Part 4 - Local Adaptation Analyses - Identification of Candidates Loci Under Selection

This suit of scripts were developed based on LanGen_pipeline

For a similar pipeline without using VCFtools v.0.1.16, check our Populational Genomics Pipeline

 

Scripts:

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.

 

INITIAL PAGE

PART 1 - GENETIC STRUCTURE AND GENETIC DIVERSITY

PART 2 - ISOLATION BY DISTANCE AND FINE-SCALE SPATIAL GENETIC STRUCTURE

PART 3 - ISOLATION BY RESISTANCE USING MLPE MODELS