genomics-pipeline

Multi-Sample Comparison Guide

Ran the pipeline on two or more people? This guide explains how to compare their results for carrier screening, family planning, and inherited disease investigation.

Carrier Screening for Partners

The most immediately useful multi-sample analysis: checking whether both partners carry pathogenic variants in the same recessive gene. If so, each child has a 25% chance of being affected.

Quick Cross-Check

PARTNER_A="sergio"
PARTNER_B="annais"

# Extract pathogenic ClinVar hits for each partner
for SAMPLE in $PARTNER_A $PARTNER_B; do
  docker run --rm -v "${GENOME_DIR}:/genome" staphb/bcftools:1.21 \
    bcftools query -f '%CHROM\t%POS\t%REF\t%ALT\t%INFO/GENEINFO\n' \
      /genome/${SAMPLE}/clinvar/isec/0002.vcf \
    > /tmp/${SAMPLE}_clinvar_genes.txt
done

# Find genes where BOTH partners have pathogenic hits
cut -f5 /tmp/${PARTNER_A}_clinvar_genes.txt | cut -d: -f1 | sort -u > /tmp/genes_a.txt
cut -f5 /tmp/${PARTNER_B}_clinvar_genes.txt | cut -d: -f1 | sort -u > /tmp/genes_b.txt
comm -12 /tmp/genes_a.txt /tmp/genes_b.txt

If the output is empty: No shared recessive carrier risk detected. This is the most common result.

If genes appear in both lists: Check whether:

  1. Both variants are in the same gene (not just nearby genes)
  2. Both are classified as Pathogenic or Likely pathogenic (not VUS)
  3. The gene follows autosomal recessive inheritance (check OMIM or ClinVar)
  4. Both partners are heterozygous (carriers), not homozygous

Common Carrier Genes (Not Usually a Concern)

These genes frequently show up in ClinVar carrier screens. Being a carrier is very common and only relevant if your partner carries the same gene:

Gene Condition Carrier Frequency
CFTR Cystic fibrosis 1 in 25 (European)
GJB2 Hearing loss (DFNB1) 1 in 30
HFE Hemochromatosis 1 in 10 (H63D), 1 in 150 (C282Y)
MUTYH Colorectal cancer risk 1 in 50
SMN1 Spinal muscular atrophy 1 in 40-60
HEXA Tay-Sachs disease 1 in 30 (Ashkenazi), 1 in 300 (general)

Comparing Pharmacogenomics

PharmCAT results can differ dramatically between partners. Compare the HTML reports side by side for genes that affect commonly prescribed medications:

Gene One Partner is Rapid, Other is Poor? Clinical Impact
CYP2C19 PPIs, SSRIs, clopidogrel Different SSRI dosing needed
CYP2D6 Codeine, tramadol, psych meds Codeine dangerous for poor metabolizers
NAT2 Isoniazid, caffeine Different caffeine sensitivity
CYP2C9 Warfarin, NSAIDs Warfarin dosing differs

Parent-Child Analysis

If you have WGS data for a parent and child, you can investigate:

Inherited vs De Novo Variants

A de novo variant is one that appeared for the first time in the child (not present in either parent). These are rare (~50-100 per genome) and occasionally clinically significant.

PARENT="parent_name"
CHILD="child_name"

# Find variants in the child that are NOT in the parent
docker run --rm -v "${GENOME_DIR}:/genome" staphb/bcftools:1.21 \
  bcftools isec -C \
    /genome/${CHILD}/vcf/${CHILD}.vcf.gz \
    /genome/${PARENT}/vcf/${PARENT}.vcf.gz \
    -p /genome/${CHILD}/vcf/de_novo_candidates/

Note: This is a rough screen. True de novo detection requires trio analysis (both parents + child) and careful filtering for sequencing errors. Many variants flagged by bcftools isec will be false positives (present in the parent but missed by the variant caller due to low coverage at that position).

Carrier Inheritance Tracing

If the child is a carrier for a recessive condition, you can check which parent contributed the variant:

GENE_REGION="chr13:20189473-20189473"  # Example: GJB2 position

for SAMPLE in $PARENT $CHILD; do
  echo "--- ${SAMPLE} ---"
  docker run --rm -v "${GENOME_DIR}:/genome" staphb/bcftools:1.21 \
    bcftools view -r "$GENE_REGION" /genome/${SAMPLE}/vcf/${SAMPLE}.vcf.gz
done

Structural Variant Comparison

SVs called by multiple callers in one person have lower false-positive rates. SVs shared between family members add further confidence:

# Compare Manta SVs between two samples
# (Simple overlap check using bedtools-style comparison)
for SAMPLE in $PARTNER_A $PARTNER_B; do
  docker run --rm -v "${GENOME_DIR}:/genome" staphb/bcftools:1.21 \
    bcftools query -f '%CHROM\t%POS\t%INFO/END\t%INFO/SVTYPE\n' \
      /genome/${SAMPLE}/manta/results/variants/diploidSV.vcf.gz \
    > /tmp/${SAMPLE}_svs.bed
done

echo "Partner A SVs: $(wc -l < /tmp/${PARTNER_A}_svs.bed)"
echo "Partner B SVs: $(wc -l < /tmp/${PARTNER_B}_svs.bed)"

# Exact position matches (most stringent)
comm -12 <(sort /tmp/${PARTNER_A}_svs.bed) <(sort /tmp/${PARTNER_B}_svs.bed) | wc -l
echo "Shared SVs (exact match)"

Expected: Partners (unrelated) share very few SVs at exact positions. Parent-child pairs share ~50% of SVs.

Mitochondrial Haplogroup Comparison

Relationship Expected mtDNA Result
Partners Different haplogroups (unless same maternal lineage)
Siblings Identical haplogroup (same mother)
Mother-child Identical haplogroup
Father-child Different haplogroups (mtDNA is maternal only)

If siblings have different mitochondrial haplogroups, it may indicate different biological mothers (adoption, etc.) or a very rare paternal mtDNA inheritance event.

Telomere Length Comparison

Telomere length (step 10) is most informative when compared between samples of similar age sequenced on the same platform:

Caution: TelomereHunter’s tel_content is a rough estimate. Only use it for relative comparisons between samples processed identically. Do not compare with values from other labs, papers, or sequencing platforms.

What This Pipeline Cannot Do (Yet)

These are planned for future pipeline versions.

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