Determining Ancestry Proportions in Complex Admixture Scenarios in South Africa Using a Novel Proxy Ancestry Selection Method

Admixed populations can make an important contribution to the discovery of disease susceptibility genes if the parental populations exhibit substantial variation in susceptibility. Admixture mapping has been used successfully, but is not designed to cope with populations that have more than two or three ancestral populations. The inference of admixture proportions and local ancestry and the imputation of missing genotypes in admixed populations are crucial in both understanding variation in disease and identifying novel disease loci. These inferences make use of reference populations, and accuracy depends on the choice of ancestral populations. Using an insufficient or inaccurate ancestral panel can result in erroneously inferred ancestry and affect the detection power of GWAS and meta-analysis when using imputation. Current algorithms are inadequate for multi-way admixed populations. To address these challenges we developed PROXYANC, an approach to select the best proxy ancestral populations. From the simulation of a multi-way admixed population we demonstrate the capability and accuracy of PROXYANC and illustrate the importance of the choice of ancestry in both estimating admixture proportions and imputing missing genotypes. We applied this approach to a complex, uniquely admixed South African population. Using genome-wide SNP data from over 764 individuals, we accurately estimate the genetic contributions from the best ancestral populations: isiXhosa , ‡Khomani SAN , European , Indian , and Chinese . We also demonstrate that the ancestral allele frequency differences correlate with increased linkage disequilibrium in the South African population, which originates from admixture events rather than population bottlenecks.
Nomenclature
The collective term for people of mixed ancestry in southern Africa is “Coloured,” and this is officially recognized in South Africa as a census term, and for self-classification. Whilst we acknowledge that some cultures may use this term in a derogatory manner, these connotations are not present in South Africa, and are certainly not intended here.

若亲本人群在疾病易感性上存在显著差异，混血人群（admixed populations）可为疾病易感基因的发现做出重要贡献。混血定位（admixture mapping）已被成功应用，但无法应对拥有超过2或3个祖先人群的群体。对混血人群的混血比例、局部祖先信息的推断以及缺失基因型的填充，在解析疾病表型差异与识别新型疾病位点两方面均至关重要。此类推断依赖参考人群，其准确性取决于祖先人群的选择。使用不足或不准确的祖先参考面板，可能会导致祖先推断错误，并在使用基因型填充时影响全基因组关联研究（Genome-Wide Association Study, GWAS）与荟萃分析的检测效能。当前算法难以适配多祖先混血人群。为解决上述挑战，我们开发了PROXYANC工具，用于筛选最优的替代祖先人群。通过模拟多祖先混血人群，我们验证了PROXYANC的性能与准确性，并阐明了祖先人群选择在估算混血比例与填充缺失基因型两方面的重要性。我们将该方法应用于一个结构复杂且具有独特混血特征的南非人群。借助来自764余名个体的全基因组单核苷酸多态性（Single Nucleotide Polymorphism, SNP）数据，我们精准估算了最优祖先人群的遗传贡献：科萨语族（isiXhosa）、科曼尼桑人（‡Khomani SAN）、欧洲人群、印度人群与中国人群。我们还证实，该南非人群的祖先等位基因频率差异与连锁不平衡水平升高呈正相关，其遗传结构源于混血事件而非种群瓶颈。

术语规范：南非地区混合祖先人群的统称是"Coloured"（有色人种），该术语在南非已被官方认定为人口普查术语与自我分类术语。尽管我们知晓部分文化群体可能以贬损方式使用该术语，但在南非语境下并无此类负面含义，本文亦绝非此意。