Population Genetics Analysis Tutorial

1. Introduction

The script performs population genetics analyses using a VCF file as input. It calculates nucleotide diversity (π), Tajima's D, and FST in sliding windows across the genome. Additionally, it generates visualizations of the results.

2. Input Files

• input.vcf: The input VCF file containing genetic variants.
• pop1.txt & pop2.txt: Text files listing individuals from two populations for FST analysis.

3. Analysis Steps

3.1 Nucleotide Diversity (π)

The script calculates nucleotide diversity (π) in 10kb sliding windows using VCFtools:

vcftools --vcf input.vcf --window-pi 10000 --window-pi-step 5000 --out population_analysis_results/stats/pi

3.2 Tajima's D

Tajima's D is calculated for 10kb windows to detect selection signals:

vcftools --vcf input.vcf --TajimaD 10000 --out population_analysis_results/stats/tajima

3.3 Windowed FST

FST values between two populations are computed in 10kb sliding windows:

vcftools --vcf input.vcf --weir-fst-pop pop1.txt --weir-fst-pop pop2.txt --fst-window-size 10000 --fst-window-step 5000 --out population_analysis_results/stats/fst

4. Visualization

The script generates plots for each analysis using Python with Matplotlib and Seaborn.

4.1 Nucleotide Diversity (π)

Scatter plot of nucleotide diversity across genomic positions:

plt.scatter(pi['POS'], pi['PI'], s=10, alpha=0.7, color='darkorange')

4.2 Tajima's D

Tajima's D values plotted along genomic coordinates:

plt.scatter(tajima['POS'], tajima['D'], s=10, alpha=0.7, color='royalblue')

4.3 Windowed FST

FST values plotted to compare population differentiation:

plt.scatter(fst['POS'], fst['FST'], s=10, alpha=0.7, color='forestgreen')

5. Output Files

• population_analysis_results/stats/pi.windowed.pi: Nucleotide diversity results.
• population_analysis_results/stats/tajima.Tajima.D: Tajima's D values.
• population_analysis_results/stats/fst.windowed.weir.fst: Windowed FST values.
• population_analysis_results/plots/*.png: Visualization plots.
• population_analysis_results/report.html: HTML summary report.

6. Conclusion

This script automates population genomics analysis, making it easier to compute diversity statistics and visualize results in a structured manner.