Burden Testing Pipeline

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📉 Overview

This pipeline supports gene-based burden testing of rare variants using SKAT and related methods. It is designed for flexibility in variant filtering, population structure correction, and real-case sample setups. It includes options for covariates, QC visualization, and multiple filtering combinations to adapt to clinical or family-based studies.

All scripts are richly commented to guide users through the logic, assumptions, and interpretation of each step.

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🧬 Variant Filtering: variant_filtering_main.R

Filtering is a critical step to ensure that the burden tests focus on variants most likely to affect gene function and contribute to disease. This script enables:

• Support for multiple file formats (e.g., subject-based tables or binary presence/absence matrices)
• Filtering by functional consequence (e.g., missense, nonsense)
• Frequency pruning using sources like gnomAD
• Prioritization by functional scores (e.g., CADD)
• Segregation filtering (e.g., ≥ N affected individuals in a family, absent in controls/outgroup)
• Optional filtering by clinical annotations, inheritance model, or panel membership
• Option to aggregate and test variants by custom genomic regions (provided as BED or TSV format), instead of by gene

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🧪 Burden Testing: burden_testing_pipeline.R

This script performs gene-based rare variant association testing with several statistical methods:

• SKAT and SKAT-O: variance-component tests
• CMC, CAST, and ACAT: burden-style tests
• Mixed Models: correct for relatedness, ancestry, batch using covariates
• Outputs: results are saved in TSV format and ready for downstream analysis

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📊 Additional Modules and Considerations

• QC Plots: PCA plot, QQ plot, variant count histograms, volcano plot
• Sample Size Guidance: Power estimation functions available in SKAT

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🧾 Usage Example

Filtering step:

Rscript variant_filtering_main.R \
  --input data/input.tsv \
  --output data/filtered.tsv \
  --format subjects \
  --min_carriers 3 \
  --af_max 0.01 \
  --cadd_min 15

Burden test step:

Rscript burden_testing_pipeline.R \
  --geno data/geno.tsv \
  --pheno data/pheno.tsv \
  --covar data/covar.tsv \
  --out results/burden_results.tsv \
  --plots results/plots \
  --summary results/summary_stats.tsv

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📚 Full Documentation

For complete explanation of methods and advanced usage:
→ docs/README.pdf

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📬 Contact & License

Author: Sally Yepes (sallyepes233@gmail.com)
License: MIT