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GWAS in the southern African context.
Yolandi Swart1, Gerald van Eeden1, Caitlin Uren1,2
1DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa.
Plos One
|September 28, 2022
Summary
Local ancestry adjusted allelic (LAAA) analysis shows promise for complex admixed populations. Researchers should use multiple genome-wide association study (GWAS) models for robust results in diverse ancestries.
Area of Science:
- Population Genetics
- Genomic Association Studies
Background:
- Standard methods for population structure adjustment in genetic studies include global ancestry or principal components.
- Admixture mapping is used to detect variants with ancestry effects, but may be insufficient for complex multi-way admixed populations.
Purpose of the Study:
- To evaluate the performance of local ancestry adjusted allelic (LAAA) analysis in three- and five-way admixed populations.
- To compare LAAA analysis with commonly used genome-wide association study (GWAS) models in complex admixture scenarios.
Main Methods:
- Simulated three- and five-way admixed populations.
- Compared the LAAA model against other standard GWAS models for variant detection and false positive rates.
Main Results:
- The LAAA model identified the most causal variants across tested phenotypes in both three- and five-way admixed populations.
- A high number of false positive hits were observed with the LAAA model, potentially due to assumed ancestry effect sizes.
Conclusions:
- The LAAA model is a robust method for discovering association signals in complex admixed populations.
- Given the variability in results across models and the unknown source of associations, using multiple GWAS models is recommended for analyzing populations with complex ancestry.
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