Bio Variant Calling Filtering Best Practices

Version Compatibility

Reference examples tested with: GATK 4.6+, bcftools 1.19+, cyvcf2 0.30+

Note: CNNScoreVariants is deprecated as of GATK 4.6.1.0 (replaced by NVScoreVariants, a PyTorch drop-in); VETS (ExtractVariantAnnotations/TrainVariantAnnotationsModel/ScoreVariantAnnotations) is BETA. Confirm tool availability with gatk --list on the installed build before scripting a pipeline.

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 my variant calls" -> Flag or remove low-quality variant sites, and separately null out untrustworthy per-sample genotypes, using a model matched to cohort size, platform, and organism.

• CLI: GATK VariantRecalibrator/ApplyVQSR (large cohorts), VariantFiltration (hard filters), ScoreVariantAnnotations (VETS), NVScoreVariants (single-sample DL); bcftools filter/view
• Python: cyvcf2 for custom per-variant logic

The governing principle

Filtering decides WHICH errors a callset keeps, not whether they exist. The two site-level paradigms fail in OPPOSITE regimes: VQSR (a ratio of two learned Gaussian-mixture densities over annotation space) collapses on small or exome cohorts; static hard thresholds discard real variants at scale. Two rules follow. First, site-level filtering ("is this SITE real?") and genotype-level filtering ("is this SAMPLE's genotype trustworthy?") are orthogonal -- both are needed, site first. Second, SNPs and indels have different error processes (base-calling/strand vs alignment-ambiguity-in-repeats) and different truth resources, so they are ALWAYS filtered separately then merged. None of these mistakes throws an error: the VCF stays structurally valid while the numbers are silently wrong.

Site-Level Filter Method Selection

Somatic data is a separate track: use GATK FilterMutectCalls, never VQSR or germline hard filters (the annotations and error model differ).

Methodology is evolving (VETS is displacing VQSR). Verify the current recommended path against the installed GATK version's "How to Filter variants" article before committing a pipeline.

VQSR -- Mechanism and Why It Breaks

Goal: Recalibrate a large jointly-genotyped human callset with a data-driven quality score.

Approach: Fit a Gaussian mixture model (GMM) to the annotation profile of known-true sites (positive model), bootstrap a second GMM on the low-probability-tail artifact sites (negative model), and score each variant by VQSLOD = log( P(annotations | positive) / P(annotations | negative) ). Then choose a truth-sensitivity TRANCHE rather than thresholding VQSLOD directly: a "99.7 tranche" is the VQSLOD cutoff that RETAINS 99.7% of the truth-set sites. Tranches are truth-set SENSITIVITIES, not FDRs.

Three load-bearing consequences the agent must respect:

• VQSR estimates full covariance matrices in ~6-8 annotation dimensions, so it needs tens of thousands of variants -- as practical GATK convention, a single deep WGS (which alone supplies millions of sites) OR ~30+ jointly-genotyped exomes. On a single exome or panel the model is non-identifiable or wildly overfit -- it may report "converged" while VQSLOD is garbage. This is the single most common real-world VQSR misuse.
• On exomes, DP must NOT be supplied as a VQSR annotation: capture depth tracks bait design, not truth, and injects a spurious signal.
• SNPs and indels are recalibrated in separate runs (-mode SNP, -mode INDEL) because indels are ~10x rarer and their GMM fails first (see governing principle).

# SNP recalibration: fit the GMM in annotation space against truth resources
gatk VariantRecalibrator \
    -R reference.fa -V cohort.vcf.gz \
    --resource:hapmap,known=false,training=true,truth=true,prior=15.0 hapmap.vcf.gz \
    --resource:omni,known=false,training=true,truth=true,prior=12.0 omni.vcf.gz \
    --resource:1000G,known=false,training=true,truth=false,prior=10.0 1000G.vcf.gz \
    --resource:dbsnp,known=true,training=false,truth=false,prior=2.0 dbsnp.vcf.gz \
    -an QD -an MQ -an MQRankSum -an ReadPosRankSum -an FS -an SOR \
    -mode SNP \
    -O snp.recal --tranches-file snp.tranches
# For exomes: OMIT -an DP (capture depth is uninformative of truth), add -an QD -an FS etc. only

# Apply the chosen truth-sensitivity tranche (keeps 99.7% of truth-set SNPs)
gatk ApplyVQSR \
    -R reference.fa -V cohort.vcf.gz \
    -mode SNP --recal-file snp.recal --tranches-file snp.tranches \
    --truth-sensitivity-filter-level 99.7 \
    -O snp.recalibrated.vcf.gz

Run the identical pair with -mode INDEL and the Mills/1000G gold-indel resource, then merge the recalibrated SNP and indel callsets. For a single exome or panel (too few variants for VQSR), replace this whole block with hard filters or NVScoreVariants.

GATK Hard Filters (SNPs and indels separately)

Goal: Flag artifacts with static, per-annotation thresholds when VQSR is inapplicable.

Approach: Split the callset by type (SelectVariants), apply type-appropriate OR-combined fail conditions with VariantFiltration, then merge. Each annotation targets an independent error mode; a variant fails if it violates ANY one.

"Filter my variants using GATK best practices" -> Apply GATK's recommended annotation cutoffs, separately for SNPs and indels.

# 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: FS loosened to 200, ReadPosRankSum tightened to -20, MQ/MQRankSum DROPPED
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"

The SNP/indel threshold difference is the point, not an inconsistency: real indels have messier local alignments in repeats, so strand bias (FS) is naturally higher and the gate is loosened to 200; spurious indels cluster at read ends, so ReadPosRankSum is tightened to -20. Mapping-quality metrics (MQ, MQRankSum) are dropped for indels because they are less diagnostic there and the truth model is weaker. Values are GATK-recommended lenient starting points -- verify against the installed version's docs and tune to the annotation histograms of the dataset.

The hom-alt "missing => PASS" trap

MQRankSum, ReadPosRankSum, and BaseQRankSum are rank-sum tests comparing ref- vs alt-supporting reads, so they are only DEFINED at heterozygous sites. At hom-alt sites there are no ref reads and the annotation is missing (.). GATK's VariantFiltration fires a filter only when the value is PRESENT and violates the cutoff -- a missing value PASSES. Anyone hand-writing the equivalent in bcftools MUST replicate this: guard every RankSum term with an explicit || INFO/X = ".", or every hom-alt variant silently fails and vanishes.

# GATK SNP hard filter (plus a QUAL>=30 floor, which is not part of GATK's canonical set)
# -- the "|| = \".\"" guard on each RankSum term lets hom-alt sites (undefined RankSums) pass
bcftools filter -i '
    QUAL >= 30 && (INFO/QD >= 2.0 || INFO/QD = ".") &&
    (INFO/FS <= 60.0 || INFO/FS = ".") && (INFO/MQ >= 40.0 || INFO/MQ = ".") &&
    (INFO/MQRankSum >= -12.5 || INFO/MQRankSum = ".") &&
    (INFO/ReadPosRankSum >= -8.0 || INFO/ReadPosRankSum = ".") &&
    (INFO/SOR <= 3.0 || INFO/SOR = ".")' raw_snps.vcf.gz -Oz -o snps_filtered.vcf.gz

Quality Metric Rationale

Site-Level vs Genotype-Level Filtering

The two are orthogonal and both are required, in order. Site filters (above) decide whether a SITE is real. Genotype filters set an individual sample's genotype to no-call (./.) when it is untrustworthy at an otherwise-passing site:

• GQ < 20 => set ./. (genotype confidence below 99%).
• DP < 8-10 => set ./. (too few reads for a confident diploid call, for WGS).
• Allele balance far from 0.5 at hets (e.g. alt fraction <0.2 or >0.8) => suspect mapping artifact, CNV, or contamination; derive from AD (GATK does not emit AB directly).

Ordering matters: apply genotype-level no-calls BEFORE computing cohort metrics (missingness, HWE, allele frequency). Computing HWE on a matrix full of low-GQ garbage genotypes manufactures spurious deviation. Pipeline: site filter -> genotype filter -> recompute cohort QC.

# Genotype-level: null out low-confidence genotypes, keeping the site
bcftools filter -S . -e 'FMT/GQ<20 | FMT/DP<8' passing_sites.vcf.gz -Oz -o gt_filtered.vcf.gz

bcftools filter -- Soft vs Hard

Goal: Flag (soft) or remove (hard) variants by expression on QUAL, INFO, and FORMAT fields.

Approach: -e excludes, -i includes; -s NAME writes a named FILTER label instead of dropping; bcftools view -f PASS extracts survivors at the end.

bcftools filter -e 'QUAL<30' input.vcf.gz -o filtered.vcf          # hard: drop failing
bcftools filter -s 'LowQual' -e 'QUAL<30' input.vcf.gz -o marked.vcf  # soft: label failing
bcftools view -f PASS marked.vcf -o passed.vcf                      # extract PASS survivors

Operators: < <= > >= = == != && || !. Aggregate over samples with MIN() MAX() AVG() SUM(). Guard against missing values explicitly (INFO/DP!="."), for the same hom-alt reason as above.

Somatic Variant Filtering

Goal: Filter tumor-normal somatic calls with the caller's own model, not germline thresholds.

Approach: Run GATK FilterMutectCalls with contamination and segmentation tables, then layer additional thresholds on TLOD and VAF.

gatk FilterMutectCalls -R reference.fa -V mutect2_raw.vcf \
    --contamination-table contamination.table \
    --tumor-segmentation segments.table \
    -O mutect2_filtered.vcf
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: Apply custom multi-metric per-variant logic in Python.

Approach: Iterate with cyvcf2, read QUAL/INFO fields, write survivors with Writer. INFO.get returns None for missing tags -- treat None as pass to avoid the hom-alt trap.

from cyvcf2 import VCF, Writer

vcf = VCF('input.vcf.gz')
writer = Writer('filtered.vcf', vcf)
for variant in vcf:
    qual = variant.QUAL or 0
    dp = variant.INFO.get('DP') or 1e9      # missing depth => do not fail on depth
    fs = variant.INFO.get('FS') or 0.0      # missing strand bias => pass (None -> 0)
    mq = variant.INFO.get('MQ') or 1e9      # missing MQ => pass
    if qual >= 30 and dp >= 10 and fs <= 60.0 and mq >= 40.0:
        writer.write_record(variant)
writer.close(); vcf.close()

Validate Filtering

Goal: Confirm filtering removed artifacts without stripping true variants.

Approach: Compare before/after bcftools stats; check Ti/Tv and Het/Hom against expected ranges and known-variant recovery. A filter that improves one metric while degrading another is miscalibrated.

bcftools stats input.vcf > before.txt
bcftools stats filtered.vcf | grep '^TSTV'                    # Ti/Tv after filtering
bcftools query -f '%FILTER\n' filtered.vcf | sort | uniq -c   # counts per FILTER label

If Ti/Tv drops after filtering, the filters are preferentially removing true transitions -- relax them. See variant-calling/vcf-statistics for the full QC panel (het/hom by ancestry, contamination, relatedness).

Region-Based Filtering

Stratify by genomic context; artifact-prone regions dominate false positives. Exclude with bcftools view -T ^regions.bed:

• ENCODE exclusion list (github.com/Boyle-Lab/Blacklist) -- anomalous-signal regions (centromeres, satellites).
• GIAB stratification BEDs -- low-complexity, segdups, tandem repeats; essential for honest benchmarking (bcftools isec against a GIAB truth set).
• LCR-hs38 (Heng Li) -- homopolymers and simple repeats where indel calling is unreliable.

Common Filtering Pitfalls

• Applying SNP thresholds to indels: distributions differ (FS, SOR, ReadPosRankSum). Always split by type first.
• Treating missing RankSum as failing: silently deletes every hom-alt site (see the missing => PASS trap).
• Running VQSR on a single exome or panel: too few variants, the GMM is non-identifiable (a single deep WGS is fine); use hard filters, VETS, or NVScoreVariants.
• Re-applying GATK hard filters on DeepVariant/DRAGEN output: their calibrated fields already encode quality; the GATK annotations may be absent or differently distributed.
• Filtering on depth alone: removes real variants in collapsed segdups; combine DP with MQ/MQRankSum.
• Choosing thresholds without looking: plot each annotation stratified by known TP (HapMap) vs likely FP and cut at the valley; GATK defaults are population-level starting points.

Common Errors

Related Skills

• variant-calling/variant-calling - Variant calling with bcftools to generate VCF files
• variant-calling/gatk-variant-calling - GATK HaplotypeCaller and joint genotyping upstream of VQSR
• variant-calling/deepvariant - Deep-learning caller whose output needs no separate site filter
• variant-calling/variant-annotation - Functional annotation after filtering
• variant-calling/variant-normalization - Left-align and decompose before filtering for consistent comparisons
• variant-calling/vcf-statistics - Ti/Tv, het/hom, contamination, and relatedness QC of filter effects
• variant-calling/vcf-basics - VCF field interpretation, PL/GQ/QUAL, and Number=A/R/G subsetting

References

• DePristo MA, Banks E, Poplin R, et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nature Genetics. 2011;43(5):491-498. -- VQSR foundational description.
• Van der Auwera GA, Carneiro MO, Hartl C, et al. From FastQ Data to High-Confidence Variant Calls: the GATK Best Practices pipeline. Current Protocols in Bioinformatics. 2013;43:11.10.1-11.10.33. -- hard-filtering and VQSR usage.
• Poplin R, Chang P-C, Alexander D, et al. A universal SNP and small-indel variant caller using deep neural networks. Nature Biotechnology. 2018;36(10):983-987. -- DeepVariant (DL-native calls need no separate filter).
• Danecek P, Bonfield JK, Liddle J, et al. Twelve years of SAMtools and BCFtools. GigaScience. 2021;10(2):giab008. -- bcftools filter/view/stats.