The malaria parasite Plasmodium falciparum invades human red blood cells via interactions between host and parasite surface proteins. By analyzing genome sequence data from human populations, including 1269 individuals from sub-Saharan Africa, we identify a diverse array of large copy number variants affecting the host invasion receptor genes GYPA and GYPB. We find that a nearby association with severe malaria is explained by a complex structural rearrangement involving the loss of GYPB and gain of two GYPB-A hybrid genes, which encode a serologically distinct blood group antigen known as Dantu. This variant reduces the risk of severe malaria by 40% and has recently risen in frequency in parts of Kenya, yet it appears to be absent from west Africa. These findings link structural variation of red blood cell invasion receptors with natural resistance to severe malaria.

Many human genetic associations with resistance to malaria have been reported but few have been reliably replicated. We collected data on 11,890 cases of severe malaria due to Plasmodium falciparum and 17,441 controls from 12 locations in Africa, Asia and Oceania. There was strong evidence of association with the HBB, ABO, ATP2B4, G6PD and CD40LG loci but previously reported associations at 22 other loci did not replicate in the multi-centre analysis. The large sample size made it possible to identify authentic genetic effects that are heterogeneous across populations or phenotypes, a striking example being the main African form of G6PD deficiency, which reduced the risk of cerebral malaria but increased the risk of severe malarial anaemia. The finding that G6PD deficiency has opposing effects on different fatal complications of P. falciparum infection indicates that the evolutionary origins of this common human genetic disorder are more complex than previously supposed.

Combining data from genome-wide association studies (GWAS) conducted at different locations, using genotype imputation and fixed-effects meta-analysis, has been a powerful approach for dissecting complex disease genetics in populations of European ancestry. Here we investigate the feasibility of applying the same approach in Africa, where genetic diversity, both within and between populations, is far more extensive. We analyse genome-wide data from approximately 5,000 individuals with severe malaria and 7,000 population controls from three different locations in Africa. Our results show that the standard approach is well powered to detect known malaria susceptibility loci when sample sizes are large, and that modern methods for association analysis can control the potential confounding effects of population structure. We show that pattern of association around the haemoglobin S allele differs substantially across populations due to differences in haplotype structure. Motivated by these observations we consider new approaches to association analysis that might prove valuable for multicentre GWAS in Africa: we relax the assumptions of SNP–based fixed effect analysis; we apply Bayesian approaches to allow for heterogeneity in the effect of an allele on risk across studies; and we introduce a region-based test to allow for heterogeneity in the location of causal alleles.