A RAPD, AFLP and SSR linkage map, and QTL analysis in European beech (Fagus sylvatica L.)

The genetic linkage map of European beech ( Fagus sylvatica L.) that we report here is the first to our knowledge. Based on a total of 312 markers (28 RAPDs, 274 AFLPs, 10 SSRs) scored in 143 individuals from a F(1) full-sib family. Two maps (one for each parent) were constructed according to a "two-way pseudo-testcross" mapping strategy. In the male map 119 markers could be clustered in 11 major groups (971 cM), while in the female map 132 markers were distributed in 12 major linkage groups (844 cM). In addition, four and one minor linkage groups (doublets and triplets) were obtained for the male and female map respectively. The two maps cover about 82% and 78% of the genome. Based on the position of 15 AFLP and 2 SSR loci segregating in both parents, seven homologous linkage groups could be identified. In the same pedigree we investigated the association with genetic markers of several quantitative traits: leaf area, leaf number and shape in 2 different years, specific leaf area, leaf carbon-isotope discrimination and tree height. A composite interval-mapping approach was used to estimate the number of QTLs, the amount of variation explained by each of them, and their position on the genetic linkage maps. Eight QTLs associated with leaf traits were found that explained between 15% and 35% of the trait variation, five on the female map and three on the male map

Data from: Effect of habitat fragmentation on the genetic diversity of peripheral populations of beech in Central Italy

Fragmentation can affect the demographic and genetic structure of populations near the boundary of their bio-geographic range. Higher genetic differentiation among populations coupled with lower level of within population variability is expected as a consequence of reduced population size and isolation. The effects of these two factors have been rarely disentangled. Given their high gene flow, anemophilous forest trees should be more affected, in terms of loss of genetic diversity, by small population size rather than geographic isolation alone. We studied the impact of distance from the main range (a measure of isolation) and reduced population size on the within and among population components of genetic variability. We assayed 11 isozyme loci in 27 marginal populations of European beech (Fagus sylvatica L.) in Central Italy. Populations were divided in three groups with an increasing level of fragmentation. In the most fragmented group the within population genetic variability was slightly smaller and the among population differentiation significantly larger than in the other two groups. These results support the role of random genetic drift having a larger impact on the most fragmented group, while gene flow seems to balance genetic drift in the two less fragmented ones. Given that average distance from the main range is not different between the intermediate and the most fragmented group, but average population size is smaller, we can conclude that gene flow is effective, even at relatively long distances, in balancing the effect of fragmentation if population size is not too small

Data and Readme

Data and Readm

Micro- and Macro-Geographic Scale Effect on the Molecular Imprint of Selection and Adaptation in Norway Spruce

<div><p>Forest tree species of temperate and boreal regions have undergone a long history of demographic changes and evolutionary adaptations. The main objective of this study was to detect signals of selection in Norway spruce (<i>Picea abies</i> [L.] Karst), at different sampling-scales and to investigate, accounting for population structure, the effect of environment on species genetic diversity. A total of 384 single nucleotide polymorphisms (SNPs) representing 290 genes were genotyped at two geographic scales: across 12 populations distributed along two altitudinal-transects in the Alps (micro-geographic scale), and across 27 populations belonging to the range of Norway spruce in central and south-east Europe (macro-geographic scale). At the macrogeographic scale, principal component analysis combined with Bayesian clustering revealed three major clusters, corresponding to the main areas of southern spruce occurrence, i.e. the Alps, Carpathians, and Hercynia. The populations along the altitudinal transects were not differentiated. To assess the role of selection in structuring genetic variation, we applied a Bayesian and coalescent-based <i>F</i>_ST-outlier method and tested for correlations between allele frequencies and climatic variables using regression analyses. At the macro-geographic scale, the <i>F</i>_ST-outlier methods detected together 11 <i>F</i>_ST-outliers. Six outliers were detected when the same analyses were carried out taking into account the genetic structure. Regression analyses with population structure correction resulted in the identification of two (micro-geographic scale) and 38 SNPs (macro-geographic scale) significantly correlated with temperature and/or precipitation. Six of these loci overlapped with <i>F</i>_ST-outliers, among them two loci encoding an enzyme involved in riboflavin biosynthesis and a sucrose synthase. The results of this study indicate a strong relationship between genetic and environmental variation at both geographic scales. It also suggests that an integrative approach combining different outlier detection methods and population sampling at different geographic scales is useful to identify loci potentially involved in adaptation.</p></div

5 Fagaceae Trees

Worldwide, there are more than 1,000 species belonging to the Fagaceae. All Fagaceae species are woody plants and are spread throughout the northern hemisphere, from the tropical to the boreal regions. The family comprises seven genera (Govaerts and Frodin 1998), and the number of species is extremely variable among genera: Castanea (12), Castanopsis (100 to 200), Chrysolepis (2), Fagus (11), Lithocarpus (300), Quercus (450 to 600), Trigonobalanus (3).Oaks (Quercus), chestnuts (Castanea), and beeches (Fagus) are widely used in forestry for wood products over the three continents (Asia, Europe, and America) and are important economic species. Consequently, they have received more attention in forest genetic research than other genera. In addition to their cultivation in forestry, chestnuts are also used for their fruit production and have been partially domesticated for that purpose. Castanopsis and Lithocarpus are important ecological components of the Asian flora and have recently been investigated for their biological diversity (Cannon and Manos 2003). The remaining genera comprise only a very few species and for the time being have been studied mainly in botany and taxonomy

Outlier detection using BayeScan results at the macro-geographic scale: populations assigned according their geographic position (<i>All populations</i>), according to all STRUCTURE clusters (<i>all-clusters</i>).

<p>Outlier detected using Arlequin with the neutral island and hierarchical island model assumptions; only loci highly significant (<i>P</i><0.0001) are reported in the table.</p><p>Outlier detection using BayeScan results at the macro-geographic scale: populations assigned according their geographic position (<i>All populations</i>), according to all STRUCTURE clusters (<i>all-clusters</i>).</p

Sampling sites included in the macro-geographic investigation with their labels (Pop ID), provenances (Country), sample sizes (N), altitude (E), geographic coordinates (Lat: latitude; Long: longitude) and values of annual mean temperature (bio01) and precipitation (bio12).

<p>Genetic variability across populations with mean values of observed (<i>H</i>_O) and expected heterozygosity (<i>H</i>_E) with its standard deviation (SD) and <i>F</i>_IS statistics per population over all loci with the gene diversity among individuals within population (1-Q_inter).</p><p>* bio01  =  Annual Mean Temperature; bio04  =  Temperature Seasonality (standard deviation *100); bio09  =  Mean Temperature of Driest Quarter; bio11  =  Mean Temperature of Coldest Quarter; bio12  =  Annual Precipitation; Precipitation data is mm.</p><p>Sampling sites included in the macro-geographic investigation with their labels (Pop ID), provenances (Country), sample sizes (N), altitude (E), geographic coordinates (Lat: latitude; Long: longitude) and values of annual mean temperature (bio01) and precipitation (bio12).</p

Bayesian cluster analysis using STRUCTURE [51].

<p>Log likelihood value (Ln(Pr(X|<i>K</i>)) of Pritchard plot is shown for micro and macro-geographic scales(<b>A</b>). Macro-geographic populations clustering according to the Bayesian method implemented in STRUCTURE (B). The population dot colours represent the cluster that includes the majority of individuals within populations. The species distribution range is in green (created using Q-GIS based on description from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0115499#pone.0115499-SchmidtVogt2" target="_blank">[25]</a>).</p

Summary of significant regression models according to the FDR (False Discovery Rate) method [66].

<p><i>P</i> is the test probability for the selected model, <i>P</i>_var is the variable (var) probability, <i>R</i> is the linear correlation coefficient. NS: not significant, NA = not present. Loci with a <i>P</i>_var<0.01 are in bold.</p><p>Summary of significant regression models according to the FDR (False Discovery Rate) method <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0115499#pone.0115499-Storey1" target="_blank">[66]</a>.</p