TESS3: fast inference of spatial population structure and genome scans for selection

Geography and landscape are important determinants of genetic variation in natural populations, and several ancestry estimation methods have been proposed to investigate population structure using genetic and geographic data simultaneously. Those approaches are often based on computer‐intensive stoc...

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Veröffentlicht in:	Molecular ecology resources Jg. 16; H. 2; S. 540 - 548
Hauptverfasser:	Caye, Kevin, Deist, Timo M., Martins, Helena, Michel, Olivier, François, Olivier
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	England Blackwell Publishing Ltd 01.03.2016 Wiley Subscription Services, Inc Wiley/Blackwell
Schlagworte:	algorithms ancestry Arabidopsis Arabidopsis - classification Arabidopsis - genetics Arabidopsis thaliana Computational Biology Computational Biology - methods control of false discoveries data collection Europe gene frequency Genealogy Genes Genetic diversity Genetic Variation Genetics Genetics, Population Genetics, Population - methods genome genome scans for selection Genome, Plant Genomes geographic variation geographical variation Geography high-throughput nucleotide sequencing inference of population structure landscapes Life Sciences Natural populations Phylogeography Phylogeography - methods Plant species Population structure Populations and Evolution statistical analysis Statistical methods genome scans for selection geographic variation inference of population structure control of false discoveries
ISSN:	1755-098X, 1755-0998, 1755-0998
Online-Zugang:	Volltext
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Zusammenfassung:	Geography and landscape are important determinants of genetic variation in natural populations, and several ancestry estimation methods have been proposed to investigate population structure using genetic and geographic data simultaneously. Those approaches are often based on computer‐intensive stochastic simulations and do not scale with the dimensions of the data sets generated by high‐throughput sequencing technologies. There is a growing demand for faster algorithms able to analyse genomewide patterns of population genetic variation in their geographic context. In this study, we present TESS3, a major update of the spatial ancestry estimation program TESS. By combining matrix factorization and spatial statistical methods, TESS3 provides estimates of ancestry coefficients with accuracy comparable to TESS and with run‐times much faster than the Bayesian version. In addition, the TESS3 program can be used to perform genome scans for selection, and separate adaptive from nonadaptive genetic variation using ancestral allele frequency differentiation tests. The main features of TESS3 are illustrated using simulated data and analysing genomic data from European lines of the plant species Arabidopsis thaliana.
Bibliographie:	ark:/67375/WNG-0XBR76L9-C French program Investissement d'avenir istex:C7AD0671F6C857F0BC1F5AC1209F0DCB9BACCD35 'Agence Nationale de la Recherche' - No. AFRICROP ANR-13-BSV7-0017 LabEx PERSYVAL-Lab - No. ANR-11-LABX-0025-01 'Ciências sem Fronteiras' scholarship program from the Brazilian government ArticleID:MEN12471 ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	1755-098X 1755-0998 1755-0998
DOI:	10.1111/1755-0998.12471