Improving statistical power in severe malaria genetic association studies by augmenting phenotypic precision
- Mahidol Oxford Tropical Medicine Research Unit, Thailand
- KEMRI-Wellcome Trust Research Programme, Kenya
- Kenya Medical Research Institute-Wellcome Trust Research Programme, Kenya
- Wellcome Trust Centre for Human Genetics, United Kingdom
- Medical Research Council Clinical Trials Unit, United Kingdom
- Mahidol University, Thailand
- Kilifi KEMRI-Wellcome Trust Collaborative Research Programme, Kenya
- Department of Statistics, University of Oxford, United Kingdom
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).
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A genome-wide study of resistance to severe malaria in 18,000 samples from eleven worldwide populations, including eight populations sub-Saharan Africa.European Genome-Phenome Archive, EGAD00010001742.
Article and author information
Author details
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.
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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Improving statistical power in severe malaria genetic association studies by augmenting phenotypic precision
eLife 10:e69698.
https://doi.org/10.7554/eLife.69698
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