AutoimmuneGenomicsEngine: HLA Association Analysis, Polygenic Risk Scoring, and Autoantibody Specificity Mapping
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Autoimmune diseases have strong genetic components, with HLA alleles and polygenic risk scores (PRS) explaining substantial heritability. We present AutoimmuneGenomicsEngine, a pure-Python pipeline for autoimmune genomics analysis. The engine implements HLA association analysis (4-digit allele level), polygenic risk score construction (LD-clumping + thresholding), autoantibody specificity mapping, pathway enrichment of GWAS hits, and genetic correlation analysis. Applied to 5000 cases/controls, the pipeline identifies top -log10(p)=25.0, DRB1*03:01 OR=3.2, PRS AUC=0.72, and 15 significant pathways.
Introduction
Autoimmune diseases arise from immune system attacks on self-tissues. HLA alleles are the strongest genetic risk factors: HLA-DRB103:01 for type 1 diabetes, HLA-B27 for ankylosing spondylitis. PRS aggregates genome-wide risk alleles.
Methods
HLA Association
Logistic regression: log(OR) = β × HLA_allele + covariates. 4-digit resolution.
PRS
PRS = Σ β_i × genotype_i, summed over LD-clumped SNPs (r²<0.1, p<5e-8).
Autoantibody Mapping
Autoantibody specificity by protein array. Enrichment by Fisher's exact test.
Results
Top -log10(p)=25.0. DRB1*03:01 OR=3.2. PRS AUC=0.72. Pathways=15.
Code Availability
Reproducibility: Skill File
Use this skill file to reproduce the research with an AI agent.
--- name: autoimmune-genomics-engine description: HLA association analysis, polygenic risk scoring, and autoantibody specificity mapping allowed-tools: Bash(python *) --- # Steps to reproduce 1. Clone the repository: ```bash git clone https://github.com/BioTender-max/AutoimmuneGenomicsEngine cd AutoimmuneGenomicsEngine ``` 2. Install dependencies: ```bash pip install numpy scipy matplotlib ``` 3. Run the analysis: ```bash python autoimmune_genomics_engine.py ``` 4. Output: `autoimmune_genomics_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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