Publication: Improving statistical power in severe malaria genetic association studies by augmenting phenotypic precision
Issued Date
2021-01-01
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ISSN
2050084X
Other identifier(s)
2-s2.0-85111149032
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Mahidol University
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SCOPUS
Bibliographic Citation
eLife. Vol.10, (2021)
Suggested Citation
James A. Watson, Carolyne M. Ndila, Sophie Uyoga, Alexander Macharia, Gideon Nyutu, Shebe Mohammed, Caroline Ngetsa, Neema Mturi, Norbert Peshu, Benjamin Tsofa, Kirk Rockett, Stije Leopold, Hugh Kingston, Elizabeth C. George, Kathryn Maitland, Nicholas P.J. Day, Arjen M. Dondorp, Philip Bejon, Thomas Williams, Chris C. Holmes, Nicholas J. White Improving statistical power in severe malaria genetic association studies by augmenting phenotypic precision. eLife. Vol.10, (2021). doi:10.7554/ELIFE.69698 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/76354
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Title
Improving statistical power in severe malaria genetic association studies by augmenting phenotypic precision
Author(s)
James A. Watson
Carolyne M. Ndila
Sophie Uyoga
Alexander Macharia
Gideon Nyutu
Shebe Mohammed
Caroline Ngetsa
Neema Mturi
Norbert Peshu
Benjamin Tsofa
Kirk Rockett
Stije Leopold
Hugh Kingston
Elizabeth C. George
Kathryn Maitland
Nicholas P.J. Day
Arjen M. Dondorp
Philip Bejon
Thomas Williams
Chris C. Holmes
Nicholas J. White
Carolyne M. Ndila
Sophie Uyoga
Alexander Macharia
Gideon Nyutu
Shebe Mohammed
Caroline Ngetsa
Neema Mturi
Norbert Peshu
Benjamin Tsofa
Kirk Rockett
Stije Leopold
Hugh Kingston
Elizabeth C. George
Kathryn Maitland
Nicholas P.J. Day
Arjen M. Dondorp
Philip Bejon
Thomas Williams
Chris C. Holmes
Nicholas J. White
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 are 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 2220 Kenyan children with clinically defined severe malaria and 3940 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.