The code (.qmd files) used to generate the following reports can be found in the folder /reports.
-
FormattingScotsPineGenomicData.html Scots pine genomic data - formatting (e.g., replacing 'AA', 'AB', 'BB' etc by 0, 1, 2); filtering (based on the proportion of missing data for each SNP/individual, minor allele count and minor allele frequencies for each SNP); SNP position on the genome; imputation of missing data based on the most common allele within the family.
-
PopulationGeneticStructureAndVariancePartioningScotsPine.html Exploring the neutral population genetic structure in Scots pine with a Principal Component Analysis (PCA) and estimating the relative contribution of climate, population genetic structure and geography in explaining Scots pine genomic variation.
-
ClimaticDataScotsPine.html Extracting and exploring climatic data at the location of the Scots pine populations.
-
ExploringFormattingCGdata.html Formatting and exploring the dataset with phenotypic data.
-
EstimatingPhenotypicPlasticityScotsPine.html Variance partioning and estimating phenotypic plasticity of Scots pine populations.
-
EstimatingNurseryEffectInverewe.html Estimating the nursery effect on height variation in 2016 and 2020 in Inverewe (FW), the field site in which trees come from three different nurseries.
-
EstimatingEvolvabilityScotsPine.html Estimating narrow-sense heritability, evolvability and family variance at the species and population level in Scots pine for height (height in 2014 and 2020).
-
ExploreChessCapeClimaticData.html Extracting and visualizing the CHESS-SCAPE climatic data at the location of the studied populations.
-
Shinny app to vizualise the differences between current and future climates at the location of the populations: https://juliettearchambeau.shinyapps.io/VizClimateDifferencesScotsPine/.
-
Shinny app to vizualise the correlations among the selected climatic variables: https://juliettearchambeau.shinyapps.io/VizClimateDifferencesScotsPine_SelectedVariables/.
-
-
IdentifyingCandidateSNPsScotsPine.html Identifying outlier SNPs (i.e., potential candidate for adaptation to climate) with Redundancy Analysis (correcting or not for population structure) and predicting the genomic offset with the RDA.