LANDSCAPE GENOMICS PIPELINE IN R

PART 4 - LOCAL ADAPTATION ANALYSES - IDENTIFICATION OF CANDIDATES LOCI UNDER SELECTION

These suit of scripts were developed based on LanGen_pipeline from @rojaff/LanGen_pipeline

Scripts in R for different analyses of Landscape Genomics using SNPS, VCF, or Genind objects. These scripts perform the following steps:

1. Filter datasets according to environmental/phenotype variables for GEA (Genome-Environment Association) and GPA (Genome-Phenotype Association) studies.

2. Predict values of a variable in the case Pilocarpine, using Machine Learning Algorithms.

3. Select variables for GEA and GPA analyses.

4a. Partial RDA for GEA analyses to identify candidate SNPs in a multivariate approach.

4b. Partial RDA for GPA analyses to identify candidate SNPs in a multivariate approach.

5a. LFMM2 for GEA analyses to identify candidate SNPs in a univariate approach.

5b. LFMM2 for GPA analyses to identify candidate SNPs in a univariate approach.

6a. Final RDA for GEA analyses comparing candidate SNPs from both approaches.

6b. Final RDA for GPA analyses comparing candidate SNPs from both approaches.

7. Venn Diagramm comparing candidate SNPs from both approaches in GEA and GPA.

8. Polygenic Scores for candidate SNPs in GPA studies.

9a. sPCA using candidate SNPs in GEA studies to map adaption in the study area.

9a. sPCA using candidate SNPs in GPA studies to map adaption in the study area.

10. Allele frequency heatmap using candidate SNPs for Pilocarpine in each population.

PART 1 - GENETIC STRUCTURE AND GENETIC DIVERSITY https://github.com/jdalapicolla/LanGen_pipeline_version2

PART 2 - ISOLATION BY DISTANCE AND FINE-SCALE SPATIAL GENETIC STRUCTURE https://github.com/jdalapicolla/IBD_models.R

PART 3 - ISOLATION BY RESISTANCE USING MLPE MODELS - https://github.com/jdalapicolla/MLPE.R

PART 4 - LOCAL ADAPTATION ANALYSES - IDENTIFICATION OF CANDIDATES LOCI UNDER SELECTION - https://github.com/jdalapicolla/LOCAL_ADAPTATION.R