Imputation-based meta-analysis of severe malaria in three African populations.
Band G., Le QS., Jostins L., Pirinen M., Kivinen K., Jallow M., Sisay-Joof F., Bojang K., Pinder M., Sirugo G., Conway DJ., Nyirongo V., Kachala D., Molyneux M., Taylor T., Ndila C., Peshu N., Marsh K., Williams TN., Alcock D., Andrews R., Edkins S., Gray E., Hubbart C., Jeffreys A., Rowlands K., Schuldt K., Clark TG., Small KS., Teo YY., Kwiatkowski DP., Rockett KA., Barrett JC., Spencer CCA., Malaria Genomic Epidemiology Network None., Malaria Genomic Epidemiological Network None.
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.