ArticleGenome biology and evolution2021
A Chromosome-Painting-Based Pipeline to Infer Local Ancestry under Limited Source Availability.
Article in Genome biology and evolution, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Opportunities and challenges of local ancestry in genetic association analyses.American journal of human genetics · 2025Review
- PANE: fast and reliable ancestral reconstruction on ancient genotype data with non-negative least square and principal component analysis.Genome biology · 2025Article
- AncestryGrapher toolkit: Python command-line pipelines to visualize global- and local- ancestry inferences from the RFMIX version 2 software.Bioinformatics (Oxford, England) · 2024Article
- Genome-Wide Admixture Mapping Identifies Wild Ancestry-of-Origin Segments in Cultivated Robusta Coffee.Genome biology and evolution · 2023Article
- Genomic variation in baboons from central Mozambique unveils complex evolutionary relationships with other Papio species.BMC ecology and evolution · 2022Article
- The genomic signatures of natural selection in admixed human populations.American journal of human genetics · 2022Article
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Authors and funding
8 authors.
Funding
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Abstract
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.
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