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Identifying genetic signatures of selection in a non-model species, alpine gentian (Gentiana nivalis L.), using a landscape genetic approach. / Bothwell, H.; Bisbing, S.; Therkildsen, Nina Overgaard; Crawford, L.; Holderegger, R.; Alvarez, N.; Manel, S.

In: Conservation Genetics, Vol. 14, No. 2, 2013, p. 467-481.

Publication: Research - peer-reviewJournal article – Annual report year: 2012

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Bothwell, H.; Bisbing, S.; Therkildsen, Nina Overgaard; Crawford, L.; Holderegger, R.; Alvarez, N.; Manel, S. / Identifying genetic signatures of selection in a non-model species, alpine gentian (Gentiana nivalis L.), using a landscape genetic approach.

In: Conservation Genetics, Vol. 14, No. 2, 2013, p. 467-481.

Publication: Research - peer-reviewJournal article – Annual report year: 2012

Bibtex

@article{820f7987686d45f9ade47dd0278ea3b3,
title = "Identifying genetic signatures of selection in a non-model species, alpine gentian (Gentiana nivalis L.), using a landscape genetic approach",
keywords = "Adaptive genetic variation, landscape genetics, Allele distribution models, outlier locus detection, principles coordinates of neighbor matrices, Climate change",
author = "H. Bothwell and S. Bisbing and Therkildsen, {Nina Overgaard} and L. Crawford and R. Holderegger and N. Alvarez and S. Manel",
year = "2013",
doi = "10.1007/s10592-012-0411-5",
volume = "14",
pages = "467--481",
journal = "Conservation Genetics",
issn = "1566-0621",
publisher = "Springer Netherlands",
number = "2",

}

RIS

TY - JOUR

T1 - Identifying genetic signatures of selection in a non-model species, alpine gentian (Gentiana nivalis L.), using a landscape genetic approach

AU - Bothwell,H.

AU - Bisbing,S.

AU - Therkildsen,Nina Overgaard

AU - Crawford,L.

AU - Holderegger,R.

AU - Alvarez,N.

AU - Manel,S.

PY - 2013

Y1 - 2013

N2 - It is generally accepted that most plant populations are locally adapted. Yet, understanding how environmental forces give rise to adaptive genetic variation is a challenge in conservation genetics and crucial to the preservation of species under rapidly changing climatic conditions. Environmental variation, phylogeographic history, and population demographic processes all contribute to spatially structured genetic variation, however few current models attempt to separate these confounding effects. To illustrate the benefits of using a spatially-explicit model for identifying potentially adaptive loci, we compared outlier locus detection methods with a recently-developed landscape genetic approach. We analyzed 157 loci from samples of the alpine herb Gentiana nivalis collected across the European Alps. Principle coordinates of neighbor matrices (PCNM), eigenvectors that quantify multi-scale spatial variation present in a data set, were incorporated into a landscape genetic approach relating AFLP frequencies with 23 environmental variables. Four major findings emerged. 1) Fifteen loci were significantly correlated with at least one predictor variable (R <sub>adj </sub><sup>2</sup> &gt; 0.5). 2) Models including PCNM variables identified eight more potentially adaptive loci than models run without spatial variables. 3) When compared to outlier detection methods, the landscape genetic approach detected four of the same loci plus 11 additional loci. 4) Temperature, precipitation, and solar radiation were the three major environmental factors driving potentially adaptive genetic variation in G. nivalis. Techniques presented in this paper offer an efficient method for identifying potentially adaptive genetic variation and associated environmental forces of selection, providing an important step forward for the conservation of non-model species under global change

AB - It is generally accepted that most plant populations are locally adapted. Yet, understanding how environmental forces give rise to adaptive genetic variation is a challenge in conservation genetics and crucial to the preservation of species under rapidly changing climatic conditions. Environmental variation, phylogeographic history, and population demographic processes all contribute to spatially structured genetic variation, however few current models attempt to separate these confounding effects. To illustrate the benefits of using a spatially-explicit model for identifying potentially adaptive loci, we compared outlier locus detection methods with a recently-developed landscape genetic approach. We analyzed 157 loci from samples of the alpine herb Gentiana nivalis collected across the European Alps. Principle coordinates of neighbor matrices (PCNM), eigenvectors that quantify multi-scale spatial variation present in a data set, were incorporated into a landscape genetic approach relating AFLP frequencies with 23 environmental variables. Four major findings emerged. 1) Fifteen loci were significantly correlated with at least one predictor variable (R <sub>adj </sub><sup>2</sup> &gt; 0.5). 2) Models including PCNM variables identified eight more potentially adaptive loci than models run without spatial variables. 3) When compared to outlier detection methods, the landscape genetic approach detected four of the same loci plus 11 additional loci. 4) Temperature, precipitation, and solar radiation were the three major environmental factors driving potentially adaptive genetic variation in G. nivalis. Techniques presented in this paper offer an efficient method for identifying potentially adaptive genetic variation and associated environmental forces of selection, providing an important step forward for the conservation of non-model species under global change

KW - Adaptive genetic variation

KW - landscape genetics

KW - Allele distribution models

KW - outlier locus detection

KW - principles coordinates of neighbor matrices

KW - Climate change

U2 - 10.1007/s10592-012-0411-5

DO - 10.1007/s10592-012-0411-5

M3 - Journal article

VL - 14

SP - 467

EP - 481

JO - Conservation Genetics

T2 - Conservation Genetics

JF - Conservation Genetics

SN - 1566-0621

IS - 2

ER -