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...
| Main Authors: | , , , , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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eLife Sciences Publications Ltd
2021-07-01
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| Series: | eLife |
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| Online Access: | https://elifesciences.org/articles/69698 |
| _version_ | 1818026725277171712 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-10T04:36:34Z |
| format | Article |
| id | doaj.art-9975f68439c94b6da46fbbde7cbac9a5 |
| institution | Directory Open Access Journal |
| issn | 2050-084X |
| language | English |
| last_indexed | 2024-12-10T04:36:34Z |
| publishDate | 2021-07-01 |
| publisher | eLife Sciences Publications Ltd |
| record_format | Article |
| series | eLife |
| spelling | doaj.art-9975f68439c94b6da46fbbde7cbac9a52022-12-22T02:01:59ZengeLife Sciences Publications LtdeLife2050-084X2021-07-011010.7554/eLife.69698Improving statistical power in severe malaria genetic association studies by augmenting phenotypic precisionJames A Watson0https://orcid.org/0000-0001-5524-0325Carolyne M Ndila1Sophie Uyoga2Alexander Macharia3Gideon Nyutu4Shebe Mohammed5Caroline Ngetsa6Neema Mturi7Norbert Peshu8Benjamin Tsofa9Kirk Rockett10Stije Leopold11https://orcid.org/0000-0002-0482-5689Hugh Kingston12https://orcid.org/0000-0003-1869-8307Elizabeth C George13Kathryn Maitland14https://orcid.org/0000-0002-0007-0645Nicholas PJ Day15https://orcid.org/0000-0003-2309-1171Arjen M Dondorp16https://orcid.org/0000-0001-5190-2395Philip Bejon17Thomas N Williams18https://orcid.org/0000-0003-4456-2382Chris C Holmes19Nicholas J White20https://orcid.org/0000-0002-1897-1978Mahidol Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand; Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United KingdomMahidol Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand; Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United KingdomKEMRI-Wellcome Trust Research Programme, Centre for Geographic Medicine Research-Coast, Kilifi, KenyaKEMRI-Wellcome Trust Research Programme, Centre for Geographic Medicine Research-Coast, Kilifi, KenyaKEMRI-Wellcome Trust Research Programme, Centre for Geographic Medicine Research-Coast, Kilifi, KenyaKEMRI-Wellcome Trust Research Programme, Centre for Geographic Medicine Research-Coast, Kilifi, KenyaKEMRI-Wellcome Trust Research Programme, Centre for Geographic Medicine Research-Coast, Kilifi, KenyaKEMRI-Wellcome Trust Research Programme, Centre for Geographic Medicine Research-Coast, Kilifi, KenyaKEMRI-Wellcome Trust Research Programme, Centre for Geographic Medicine Research-Coast, Kilifi, KenyaKEMRI-Wellcome Trust Research Programme, Centre for Geographic Medicine Research-Coast, Kilifi, KenyaThe Wellcome Sanger Institute, Cambridge, United Kingdom; Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United KingdomMahidol Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand; Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United KingdomMahidol Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand; Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United KingdomMedical Research Council Clinical Trials Unit, University College London, London, United KingdomKEMRI-Wellcome Trust Research Programme, Centre for Geographic Medicine Research-Coast, Kilifi, Kenya; Institute of Global Health Innovation, Imperial College, London, London, United KingdomMahidol Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand; Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United KingdomMahidol Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand; Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United KingdomCentre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom; KEMRI-Wellcome Trust Research Programme, Centre for Geographic Medicine Research-Coast, Kilifi, KenyaKEMRI-Wellcome Trust Research Programme, Centre for Geographic Medicine Research-Coast, Kilifi, Kenya; Institute of Global Health Innovation, Imperial College, London, London, United KingdomNuffield Department of Medicine, University of Oxford, Oxford, United Kingdom; Department of Statistics, University of Oxford, Oxford, United KingdomMahidol Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand; Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United KingdomSevere 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.https://elifesciences.org/articles/69698severe malariaGWASdiagnosiscomplete blood count |
| spellingShingle | 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 eLife severe malaria GWAS diagnosis complete blood count |
| title | 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_short | 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 |
| topic | severe malaria GWAS diagnosis complete blood count |
| url | https://elifesciences.org/articles/69698 |
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