DSI-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.
Centre for Bioinformatics and Computational Biology, Stellenbosch University, Stellenbosch, South Africa.
PLoS One. 2022 Sep 28;17(9):e0264657. doi: 10.1371/journal.pone.0264657. eCollection 2022.
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 positive hits. 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 genome-wide association studies (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 positive hits 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.
研究人员通常会通过考虑总体祖先比例或主要成分来调整群体结构的可能混杂效应。或者，研究人员会进行混合映射，以提高检测具有祖先效应的变体的能力。在简单的混合情况下，这是足够的，然而，来自南非的人群可能是复杂的多向混合人群。 Duan 等人（2018 年）首次描述了局部祖先调整等位基因（LAAA）分析作为发现关联信号的稳健方法，同时产生最小的假阳性命中。然而，他们的模拟研究仅限于双向混合人群。意识到他们的发现可能不适用于其他混合情况，我们模拟了一个三向和五向混合人群，以将 LAAA 模型与全基因组关联研究（GWAS）中常用的其他模型进行比较。我们发现，考虑到我们的混合情况，LAAA 模型在我们测试的大多数表型中识别出了大多数因果变体，无论是在三向混合人群还是五向混合人群中。LAAA 模型还产生了大量的假阳性命中，这可能是由于我们假设的祖先效应大小造成的。考虑到测试的各种模型在结果上的差异程度，以及给定关联的来源未知，我们建议研究人员在分析具有复杂祖先的人群时使用多种 GWAS 模型。
