1)
  4. Version of Record published: July 27, 2021 (version 2)
  5. Version of Record updated: November 22, 2021 (version 3)

Copyright

© 2021, Watson et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Further reading

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    Background: Lipid metabolism plays an important role in viral infections. We aimed to assess the causal effect of lipid-lowering drugs (HMGCR inhibitiors, PCSK9 inhibitiors and NPC1L1 inhibitior) on COVID-19 outcomes using 2-sample Mendelian Randomization (MR) study.

    Methods: We used two kinds of genetic instruments to proxy the exposure of lipid-lowering drugs, including eQTLs of drugs target genes, and genetic variants within or nearby drugs target genes associated with LDL cholesterol from GWAS. Summary-data-based MR (SMR) and inverse-variance weighted MR (IVW-MR) were used to calculate the effect estimates.

    Results: SMR analysis found that a higher expression of HMGCR was associated with a higher risk of COVID-19 hospitalization (OR=1.38, 95%CI=1.06-1.81). Similarly, IVW-MR analysis observed a positive association between HMGCR-mediated LDL cholesterol and COVID-19 hospitalization (OR=1.32, 95%CI=1.00-1.74). No consistent evidence from both analyses was found for other associations.

    Conclusions: This 2-sample MR study suggested a potential causal relationship between HMGCR inhibition and the reduced risk of COVID-19 hospitalization.

    Funding: Fujian Province Major Science and Technology Program.

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