Bio Variant Calling Filtering Best Practices

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