Estonian Biocentre, Institute of Genomics, University of Tartu, Estonia.
Institute of Molecular and Cell Biology, University of Tartu, Estonia.
Genome Biol Evol. 2021 Apr 5;13(4). doi: 10.1093/gbe/evab025.
Contemporary individuals are the combination of genetic fragments inherited from ancestors belonging to multiple populations, as the result of migration and admixture. Isolating and characterizing these layers are crucial to the understanding of the genetic history of a given population. Ancestry deconvolution approaches make use of a large amount of source individuals, therefore constraining the performance of Local Ancestry Inferences when only few genomes are available from a given population. Here we present WINC, a local ancestry framework derived from the combination of ChromoPainter and NNLS approaches, as a method to retrieve local genetic assignments when only a few reference individuals are available. The framework is aided by a score assignment based on source differentiation to maximize the amount of sequences retrieved and is capable of retrieving accurate ancestry assignments when only two individuals for source populations are used.
当代人是由来自多个群体的祖先遗传片段组合而成的，这是迁移和混合的结果。分离和描述这些层对于理解特定人群的遗传历史至关重要。祖先分解方法利用了大量的源个体，因此当给定群体只有少数基因组可用时，限制了局部祖先推断的性能。在这里，我们提出了 WINC，这是一种源自 ChromoPainter 和 NNLS 方法组合的局部祖先框架，是一种在只有少数参考个体可用时检索局部遗传分配的方法。该框架通过基于源分化的得分分配来辅助，以最大限度地检索到序列，并能够在仅使用两个源群体个体时准确地检索到祖先分配。
