Imperial College London
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Professor Thomas N Williams

Faculty of Medicine, Department of Surgery & Cancer

Chair in Haemoglobinopathy Research
 
 
 
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Contact

 

tom.williams Website

 
 
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Location

 

Norfolk PlaceSt Mary's Campus

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Summary

 

Publications

Citation

BibTex format

@article{Watson:2021:10.7554/eLife.69698,
author = {Watson, JA and Ndila, CM and Uyoga, S and Macharia, A and Nyutu, G and Mohammed, S and Ngetsa, C and Mturi, N and Peshu, N and Tsofa, B and Rockett, K and Leopold, S and Kingston, H and George, EC and Maitland, K and Day, NP and Dondorp, AM and Bejon, P and Williams, T and Holmes, CC and White, NJ},
doi = {10.7554/eLife.69698},
journal = {eLife},
pages = {1--39},
title = {Improving statistical power in severe malaria genetic association studies by augmenting phenotypic precision.},
url = {http://dx.doi.org/10.7554/eLife.69698},
volume = {10},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
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, 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.
AU  - Watson,JA
AU  - Ndila,CM
AU  - Uyoga,S
AU  - Macharia,A
AU  - Nyutu,G
AU  - Mohammed,S
AU  - Ngetsa,C
AU  - Mturi,N
AU  - Peshu,N
AU  - Tsofa,B
AU  - Rockett,K
AU  - Leopold,S
AU  - Kingston,H
AU  - George,EC
AU  - Maitland,K
AU  - Day,NP
AU  - Dondorp,AM
AU  - Bejon,P
AU  - Williams,T
AU  - Holmes,CC
AU  - White,NJ
DO  - 10.7554/eLife.69698
EP  - 39
PY  - 2021///
SN  - 2050-084X
SP  - 1
TI  - Improving statistical power in severe malaria genetic association studies by augmenting phenotypic precision.
T2  - eLife
UR  - http://dx.doi.org/10.7554/eLife.69698
UR  - https://www.ncbi.nlm.nih.gov/pubmed/34225842
UR  - https://elifesciences.org/articles/69698
UR  - http://hdl.handle.net/10044/1/90622
VL  - 10
ER  -
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