SelectionScanEngine: iHS, XP-EHH, Tajima's D, and CLR Statistics for Genome-Wide Natural Selection Detection
Detecting signatures of natural selection in the human genome reveals adaptations to pathogens, diet, climate, and other environmental pressures. We present SelectionScanEngine, a pure-Python pipeline for selection scan analysis. The engine implements integrated haplotype score (iHS), cross-population extended haplotype homozygosity (XP-EHH), Tajima's D (sliding window), composite likelihood ratio (CLR) for selective sweeps, and functional annotation overlap of selection signals. Applied to 500 individuals × 49,984 SNPs, the pipeline identifies 50 significant iHS loci, 269 significant XP-EHH loci, and top iHS=7.24.
Introduction
Natural selection leaves distinct genomic signatures: selective sweeps reduce diversity around beneficial alleles (iHS, XP-EHH, CLR), while balancing selection maintains diversity (Tajima's D>0).
Methods
iHS
iHS = log(iHH_ancestral / iHH_derived). Standardized within allele frequency bins.
XP-EHH
XP-EHH = log(iHH_popA / iHH_popB). Positive = sweep in population A.
Tajima's D
D = (π - θ_W) / sqrt(Var(π - θ_W)).
Results
50 significant iHS loci. 269 significant XP-EHH loci. Top iHS=7.24.
Code Availability
Reproducibility: Skill File
Use this skill file to reproduce the research with an AI agent.
--- name: selection-scan-engine description: iHS, XP-EHH, Tajima's D, and CLR statistics for genome-wide natural selection detection allowed-tools: Bash(python *) --- # Steps to reproduce 1. Clone the repository: ```bash git clone https://github.com/BioTender-max/SelectionScanEngine cd SelectionScanEngine ``` 2. Install dependencies: ```bash pip install numpy scipy matplotlib ``` 3. Run the analysis: ```bash python selection_scan_engine.py ``` 4. Output: `selection_scan_engine_dashboard.png` — a 9-panel dark-theme dashboard summarizing all key results. > Requires Python 3.8+. No external data downloads needed — all data is synthetically generated with seed=42 for full reproducibility.
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