Genetic differentiation is related to the environmental heterogeneity and to the equilibrium between selection and gene flow. Both environmental variables and geography may affect genetic structure. Here, a single nucleotide polymorphism (SNP) genotyping was conduced on Abies alba Mill., Larix decidua L., Pinus cembra L. and Pinus mugo Turra, across their natural range in the Italian Alps and Apennines. Firstly, the presence of patterns of population structure was tested with Bayesian program, showing values of K between 3 and 5. Next, the genetic structure was correlated to a geographic factor, showing significant result only for P. cembra. To reduce dimensionality for the environmental variables a principal component analysis was used. Significant principal components (PC) were testing for association with SNPs through a Bayesian generalized linear mixed model. In all species, several SNPs were identified to be associated to PC1, corresponding to temperature and precipitation, in particular winter precipitation and seasonal minimum temperature. In A. alba, most SNPs were associated to PC2, corresponding to the seasonal minimum temperature.
Mosca, E.; Eckert, A.J.; Di Pierro, E.A.; Rocchini, D.; La Porta, N.; Neale, D.B. (2012). Patterns of genetic variation across four forest species. In: International Conference "Molecular Ecology", Vienna, 4-7 February 2012. handle: http://hdl.handle.net/10449/22477
Patterns of genetic variation across four forest species
Mosca, Elena;Di Pierro, Erica Adele;Rocchini, Duccio;La Porta, Nicola;
2012-01-01
Abstract
Genetic differentiation is related to the environmental heterogeneity and to the equilibrium between selection and gene flow. Both environmental variables and geography may affect genetic structure. Here, a single nucleotide polymorphism (SNP) genotyping was conduced on Abies alba Mill., Larix decidua L., Pinus cembra L. and Pinus mugo Turra, across their natural range in the Italian Alps and Apennines. Firstly, the presence of patterns of population structure was tested with Bayesian program, showing values of K between 3 and 5. Next, the genetic structure was correlated to a geographic factor, showing significant result only for P. cembra. To reduce dimensionality for the environmental variables a principal component analysis was used. Significant principal components (PC) were testing for association with SNPs through a Bayesian generalized linear mixed model. In all species, several SNPs were identified to be associated to PC1, corresponding to temperature and precipitation, in particular winter precipitation and seasonal minimum temperature. In A. alba, most SNPs were associated to PC2, corresponding to the seasonal minimum temperature.File | Dimensione | Formato | |
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