Advanced Analysis

This section covers advanced workflows and analysis modules extending the standard WGS/WES pipeline. These include family-based (trio/quad) analyses, joint-calling strategies for large cohorts, copy-number and mitochondrial genome analysis, and somatic tumor purity assessment and adjustment.

Trio and Family-Based Analysis

Overview

Family-based designs (e.g., trios: proband + parents; quads: proband + parents + sibling) enhance the detection of de novo variants, phase genotypes, and improve filtering accuracy.

Workflow

  1. Joint Genotyping Run HaplotypeCaller in GVCF mode for all family members → GenotypeGVCFs → single multi-sample VCF.

  2. Relationship Verification Use KING, PLINK, or Somalier to verify reported relationships and detect swaps.

  3. De Novo Detection Tools: - GATK VariantAnnotator with -A PossibleDeNovo - DeNovoGear - TrioDeNovo

    Example (GATK de novo annotation):

    gatk VariantAnnotator \
  -R refs/GRCh38.fa \
  -V family.joint.vcf.gz \
  -A PossibleDeNovo \
  --pedigree family.ped \
  -O family.denovo_annotated.vcf.gz

  4. Mendelian Inconsistency Checks Use bcftools +mendelian plugin to identify violations.

  5. Filtering and Prioritization - Restrict to high-confidence de novo calls (PASS, depth >10 in all samples). - Annotate with VEP or ANNOVAR to assess functional impact and allele frequency.

Tips - Include only variants in high-confidence regions (e.g., GIAB). - For exomes, restrict analysis to targets ± padding.

Joint Calling for Large Cohorts

Advantages - Consistent genotyping across samples. - Improved variant quality metrics (VQSR modeling). - Better sensitivity for rare variant discovery.

Best Practices - Always call variants in GVCF mode per sample. - Use GenomicsDBImport or CombineGVCFs for aggregation. - For >500 samples, use scatter/gather and sharded GenomicsDB workspaces.

Example:

gatk GenomicsDBImport \
  --genomicsdb-workspace-path cohort_gdb \
  --sample-name-map gvcf_samples.map \
  --reader-threads 8 \
  --batch-size 50

gatk GenotypeGVCFs \
  -R refs/GRCh38.fa \
  -V gendb://cohort_gdb \
  -O cohort.unfiltered.vcf.gz

Note

VQSR requires several thousand high-quality SNPs/indels for stable model training. For small cohorts, use hard filters.

Copy Number Variation (CNV) Analysis

Germline CNVs - Tools: GATK gCNV, XHMM, ExomeDepth (WES-specific). - Requires matched control cohort or PoN.

Example (GATK gCNV WES mode):

gatk PreprocessIntervals \
  -R refs/GRCh38.fa \
  --interval-merging-rule OVERLAPPING_ONLY \
  -L refs/targets_exome.bed \
  -O targets.interval_list

gatk AnnotateIntervals \
  -R refs/GRCh38.fa \
  -L targets.interval_list \
  -O targets.annotated.tsv

gatk CollectReadCounts \
  -I SAMPLE.recal.bam \
  -L targets.interval_list \
  --interval-merging-rule OVERLAPPING_ONLY \
  -O SAMPLE.counts.hdf5

Somatic CNVs - Tool: GATK ModelSegments with matched tumor-normal or tumor-only modes. - Input: read counts + allele counts from common SNPs.

Example (somatic CNV):

gatk CollectReadCounts \
  -I tumor.bam -L genome_intervals.interval_list \
  --interval-merging-rule OVERLAPPING_ONLY \
  -O tumor.counts.hdf5

gatk CollectAllelicCounts \
  -I tumor.bam -R refs/GRCh38.fa \
  -L snps.interval_list \
  -O tumor.allelicCounts.tsv

gatk ModelSegments \
  --denoised-copy-ratios tumor.denoisedCR.tsv \
  --allelic-counts tumor.allelicCounts.tsv \
  -O cnv_segments/

gatk CallCopyRatioSegments \
  -I cnv_segments/called_copy_ratios.seg \
  -O cnv_segments/cnv_calls.seg

Mitochondrial Variant Analysis

Mitochondrial variants can be called from WGS (and sometimes WES) using GATK Mutect2 in mitochondrial mode.

Example:

gatk Mutect2 \
  -R refs/GRCh38.fa \
  -I SAMPLE.recal.bam \
  --mitochondria-mode \
  -O SAMPLE.mitochondria.unfiltered.vcf.gz

gatk FilterMutectCalls \
  -V SAMPLE.mitochondria.unfiltered.vcf.gz \
  -O SAMPLE.mitochondria.filtered.vcf.gz

Note

For mitochondrial analysis, use a dedicated circularized mtDNA reference to minimize alignment artifacts.

Tumor Purity Estimation & Adjustment

Why it matters - Low purity can reduce somatic variant allele fractions (VAFs), impacting sensitivity.

Tools - PureCN (integrates CNV + VAF). - FACETS, ABSOLUTE. - Sequenza (tumor/normal only).

Example (FACETS tumor-normal):

Rscript run_facets.R \
  --tumor tumor.bam \
  --normal normal.bam \
  --genome hg38 \
  --output facets_results/

Adjustment Strategies - Lower –min-af in Mutect2 for low-purity tumors. - Use CNV-aware filtering to retain real variants with low VAF in amplified regions. - Flag likely subclonal variants for separate interpretation.

Integration and Reporting

  • Combine SNVs/indels, CNVs, and mtDNA calls into a unified multi-omic report.

  • Summarize per-sample key metrics: purity, ploidy, mutation burden, % genome altered.

  • Annotate variants with clinical relevance (COSMIC, ClinVar, OncoKB) — interpretation only.

Next Steps