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Genome-wide association testing in malaria studies in the presence of overdominance

BACKGROUND: In human genetics, heterozygote advantage (heterosis) has been detected in studies that focused on specific genes but not in genome-wide association studies (GWAS). For example, heterosis is believed to confer resistance to certain strains of malaria in patients heterozygous for the sick...

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Autores principales: Akoth, Morine, Odhiambo, John, Omolo, Bernard
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10084622/
https://www.ncbi.nlm.nih.gov/pubmed/37038187
http://dx.doi.org/10.1186/s12936-023-04533-2
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author Akoth, Morine
Odhiambo, John
Omolo, Bernard
author_facet Akoth, Morine
Odhiambo, John
Omolo, Bernard
author_sort Akoth, Morine
collection PubMed
description BACKGROUND: In human genetics, heterozygote advantage (heterosis) has been detected in studies that focused on specific genes but not in genome-wide association studies (GWAS). For example, heterosis is believed to confer resistance to certain strains of malaria in patients heterozygous for the sickle-cell gene, haemoglobin S (HbS). Yet the power of allelic tests can be substantially diminished by heterosis. Since GWAS (and haplotype-associations) also utilize allelic tests, it is unclear to what degree GWAS could underachieve because heterosis is ignored. METHODS: In this study, a two-step approach to genetic association testing in malaria studies in a GWAS setting that may enhance the power of the tests was proposed, by identifying the underlying genetic model first before applying the association tests. Generalized linear models for dominant, recessive, additive, and heterotic effects were fitted and model selection was performed. This was achieved via tests of significance using the MAX and allelic tests, noting the minimum p-values across all the models and the proportion of tests that a given genetic model was deemed the best. An example dataset, based on 17 SNPs, from a robust genetic association study and simulated genotype datasets, were used to illustrate the method. Case–control genotype data on malaria from Kenya and Gambia were used for validation. RESULTS AND CONCLUSION: Results showed that the allelic test returned some false negatives under the heterosis model, suggesting reduced power in testing genetic association. Disparities were observed for some chromosomes in the Kenyan and Gambian datasets, including the sex chromosomes. Thus, GWAS and haplotype associations should be treated with caution, unless the underlying genetic model had been determined. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12936-023-04533-2.
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spelling pubmed-100846222023-04-11 Genome-wide association testing in malaria studies in the presence of overdominance Akoth, Morine Odhiambo, John Omolo, Bernard Malar J Research BACKGROUND: In human genetics, heterozygote advantage (heterosis) has been detected in studies that focused on specific genes but not in genome-wide association studies (GWAS). For example, heterosis is believed to confer resistance to certain strains of malaria in patients heterozygous for the sickle-cell gene, haemoglobin S (HbS). Yet the power of allelic tests can be substantially diminished by heterosis. Since GWAS (and haplotype-associations) also utilize allelic tests, it is unclear to what degree GWAS could underachieve because heterosis is ignored. METHODS: In this study, a two-step approach to genetic association testing in malaria studies in a GWAS setting that may enhance the power of the tests was proposed, by identifying the underlying genetic model first before applying the association tests. Generalized linear models for dominant, recessive, additive, and heterotic effects were fitted and model selection was performed. This was achieved via tests of significance using the MAX and allelic tests, noting the minimum p-values across all the models and the proportion of tests that a given genetic model was deemed the best. An example dataset, based on 17 SNPs, from a robust genetic association study and simulated genotype datasets, were used to illustrate the method. Case–control genotype data on malaria from Kenya and Gambia were used for validation. RESULTS AND CONCLUSION: Results showed that the allelic test returned some false negatives under the heterosis model, suggesting reduced power in testing genetic association. Disparities were observed for some chromosomes in the Kenyan and Gambian datasets, including the sex chromosomes. Thus, GWAS and haplotype associations should be treated with caution, unless the underlying genetic model had been determined. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12936-023-04533-2. BioMed Central 2023-04-10 /pmc/articles/PMC10084622/ /pubmed/37038187 http://dx.doi.org/10.1186/s12936-023-04533-2 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Akoth, Morine
Odhiambo, John
Omolo, Bernard
Genome-wide association testing in malaria studies in the presence of overdominance
title Genome-wide association testing in malaria studies in the presence of overdominance
title_full Genome-wide association testing in malaria studies in the presence of overdominance
title_fullStr Genome-wide association testing in malaria studies in the presence of overdominance
title_full_unstemmed Genome-wide association testing in malaria studies in the presence of overdominance
title_short Genome-wide association testing in malaria studies in the presence of overdominance
title_sort genome-wide association testing in malaria studies in the presence of overdominance
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10084622/
https://www.ncbi.nlm.nih.gov/pubmed/37038187
http://dx.doi.org/10.1186/s12936-023-04533-2
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