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

eLife

Philip Bejon¹  Shebe Mohammed²  Norbert Peshu²  Caroline Ngetsa²  Benjamin Tsofa²  Sophie Uyoga²  Neema Mturi²  Alexander Macharia²  Gideon Nyutu²  Thomas Williams³  Kathryn Maitland³  Hugh Kingston⁴  Arjen M Dondorp⁴  Nicholas J White⁴  Nicholas PJ Day⁴  Carolyne M Ndila⁴  James A Watson⁴  Stije Leopold⁴  Elizabeth C George⁵  Chris C Holmes⁶  Kirk Rockett⁷ 

[1] Centre 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, Kenya;KEMRI-Wellcome Trust Research Programme, Centre for Geographic Medicine Research-Coast, Kilifi, Kenya;KEMRI-Wellcome Trust Research Programme, Centre for Geographic Medicine Research-Coast, Kilifi, Kenya;Institute of Global Health Innovation, Imperial College, London, London, United Kingdom;Mahidol Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahido University, Bangkok, Thailand;Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom;Medical Research Council Clinical Trials Unit, University College London, London, United Kingdom;Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom;Department of Statistics, University of Oxford, Oxford, United Kingdom;The Wellcome Sanger Institute, Cambridge, United Kingdom;Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom;

关键词: severe malaria;    GWAS;    diagnosis;    complete blood count;    Human;   

DOI  :  10.7554/eLife.69698

来源: eLife Sciences Publications, Ltd

【 摘 要 】

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.

【 授权许可】

CC BY