GWAS in the southern African context

Abstract

Researchers would generally adjust for the possible confounding effect of population structure by considering global ancestry proportions or top principle components. Alternatively, researchers would conduct admixture mapping to increase the power to detect variants with an ancestry effect. This is sufficient in simple admixture scenarios, however, populations from southern Africa can be complex multi-way admixed populations. Duan et al. (2018) first described local ancestry adjusted allelic (LAAA) analysis as a robust method for discovering association signals, while producing minimal false-positives. Their simulation study, however, was limited to a two-way admixed population. Realizing that their findings might not translate to other admixture scenarios, we simulated a three- and five-way admixed population to compare the LAAA model to other models commonly used in GWAS. We found that, given our admixture scenarios, the LAAA model identifies the most causal variants in most of the phenotypes we tested across both the three-way and five-way admixed populations. The LAAA model also produced a high number of false-positives which was potentially caused by the ancestry effect size that we assumed. Considering the extent to which the various models tested differed in their results and considering that the source of a given association is unknown, we recommend that researchers use multiple GWAS models when analysing populations with complex ancestry.

Confirmation of HLA-II associations with TB susceptibility in admixed African samples

“…Separate regression models for each ancestral contribution were fitted to investigate which ancestral contribution is associated with TB susceptibility. Details regarding the models have been described elsewhere (Swart, van Eeden, et al, 2022); but in summary, four regression models were tested to detect the source of the association signals observed: Null model or global ancestry (GA) model: The null model only includes global ancestry, sex and age covariates. This test investigates whether an additive allelic dose exerts an effect on the phenotype (without including local ancestry of the allele). Local ancestry (LA) model: The LA model includes the number of alleles of a specific ancestry at a locus as covariates.…”

“…Separate regression models for each ancestral contribution were fitted to investigate which ancestral contribution is associated with TB susceptibility. Details regarding the models have been described elsewhere (Swart, van Eeden, et al, 2022); but in summary, four regression models were tested to detect the source of the association signals observed:…”

“…The diverse genetic background of admixed individuals can lead to population stratification, potentially introducing confounding variables. However, the power to detect statistically significant loci in admixed populations can be improved by leveraging admixture-induced local ancestry (Swart et al, 2021; Swart, van Eeden, et al, 2022). Since previous computational algorithms were not able to include local ancestry as a covariate for GWASs, the local ancestry allelic adjusted association model (LAAA) was developed to overcome this limitation (Duan et al, 2018).…”

“…The LAAA model identifies ancestry-specific alleles associated with the phenotype by including the minor alleles and the corresponding ancestry of the minor alleles (obtained by local ancestry inference) as covariates. The LAAA model has been successfully applied in a cohort of multi-way admixed SAC individuals to identify novel variants associated with TB susceptibility (Swart et al, 2021; Swart, van Eeden, et al, 2022).…”

Novel Populations from Simulated Admixed Populations

“…The two main approaches of simulation [26] are forward time simulation, where the evolution of initial population is tracked under various genetic models over multiple generations; and backward time (also known as coalescent simulation), where the simulation begins with the current time and work backwards into history. Hence, it is plausible to study the emergence of novel populations from simulated admixed populations but studies in this area have been sparse as recent simulation studies mainly focus on the admixture models [27][28][29][30][31] or determining population history [29,[31][32][33][34] and adaptations to novel environments [35].…”