Bio Vcf Statistics

bcftools stats

Goal: Generate comprehensive variant statistics including counts, Ti/Tv ratio, and quality distributions.

Approach: Run bcftools stats and parse section-tagged output lines (SN, TSTV, AF, QUAL, DP).

"How many variants are in this VCF?" → Compute summary counts, substitution types, and quality distributions from variant records.

Basic Statistics

bcftools stats input.vcf.gz > stats.txt

View Key Metrics

bcftools stats input.vcf.gz | grep "^SN"

Output sections:

• SN - Summary numbers
• TSTV - Transitions/transversions
• SiS - Singleton stats
• AF - Allele frequency distribution
• QUAL - Quality distribution
• IDD - Indel distribution
• ST - Substitution types
• DP - Depth distribution

Summary Numbers (SN)

bcftools stats input.vcf.gz | grep "^SN" | cut -f3-

Reports:

• Number of samples
• Number of records
• Number of SNPs
• Number of indels
• Number of multiallelic sites
• Number of multiallelic SNPs

Transition/Transversion Ratio

bcftools stats input.vcf.gz | grep "^TSTV"

Expected Ti/Tv ratio:

• Whole genome: ~2.0-2.1
• Exome: ~2.8-3.3

Per-Sample Statistics

bcftools stats -s - input.vcf.gz > per_sample.txt

Compare Two VCFs

bcftools stats input1.vcf.gz input2.vcf.gz > comparison.txt

Region-Specific Stats

bcftools stats -r chr1:1000000-2000000 input.vcf.gz > region_stats.txt

Exome Statistics

bcftools stats -R exome.bed input.vcf.gz > exome_stats.txt

Plotting Statistics

Goal: Visualize variant statistics as publication-quality plots.

Approach: Pipe bcftools stats output to plot-vcfstats to generate PDF and PNG plots.

Generate Plots

bcftools stats input.vcf.gz > stats.txt
plot-vcfstats -p output_dir stats.txt

Creates:

• output_dir/summary.pdf
• Individual PNG files

Comparison Plots

bcftools stats file1.vcf.gz file2.vcf.gz > comparison.txt
plot-vcfstats -p comparison_dir comparison.txt

bcftools gtcheck

Goal: Verify sample identity and detect sample swaps by comparing genotype concordance.

Approach: Use bcftools gtcheck to compute pairwise discordance rates between samples or against a reference panel.

Check Sample Identity

bcftools gtcheck -g reference.vcf.gz query.vcf.gz

Reports concordance between samples.

Detect Sample Swaps

bcftools gtcheck -G 1 input.vcf.gz > relatedness.txt

Compares all samples pairwise.

Output Format

DC  0  sample1  sample2  0.95  1234  1200

Fields:

• DC: Data type (discordance)
• Index
• Sample 1
• Sample 2
• Discordance rate
• Sites compared
• Discordant sites

Check Against Reference Panel

bcftools gtcheck -g 1000genomes.vcf.gz unknown_sample.vcf.gz

Quick Statistics with Query

Goal: Compute targeted summary statistics using bcftools query and shell tools.

Approach: Extract specific fields with bcftools query/view and aggregate with awk for counts, means, and distributions.

Count Variants

bcftools view -H input.vcf.gz | wc -l

Count by Type

# SNPs
bcftools view -v snps -H input.vcf.gz | wc -l

# Indels
bcftools view -v indels -H input.vcf.gz | wc -l

Count PASS Variants

bcftools view -f PASS -H input.vcf.gz | wc -l

Quality Distribution

bcftools query -f '%QUAL\n' input.vcf.gz | \
    awk '{sum+=$1; count++} END {print "Mean QUAL:", sum/count}'

Depth Distribution

bcftools query -f '%INFO/DP\n' input.vcf.gz | \
    awk '{sum+=$1; count++} END {print "Mean DP:", sum/count}'

Genotype Counts

# Count heterozygous sites per sample
bcftools query -f '[%GT\t]\n' input.vcf.gz | \
    awk -F'\t' '{for(i=1;i<=NF;i++) if($i=="0/1" || $i=="0|1") het[i]++}
        END {for(i in het) print "Sample", i, "het:", het[i]}'

Allele Frequency Spectrum

bcftools query -f '%INFO/AF\n' input.vcf.gz | \
    awk '{
        if($1<0.01) rare++
        else if($1<0.05) low++
        else if($1<0.5) common++
        else freq++
    } END {
        print "Rare (<1%):", rare
        print "Low (1-5%):", low
        print "Common (5-50%):", common
        print "Frequent (>50%):", freq
    }'

Sample Statistics

Goal: Compute per-sample variant counts, genotype distributions, and missingness rates.

Approach: Use bcftools query/view/stats per sample to tabulate sample-level metrics.

List Samples

bcftools query -l input.vcf.gz

Count Samples

bcftools query -l input.vcf.gz | wc -l

Per-Sample Variant Counts

for sample in $(bcftools query -l input.vcf.gz); do
    count=$(bcftools view -s "$sample" -H input.vcf.gz | \
        bcftools view -c 1 -H | wc -l)
    echo "$sample: $count"
done

Missing Genotypes per Sample

bcftools stats -s - input.vcf.gz | grep "^PSC"

cyvcf2 Statistics

Goal: Compute variant statistics programmatically in Python for custom analyses.

Approach: Iterate variants with cyvcf2, accumulate counts by type, and compute quality/genotype distributions with numpy.

Basic Counts

from cyvcf2 import VCF

stats = {'snps': 0, 'indels': 0, 'other': 0}

for variant in VCF('input.vcf.gz'):
    if variant.is_snp:
        stats['snps'] += 1
    elif variant.is_indel:
        stats['indels'] += 1
    else:
        stats['other'] += 1

print(f'SNPs: {stats["snps"]}')
print(f'Indels: {stats["indels"]}')
print(f'Other: {stats["other"]}')

Quality Statistics

from cyvcf2 import VCF
import numpy as np

quals = []
for variant in VCF('input.vcf.gz'):
    if variant.QUAL:
        quals.append(variant.QUAL)

quals = np.array(quals)
print(f'Mean QUAL: {np.mean(quals):.1f}')
print(f'Median QUAL: {np.median(quals):.1f}')
print(f'Min QUAL: {np.min(quals):.1f}')
print(f'Max QUAL: {np.max(quals):.1f}')

Genotype Distribution

from cyvcf2 import VCF

vcf = VCF('input.vcf.gz')
samples = vcf.samples

hom_ref = [0] * len(samples)
het = [0] * len(samples)
hom_alt = [0] * len(samples)
missing = [0] * len(samples)

for variant in vcf:
    for i, gt in enumerate(variant.gt_types):
        if gt == 0:
            hom_ref[i] += 1
        elif gt == 1:
            het[i] += 1
        elif gt == 3:
            hom_alt[i] += 1
        else:
            missing[i] += 1

for i, sample in enumerate(samples):
    print(f'{sample}: HOM_REF={hom_ref[i]}, HET={het[i]}, HOM_ALT={hom_alt[i]}, MISS={missing[i]}')

Transition/Transversion Calculation

from cyvcf2 import VCF

transitions = 0
transversions = 0

ti_pairs = {('A', 'G'), ('G', 'A'), ('C', 'T'), ('T', 'C')}

for variant in VCF('input.vcf.gz'):
    if not variant.is_snp:
        continue
    ref = variant.REF
    alt = variant.ALT[0]
    if (ref, alt) in ti_pairs:
        transitions += 1
    else:
        transversions += 1

ratio = transitions / transversions if transversions > 0 else 0
print(f'Transitions: {transitions}')
print(f'Transversions: {transversions}')
print(f'Ti/Tv ratio: {ratio:.2f}')

Common Workflows

Goal: Run standard QC and comparison workflows for variant call evaluation.

Approach: Combine bcftools stats, grep, and plot-vcfstats for before/after filtering comparisons and sample relatedness checks.

Quality Control Report

# Generate stats
bcftools stats input.vcf.gz > stats.txt

# Extract key metrics
echo "=== VCF Summary ==="
grep "^SN" stats.txt | cut -f3-

echo ""
echo "=== Ti/Tv Ratio ==="
grep "^TSTV" stats.txt | cut -f5

# Generate plots
plot-vcfstats -p qc_plots stats.txt

Compare Before/After Filtering

bcftools stats raw.vcf.gz filtered.vcf.gz > comparison.txt

echo "=== Before Filtering ==="
grep "^SN.*raw" comparison.txt | cut -f3-

echo ""
echo "=== After Filtering ==="
grep "^SN.*filtered" comparison.txt | cut -f3-

Sample Relatedness Check

bcftools gtcheck -G 1 cohort.vcf.gz > relatedness.txt
cat relatedness.txt

Quick Reference

Common Errors

Related Skills

• vcf-basics - View and query VCF files
• filtering-best-practices - Evaluate filter impact
• vcf-manipulation - Compare call sets
• variant-calling - Assess calling quality