Variant Calling (GATK/BCFtools): From Alignments to VCF

Variant calling converts aligned reads into hypotheses about differences from the reference. The hard part is not producing a VCF—it’s distinguishing true variants from artifacts (mapping bias, PCR, strand bias, low depth, contamination).

Typical DNA variant pipeline (high level)

QC & trim
Align to reference
Mark duplicates (if applicable)
Call variants
Filter (hard filters or VQSR)
Annotate + interpret

Example commands (illustrative)

# Basic calling with bcftools (example)
samtools mpileup -f reference.fa sample.bam \
  | bcftools call -mv -Oz -o sample.vcf.gz
bcftools index sample.vcf.gz

# Basic filtering idea (thresholds depend on experiment!)
bcftools filter -e 'DP<10 || QUAL<30' sample.vcf.gz -Oz -o sample.filtered.vcf.gz

How to read a VCF genotype

Genotypes in diploid samples are commonly:

0/0 homozygous reference
0/1 heterozygous
1/1 homozygous alternate
./. missing

Key fields

Field	Meaning
`DP`	Total depth
`AD`	Allelic depths (REF, ALT…)
`GQ`	Genotype quality
`AF`	Allele fraction (caller-defined)

Ti/Tv ratio across samples (example)

Ti/Tv is a quick plausibility metric (especially for exomes). Strange values can indicate calling issues or contamination.

Variant allele fraction histogram (example)

VAF shapes differ across germline vs somatic experiments, purity, copy number, and filters.

Common artifact checklist

Technical

Low complexity regions, repeats, segmental duplications
Strand bias (ALT supported mostly on one strand)
Position bias (ALT mostly at read ends)
Read-mapping ambiguity (low MAPQ)

Biological / design

Sample contamination / swaps
Unexpected ploidy / sex chromosomes
Tumor purity and subclonality (somatic)
Batch effects in capture/coverage