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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Bibliographic Details
Published in:Molecular ecology resources Vol. 16; no. 2; pp. 540 - 548
Main Authors: Caye, Kevin, Deist, Timo M., Martins, Helena, Michel, Olivier, François, Olivier
Format: Journal Article
Language:English
Published: England Blackwell Publishing Ltd 01.03.2016
Wiley Subscription Services, Inc
Wiley/Blackwell
Subjects:
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 Access:Get full text
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Summary: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.
Bibliography: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
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ISSN:1755-098X
1755-0998
1755-0998
DOI:10.1111/1755-0998.12471