James A. WatsonCarolyne M. NdilaSophie UyogaAlexander MachariaGideon NyutuShebe MohammedCaroline NgetsaNeema MturiNorbert PeshuBenjamin TsofaKirk RockettStije LeopoldHugh KingstonElizabeth C. GeorgeKathryn MaitlandNicholas P.J. DayArjen M. DondorpPhilip BejonThomas WilliamsChris C. HolmesNicholas J. WhiteFaculty of Tropical Medicine, Mahidol UniversityThe Wellcome Centre for Human GeneticsCentre for Geographic Medicine ResearchUniversity of OxfordUniversity College LondonImperial College LondonNuffield Department of MedicineWellcome Sanger Institute2022-08-042022-08-042021-01-01eLife. Vol.10, (2021)2050084X2-s2.0-85111149032https://repository.li.mahidol.ac.th/handle/20.500.14594/76354Severe 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.Mahidol UniversityBiochemistry, Genetics and Molecular BiologyImmunology and MicrobiologyNeuroscienceArticleSCOPUS10.7554/ELIFE.69698