Bio Variant Calling Filtering Best Practices

Version Compatibility

Reference examples tested with: GATK 4.5+, bcftools 1.19+, numpy 1.26+

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show <package> then help(module.function) to check signatures
• CLI: <tool> --version then <tool> --help to confirm flags

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Variant Filtering Best Practices

Filter Selection Decision Tree

Is dataset large enough for VQSR? (>30 exomes or WGS)
├── Yes → Use VQSR (machine learning)
└── No → Use hard filters
    ├── Germline → GATK recommended thresholds
    └── Somatic → Caller-specific filters + manual review

GATK Hard Filter Thresholds

Goal: Apply GATK-recommended annotation thresholds to separate true variants from artifacts.

Approach: Use VariantFiltration with per-metric filter expressions for SNPs and indels separately.

"Filter my variants using GATK best practices" → Apply fixed annotation thresholds (QD, FS, MQ, SOR, RankSum) to flag low-quality variants.

# SNPs
gatk VariantFiltration \
    -R reference.fa \
    -V raw_snps.vcf \
    -O filtered_snps.vcf \
    --filter-expression "QD < 2.0" --filter-name "QD2" \
    --filter-expression "FS > 60.0" --filter-name "FS60" \
    --filter-expression "MQ < 40.0" --filter-name "MQ40" \
    --filter-expression "MQRankSum < -12.5" --filter-name "MQRankSum-12.5" \
    --filter-expression "ReadPosRankSum < -8.0" --filter-name "ReadPosRankSum-8" \
    --filter-expression "SOR > 3.0" --filter-name "SOR3"

# Indels
gatk VariantFiltration \
    -R reference.fa \
    -V raw_indels.vcf \
    -O filtered_indels.vcf \
    --filter-expression "QD < 2.0" --filter-name "QD2" \
    --filter-expression "FS > 200.0" --filter-name "FS200" \
    --filter-expression "ReadPosRankSum < -20.0" --filter-name "ReadPosRankSum-20" \
    --filter-expression "SOR > 10.0" --filter-name "SOR10"

Understanding Quality Metrics

bcftools filter

Goal: Filter variants using bcftools expression syntax with soft or hard removal.

Approach: Use -e (exclude) or -i (include) with expressions on QUAL, INFO, and FORMAT fields; use -s for soft filtering.

Soft vs Hard Filtering

# Hard filter (remove variants)
bcftools filter -e 'QUAL<30' input.vcf.gz -o filtered.vcf

# Soft filter (mark, don't remove)
bcftools filter -s 'LowQual' -e 'QUAL<30' input.vcf.gz -o marked.vcf
# Variants failing filter get "LowQual" in FILTER column

# Include instead of exclude
bcftools filter -i 'QUAL>=30' input.vcf.gz -o filtered.vcf

Expression Syntax

Aggregate Functions

Common bcftools Filters

# Basic quality filter
bcftools filter -i 'QUAL>30 && DP>10' input.vcf -o filtered.vcf

# Complex filter with multiple metrics
bcftools filter -i 'QUAL>30 && INFO/DP>10 && INFO/DP<500 && \
    (INFO/FS<60 || INFO/FS=".") && INFO/MQ>40' input.vcf -o filtered.vcf

# Genotype-level filters
bcftools filter -i 'FMT/DP>10 && FMT/GQ>20' input.vcf -o filtered.vcf

# Remove filtered sites
bcftools view -f PASS input.vcf -o passed.vcf

# Keep only biallelic SNPs
bcftools view -m2 -M2 -v snps input.vcf -o biallelic_snps.vcf

# Check for missing values
bcftools filter -e 'QUAL="."' input.vcf.gz  # Exclude missing QUAL
bcftools filter -i 'INFO/DP!="."' input.vcf.gz  # Include only if DP exists

bcftools view Filtering

Goal: Select variants by type, region, or sample using bcftools view.

Approach: Use type flags (-v/-V), region flags (-r/-R), and sample flags (-s/-S) for structured subsetting.

Filter by Variant Type

# SNPs only
bcftools view -v snps input.vcf.gz -o snps.vcf.gz

# Indels only
bcftools view -v indels input.vcf.gz -o indels.vcf.gz

# Exclude SNPs
bcftools view -V snps input.vcf.gz -o no_snps.vcf.gz

Filter by Region

bcftools view -r chr1:1000000-2000000 input.vcf.gz -o region.vcf.gz

# Multiple regions
bcftools view -r chr1:1000-2000,chr2:3000-4000 input.vcf.gz

Filter by Samples

# Include samples
bcftools view -s sample1,sample2 input.vcf.gz -o subset.vcf.gz

# Exclude samples
bcftools view -s ^sample3,sample4 input.vcf.gz -o subset.vcf.gz

Depth Filtering

Goal: Remove variants at extreme depth values that suggest mapping artifacts or duplications.

Approach: Calculate depth percentiles, then filter to the middle 90% of the distribution.

# Calculate depth percentiles
bcftools query -f '%DP\n' input.vcf | \
    sort -n | \
    awk '{a[NR]=$1} END {print "5th:", a[int(NR*0.05)], "95th:", a[int(NR*0.95)]}'

# Filter to middle 90% of depth distribution
bcftools filter -i 'INFO/DP>10 && INFO/DP<200' input.vcf -o depth_filtered.vcf

Allele Frequency Filters

Goal: Filter variants by minor allele frequency and allelic balance.

Approach: Apply thresholds on INFO/AF for population frequency and AD ratios for heterozygote balance.

# Minor allele frequency filter (population data)
bcftools filter -i 'INFO/AF>0.01 && INFO/AF<0.99' input.vcf -o maf_filtered.vcf

# Allele balance for heterozygotes
bcftools filter -i 'GT="het" -> (AD[1]/(AD[0]+AD[1]) > 0.2 && AD[1]/(AD[0]+AD[1]) < 0.8)' \
    input.vcf -o ab_filtered.vcf

Region-Based Filtering

Goal: Include or exclude variants based on genomic region annotations.

Approach: Use bcftools view with BED files to restrict to target regions or exclude blacklisted areas.

# Exclude problematic regions
bcftools view -T ^blacklist.bed input.vcf -o filtered.vcf

# Keep only exonic regions
bcftools view -R exons.bed input.vcf -o exonic.vcf

Sample-Level Filtering

Goal: Remove low-quality samples and sites with excessive missing genotypes.

Approach: Compute per-sample missingness with bcftools stats, exclude failing samples, and filter sites by F_MISSING threshold.

# Missing genotype rate per sample
bcftools stats -s - input.vcf | grep ^PSC | cut -f3,14

# Filter samples with >10% missing
bcftools view -S good_samples.txt input.vcf -o sample_filtered.vcf

# Filter sites with >5% missing genotypes
bcftools filter -i 'F_MISSING<0.05' input.vcf -o site_filtered.vcf

Variant Type-Specific Filters

Goal: Apply different quality thresholds to SNPs and indels.

Approach: Separate by type, apply type-appropriate thresholds (e.g., FS<60 for SNPs, FS<200 for indels), then merge back.

# Separate SNPs and indels for different filters
bcftools view -v snps input.vcf -o snps.vcf
bcftools view -v indels input.vcf -o indels.vcf

# Apply different thresholds
bcftools filter -i 'QUAL>30 && INFO/FS<60' snps.vcf -o snps_filtered.vcf
bcftools filter -i 'QUAL>30 && INFO/FS<200' indels.vcf -o indels_filtered.vcf

# Merge back
bcftools concat snps_filtered.vcf indels_filtered.vcf | bcftools sort -o merged.vcf

Multi-Step Pipeline Filtering

Goal: Chain multiple filter criteria in a single pipeline with optional soft filter labels.

Approach: Pipe through successive bcftools filter commands, using -s for named soft filters and -f PASS for final hard extraction.

bcftools filter -e 'QUAL<30' input.vcf.gz | \
    bcftools filter -e 'INFO/DP<10' | \
    bcftools view -v snps -Oz -o filtered_snps.vcf.gz

# Named soft filters
bcftools filter -s 'LowQual' -e 'QUAL<30' input.vcf.gz | \
    bcftools filter -s 'LowDepth' -e 'INFO/DP<10' -Oz -o marked.vcf.gz

# Later, remove all filtered variants
bcftools view -f PASS marked.vcf.gz -Oz -o pass_only.vcf.gz

Somatic Variant Filters

Goal: Filter somatic variants from tumor-normal calling with caller-specific criteria.

Approach: Use GATK FilterMutectCalls with contamination/segmentation tables, then apply additional bcftools thresholds on TLOD and VAF.

# Mutect2 specific
gatk FilterMutectCalls \
    -R reference.fa \
    -V mutect2_raw.vcf \
    --contamination-table contamination.table \
    --tumor-segmentation segments.table \
    -O mutect2_filtered.vcf

# Additional somatic filters
bcftools filter -i 'INFO/TLOD>6.3 && FMT/AF[0]>0.05 && FMT/DP[0]>20' \
    mutect2_filtered.vcf -o somatic_final.vcf

Python Filtering (cyvcf2)

Goal: Filter variants programmatically in Python for custom multi-metric criteria.

Approach: Iterate with cyvcf2, evaluate QUAL/INFO/FORMAT fields per variant, and write passing records with Writer.

from cyvcf2 import VCF, Writer
import numpy as np

vcf = VCF('input.vcf.gz')
writer = Writer('filtered.vcf', vcf)

for variant in vcf:
    qual = variant.QUAL or 0
    dp = variant.INFO.get('DP', 0)
    fs = variant.INFO.get('FS', 0)
    mq = variant.INFO.get('MQ', 0)

    if qual >= 30 and dp >= 10 and fs <= 60 and mq >= 40:
        writer.write_record(variant)

writer.close()
vcf.close()

Filter by Genotype

from cyvcf2 import VCF, Writer

vcf = VCF('input.vcf.gz')
writer = Writer('filtered.vcf', vcf)

for variant in vcf:
    # Keep if at least one sample is heterozygous
    # gt_types: 0=HOM_REF, 1=HET, 2=UNKNOWN, 3=HOM_ALT
    if 1 in variant.gt_types:
        writer.write_record(variant)

writer.close()
vcf.close()

Filter by Sample Depth

from cyvcf2 import VCF, Writer
import numpy as np

vcf = VCF('input.vcf.gz')
writer = Writer('filtered.vcf', vcf)

for variant in vcf:
    depths = variant.format('DP')
    if depths is not None and np.min(depths) >= 10:
        writer.write_record(variant)

writer.close()
vcf.close()

Validate Filtering

Goal: Assess the impact of filtering on variant quality and retention.

Approach: Compare before/after bcftools stats, check Ti/Tv ratio improvement, and count variants per filter label.

# Compare before/after stats
bcftools stats input.vcf > before_stats.txt
bcftools stats filtered.vcf > after_stats.txt

# Ti/Tv ratio (should be ~2.1 for WGS, ~2.8-3.3 for exomes)
bcftools stats filtered.vcf | grep 'TSTV'

# Count variants per filter
bcftools query -f '%FILTER\n' filtered.vcf | sort | uniq -c

Quick Reference

Common Errors

Related Skills

• variant-calling/variant-calling - Generate VCF files
• variant-calling/gatk-variant-calling - GATK VQSR
• variant-calling/variant-annotation - Annotation after filtering
• variant-calling/vcf-statistics - Evaluate filter effects