You've run the pipeline. Now you have directories full of VCFs, TSVs, and HTML reports. This guide explains what to look at first and what it all means — no bioinformatics degree required.
If this is your first time looking at your own genomic data, the numbers can be alarming. Here is what a completely normal, healthy person's genome looks like:
| Finding | Normal Range | Why It Seems Scary |
|---|---|---|
| Total variants | 4.5-5.5 million | Sounds like millions of "mutations" — but >99.9% are normal human variation |
| ClinVar pathogenic hits (step 6) | 0-10 | Step 6 screens against Pathogenic/Likely_pathogenic only. Most hits are recessive carriers — you need TWO copies to be affected |
| HIGH impact variants (VEP) | 100-150 | Most are heterozygous in non-essential genes. For recessive genes, one copy is typically tolerated (but see haploinsufficiency). |
| Structural variants | 5,000-10,000 | Most are in non-coding regions. Your parents had them too. |
| Heteroplasmic mitochondrial variants | 20-40 | Low-level heteroplasmy (<5%) is universal and age-related |
| VUS (Variants of Uncertain Significance) | 20-200+ | "Uncertain" means not enough data yet, not "probably bad" |
The single biggest source of unnecessary anxiety in personal genomics is VUS — Variants of Uncertain Significance. These are variants where:
- There is not enough scientific evidence to classify them as either pathogenic or benign
- The vast majority will eventually be reclassified as benign as more data accumulates
- They are not actionable — no clinical decision should be made based on a VUS
- CPSR may report dozens or hundreds of VUS. This is normal and expected.
Rule of thumb: If a variant is classified as VUS, treat it the same as if it were not tested. Do not change screening or management based on a VUS. Check back in 1-2 years when ClinVar may have reclassified it.
One nuance: If you have a strong family history of a condition AND a VUS appears in the relevant high-penetrance gene (e.g., BRCA1/2, TP53, MLH1/MSH2), it may be worth mentioning to a genetic counselor — not to act on the VUS, but because the family history itself may warrant enhanced screening regardless of the variant's classification.
Not all ClinVar classifications are equally reliable. Each entry has a review status indicated by stars:
| Stars | Review Status | Reliability |
|---|---|---|
| 0 | No assertion criteria | Low — submitter did not explain their reasoning |
| 1 | Single submitter, criteria provided | Moderate — one lab's interpretation |
| 2 | Two or more submitters, no conflict | Good — multiple labs agree |
| 3 | Expert panel reviewed | High — reviewed by specialists |
| 4 | Practice guideline | Highest — established clinical standard |
Focus on 2+ star entries. Single-submitter (1-star) pathogenic calls are sometimes reclassified. If you find a scary-looking pathogenic variant with 0-1 stars, check the ClinVar entry directly at ncbi.nlm.nih.gov/clinvar — look at the "Review status" and "Last evaluated" date.
The most common "pathogenic" finding in any genome is heterozygous carrier status for recessive conditions. This means:
- You have ONE copy of a variant that causes disease when BOTH copies are affected
- For most recessive conditions, heterozygous carriers are not clinically affected (though some carrier states confer subtle phenotypic effects — e.g., sickle cell trait, HFE carriers and iron loading)
- The primary relevance is for family planning: if your partner carries the same gene, each child has a 25% chance of being affected
- Note — MUTYH: Biallelic (homozygous or compound het) MUTYH carriers have a well-established high colorectal cancer risk. For monoallelic (single-copy) carriers, the evidence is more nuanced: some meta-analyses show a modest risk increase, but a counseling framework for moderate-penetrance CRC genes (Genetics in Medicine) notes that risk estimates for monoallelic MUTYH are conflicting and that screening recommendations (e.g., earlier colonoscopy) were historically tied to carriers with a CRC family history. Discuss with a genetic counselor, especially if you have a family history of CRC
- Examples: GJB2 (hearing loss), CFTR (cystic fibrosis), HFE (hemochromatosis)
What it tells you: Known pathogenic variants in your genome, as classified by ClinVar (NCBI's public database of clinically significant variants).
Where to look: ${SAMPLE}/clinvar/
How to read it:
- Each line in the output VCF is a variant in your genome that matches a known ClinVar entry
- Step 6 intersects against the pathogenic-only ClinVar subset (Pathogenic + Likely_pathogenic). Every hit in this output is at a position ClinVar classifies as disease-associated — benign/VUS entries are excluded at the database level
- The
CLNSIGfield confirms the classification
What to expect:
- 0-10 pathogenic/likely pathogenic hits is typical for a 30X WGS
- Most pathogenic hits are carrier status (heterozygous) for recessive conditions — you carry one copy but aren't affected
- A heterozygous pathogenic variant in a recessive gene (like GJB2 for hearing loss) means you're a carrier, not affected
- A homozygous pathogenic variant, or a heterozygous variant in a dominant gene, needs attention
When to worry:
- Pathogenic variant in a dominant gene (one copy is enough to cause disease)
- Two pathogenic variants in the same recessive gene (one from each parent)
- Any variant in cancer predisposition genes (BRCA1, BRCA2, MLH1, MSH2, etc.)
What it tells you: How your genes affect drug metabolism. These results are clinically relevant and should be shared with your prescribing physician.
Where to look: ${SAMPLE}/pharmcat/ (Nextflow) or ${SAMPLE}/vcf/ (bash scripts) — PharmCAT writes its reports there. Open the HTML report in a browser.
Key genes to check:
| Gene | Affects | Common Impact |
|---|---|---|
| CYP2C19 | PPIs, clopidogrel, SSRIs, voriconazole | Rapid metabolizers burn through drugs too fast |
| CYP2D6 | Codeine, tramadol, tamoxifen, many psych meds | Poor metabolizers get toxic buildup |
| CYP2C9 | Warfarin, NSAIDs, phenytoin | Dose adjustment needed |
| DPYD | 5-fluorouracil (cancer drug) | Poor metabolizers can die from standard doses |
| SLCO1B1 | Statins (simvastatin, atorvastatin) | Increased myopathy risk |
| NAT2 | Isoniazid (TB), caffeine | Slow acetylators have more side effects |
| UGT1A1 | Irinotecan, atazanavir | *28/*28 = Gilbert syndrome (elevated bilirubin) |
What to do: Share the PharmCAT report with your prescribing physician or pharmacist. PharmCAT is a research tool — its authors explicitly note that missing positions, unphased input, and undetected structural variation (especially CYP2D6) can affect genotype and phenotype calls. The report is a valuable starting point for pharmacogenomic-guided prescribing, but clinical confirmation may be warranted before making medication changes, especially for high-risk drugs (DPYD, CYP2D6-dependent opioids).
What it tells you: Cancer predisposition screening using CPSR's curated cancer gene panels (panel 0 covers 500+ genes). This is broader than the 81-gene ACMG SF v3.2 list and focused specifically on cancer predisposition.
Where to look: ${SAMPLE}/cpsr/ — open the HTML report in a browser.
How to read it:
- Variants are classified into tiers:
- Tier 1: Pathogenic / Likely pathogenic — needs clinical attention
- Tier 2: Variant of Uncertain Significance (VUS) with some evidence
- Tier 3: VUS with limited evidence
- Tier 4: Likely benign / Benign
- Focus on Tier 1 variants only for clinical action
Unlike SNPs (single letter changes), structural variants are large rearrangements:
- Deletions (DEL): A chunk of DNA is missing
- Duplications (DUP): A chunk is copied extra times
- Inversions (INV): A chunk is flipped backwards
- Translocations (BND): A chunk moved to a different chromosome
- Insertions (INS): New DNA inserted
If you run Manta or Delly, you will see many BND calls — often hundreds or thousands. BND indicates a "breakend" where one end of a read pair maps to a different chromosome or a distant location. This sounds alarming ("translocation!") but:
- Most BND calls are artifacts of repetitive regions, segmental duplications, or mobile elements
- A typical genome has 1,000-3,000 BND calls from Manta and 5,000+ from Delly
- Fewer than 5 are likely real inter-chromosomal translocations in a healthy genome
- BND calls require multi-caller support (called by both Manta and Delly at overlapping breakpoints) to be considered credible
- Unless a BND disrupts a known disease gene AND is confirmed by a second caller, it can be safely ignored
A typical human genome has:
- ~5,000-10,000 structural variants total
- Most are in non-coding regions and harmless
- ~5-20 may affect genes
- 0-2 may be clinically significant
If you ran multiple SV callers:
- Called by 2+ callers (Manta + Delly, or Manta + CNVnator): Lower false-positive rate
- Called by 1 caller only: Lower confidence, may be false positive
- duphold DHFFC < 0.7 for deletions: High confidence (depth drops as expected)
- duphold DHBFC > 1.3 for duplications: High confidence (depth rises as expected)
The AnnotSV TSV (step 5) adds clinical annotations to each SV:
ACMG_class: 1 (benign) to 5 (pathogenic)Overlapped_CDS_percent: How much of a gene is affected- Focus on SVs with
ACMG_class4 or 5 that overlap known disease genes
Some regions of DNA have short sequences repeated many times (e.g., CAG CAG CAG...). When the number of repeats exceeds a threshold, it can cause disease.
The output VCF lists each tested locus with the number of repeats found. Key loci:
| Locus | Gene | Normal | Intermediate / Premutation | Pathogenic | Disease |
|---|---|---|---|---|---|
| HTT | HTT | <=26 | 27-35 (mutable normal); 36-39 (reduced penetrance) | >=40 (full penetrance) | Huntington's disease |
| FMR1 | FMR1 | <45 | 45-54 (intermediate); 55-200 (premutation) | >200 | Fragile X syndrome |
| ATXN1 | ATXN1 | <33 | — | >39 | Spinocerebellar ataxia 1 |
| C9orf72 | C9orf72 | <24 | 24-30 (gray zone, lab cutoffs vary) | Typically hundreds-thousands | ALS / Frontotemporal dementia |
| DMPK | DMPK | <35 | 35-49 (premutation) | >=50 | Myotonic dystrophy type 1 |
HTT 36-39 repeats (reduced penetrance): Individuals in this range may or may not develop Huntington's disease. The risk increases with repeat length but is not certain. GeneReviews classifies >=40 as full penetrance and 36-39 as reduced penetrance. Alleles of 27-35 ("mutable normal") do not cause disease but may expand in offspring.
C9orf72 gray zone: The exact pathogenic threshold for C9orf72 is not established. Laboratory cutoffs are discordant (JNNP 2021 review). Clearly pathogenic expansions are typically hundreds to thousands of repeats. Short-read WGS has limited ability to size very large expansions accurately.
FMR1 intermediate zone (45-54 repeats): Not affected, but repeats may expand in offspring. Carriers should receive genetic counseling. Premutation (55-200) carries risk of FXTAS (males >50) and FXPOI.
"ALL CLEAR" means no locus exceeded its clearly pathogenic threshold. Intermediate-range results should be discussed with a genetic counselor.
Telomeres are protective caps at the ends of chromosomes that shorten with cell division. TelomereHunter measures tel_content — the normalized telomere read count — as a proxy for relative telomere content.
- Higher
tel_content= more telomeric reads (generally correlates with longer telomeres) - Lower
tel_content= fewer telomeric reads (generally correlates with shorter telomeres) - There is no universal "normal" range — compare between samples of similar age, sequenced on the same platform
- Typical
tel_contentfor 30X WGS: 300-800 (varies by sequencing platform and coverage)
- Not a clinical telomere length measurement. TelomereHunter was developed for cancer genome analysis, not as a validated healthy-population aging assay
- Not a "biological age" readout. While telomere length correlates with aging at a population level, it is a rough estimate of aging rate and is not established as a clinically important standalone risk marker for individuals (see Vaiserman & Krasnienkov, "Telomere Length as a Marker of Biological Age," 2021)
- Short-read WGS systematically underestimates telomere length compared to dedicated assays (TRF, FlowFISH)
- Useful only for relative comparisons between samples run on the same platform — not absolute measurements and not individual health predictions
Runs of Homozygosity (ROH) are long stretches where both copies of your DNA are identical. Everyone has some ROH, but extensive ROH can indicate:
- Parental relatedness (consanguinity)
- Uniparental disomy
- Population bottleneck effects
- Total ROH > 300 Mb: Suggests parental relatedness (first-cousin equivalent)
- Total ROH > 100 Mb but < 300 Mb: May indicate distant relatedness
- Total ROH < 100 Mb with all segments < 10 Mb: Normal for outbred populations
- Individual ROH segments > 10 Mb: Recent inbreeding event
- Many small ROH segments (1-5 Mb): Population-level background (Ashkenazi, Finnish, etc.)
Some apparent ROH near centromeres are artifacts of low-coverage sequencing in repetitive regions. These can be ignored.
Your mitochondrial haplogroup traces your maternal ancestry lineage. It's determined by the specific set of variants in your mitochondrial DNA (inherited only from your mother).
| Haplogroup | Origin | Notes |
|---|---|---|
| H | Western Europe | Most common in Europe (~40%) |
| U | Northern/Eastern Europe | Second most common (~15%) |
| T | Near East / Mediterranean | ~10% of Europeans |
| K | Near East | ~6% of Europeans, Ashkenazi ~30% |
| J | Near East | ~8% of Europeans |
| V | Iberian Peninsula / Scandinavia | ~4% |
| I | Near East / Europe | ~3% |
Mitochondrial haplogroups have weak associations with some diseases (Parkinson's, diabetes, longevity), but these are population-level statistics, not individual predictions. The main clinical value is in step 20 (GATK Mutect2 mitochondrial mode), which detects disease-causing mitochondrial variants and heteroplasmy.
VEP annotates every variant with:
- Consequence type: missense, nonsense, synonymous, splice site, etc.
- SIFT score: Predicts if amino acid change is tolerated (>0.05) or damaging (<0.05)
- PolyPhen score: Predicts if change is benign (<0.15), possibly damaging (0.15-0.85), or probably damaging (>0.85)
- gnomAD exome frequency: How common this variant is in gnomAD exome data
The single most useful annotation VEP adds is the gnomAD allele frequency — how common a variant is in the general population. This pipeline uses VEP's --af_gnomade flag, which annotates with gnomAD exome frequencies only (not the combined exome+genome dataset). This means non-coding variants outside exome capture regions will lack gnomAD frequency annotations even if they appear in the gnomAD genome dataset. For coding variants (the most clinically relevant), exome frequencies are well-powered.
Key principle: A variant that is common in healthy people is almost certainly benign, regardless of what any prediction tool says.
| gnomAD AF | Interpretation | Action |
|---|---|---|
| > 5% (0.05) | Common polymorphism | Benign. Ignore. |
| 1-5% | Low-frequency variant | Almost certainly benign |
| 0.1-1% | Uncommon | Probably benign, but check ClinVar |
| 0.01-0.1% | Rare | Worth investigating if in a disease gene |
| < 0.01% | Very rare | Potentially significant. Check ClinVar + literature |
| Absent | Novel or ultra-rare | Could be significant OR a sequencing artifact. Verify with a second method |
If a variant is "pathogenic" in ClinVar but has gnomAD AF > 1%: The ClinVar entry may be outdated or wrong. Truly pathogenic variants for severe diseases are almost always rare (< 0.1%) because natural selection removes them from the population.
For finding potentially significant variants in the annotated VCF:
# High-impact variants (loss of function: stop-gain, frameshift, splice donor/acceptor)
grep "HIGH" ${SAMPLE}_vep.vcf | grep -v "^#"
# Rare missense variants predicted damaging by both SIFT and PolyPhen
grep "missense_variant" ${SAMPLE}_vep.vcf | grep "deleterious" | grep "probably_damaging"
# Variants absent from gnomAD (novel/ultra-rare)
grep "missense_variant" ${SAMPLE}_vep.vcf | grep -v "gnomAD_AF"VEP classifies variant impact as:
| Impact | Types | Interpretation |
|---|---|---|
| HIGH | Stop gained, frameshift, splice donor/acceptor | Likely breaks the protein |
| MODERATE | Missense, in-frame insertion/deletion | Changes the protein, may or may not matter |
| LOW | Synonymous, splice region | Probably no functional effect |
| MODIFIER | Intronic, intergenic, UTR | Usually non-functional |
Everyone has ~100-150 HIGH impact variants. Most are in one copy (heterozygous) of non-essential genes. Don't panic at the number.
If you ran step 30, your VCF now includes quantitative pathogenicity scores beyond VEP's qualitative SIFT/PolyPhen predictions. These scores are widely used as computational evidence in variant interpretation (see ClinGen's PP3/BP4 calibration framework).
Scores all variant types (coding, non-coding, splice, regulatory). Uses a PHRED-like scale where higher = more deleterious.
| CADD PHRED | Interpretation | Context |
|---|---|---|
| < 10 | Likely benign | Bottom 90% of genome variation |
| 10-20 | Uncertain | Top 10%, but most are still benign |
| 20-25 | Potentially deleterious | Top 1% — investigate if in a disease gene |
| 25-30 | Likely deleterious | Top 0.3% — strong candidate for pathogenicity |
| > 30 | Highly deleterious | Top 0.1% — likely damaging if in a constrained gene |
When to use CADD: Best for non-coding and splice-region variants where SIFT/PolyPhen don't apply. For missense variants, REVEL and AlphaMissense are more specific.
Scores missense variants only. Combines 13 individual tools into a single 0-1 score. Recommended by ClinGen for ACMG PP3/BP4 evidence.
| REVEL Score | ClinGen Evidence Level | Interpretation |
|---|---|---|
| < 0.290 | BP4_Supporting | Supporting evidence of benign |
| 0.290-0.644 | No evidence | Uncertain significance |
| 0.644-0.773 | PP3_Moderate | Moderate evidence of pathogenicity |
| 0.773-0.932 | PP3_Strong | Strong evidence of pathogenicity |
| > 0.932 | PP3_Very Strong | Very strong evidence of pathogenicity |
When to use REVEL: First-line score for evaluating missense variants. If REVEL >= 0.644, investigate the variant seriously.
DeepMind's protein-structure-informed missense classifier. Uses AlphaFold2 protein structure predictions to assess amino acid substitution impact.
| am_pathogenicity | am_class | Interpretation |
|---|---|---|
| < 0.34 | likely_benign | Predicted benign by protein structure analysis |
| 0.34-0.564 | ambiguous | Uncertain — use other evidence |
| > 0.564 | likely_pathogenic | Predicted damaging based on protein structure |
When to use AlphaMissense: Complements REVEL. If both REVEL and AlphaMissense agree a variant is damaging, this strengthens the computational evidence (ClinGen PP3). If they disagree, investigate further.
Deep learning model predicting splice-altering variants. Scores four types of splice disruption: acceptor gain (AG), acceptor loss (AL), donor gain (DG), donor loss (DL).
| Max Delta Score | Interpretation |
|---|---|
| < 0.2 | Unlikely to affect splicing |
| 0.2-0.5 | May affect splicing — investigate |
| 0.5-0.8 | Likely affects splicing |
| > 0.8 | Strong evidence of splice disruption |
When to use SpliceAI: VEP already flags canonical splice site variants (GT/AG dinucleotides). SpliceAI catches cryptic splice variants — intronic or exonic variants that create new splice sites or disrupt existing ones through more subtle mechanisms.
These are per-gene metrics (not per-variant) added to the clinical filter summary TSV. They measure how intolerant a gene is to different types of mutations.
| Metric | Threshold | Meaning |
|---|---|---|
| LOEUF < 0.35 | Constrained for loss-of-function | Gene is intolerant to LoF mutations — a HIGH impact variant here is more likely to cause disease |
| pLI >= 0.9 | Loss-of-function intolerant | Same as LOEUF but older metric. LOEUF is preferred. |
| mis_Z > 3.09 | Constrained for missense | Gene is intolerant to missense mutations — a REVEL-high missense here is more concerning |
Combining scores: A rare variant (gnomAD AF < 0.01%) with CADD > 25, in a constrained gene (LOEUF < 0.35), with ClinVar pathogenic classification, is a high-confidence pathogenic finding. Any one of these alone is insufficient.
Copy-paste these commands to extract the most clinically relevant variants. All assume your VEP-annotated VCF is at ${GENOME_DIR}/${SAMPLE}/vep/${SAMPLE}_vep.vcf.
VEP_VCF="${GENOME_DIR}/${SAMPLE}/vep/${SAMPLE}_vep.vcf"
# 1. Homozygous loss-of-function variants (most likely to cause disease)
grep -v "^#" "$VEP_VCF" | grep "HIGH" | grep "1/1" | head -20
# 2. Rare HIGH-impact variants (gnomAD AF < 0.1%)
# These are the variants most likely to be clinically significant
grep -v "^#" "$VEP_VCF" | grep "HIGH" | grep -v "gnomADe_AF=0\.[0-9]" | head -20
# 3. Compound heterozygous candidates: genes with 2+ heterozygous variants
# (potential autosomal recessive — needs manual curation)
grep -v "^#" "$VEP_VCF" | grep "0/1" | grep -oP 'SYMBOL=[^;|]+' | \
sort | uniq -c | sort -rn | awk '$1 >= 2' | head -20
# 4. Known ACMG actionable genes (81 genes in ACMG SF v3.2)
# Quick check if any HIGH/MODERATE variants land in these genes
# Note: this is a partial list of cancer-related genes for illustration.
# See https://www.nature.com/articles/s41436-023-02171-w for the full 81-gene list.
ACMG_GENES="BRCA1|BRCA2|MLH1|MSH2|MSH6|PMS2|APC|MUTYH|TP53|RB1|MEN1|RET|VHL|SDHB|SDHD|TSC1|TSC2|WT1|NF2|PTEN|STK11|BMPR1A|SMAD4|CDH1|PALB2|CHEK2|ATM|NBN|BARD1|RAD51C|RAD51D|BRIP1"
grep -v "^#" "$VEP_VCF" | grep -E "HIGH|MODERATE" | grep -E "$ACMG_GENES" | head -20
# 5. PharmCAT-relevant variants not caught by step 7
# (PharmCAT misses some alleles — check CYP2D6, DPYD, UGT1A1 manually)
grep -v "^#" "$VEP_VCF" | grep -E "CYP2D6|CYP2C19|CYP2C9|DPYD|UGT1A1|SLCO1B1|TPMT|NUDT15" | head -20Important: These are starting points, not definitive screens. Any interesting finding should be cross-referenced with ClinVar and ideally confirmed by a second method (Sanger sequencing or a clinical lab).
CNVnator detects copy number variants using read depth analysis — complementary to Manta's paired-end/split-read approach.
Where to look: ${SAMPLE}/cnvnator/${SAMPLE}_cnvs.txt
Format: Each line has: type, region, size, normalized_RD, e-value1, e-value2, e-value3, e-value4, q0
What to expect:
- 3,000-4,000 total CNVs (mostly deletions)
- 1,500-2,000 significant (e-value < 0.01)
- Calls at chromosome starts (chr1:1-10000) are telomeric artifacts — ignore them
Filtering:
# Significant CNVs only (e-value < 0.01)
awk '$5 < 0.01' ${SAMPLE}_cnvs.txt
# Large deletions (>100kb, potentially clinically relevant)
awk '$1 == "deletion" && $3 > 100000 && $5 < 0.01' ${SAMPLE}_cnvs.txt
# Large duplications
awk '$1 == "duplication" && $3 > 100000 && $5 < 0.01' ${SAMPLE}_cnvs.txtMulti-caller overlap: CNVs found by both Manta AND CNVnator have lower false-positive rates. Cross-reference by checking if the same genomic region appears in both output files.
Delly is a third structural variant caller, detecting deletions, duplications, inversions, and translocations.
Where to look: ${SAMPLE}/delly/${SAMPLE}_sv.vcf.gz
Quick summary:
# Count SVs by type
bcftools query -f '%INFO/SVTYPE\n' ${SAMPLE}_sv.vcf.gz | sort | uniq -c
# Filter PASS variants only
bcftools view -f PASS ${SAMPLE}_sv.vcf.gz | grep -cv '^#'What to expect:
- 5,000-15,000 total SV calls
- Most are small deletions (<1kb)
- PASS filter reduces count significantly
Multi-caller overlap: SVs detected by Manta + Delly + CNVnator (or any 2 of 3) have substantially lower false-positive rates. Single-caller calls, especially large ones, should be viewed with caution.
GATK Mutect2 in mitochondrial mode detects variants with heteroplasmy fractions — the proportion of your mitochondria carrying each variant.
Where to look: ${SAMPLE}/mito/${SAMPLE}_chrM_filtered.vcf.gz
Key field: AF (allele fraction) indicates heteroplasmy level:
| AF Level | Meaning |
|---|---|
| >0.95 | Homoplasmic — fixed in all mitochondria (haplogroup-defining) |
| 0.10-0.95 | Heteroplasmic — clinically significant range |
| 0.03-0.10 | Low-level heteroplasmy — often age-related somatic |
| <0.03 | Near detection limit |
What to expect:
- 50-70 PASS variants total
- 25-35 homoplasmic (haplogroup variants)
- 25-35 low-level heteroplasmic (mostly <5%)
- Poly-C tract variants at positions 302-310 are sequencing artifacts
When to investigate further:
- Heteroplasmic variant at a known disease position (check MitoMap)
- m.3243A>G (MELAS) or m.8344A>G (MERRF) at detectable heteroplasmy levels
- Any position in MT-ATP6, MT-ND genes with AF >0.10
Important caveats about heteroplasmy thresholds:
- There is no single absolute heteroplasmy threshold that determines clinical significance. Thresholds vary by variant and by tissue (ClinGen/MSeqDR mtDNA interpretation specifications)
- Blood underrepresents heteroplasmy for many mitochondrial diseases. WGS from blood-derived DNA may show lower heteroplasmy levels than affected tissues (muscle, nerve). m.3243A>G in particular shows different clinical phenotypes at very different heteroplasmy levels across tissues
- The AF values from this pipeline reflect blood-derived DNA only. A low or absent heteroplasmy level in blood does not rule out clinically significant heteroplasmy in other tissues
- For any detected pathogenic mtDNA variant, discuss with a specialist who can order tissue-specific testing if warranted
Cross-reference: Compare with step 12 (haplogrep3) — your homoplasmic variants should match your assigned haplogroup.
Mutect2 in tumor-only mode looks for somatic mutations -- variants acquired during your lifetime rather than inherited. From blood-derived WGS, the main category of interest is clonal hematopoiesis (CHIP).
Where to look: ${SAMPLE}/somatic/${SAMPLE}_somatic_filtered.vcf.gz
Key field: AF (allele fraction) indicates the clone size:
| AF Range | Likely Source |
|---|---|
| 0.45-0.55 | Heterozygous germline (false positive) |
| ~1.0 | Homozygous germline (false positive) |
| 0.01-0.10 | Potential low-frequency somatic (CHIP candidate) |
| 0.10-0.40 | Ambiguous -- could be somatic, mosaic, or noisy germline |
What to expect:
- Thousands of PASS calls in a healthy individual -- the vast majority are germline false positives
- True somatic variants are rare: a healthy 40-year-old might have 0-20 genuine CHIP mutations detectable at 30X
- Without a matched normal sample, germline variants that are rare in gnomAD will often pass all filters
CHIP genes to check: DNMT3A, TET2, ASXL1, TP53, JAK2, SF3B1, SRSF2, PPM1D, CBL. CHIP prevalence increases with age and is associated with elevated cardiovascular risk and risk of hematologic malignancies.
Important caveats:
- This step has a much higher false positive rate than any other step in the pipeline
- Do not interpret PASS variants as confirmed somatic without cross-referencing with the germline VCF (step 3) and gnomAD frequencies (step 13)
- If a variant is also called at ~50% AF by DeepVariant in step 3, it is almost certainly germline
- Share your PharmCAT report with your prescribing physician or pharmacist
- Review ClinVar pathogenic hits — check if any are in dominant genes or if you're homozygous for recessive genes
- Read the CPSR HTML report — it's designed for clinical interpretation and will highlight anything that needs attention
- If you find something concerning: Don't panic. Discuss with a genetic counselor. Many "pathogenic" variants have incomplete penetrance (not everyone with the variant gets the disease)
- For carrier status findings: Relevant mainly for family planning. If both partners carry the same recessive condition, each child has a 25% chance of being affected
Genomic databases are updated continuously. Variants classified as VUS today may be reclassified next year. Periodic re-analysis is one of the most valuable things you can do.
| Database | Update Frequency | Pipeline Steps Affected | How to Update |
|---|---|---|---|
| ClinVar | Weekly | Step 6 (ClinVar screen) | Re-download from NCBI FTP (see 00-reference-setup.md) |
| VEP cache | Every 6 months | Step 13 (VEP annotation) | Download new release from Ensembl FTP |
| PCGR/CPSR data | Every 6-12 months | Step 17 (CPSR) | Download new bundle from PCGR GitHub releases |
| PharmCAT | Every few months | Step 7 (pharmacogenomics) | Pull new Docker image (docker pull pgkb/pharmcat:3.2.0) |
- Every 6 months: Re-run steps 6 (ClinVar) and 17 (CPSR) with updated databases. These are the fastest steps (~35 minutes total) and the most likely to have new classifications.
- Every 12 months: Re-run step 13 (VEP) with updated cache for new gnomAD frequencies and consequence predictions.
- After major database releases: ClinVar periodically reclassifies large batches of variants. Follow @ClinVarUpdates or check the NCBI blog for announcements.
- Steps 2-3 (alignment + variant calling): Your variants don't change. Only re-run if a major DeepVariant version is released with improved accuracy.
- Steps 4, 18, 19 (SV callers): Structural variant calling is compute-intensive and results don't change with database updates.
- Step 10 (telomere): Telomere content doesn't change with database updates.
Sanitized examples from a real 30X WGS run, so you know what to expect.
bcftools stats output:
SN 0 number of samples: 1
SN 0 number of records: 5560412
SN 0 number of SNPs: 4198753
SN 0 number of indels: 1361659
SN 0 number of multiallelic sites: 45231
# PASS variants only: 4,650,000-4,700,000
# Ti/Tv ratio: 2.05-2.10 (if < 1.8, something is wrong)
Pathogenic/Likely Pathogenic hits: 4
chr2:47637270 T>C (rs80338939) — GJB2 carrier (hearing loss, recessive)
chr1:45331175 G>A (rs36053993) — MUTYH carrier (CRC risk, recessive)
chr10:124774641 C>T (rs28936670) — ACADSB carrier (metabolic, recessive)
All heterozygous (0/1) = carrier status only. This is a completely normal result.
The HTML report will show a table like:
Gene Diplotype Phenotype Affected Drugs
CYP2C19 *1/*17 Rapid Metabolizer PPIs, SSRIs, clopidogrel
CYP2C9 *1/*1 Normal Metabolizer Warfarin, NSAIDs
NAT2 *5/*6 Slow Acetylator Isoniazid, caffeine
DPYD *1/*1 Normal Metabolizer 5-FU (safe at standard dose)
SLCO1B1 *1a/*1a Normal Function Statins (standard dosing)
Typically 18-21 of 23 genes will have confident calls. CYP2D6 may be "Inconclusive" from short-read WGS (known limitation).
{
"LocusResults": {
"HTT": { "Genotype": "17/19" },
"FMR1": { "Genotype": "29" },
"C9orf72": { "Genotype": "2/3" },
"ATXN1": { "Genotype": "29/29" },
"DMPK": { "Genotype": "12/13" }
}
}All values well below disease thresholds = ALL CLEAR.
The HTML report tier summary:
Tier 1 (Pathogenic/Likely pathogenic): 0 variants
Tier 2 (VUS with evidence): 3 variants
Tier 3 (VUS limited evidence): 21 variants
Tier 4 (Likely benign/Benign): ~53,000 variants
Zero Tier 1 = ALL CLEAR for cancer predisposition. The VUS count varies widely (20-200+) and is not cause for concern.
# Autosomal ROH > 5 MB: 0
# Total autosomal ROH: 47.3 MB (all segments < 3 MB)
# Conclusion: No evidence of parental relatedness
Normal outbred individual. If total ROH > 100 MB or any segment > 10 MB, investigate further.
tel_content: 553.52
No universal "normal" range — compare between samples of the same age, sequenced on the same platform.
Found something interesting? These free tools help you dig deeper:
- IGV Web — Load your BAM file (or a region of it) to visually inspect read-level evidence for a variant. Essential for confirming structural variants and checking for sequencing artifacts.
- gene.iobio — Clinically-driven variant interrogation tool. Load your VCF and BAM, search for specific genes, and see coverage, variant calls, and population frequency in one view.
- UCSC Genome Browser — Search for any genomic coordinate to see the surrounding genes, conservation, regulatory elements, and known variants.
- ClinVar — Search by rsID, gene name, or genomic position. Check the review status (stars) and submission history.
- gnomAD — Search any variant to see its population frequency across 800,000+ individuals (v4). If common in gnomAD, almost certainly benign.
- OMIM — The definitive catalog of genetic disorders. Search by gene name to understand what conditions it causes and the inheritance pattern.
- GeneReviews — Expert-written disease descriptions for genetic conditions. The single best resource for understanding a specific genetic disease.
An important caveat: different annotation tools may classify the same variant differently.
VEP (used in step 13), SnpEff, and ANNOVAR are the three most common variant annotation tools. Studies have shown that they disagree on consequence predictions for ~5-10% of variants, particularly at splice sites and multi-transcript genes.
What this means for you:
- If VEP says a variant is "HIGH impact" but ClinVar says it is benign, trust ClinVar (human-reviewed evidence > computational prediction)
- If VEP and ClinVar agree on pathogenicity, this strengthens the interpretation
- If you find a potentially significant variant using VEP that is NOT in ClinVar, search gnomAD for its population frequency before drawing conclusions
- For the most important findings, consider running a second annotation tool as validation
The pipeline uses VEP because it is the most widely used and well-maintained tool, with direct gnomAD frequency integration. But no single tool is perfect.
- This is not a clinical diagnosis. These tools use the same algorithms as clinical labs, but the pipeline has not been clinically validated.
- False positives exist. Short-read WGS has limitations in repetitive regions, homologous genes (CYP2D6, HLA), and structural variants.
- False negatives exist. Some pathogenic variants are in regions that short reads can't cover (deep intronic, repeat expansions beyond read length, large structural variants).
- VUS (Variants of Uncertain Significance) are not actionable. They may be reclassified in the future as more data accumulates.
- ClinVar classifications can change. A variant classified as pathogenic today may be reclassified as benign (or vice versa) as new evidence emerges. Re-run step 6 periodically with updated ClinVar databases.