Unsupervised detection of ancestry tracks with the GHap r package

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dc.contributor.authorUtsunomiya, Yuri Tani [UNESP]
dc.contributor.authorMilanesi, Marco [UNESP]
dc.contributor.authorBarbato, Mario
dc.contributor.authorUtsunomiya, Adam Taiti Harth [UNESP]
dc.contributor.authorSölkner, Johann
dc.contributor.authorAjmone-Marsan, Paolo
dc.contributor.authorGarcia, José Fernando [UNESP]
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionInternational Atomic Energy Agency (IAEA) Collaborating Centre on Animal Genomics and Bioinformatics
dc.contributor.institutionUniversità Cattolica del Sacro Cuore
dc.contributor.institutionBOKU—University of Natural Resources and Life Sciences
dc.date.accessioned2020-12-12T02:21:44Z
dc.date.available2020-12-12T02:21:44Z
dc.date.issued2020-01-01
dc.description.abstractThe identification of ancestry tracks is a powerful tool to assist the inference of evolutionary events in the genomes of animals and plants. However, algorithms for ancestry track detection typically require labelled reference population data. This dependency prevents the analysis of genomic data lacking prior information on genetic structure, and may produce classification bias when samples in the reference data are inadvertently admixed. We combined heuristics with K-means clustering to deploy a method that can detect ancestry tracks without the provision of lineage labels for reference population data. The resulting algorithm uses phased genotypes to infer individual ancestry proportions and local ancestry. By piling up ancestry tracks across individuals, our method also allows for mapping loci with excess or deficit ancestry from specific lineages. Using both simulated and real genomic data, we found that the proposed method was accurate in inferring genetic structure, assigning chromosomal segments to lineages and estimating individual ancestry, especially in cases where ancestry tracks resulted from recent admixture of highly divergent lineages. The method is implemented as part of the v2 release of the GHap r package (available at https://cran.r-project.org/package=GHap and https://bitbucket.org/marcomilanesi/ghap/src/master/).en
dc.description.affiliationDepartment of Support Production and Animal Health School of Veterinary Medicine of Araçatuba São Paulo State University (Unesp)
dc.description.affiliationInternational Atomic Energy Agency (IAEA) Collaborating Centre on Animal Genomics and Bioinformatics
dc.description.affiliationDepartment of Animal Science Food and Nutrition—DIANA and Nutrigenomics and Proteomics Research Center Università Cattolica del Sacro Cuore
dc.description.affiliationDivision of Livestook Sciences Department of Sustainable Agriculture System BOKU—University of Natural Resources and Life Sciences
dc.description.affiliationDepartment of Preventive Veterinary Medicine and Animal Reproduction School of Agricultural and Veterinarian Sciences São Paulo State University (Unesp)
dc.description.affiliationUnespDepartment of Support Production and Animal Health School of Veterinary Medicine of Araçatuba São Paulo State University (Unesp)
dc.description.affiliationUnespDepartment of Preventive Veterinary Medicine and Animal Reproduction School of Agricultural and Veterinarian Sciences São Paulo State University (Unesp)
dc.identifierhttp://dx.doi.org/10.1111/2041-210X.13467
dc.identifier.citationMethods in Ecology and Evolution.
dc.identifier.doi10.1111/2041-210X.13467
dc.identifier.issn2041-210X
dc.identifier.scopus2-s2.0-85090309413
dc.identifier.urihttp://hdl.handle.net/11449/201006
dc.language.isoeng
dc.relation.ispartofMethods in Ecology and Evolution
dc.sourceScopus
dc.subjectadmixture
dc.subjectchromosome painting
dc.subjectpopulation structure
dc.subjectsingle-nucleotide polymorphism
dc.titleUnsupervised detection of ancestry tracks with the GHap r packageen
dc.typeArtigo
unesp.author.orcid0000-0002-6526-8337[1]

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