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

• Published in: Malaria Journal
• Main Authors: Morine Akoth, John Odhiambo, Bernard Omolo
• Format: Article in Journal/Newspaper
• Language: English
• Published: BMC 2023
• Subjects: Allelic test; Case–control study; Genome-wide association; Malaria; MAX test; Arctic medicine. Tropical medicine; RC955-962; Infectious and parasitic diseases; RC109-216; Arctic; Sickle
• Online Access: https://doi.org/10.1186/s12936-023-04533-2; https://doaj.org/article/4cbf58317c23400fa032cc75dc1b274e

Abstract 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.