Imputation-based meta-analysis of severe malaria in three African populations.

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 app...

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Main Authors: Gavin Band, Quang Si Le, Luke Jostins, Matti Pirinen, Katja Kivinen, Muminatou Jallow, Fatoumatta Sisay-Joof, Kalifa Bojang, Margaret Pinder, Giorgio Sirugo, David J Conway, Vysaul Nyirongo, David Kachala, Malcolm Molyneux, Terrie Taylor, Carolyne Ndila, Norbert Peshu, Kevin Marsh, Thomas N Williams, Daniel Alcock, Robert Andrews, Sarah Edkins, Emma Gray, Christina Hubbart, Anna Jeffreys, Kate Rowlands, Kathrin Schuldt, Taane G Clark, Kerrin S Small, Yik Ying Teo, Dominic P Kwiatkowski, Kirk A Rockett, Jeffrey C Barrett, Chris C A Spencer, Malaria Genomic Epidemiology Network, Malaria Genomic Epidemiological Network
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2013-05-01
Series:PLoS Genetics
Online Access:https://journals.plos.org/plosgenetics/article/file?id=10.1371/journal.pgen.1003509&type=printable
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author Gavin Band
Quang Si Le
Luke Jostins
Matti Pirinen
Katja Kivinen
Muminatou Jallow
Fatoumatta Sisay-Joof
Kalifa Bojang
Margaret Pinder
Giorgio Sirugo
David J Conway
Vysaul Nyirongo
David Kachala
Malcolm Molyneux
Terrie Taylor
Carolyne Ndila
Norbert Peshu
Kevin Marsh
Thomas N Williams
Daniel Alcock
Robert Andrews
Sarah Edkins
Emma Gray
Christina Hubbart
Anna Jeffreys
Kate Rowlands
Kathrin Schuldt
Taane G Clark
Kerrin S Small
Yik Ying Teo
Dominic P Kwiatkowski
Kirk A Rockett
Jeffrey C Barrett
Chris C A Spencer
Malaria Genomic Epidemiology Network
Malaria Genomic Epidemiological Network
author_facet Gavin Band
Quang Si Le
Luke Jostins
Matti Pirinen
Katja Kivinen
Muminatou Jallow
Fatoumatta Sisay-Joof
Kalifa Bojang
Margaret Pinder
Giorgio Sirugo
David J Conway
Vysaul Nyirongo
David Kachala
Malcolm Molyneux
Terrie Taylor
Carolyne Ndila
Norbert Peshu
Kevin Marsh
Thomas N Williams
Daniel Alcock
Robert Andrews
Sarah Edkins
Emma Gray
Christina Hubbart
Anna Jeffreys
Kate Rowlands
Kathrin Schuldt
Taane G Clark
Kerrin S Small
Yik Ying Teo
Dominic P Kwiatkowski
Kirk A Rockett
Jeffrey C Barrett
Chris C A Spencer
Malaria Genomic Epidemiology Network
Malaria Genomic Epidemiological Network
author_sort Gavin Band
collection DOAJ
description 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.
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publisher Public Library of Science (PLoS)
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spelling doaj-art-dd877dc78f1d428cbcceaa37cc85a3252025-08-20T02:05:37ZengPublic Library of Science (PLoS)PLoS Genetics1553-73901553-74042013-05-0195e100350910.1371/journal.pgen.1003509Imputation-based meta-analysis of severe malaria in three African populations.Gavin BandQuang Si LeLuke JostinsMatti PirinenKatja KivinenMuminatou JallowFatoumatta Sisay-JoofKalifa BojangMargaret PinderGiorgio SirugoDavid J ConwayVysaul NyirongoDavid KachalaMalcolm MolyneuxTerrie TaylorCarolyne NdilaNorbert PeshuKevin MarshThomas N WilliamsDaniel AlcockRobert AndrewsSarah EdkinsEmma GrayChristina HubbartAnna JeffreysKate RowlandsKathrin SchuldtTaane G ClarkKerrin S SmallYik Ying TeoDominic P KwiatkowskiKirk A RockettJeffrey C BarrettChris C A SpencerMalaria Genomic Epidemiology NetworkMalaria Genomic Epidemiological NetworkCombining 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.https://journals.plos.org/plosgenetics/article/file?id=10.1371/journal.pgen.1003509&type=printable
spellingShingle Gavin Band
Quang Si Le
Luke Jostins
Matti Pirinen
Katja Kivinen
Muminatou Jallow
Fatoumatta Sisay-Joof
Kalifa Bojang
Margaret Pinder
Giorgio Sirugo
David J Conway
Vysaul Nyirongo
David Kachala
Malcolm Molyneux
Terrie Taylor
Carolyne Ndila
Norbert Peshu
Kevin Marsh
Thomas N Williams
Daniel Alcock
Robert Andrews
Sarah Edkins
Emma Gray
Christina Hubbart
Anna Jeffreys
Kate Rowlands
Kathrin Schuldt
Taane G Clark
Kerrin S Small
Yik Ying Teo
Dominic P Kwiatkowski
Kirk A Rockett
Jeffrey C Barrett
Chris C A Spencer
Malaria Genomic Epidemiology Network
Malaria Genomic Epidemiological Network
Imputation-based meta-analysis of severe malaria in three African populations.
PLoS Genetics
title Imputation-based meta-analysis of severe malaria in three African populations.
title_full Imputation-based meta-analysis of severe malaria in three African populations.
title_fullStr Imputation-based meta-analysis of severe malaria in three African populations.
title_full_unstemmed Imputation-based meta-analysis of severe malaria in three African populations.
title_short Imputation-based meta-analysis of severe malaria in three African populations.
title_sort imputation based meta analysis of severe malaria in three african populations
url https://journals.plos.org/plosgenetics/article/file?id=10.1371/journal.pgen.1003509&type=printable
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