Abstract
Original language | English |
---|---|
Article number | e69698 |
Journal | eLife |
Volume | 10 |
DOIs | |
Publication status | Published - 2021 |
Externally published | Yes |
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In: eLife, Vol. 10, e69698, 2021.
Research output: Contribution to journal › Article › Academic › peer-review
TY - JOUR
T1 - Improving statistical power in severe malaria genetic association studies by augmenting phenotypic precision
AU - Watson, James A.
AU - Ndila, Carolyne M.
AU - Uyoga, Sophie
AU - Macharia, Alexander
AU - Nyutu, Gideon
AU - Mohammed, Shebe
AU - Ngetsa, Caroline
AU - Mturi, Neema
AU - Peshu, Norbert
AU - Tsofa, Benjamin
AU - Rockett, Kirk
AU - Leopold, Stije
AU - Kingston, Hugh
AU - George, Elizabeth C.
AU - Maitland, Kathryn
AU - Day, Nicholas P. J.
AU - Dondorp, Arjen M.
AU - Bejon, Philip
AU - Williams, Thomas
AU - Holmes, Chris C.
AU - White, Nicholas J.
N1 - Funding Information: The human data used in this study was generated through the Malaria Genomic Epidemiology Network (https://www.MalariaGEN.net) Consortial Project 1, for which a full list of Consortium members is provided at https://www.malariagen.net/projects/consortial-project-1/malariagen-consortium-members. The Malaria Genomic Epidemiology Network study of severe malaria was supported by Wellcome (WT077383/Z/05/Z) and the Bill and Melinda Gates Foundation (https://www.gate-sfoundation.org/) through the Foundations of the National Institutes of Health (https://fnih.org/) as part of the Grand Challenges in Global Health Initiative. The Resource Centre for Genomic Epidemiology of Malaria is supported by Wellcome (090770/Z/09/Z; 204911/Z/16/Z). This research was supported by the Medical Research Council (G0600718; G0600230; MR/M006212/1). Wellcome also provides core awards to the Wellcome Centre for Human Genetics (203141/Z/16/Z) and the Wellcome Sanger Institute (206194). Funding Information: This research was funded by The Wellcome Trust. A CC BY or equivalent licence is applied to the author accepted manuscript arising from this submission, in accordance with the grant?s open access conditions. This work was done as part of SMAART (Severe Malaria Africa ? A consortium for Research and Trials) funded by a Wellcome Collaborative Award in Science grant (209265/Z/17/Z) held in part by KM, NPJD and AD. TNW and NJW are senior and principal research fellows respectively funded by the Wellcome Trust (202800/Z/16/Z and 093956/Z/10/C, respectively). ECG acknowledges funding from a core grant to the MRC CTU at UCL from the MRC (MC_UU_12023/ 26). The human data used in this study was generated through the Malaria Genomic Epidemiology Network (https://www.MalariaGEN.net) Consortial Project 1, for which a full list of Consortium members is provided at https://www.malariagen.net/projects/consortial-project-1/malariagen-consor-tium-members. The Malaria Genomic Epidemiology Network study of severe malaria was supported by Wellcome (WT077383/Z/05/Z) and the Bill and Melinda Gates Foundation (https://www.gate-sfoundation.org/) through the Foundations of the National Institutes of Health (https://fnih.org/) as part of the Grand Challenges in Global Health Initiative. The Resource Centre for Genomic Epidemiology of Malaria is supported by Wellcome (090770/Z/09/Z; 204911/Z/16/Z). This research was supported by the Medical Research Council (G0600718; G0600230; MR/M006212/1). Wellcome also provides core awards to the Wellcome Centre for Human Genetics (203141/Z/16/Z) and the Wellcome Sanger Institute (206194). This study also makes use of data from the FEAST trial. The FEAST trial was supported by a grant (G0801439) from the Medical Research Council, UK, provided through the (MRC) DFID concordat. KM and ECG were supported by this grant. Funding Information: This research was funded by The Wellcome Trust. A CC BY or equivalent licence is applied to the author accepted manuscript arising from this submission, in accordance with the grant’s open access conditions. This work was done as part of SMAART (Severe Malaria Africa – A consortium for Research and Trials) funded by a Wellcome Collaborative Award in Science grant (209265/Z/17/Z) held in part by KM, NPJD and AD. TNW and NJW are senior and principal research fellows respectively funded by the Wellcome Trust (202800/Z/16/Z and 093956/Z/10/C, respectively). ECG acknowledges funding from a core grant to the MRC CTU at UCL from the MRC (MC_UU_12023/ 26). Funding Information: This study also makes use of data from the FEAST trial. The FEAST trial was supported by a grant (G0801439) from the Medical Research Council, UK, provided through the (MRC) DFID concordat. KM and ECG were supported by this grant. Publisher Copyright: © Watson et al.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85111149032&partnerID=8YFLogxK
U2 - https://doi.org/10.7554/ELIFE.69698
DO - https://doi.org/10.7554/ELIFE.69698
M3 - Article
C2 - 34225842
SN - 2050-084X
VL - 10
JO - eLife
JF - eLife
M1 - e69698
ER -