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Swart, Y.

Publications and source records attributed to Swart, Y..

2 recordsLinked to original sources
bioRxiv2022

cis-eQTL mapping of TB-T2D comorbidity elucidates the involvement of African ancestry in TB susceptibility

The validation of genome-wide association signals for tuberculosis (TB) susceptibility and the development of type 2 diabetes (T2D) across diverse populations remain problematic. The ancestry-specific variants (coding and non-coding) that contribute to previously identified differentially expressed genes (DEG) in patients with TB, T2D and comorbid TB-T2D, remain unknown. Identifying ancestry-specific expression quantitative trait loci (eQTLs) can aid in distinguishing the most probable disease-causing variants for population-specific therapeutic interventions. Therefore, this study conducted cis-eQTL mapping in TB, T2D and TB-T2D patients to identify variants associated with DEG. Both genotyping (Infinium H3A array with [~]2.3 M markers) and RNA sequencing data of 96 complex multi-way admixed South Africans were used for this purpose. Importantly, both global-and local ancestry adjustment were included in statistical analysis to account for complex admixture. Unique gene-variant pairs were associated with TB-T2D on chromosome 7p22 whilst adjusting for Bantu-speaking African ancestry (PRKAR1B:rs4464850; P=7.68e-07) and Khoe-San ancestry (PRKAR1B:rs117842122; P=3.66e-07). In addition, IFITM3 (a biomarker for the development of TB) was associated with three SNPs (rs11025530, rs3808990, and rs10896664) on chromosome 11p15 while adjusting for Khoe-San ancestry. Our results also indicated that the upregulation of the NLRP6 inflammasome is strongly associated with people with TB-T2D while adjusting for Khoe-San ancestry. Three African-specific eGenes (NLRP6, IFITM3 and PRKAR1B) would have been missed if local ancestry adjustment was not conducted. This study determined a list of ancestry-specific eQTLs in TB-T2D patients that could potentially guide the search for new therapeutic targets for TB-T2D in African populations. Author SummaryThe limitation of genome-wide association study (GWAS) is that the particular biological pathway impacted by a variant might not be evident. eQTL mapping can be conducted to determine the impact that a genetic variant might have on the expression of a specific gene in a biological pathway. In this study the use of cis-eQTL mapping was explored to elucidate the underlying genetic variants that regulate gene expression between TB-T2D and T2D patients, and between TB patients and healthy controls with multi-way genetic admixture from South Africa. Using RNA sequencing data and newly genotyped dataset of 96 individuals (Illumina Infinium H3Africa array with [~]2.5 M markers), we were able to identify ancestry-specific eQTLs. eQTLs of indigenous Khoe-San ancestral origin were identified in genetic regions previously implicated in TB and T2D in African populations. If local ancestry was not incorporated in the cis-eQTL mapping analysis these important African-specific eQTLs would have been missed. Our results provide a list of possible ancestry-specific causal variants associated with TB-T2 comorbidity that could guide the search for new therapeutic targets for African-specific populations. Including populations with complex ancestry and admixture in genetic studies is necessary to improve the quality of genetic research in sub-Saharan African groups.

genetics↗
bioRxiv2022

GWAS in the Southern African context

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.

genetics↗