Imputation-Based Meta-Analysis of Severe Malaria in Three African Populations

de Bakker, Paul I. W., Band, Gavin, Le, Quang Si, Jostins, Luke, Pirinen, Matti, Kivinen, Katja, Jallow, Muminatou, Sisay-Joof, Fatoumatta, Bojang, Kalifa, Pinder, Margaret, Sirugo, Giorgio, Conway, David J., Nyirongo, Vysaul, Kachala, David, Molyneux, Malcolm E, Taylor, Terrie, Ndila, Carolyne, Peshu, Norbert, Marsh, Kevin, Williams, Thomas N., Alcock, Daniel, Andrews, Robert, Edkins, Sarah, Gray, Emma, Hubbart, Christina, Jeffreys, Anna, Rowlands, Kate, Schuldt, Kathrin, Clark, Taane G., Small, Kerrin S., Teo, Yik Ying, Kwiatkowski, Dominic P., Rockett, Kirk A., Barrett, Jeffrey C. and Spencer, Chris C. A. (2013) 'Imputation-Based Meta-Analysis of Severe Malaria in Three African Populations'. PLoS Genetics, Vol 9, Issue 5, e1003509.

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Abstract

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.

Item Type:	Article
Subjects:	QU Biochemistry > Genetics > QU 500 Genetic phenomena QU Biochemistry > Genetics > QU 550 Genetic techniques. PCR. Chromosome mapping WA Public Health > Statistics. Surveys > WA 950 Theory or methods of medical statistics. Epidemiologic methods WC Communicable Diseases > Tropical and Parasitic Diseases > WC 750 Malaria
Faculty: Department:	Clinical Sciences & International Health > Clinical Sciences Department
Digital Object Identifer (DOI):	https://doi.org/10.1371/journal.pgen.1003509
Depositing User:	Lynn Roberts-Maloney
Date Deposited:	12 Feb 2015 12:57
Last Modified:	17 Aug 2022 08:57
URI:	https://archive.lstmed.ac.uk/id/eprint/4888

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