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 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.

Automated summary

Severe falciparum malaria has significant impact on human evolution, but genetic susceptibility studies are limited by phenotypic imprecision. Diagnostic uncertainty in young children in high malaria transmission areas prompted the development of a probabilistic diagnostic model, improving accuracy. The proposed data-tilting approach in case-control studies with phenotype mis-labeling can reduce false discovery rates and enhance statistical power in genetic association studies.