Improving statistical power in severe malaria genetic association studies by augmenting phenotypic precision

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 discove...

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Autores principales:	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 PJ Day, Arjen M Dondorp, Philip Bejon, Thomas N Williams, Chris C Holmes, Nicholas J White
Formato:	article
Lenguaje:	EN
Publicado:	eLife Sciences Publications Ltd 2021
Materias:	severe malaria GWAS diagnosis complete blood count Medicine R Science Q Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/9975f68439c94b6da46fbbde7cbac9a5
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spelling	oai:doaj.org-article:9975f68439c94b6da46fbbde7cbac9a52021-11-22T15:13:43ZImproving statistical power in severe malaria genetic association studies by augmenting phenotypic precision10.7554/eLife.696982050-084Xe69698https://doaj.org/article/9975f68439c94b6da46fbbde7cbac9a52021-07-01T00:00:00Zhttps://elifesciences.org/articles/69698https://doaj.org/toc/2050-084XSevere 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.James A WatsonCarolyne M NdilaSophie UyogaAlexander MachariaGideon NyutuShebe MohammedCaroline NgetsaNeema MturiNorbert PeshuBenjamin TsofaKirk RockettStije LeopoldHugh KingstonElizabeth C GeorgeKathryn MaitlandNicholas PJ DayArjen M DondorpPhilip BejonThomas N WilliamsChris C HolmesNicholas J WhiteeLife Sciences Publications Ltdarticlesevere malariaGWASdiagnosiscomplete blood countMedicineRScienceQBiology (General)QH301-705.5ENeLife, Vol 10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	severe malaria GWAS diagnosis complete blood count Medicine R Science Q Biology (General) QH301-705.5
spellingShingle	severe malaria GWAS diagnosis complete blood count Medicine R Science Q Biology (General) QH301-705.5 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 PJ Day Arjen M Dondorp Philip Bejon Thomas N Williams Chris C Holmes Nicholas J White Improving statistical power in severe malaria genetic association studies by augmenting phenotypic precision
description	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.
format	article
author	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 PJ Day Arjen M Dondorp Philip Bejon Thomas N Williams Chris C Holmes Nicholas J White
author_facet	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 PJ Day Arjen M Dondorp Philip Bejon Thomas N Williams Chris C Holmes Nicholas J White
author_sort	James A Watson
title	Improving statistical power in severe malaria genetic association studies by augmenting phenotypic precision
title_short	Improving statistical power in severe malaria genetic association studies by augmenting phenotypic precision
title_full	Improving statistical power in severe malaria genetic association studies by augmenting phenotypic precision
title_fullStr	Improving statistical power in severe malaria genetic association studies by augmenting phenotypic precision
title_full_unstemmed	Improving statistical power in severe malaria genetic association studies by augmenting phenotypic precision
title_sort	improving statistical power in severe malaria genetic association studies by augmenting phenotypic precision
publisher	eLife Sciences Publications Ltd
publishDate	2021
url	https://doaj.org/article/9975f68439c94b6da46fbbde7cbac9a5
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Improving statistical power in severe malaria genetic association studies by augmenting phenotypic precision

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