bsaseq 1.0.0

Bulk Segregant Analysis for QTL mapping from pooled whole-genome sequencing

bsaseq

Bulk Segregant Analysis for QTL mapping from pooled whole-genome sequencing data.

bsaseq identifies genomic loci controlling traits by comparing allele frequencies between phenotypically distinct bulked DNA pools. It provides a complete workflow from VCF input to annotated candidate genes.

Features

• Multi-sample bulk support (pool technical replicates)
• Sliding window analysis with tricube smoothing
• Z-score and G-statistic for candidate region detection
• Publication-quality genome-wide and regional plots
• Optional snpEff integration for variant annotation
• Gene-level candidate ranking

Installation

From PyPI (recommended)

pip install bsaseq

From source

git clone https://github.com/username/bsaseq.git
cd bsaseq
pip install -e .

Dependencies

Required:

• Python 3.9-3.13
• cyvcf2, numpy, scipy, matplotlib, click, rich

Optional:

• snpEff (for variant annotation)

Quick start

# Basic analysis
bsaseq run \
    --vcf joint_calls.vcf.gz \
    --high-bulk mutant_pool \
    --low-bulk wildtype_pool \
    --out results/my_analysis

# With multiple samples per bulk
bsaseq run \
    --vcf joint_calls.vcf.gz \
    --high-bulk "mut_rep1,mut_rep2" \
    --low-bulk "wt_rep1,wt_rep2" \
    --out results/my_analysis

# With annotation
bsaseq run \
    --vcf joint_calls.vcf.gz \
    --high-bulk mutant_pool \
    --low-bulk wildtype_pool \
    --out results/my_analysis \
    --annotate \
    --snpeff-db Sorghum_bicolor

Commands

Command	Description
bsaseq run	Complete BSA analysis pipeline
bsaseq samples	List sample names in VCF
bsaseq plot	Regenerate plots from existing output
bsaseq annotate	Annotate candidates with snpEff
bsaseq check-snpeff	Verify snpEff installation

Output files

File	Description
*_variants.tsv	Per-variant allele frequencies
*_windows.tsv	Sliding window statistics
*_regions.tsv	Candidate genomic regions
*_regions.bed	Candidate regions in BED format
*_candidates.tsv	Filtered candidate variants
*_annotated_candidates.tsv	Candidates with snpEff annotation
*_candidate_genes.tsv	Gene-level summary
*_summary.txt	Analysis summary report
*_genome_wide.png/pdf	Genome-wide Manhattan plot
*_region_*.png/pdf	Zoomed candidate region plots
*_af_distribution.png	Allele frequency diagnostics
*_depth_distribution.png	Read depth diagnostics

Parameters

Filtering

Parameter	Default	Description
--min-dp	10	Minimum read depth per sample
--max-dp	200	Maximum read depth (excludes repeats)
--min-gq	20	Minimum genotype quality
--min-qual	30	Minimum variant QUAL score

Window analysis

Parameter	Default	Description
--window-size	1000000	Window width in bp
--step-size	250000	Step between windows in bp
--min-variants	5	Minimum variants per window

Candidate detection

Parameter	Default	Description
--z-threshold	3.0	Z-score cutoff for significance
--mode	recessive	Inheritance mode (recessive/dominant)

Methodology

Delta allele frequency (delta-AF)

For each SNP, bsaseq calculates the difference in alternate allele frequency between the high bulk (mutant pool) and low bulk (wild-type pool):

delta_AF = AF_high - AF_low

For a recessive causal mutation:

• In the mutant bulk: individuals are homozygous mutant, so AF ~ 1.0
• In the wild-type bulk: individuals are homozygous reference, so AF ~ 0.0
• Expected delta_AF ~ 1.0 at the causal locus

Sliding window analysis

Variants are analyzed in overlapping genomic windows to reduce noise:

1. Tricube-weighted smoothing: Variants near window center contribute more
2. G-statistic: Tests for significant allele frequency differences
3. Z-score normalization: Genome-wide standardization for peak calling

Candidate region detection

Regions are identified where consecutive windows exceed the Z-score threshold. Adjacent significant regions within 500 kb are merged.

Candidate variant filtering

Within candidate regions, variants are filtered based on inheritance mode:

Mode	min_delta_AF	min_AF_high	max_AF_low
Recessive	0.8	0.9	0.1
Dominant	0.3	0.4	0.1

Input requirements

The VCF file must contain:

• Biallelic SNPs (multiallelic and indels are skipped)
• AD (allelic depth) FORMAT field for allele frequency calculation
• GQ (genotype quality) FORMAT field for filtering (optional but recommended)

Recommended variant calling:

# GATK HaplotypeCaller (recommended)
gatk HaplotypeCaller -R ref.fa -I mutant_pool.bam -I wildtype_pool.bam -O calls.vcf.gz

# bcftools mpileup (alternative)
bcftools mpileup -f ref.fa mutant_pool.bam wildtype_pool.bam | bcftools call -mv -O z -o calls.vcf.gz

Examples

Example 1: Basic recessive mutation mapping

bsaseq run \
    --vcf pooled_calls.vcf.gz \
    --high-bulk mutant \
    --low-bulk wildtype \
    --out results/analysis \
    --mode recessive

Example 2: Dominant trait with relaxed thresholds

bsaseq run \
    --vcf pooled_calls.vcf.gz \
    --high-bulk affected \
    --low-bulk unaffected \
    --out results/dominant \
    --mode dominant \
    --z-threshold 2.5 \
    --min-dp 5

Example 3: High-coverage data with annotation

bsaseq run \
    --vcf deep_seq.vcf.gz \
    --high-bulk mut1,mut2 \
    --low-bulk wt1,wt2 \
    --out results/annotated \
    --min-dp 30 \
    --max-dp 500 \
    --annotate \
    --snpeff-db Arabidopsis_thaliana

Example 4: Regenerate plots with different format

bsaseq plot \
    --windows results/analysis_windows.tsv \
    --regions results/analysis_regions.tsv \
    --variants results/analysis_variants.tsv \
    --out new_plots \
    --format pdf

Development

# Install with dev dependencies
pip install -e ".[dev]"

# Run tests
pytest tests/ -v

# Run with coverage
pytest tests/ --cov=bsaseq --cov-report=term-missing

# Type checking
mypy src/bsaseq

Citation

If you use bsaseq in your research, please cite:

bsaseq: A tool for Bulk Segregant Analysis of pooled sequencing data (in preparation)

License

MIT License - see LICENSE for details.