Gavin Band (University of Oxford): Bayesian analysis of genetic association with severe malaria

Malaria is an infectious disease prevalent in sub-Saharan Africa, south-east Asia and elsewhere, causing high rates of morbidity and an estimated 600,000 deaths each year, most of which are among young children in Africa. Although several genetic susceptibility loci are known, much remains to be discovered about both host and parasite genetics of the disease. As part of the MalariaGEN consortium, we describe a Bayesian analysis of human genetic loci associated with severe P.falciparum malaria, using 10,000 cases and 15,000 controls from 10 study sites located across sub-Saharan Africa. By choosing suitable prior distributions, we build and test a model of association accounting for uncertainty in the level of heterogeneity of effect between study sites and between severe malaria subtypes, as well as uncertainty in the mode of inheritance. We present evidence that there is substantial heterogeneity of effect at at least two well-known susceptibility loci. By making use of approximate Bayes factors and conjugate priors, we scale this analysis up across millions of SNPs, applying it to a genome-wide association analysis of severe malaria in Africa.

Keywords: malaria, Meta-analysis, Genome-wide association study.