Analysis Workflow
Mitochondrial DNA Analysis
Mitochondrial DNA biology differs fundamentally from the nuclear genome. Maternal inheritance, heteroplasmy with tissue-specific threshold effects, lack of recombination, and haplogroup structure all change how variants should be classified. Several ACMG criteria designed for autosomal genetics are not applicable. Several others require mtDNA-specific thresholds and tools. Helena routes mitochondrial variants through a dedicated classifier following the MMDWG 2020 specifications.
The MMDWG 2020 framework is the authoritative consensus document of the ClinGen Mitochondrial Disease Variant Curation Expert Panel. It revisits all 28 ACMG/AMP criteria for mtDNA applicability, excludes seven with verbatim biological rationale, and specifies fourteen with mtDNA-specific thresholds. Combining rules from Richards 2015 are preserved unchanged. Helena ships strict MMDWG 2020 compliance, with no platform-specific extensions to the published specification.
Contents
Why mtDNA Needs Its Own Framework
Treating a mitochondrial variant through a nuclear classifier produces clinically incorrect results. The MMDWG 2020 specifications exist precisely because mtDNA biology is sufficiently different that several ACMG criteria are inapplicable and others require mtDNA-specific thresholds.
A Worked Example
The classical MELAS variant m.3243A>G has over a thousand published cases, a 4-star ClinVar pathogenic assertion, and is the textbook mitochondrial encephalomyopathy variant. Run through a nuclear ACMG classifier without mtDNA awareness, the criteria string is empty, the confidence is zero, and the assignment is VUS. Run through the MMDWG 2020 classifier, the same variant correctly receives the criteria that apply to it and is classified as Pathogenic with full evidence trace.
The difference is not a matter of preference. A laboratory or clinical platform that treats mtDNA variants through a nuclear classifier delivers wrong classifications. Helena routes mitochondrial variants through a dedicated mtDNA classifier so the same dataset, evaluated by Helena, returns clinically correct assignments for both nuclear and mitochondrial variants.
Unique Mitochondrial Biology
Five biological features distinguish mtDNA from the nuclear genome and shape the MMDWG 2020 specifications.
Maternal Inheritance
Mitochondrial DNA is exclusively maternally inherited through the female germline. There is no paternal contribution and no autosomal recessive pattern. Several ACMG criteria designed for autosomal genetics are inapplicable as a result.
Heteroplasmy
A cell contains many mitochondria, and each mitochondrion contains many copies of mtDNA. A pathogenic variant may be present at any heteroplasmy level from a few percent to homoplasmy. Phenotypic expression often follows a tissue-specific threshold effect: a variant may be silent at low heteroplasmy and severe at high heteroplasmy in the same individual.
No Splicing, No Recombination
mtDNA genes do not undergo splicing, and the mtDNA genome does not undergo homologous recombination. Splice prediction is irrelevant. Variants accumulate without recombination smoothing, producing high natural variability across haplogroups.
Haplogroup Structure
Because mtDNA does not recombine, variants cluster into fixed lineages called haplogroups. Some variants are top-level haplogroup defining and reach high allele frequency in their lineage while remaining absent elsewhere. A pathogenic variant in one haplogroup may be a benign defining variant in another, requiring haplogroup-aware interpretation.
Threshold Effect
A phenotype may only manifest when a variant reaches a particular heteroplasmy threshold in a given tissue. Below the threshold the carrier is asymptomatic; above it, the disease manifests. This shapes the clinical evidence: variant heteroplasmy correlates with disease severity in segregation studies.
Dual Classifier Routing
Every variant in a session is routed to either the nuclear or the mitochondrial classifier based on chromosome and gene symbol context. Edge cases including D-loop annotation gaps and NUMT pseudogene candidates are handled explicitly with informational flags rather than fail-hard errors.
Mitochondrial Path
When: Mitochondrial chromosome with a mitochondrial gene symbol
Variant is routed to the MMDWG 2020 classifier. Criteria specifications are mtDNA-aware. Combining rules from Richards 2015 are preserved unchanged. Output is labelled with the mtDNA framework so downstream services and reports treat it as such.
Nuclear Path
When: Nuclear chromosome with a non-mitochondrial gene symbol
Variant is routed to the standard ACMG 2015 classifier. Unchanged. The vast majority of variants in a typical WGS or WES session follow this path.
D-Loop and Annotation Gaps
When: Mitochondrial chromosome but no mitochondrial gene symbol assigned by upstream annotation
Routed to the mtDNA classifier with an explicit annotation flag. The mtDNA D-loop is a regulatory region without protein-coding genes; some annotation pipelines do not assign gene symbols to D-loop variants. The classifier handles this case explicitly.
NUMT Pseudogene Candidates
When: Nuclear chromosome with a mitochondrial-prefixed gene symbol
NUMTs are nuclear copies of mitochondrial DNA segments. They are a known biological reality and a recognised cause of false-positive mtDNA calls. The variant is routed to the nuclear classifier with an explicit NUMT pseudogene candidate flag, plus additional informational markers when low heteroplasmy, low depth, or known NUMT-prone region indicators are present. Decision remains with the geneticist.
Criteria Applied to mtDNA
Twenty ACMG/AMP criteria are applicable to mtDNA variants per MMDWG 2020. Several have mtDNA-specific weight or threshold differences from the nuclear classifier. Several require manual evidence input from the geneticist.
Very Strong evidence for pathogenicity. For mtDNA, a large heteroplasmic deletion encompassing at least one full gene is treated as Very Strong. Smaller protein-coding truncations follow the Abou Tayoun decision tree, which produces Strong, Moderate, or Supporting weight depending on transcript impact and truncated fraction. Not applicable to single nucleotide changes in tRNA or rRNA.
Strong. Same nucleotide change as a previously established pathogenic variant in a protein-coding mitochondrial gene. Restricted to the protein-coding mtDNA gene set.
Functional studies. MMDWG 2020 caps PS3 at Supporting evidence weight for mtDNA, citing the absence of standard or universal parameters for objectively analysing cybrid studies. Available as a manual evidence placeholder for geneticist annotation.
Case prevalence in unrelated probands across diverse top-level haplogroups. Manual evidence placeholder. Affected proband definition follows MMDWG 2020 exactly: classic mitochondrial disease syndromes or red flag features per Haas 2007 and 2008.
Population frequency below 1 in 50,000 from controls in reliable mitochondrial databases. MMDWG 2020 specifies Supporting weight for this criterion in mtDNA, a single-tier downgrade from the nuclear PM2 Moderate. gnomAD does not contain mtDNA frequencies; MITOMAP and HmtDB are the primary frequency sources.
In-frame insertion or deletion in a non-repeat region or stop-loss. Restricted to protein-coding mtDNA genes.
Different change at the same position as a known pathogenic variant. Moderate weight in protein-coding genes; Supporting weight in tRNA and rRNA.
Assumed de novo without confirmation of maternity by full mtDNA sequencing. Manual evidence placeholder.
Segregation in maternal family members with heteroplasmy correlation. Cannot be applied when the variant is homoplasmic in all family members. Manual evidence placeholder. Heteroplasmy must segregate with disease severity.
In silico predictors. Tool selection depends on gene type: APOGEE2 for protein-coding mRNA genes, MitoTIP and HmtVAR for tRNA genes. Discordant predictions yield no contribution. rRNA in silico prediction is not currently supported.
Phenotype-specific evidence. For mtDNA, requires decreased electron transport chain enzyme activity below 20 percent of control mean from a CLIA-approved laboratory in muscle, liver, or fibroblasts. Manual evidence placeholder.
Stand-alone benign evidence. Allele frequency above 1 percent in mitochondrial population databases, OR a top-level haplogroup defining variant in an individual whose predicted haplogroup matches. The haplogroup-defining condition is dual: a variant is a defining marker for haplogroup X but pathogenic in haplogroup Y for an unrelated condition. Conflict detection prevents incorrect application.
Strong benign. Allele frequency between 0.5 and 0.99 percent in mitochondrial population databases. Strict range, not an open-ended threshold.
Heteroplasmy comparison. Variant observed at higher heteroplasmy in a healthy adult than in the same tissue of an affected individual. Manual evidence placeholder.
Another mtDNA variant in the same individual is independently confirmed as pathogenic. Symmetrical: both variants receive this annotation against each other.
In silico predictors point to benign. Mirror of PP3 with the same gene-type-specific tool selection.
Alternate molecular cause. Variant found in an individual with a confirmed nuclear-DNA-related mitochondrial disease. Manual evidence placeholder.
Synonymous variant in a protein-coding mtDNA gene. MMDWG 2020 specifies that mtDNA synonymous variants are supporting benign evidence because cryptic splice site disruption, the rationale for the nuclear BP7 conservation check, does not apply to mtDNA.
Functional studies showing no effect. Manual evidence placeholder.
Lack of segregation in affected family members, or paternal segregation. Paternal segregation is itself disqualifying for an mtDNA pathogenic variant. Manual evidence placeholder.
Criteria Explicitly Excluded
Seven ACMG criteria are excluded for mtDNA per MMDWG 2020. Each exclusion has biological rationale published verbatim in the framework. Helena preserves these exclusions strictly.
Conservation, domain, and structural information is incorporated into the in silico predictors used in PP3 (APOGEE2, MitoTIP, HmtVAR). Including PM1 separately would double-count the same evidence.
Compound heterozygous and recessive inheritance does not apply to mtDNA. The genome is maternally inherited as a single haploid unit.
Low rate of benign missense in the gene of interest does not apply to mtDNA. High variability across haplogroups is well documented; mtDNA tolerates high missense rates because it lacks recombination and has a relatively high mutation rate without histone protection.
ClinGen Sequence Variant Interpretation guidance: any variant observed in a database should be assessed using its underlying evidence, not relying on the assertion alone. This is general SVI policy, applied to both nuclear and mitochondrial paths.
Most variants in protein-coding mtDNA genes are missense, not truncating. The premise that missense variants in a gene where loss of function is the main mechanism are likely benign does not apply.
In-frame indels in repetitive regions of mtDNA are concentrated in two well-known hypervariable regions and are routinely excluded from clinical interpretation pre-classification.
Same rationale as PP5: assertions in databases should not be relied on without underlying evidence per ClinGen SVI policy.
PVS1 for mtDNA Variants
PVS1 is the strongest single criterion in the ACMG framework. For mtDNA, MMDWG 2020 specifies three implementation paths reflecting the biological differences between large deletion syndromes, protein-coding truncations, and tRNA or rRNA variants.
Path 1, Large Deletion (Very Strong)
A heteroplasmic mtDNA deletion that encompasses at least one full mitochondrial gene receives PVS1 at Very Strong weight. This corresponds clinically to single large mtDNA deletion syndromes including KSS, Pearson, and CPEO. The detection criterion is functional, encompassing a full gene rather than a base-pair threshold.
Path 2, Protein-Coding Truncation (Abou Tayoun decision tree)
Frameshift, nonsense, and small deletion variants in protein-coding mtDNA genes follow the Abou Tayoun 2018 PVS1 decision tree. Nonsense-mediated decay does not occur for mtDNA, so the truncated fraction of the protein governs the assigned weight. Variants leaving more than ninety percent of the coding region intact typically receive PVS1 at Moderate weight.
Path 3, tRNA and rRNA
Single nucleotide variants in tRNA or rRNA genes are not eligible for PVS1 unless they are part of a deletion encompassing a full gene as in Path 1. MMDWG 2020 is explicit that, with the exception of large deletions, this criterion cannot be applied to tRNA or rRNA variants.
In Silico Predictors by Gene Type
MMDWG 2020 specifies different predictor sets for different mtDNA gene types. Tools optimised for nuclear protein-coding variants are not transferable to mitochondrial tRNA structures.
Protein-Coding (mRNA)
APOGEE2 is the recommended predictor. A pathogenic prediction supports PP3; a neutral prediction supports BP4. Discordant ensemble predictions yield no contribution.
Transfer RNA (tRNA)
MitoTIP and HmtVAR are evaluated jointly. Concordant pathogenic prediction across both tools supports PP3. Concordant benign prediction supports BP4. Discordant predictions yield no contribution.
Ribosomal RNA (rRNA)
No standardised in silico predictors for rRNA are currently in use. PP3 and BP4 are not evaluated for rRNA variants in this framework. Future predictors based on rRNA secondary structure may be incorporated as the field matures.
Haplogroup-Aware Frequency
Because mtDNA does not recombine, allele frequency aggregated globally can mask haplogroup-specific patterns. A variant may reach high frequency in one haplogroup while remaining absent in others. The classifier handles this explicitly.
BA1 Stand-Alone Benign, Two Conditions
BA1 fires when global allele frequency exceeds one percent in mitochondrial population databases. It also fires when the variant is a top-level haplogroup-defining marker and the patient predicted haplogroup matches. Both pathways are tracked in the evidence trace.
Conflict Detection
A variant defining haplogroup X may be pathogenic when it appears outside that lineage. MMDWG 2020 explicitly notes that BA1 must not fire when there is conflicting evidence of pathogenicity in a different haplogroup context. The classifier checks this and surfaces a haplogroup conflict warning to the geneticist.
Frequency Sources
MITOMAP and HmtDB are the primary mtDNA population frequency databases per MMDWG 2020. gnomAD does not provide mtDNA frequencies. PM2 supporting evidence requires frequency below one in fifty thousand, BS1 strong benign requires the strict range from 0.5 to 0.99 percent, and BA1 stand-alone benign requires above one percent.
Manual Evidence Placeholders
Several MMDWG 2020 criteria require evidence not directly extractable from variant annotation. Examples include functional studies, segregation in maternal family members, ETC enzyme activity, and confirmed maternity by full mtDNA sequencing. The classifier exposes these as manual evidence placeholders for geneticist annotation.
The placeholders include PS3 (functional studies, capped at Supporting weight), PS4 (case prevalence with haplogroup diversity), PM6 (assumed de novo without confirmation), PP1 (segregation with heteroplasmy correlation), PP4 (phenotype-specific ETC deficiency in CLIA-approved laboratory testing), BS2 (heteroplasmy comparison between healthy and affected family members), BS3 (functional studies showing no effect), BS4 (lack of segregation or paternal segregation), and BP5 (alternate molecular cause from nuclear DNA).
Geneticist annotations are recorded alongside the automated criteria with timestamp and user identity. Re-running the classifier preserves manual annotations and re-evaluates only the automated criteria.
Inputs and Outputs
What the service consumes from the upstream pipeline and from the geneticist, and what it produces for review.
Inputs from the Pipeline
Pre-annotated variants from the upstream variant analysis pipeline
Per-variant gene symbol, consequence, transcript context, and population frequency
In silico predictor scores when applicable to the gene type
ClinVar context and review status when available
Heteroplasmy and read depth when present in the source VCF
Inputs from the Geneticist
Manual evidence annotations for criteria requiring external data: PS3, PS4, PM6, PP1, PP4, BS2, BS3, BS4, BP5
Optional inheritance hypothesis context (sporadic, maternally inherited, unknown)
Optional haplogroup designation when established by external testing
Outputs for the Geneticist
ACMG class assignment: Pathogenic, Likely Pathogenic, VUS, Likely Benign, or Benign
Applied criteria string with explicit framework label distinguishing mtDNA from nuclear classifications
Per-criterion evidence trace including triggering data points
Confidence score reflecting evidence strength and consistency
NUMT pseudogene candidate flags when applicable for variants on nuclear chromosomes with mitochondrial-prefixed gene symbols
Haplogroup conflict warnings when a variant is haplogroup-defining for one lineage but appears in a different lineage
Standards and Boundaries
The classifier operates against published standards and within explicit clinical boundaries.
MMDWG 2020 (McCormick et al.)
The complete classification framework for mitochondrial DNA variants. Produced by the ClinGen Mitochondrial Disease Variant Curation Expert Panel and the Mitochondrial Disease Sequence Data Resource Consortium with eighteen co-authors across leading clinical and academic centres. Approved by ClinGen SVI and Clinical Domain Working Group Oversight Committee.
Reference: McCormick et al., Hum Mutat. 2020;41(12):2028-2057, PMID: 33058415
ACMG/AMP 2015
Original ACMG/AMP classification framework. MMDWG 2020 preserves Richards 2015 combining rules unchanged and specifies which of the original 28 criteria apply to mtDNA, which are excluded with verbatim rationale, and which require mtDNA-specific thresholds.
Reference: Richards et al., Genetics in Medicine, 2015, PMID: 25741868
Abou Tayoun 2018 (PVS1)
Decision tree for PVS1 application to truncating variants. MMDWG 2020 references this for protein-coding mtDNA frameshift, nonsense, and small deletion variants. Used unchanged for the protein-coding path.
Reference: Abou Tayoun et al., Hum Mutat. 2018, PMID: 30192042 (PVS1 decision tree)
APOGEE2
In silico pathogenicity predictor for mitochondrial protein-coding variants, used per the MMDWG 2020 PP3 specification.
Reference: Castellana et al., MitImpact / APOGEE2 (in silico mtDNA pathogenicity predictor)
MitoTIP and HmtVAR
In silico pathogenicity predictors for mitochondrial tRNA variants, used per the MMDWG 2020 PP3 specification with concordance requirements.
Reference: Sonney et al., PLoS Comput Biol. 2017, PMID: 28732077 (MitoTIP for tRNA variants)
MITOMAP and HmtDB
Primary mitochondrial population frequency databases. The MMDWG 2020 specification explicitly identifies these as the reliable sources for mtDNA allele frequency. gnomAD is not used for mtDNA frequency.
Scope Boundary
Helena mtDNA analysis is for primary mitochondrial disease, matching the MMDWG 2020 scope statement verbatim. The framework is not designed for and is not used for cancer somatic mtDNA variants, longevity studies, or complex disease predisposition contexts.
Reporting Boundary
The service produces classification, applied criteria, and evidence outputs. It does not generate clinical interpretations, does not make diagnostic calls, and does not replace clinical review. All output is for review by a qualified clinical geneticist before any clinical action.
Data Residency
The service runs within the Helena platform on EU-based infrastructure compliant with GDPR Article 9 and 1+MG technical requirements.
What Sets It Apart
Eight design choices that make Helena mtDNA analysis distinct from generic ACMG classifiers extended ad hoc to mitochondrial variants.
Dedicated mtDNA classifier
Mitochondrial variants are routed to a separate classification engine that follows the MMDWG 2020 specifications. They are not processed through nuclear ACMG with its inapplicable criteria.
Haplogroup-aware BA1
A variant defining one haplogroup may cause disease in another lineage. The classifier checks both global frequency and haplogroup-defining status against the patient predicted haplogroup, and detects conflicts where the same variant has dual evidence.
Heteroplasmy in the workflow
Heteroplasmy is captured per variant where present in the source data and is presented to the geneticist alongside the classification. Segregation evidence (PP1) requires heteroplasmy correlation with disease severity.
NUMT awareness
Nuclear copies of mitochondrial DNA segments are a known cause of false-positive mtDNA calls. The classifier detects nuclear-chromosome variants with mitochondrial-prefixed gene symbols and surfaces NUMT candidate flags with low-heteroplasmy, low-depth, and known-region indicators.
Gene-type-specific predictors
APOGEE2 for protein-coding mRNA genes. MitoTIP and HmtVAR for tRNA genes. rRNA in silico prediction is explicitly not supported, matching the MMDWG 2020 scope.
Verbatim exclusion rationale
Seven ACMG criteria are excluded for mtDNA. Each exclusion carries the MMDWG 2020 rationale verbatim, preserved in the system documentation and exposed in the Standards reference. The geneticist sees why a criterion did not fire.
No Helena-specific extensions
The classifier ships strict MMDWG 2020 compliance. Combining rules from Richards 2015 are preserved unchanged. No platform-specific thresholds, no novel criteria combinations, no informal rules layered on top of the published specification.
Manual evidence with audit trail
Criteria requiring external data (functional studies, segregation, ETC enzyme activity, alternate molecular cause) are exposed as manual evidence placeholders. Geneticist annotations are recorded with timestamp and user identity for audit.
See mtDNA Analysis in Practice
Request a demo to see Helena classify a real mitochondrial dataset against the MMDWG 2020 framework, with full criteria trace, haplogroup awareness, and NUMT detection visible alongside every variant.