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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Vydané v:	Molecular ecology resources Ročník 16; číslo 2; s. 540 - 548
Hlavní autori:	Caye, Kevin, Deist, Timo M., Martins, Helena, Michel, Olivier, François, Olivier
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	England Blackwell Publishing Ltd 01.03.2016 Wiley Subscription Services, Inc Wiley/Blackwell
Predmet:	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
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Abstract	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.
AbstractList	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.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. 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. 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 .
Author	Caye, Kevin Deist, Timo M. Michel, Olivier Martins, Helena François, Olivier
Author_xml	– sequence: 1 givenname: Kevin surname: Caye fullname: Caye, Kevin organization: Centre National de la Recherche Scientifique, Université Grenoble-Alpes, TIMC-IMAG UMR 5525, 38042, Grenoble, France – sequence: 2 givenname: Timo M. surname: Deist fullname: Deist, Timo M. organization: Centre National de la Recherche Scientifique, Université Grenoble-Alpes, TIMC-IMAG UMR 5525, 38042, Grenoble, France – sequence: 3 givenname: Helena surname: Martins fullname: Martins, Helena organization: Centre National de la Recherche Scientifique, Université Grenoble-Alpes, TIMC-IMAG UMR 5525, 38042, Grenoble, France – sequence: 4 givenname: Olivier surname: Michel fullname: Michel, Olivier organization: Centre National de la Recherche Scientifique, Université Grenoble-Alpes, GIPSA-lab UMR 5216, 38042, Grenoble, France – sequence: 5 givenname: Olivier surname: François fullname: François, Olivier email: olivier.francois@imag.fr organization: Centre National de la Recherche Scientifique, Université Grenoble-Alpes, TIMC-IMAG UMR 5525, 38042, Grenoble, France
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References	Malécot G (1948) Les Mathématiques de l'Hérédité. Masson, Paris. Kim J, Park H (2011) Fast nonnegative matrix factorization: an active-set-like method and comparisons. SIAM Journal on Scientific Computing, 33, 3261-3281. Wright S (1943) Isolation by distance. Genetics, 28, 114. Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society, 57, 289-300. Frichot E, François O (2015) LEA: an R package for Landscape and Ecological Association studies. Methods in Ecology and Evolution, 6, 925-929. Belkin M, Niyogi P (2003) Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation, 15, 1373-1396. Wollstein A, Lao O (2015) Detecting individual ancestry in the human genome. Investigative Genetics, 6, 1-12. Durand E, Jay F, Gaggiotti OE, François O (2009) Spatial inference of admixture proportions and secondary contact zones. 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References_xml	– reference: François O, Ancelet S, Guillot G (2006) Bayesian clustering using hidden Markov random fields in spatial population genetics. Genetics, 174, 805-816. – reference: Jay F, Manel S, Alvarez N, et al. (2012) Forecasting changes in population genetic structure of alpine plants in response to global warming. Molecular Ecology, 21, 2354-2368. – reference: Atwell S, Huang YS, Vilhjálmsson BJ, et al. (2010) Genome-wide association study of 107 phenotypes in Arabidopsis thaliana inbred lines. Nature, 465, 627-631. – reference: Kim J, Park H (2011) Fast nonnegative matrix factorization: an active-set-like method and comparisons. SIAM Journal on Scientific Computing, 33, 3261-3281. – reference: Bazin E, Dawson KJ, Beaumont MA (2010) Likelihood-free inference of population structure and local adaptation in a Bayesian hierarchical model. Genetics, 185, 587-602. – reference: François O, Blum MG, Jakobsson M, Rosenberg NA (2008) Demographic history of European populations of Arabidopsis thaliana. PLoS Genetics, 4, e1000075. – reference: Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society, 57, 289-300. – reference: Frichot E, Mathieu F, Trouillon T, Bouchard G, François O (2014) Fast and efficient estimation of individual ancestry coefficients. Genetics, 196, 973-983. – reference: Wright S (1943) Isolation by distance. Genetics, 28, 114. – reference: Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics, 155, 945-959. – reference: Wollstein A, Lao O (2015) Detecting individual ancestry in the human genome. 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10.1534/genetics.109.112391 – ident: e_1_2_6_29_1 doi: 10.1007/s10592-009-0044-5 – ident: e_1_2_6_2_1 doi: 10.1186/1471-2105-12-246 – ident: e_1_2_6_19_1 doi: 10.1038/nrg2611 – volume-title: Les Mathématiques de l'Hérédité year: 1948 ident: e_1_2_6_24_1 – ident: e_1_2_6_28_1 doi: 10.1534/genetics.114.164350 – volume-title: Genetic Data Analysis II year: 1996 ident: e_1_2_6_30_1 – ident: e_1_2_6_5_1 doi: 10.1162/089976603321780317 – ident: e_1_2_6_25_1 doi: 10.1111/j.1365-294X.2010.04717.x – ident: e_1_2_6_26_1 doi: 10.1105/tpc.104.028530 – ident: e_1_2_6_15_1 doi: 10.1534/genetics.106.059923 – ident: e_1_2_6_31_1 doi: 10.1186/s13323-015-0019-x – volume-title: Spectral Graph Theory, Vol. 92 of Regional Conference Series in Mathematics year: 1997 ident: e_1_2_6_10_1 – ident: e_1_2_6_3_1 doi: 10.1038/nature08800 – ident: e_1_2_6_17_1 doi: 10.1111/2041-210X.12382 – ident: e_1_2_6_9_1 doi: 10.1111/j.1471-8286.2007.01769.x
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Snippet	Geography and landscape are important determinants of genetic variation in natural populations, and several ancestry estimation methods have been proposed to...
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SubjectTerms	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
Title	TESS3: fast inference of spatial population structure and genome scans for selection
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