Improving statistical power in severe malaria genetic association studies by augmenting phenotypic precision
James A Watson, Carolyne Ndila, Sophie Uyoga, Alexander W. Macharia, Gideon Nyutu, Shebe Mohammed, Caroline Ngetsa, Neema Mturi, Norbert Peshu, Benjamin Tsofa, Kirk A. Rockett, Stije J. Leopold, Hugh Kingston, Elizabeth C. George, Kathryn Maitland, Nicholas Day, Arjen M. Dondorp, Philip Bejon, Thomas N. Williams, Chris Holmes, Nicholas J. White
Abstract
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.