Analysis Workflow
Clinical Screening
ACMG classification tells you which variants are pathogenic. Phenotype Matching tells you which ones explain the patient. Clinical Screening answers a third question: given this patient full clinical context, age, sex, ethnicity, family history, sample structure, and chosen gene panels, which variants deserve action, and at what level of urgency?
The same variant in a neonate, an adult cancer screen, and a pre-conceptional carrier check should not rank the same. In Helena, it does not. The Screening Service adapts its scoring to the case in front of you, with transparent per-component breakdowns and bounded clinical-context boosts that never override the underlying biological evidence.
Clinical Positioning
Variant analysis produces classifications. Phenotype matching identifies which variants align with a patient symptoms. But several real-world clinical questions remain unanswered.
Four Clinical Questions
A neonate is in ICU with no usable phenotype yet. Which variants on the genome should the team look at first?
A 45-year-old presents for proactive health screening with family history of breast cancer. Which variants matter for her, and which actionability level applies to each?
Two parents are planning pregnancy. Which carrier findings need a reproductive counseling conversation?
A trio sample with both parents sequenced. Which de novo variants deserve elevated priority?
Each requires different prioritisation logic. A pediatric early-onset disease gene is highly relevant in a neonate and largely irrelevant in an elderly patient. A founder mutation in a specific ancestry warrants different weighting than the same variant in a different population. A heterozygous pathogenic variant in a recessive gene is a carrier finding, not a diagnosis, and the system must recognise that. The Screening Service automates this clinical reasoning and produces a tiered, transparent, justification-bearing prioritisation tailored to the case.
Two Operational Modes
Same underlying engine, very different weight distributions. Diagnostic mode is phenotype-driven. Screening mode is proactive, with five sub-modes for the major use cases.
Diagnostic Mode
When: Patient has structured phenotype data (HPO terms)
Phenotype match dominates the score. Other components support or qualify it. Age relevance contributes nothing because phenotype provides direct evidence. Used for routine diagnostic cases referred for a specific clinical indication.
Neonatal Screening
When: Newborn or infant, no usable phenotype yet
Age relevance, gene constraint, and dosage sensitivity dominate. Optimised for early-onset, treatable, time-critical conditions. Phenotype contributes only as gene-disease burden.
Pediatric Screening
When: Child, proactive or non-specific indication
Similar weight profile to neonatal but tuned to childhood-onset disease genes.
Proactive Adult Screening
When: Adult, ACMG SF v3.2 or hereditary cancer / cardiac questions
Deleteriousness and age relevance dominate. ACMG SF gene set, hereditary cancer panels, and hereditary cardiac conditions are the primary signal sources.
Carrier Screening
When: Pre-conceptional, reproductive counseling
Recessive carrier findings prioritised for reproductive counselling. Compound heterozygote detection elevated. Inheritance-aware demotion still applies for non-reproductive contexts within the same case.
Pharmacogenomics
When: Drug response and metabolism (when enabled)
Pharmacogene variants prioritised. Run alongside other modes when relevant.
The Seven Scoring Components
Every variant is scored across seven independent dimensions. Components are then combined with context-appropriate weights to produce the total score. The geneticist sees the full per-component breakdown for every variant.
Gene Constraint
How intolerant the gene is to loss-of-function and missense variation. Genes that have been depleted of damaging variants by selection are more likely to cause disease when damaged. Coding and splicing variants exploit this signal; non-coding variants are heavily discounted regardless of gene constraint.
Deleteriousness
Consensus across multiple in silico predictors covering distinct biological signals: protein-structure-based methods, evolutionary conservation, splice prediction, and ClinGen-calibrated meta-predictors. Robust to individual predictor failure, when the strongest signal is unavailable, others are re-weighted to maintain coverage.
Phenotype
In diagnostic mode, the overlap between patient HPO terms and the gene phenotypic spectrum. In screening mode, a discounted gene-disease burden signal capped well below the diagnostic ceiling, full phenotype credit is reserved for cases where actual HPO matching has been done.
Dosage Sensitivity
Whether the gene is haploinsufficient (one functional copy is not enough) or triplosensitive (an extra copy causes disease). Combined with consequence type, a loss-of-function variant in a haploinsufficient gene is far more concerning than the same variant in a dosage-tolerant gene.
Consequence Severity
A graded score reflecting the predicted impact on protein function, from stop-gained and frameshift down through missense and synonymous. Non-protein-coding consequences receive minimal credit.
Compound Heterozygote Detection
Identifies pairs of heterozygous coding/splicing variants in the same gene that may form a biallelic loss of function, the diagnostic answer for many recessive conditions. Intronic, synonymous, and regulatory variants are correctly excluded from compound het candidacy.
Age-Appropriate Disease Context
Whether the gene is known to cause disease at the patient life stage. A pediatric-onset metabolic disease gene scores high in a neonate, low in an adult; an adult-onset cancer predisposition gene scores high in a 50-year-old and low in a newborn. When a curated gene panel is in use, panel metadata (age relevance, ClinGen evidence level) takes precedence over generic gene lists.
Context-Aware Weighting
The seven components do not have fixed weights. The engine selects a weight distribution appropriate to the case. The same variant, evaluated for the same patient in two different clinical questions, will receive two different priority scores. That is the correct behaviour, clinical relevance is a function of the question being asked, not a property of the variant alone.
| Mode | Weight Behaviour |
|---|---|
| Diagnostic case (phenotype present) | Phenotype dominates. Age relevance contributes nothing. |
| Neonatal screening | Age relevance, gene constraint, and dosage sensitivity dominate. Phenotype contributes only as gene-disease burden. |
| Pediatric screening | Similar to neonatal but tuned to childhood-onset disease genes. |
| Proactive adult screening | Deleteriousness and age relevance (ACMG SF cancer/cardiac genes) dominate. |
| Elderly screening | Age relevance dominates more aggressively, since most clinically actionable variants at this age are a small set of high-evidence cardiac and cancer genes. |
Clinical Context Boosts
After the seven-component score is computed, additional boosts are applied based on the patient full clinical profile. Boosts add to the underlying score with sensible upper bounds, so no single contextual factor can override the underlying biological evidence.
ACMG class
Pathogenic and Likely Pathogenic variants receive the largest boost. VUS with PVS1 (strong null variant) evidence receive a smaller boost reflecting strong supporting evidence even at uncertain classification.
Phenotype tier
When the Phenotype Matching Service has been run, its Tier 1 and Tier 2 assignments translate into boosts that elevate phenotype-relevant variants in the screening output.
Ethnicity-aware
Targeted increases for variants in genes with established founder mutations in the patient ancestry: Ashkenazi Jewish, African, East Asian, South Asian, European-specific. Used carefully and modestly, never as a substitute for the underlying evidence.
Family history
When a family history is reported, variants in genes consistent with that history receive a measured boost. Bounded so it cannot, on its own, elevate a weak signal to a top tier.
Sex-linked
X-linked variants receive different boosts in male and female patients, reflecting the difference between hemizygous (affected) and heterozygous (often carrier) status.
Consanguinity
When consanguinity is reported, homozygous variants receive elevated weighting consistent with autosomal recessive disease in consanguineous families.
De novo
For trio or duo samples with parental sequencing, variants in constrained genes receive a boost reflecting the disproportionate clinical impact of de novo mutations in such genes.
Pregnancy and family planning
When the patient is pregnant or in family planning, variants in recessive genes commonly screened pre-conceptionally and prenatally receive a boost focused on reproductive actionability.
Gene panel
Membership in the chosen gene panel, modulated by ClinGen evidence level (Definitive, Strong, Moderate, Limited, Disputed, Refuted). Variants in strongly evidenced panel genes are favoured over those in genes with weaker evidence within the same panel.
Inheritance-Aware Demotion
Pathogenic-or-likely-pathogenic alone does not earn Tier 1 in a recessive context.
A heterozygous P/LP variant in an autosomal recessive gene, without a compound heterozygous partner, is a carrier finding, not the diagnostic answer, and is demoted out of Tier 1 to reflect that.
The exception is a confirmed PVS1 (strong null variant) call, which is treated as a safety net for cases where upstream classification has independently confirmed a dominant loss-of-function mechanism.
Why this matters: this single rule prevents one of the most common and consequential mis-prioritisations in automated screening systems, pathogenic carriers presented to clinicians as if they were diagnoses.
Tiers and Actionability, Two Axes, Not One
Screening tier answers how strongly does the evidence point to this variant? Clinical actionability answers what should be done about it, on what timeline? A Tier 1 finding is not automatically Immediate, a Tier 1 VUS in an actionable gene is Monitoring, not Immediate. The two axes are independent on purpose.
| Tier | Label | Clinical Meaning |
|---|---|---|
| Tier 1 | High Priority | Immediate review. Strong combined evidence across multiple components. The variants the geneticist should see first. |
| Tier 2 | Moderate Priority | Monitor and follow up. Substantive evidence but not conclusive on its own. Worth active surveillance and additional confirmation. |
| Tier 3 | Low Priority | Future consideration. Weak evidence in this clinical context. Worth revisiting if the clinical picture changes or new evidence emerges. |
| Tier 4 | Very Low Priority | Likely benign or clinically irrelevant in this context. |
Clinical Actionability
Immediate
Known pathogenic in an ACMG SF or otherwise actionable gene. Should trigger an urgent clinical pathway.
Monitoring
Significant evidence. Appropriate for active surveillance and confirmatory testing.
Future
Moderate evidence. Revisit as the clinical picture evolves or as new evidence emerges.
Research
Exploratory. Not currently actionable but may become so.
Gene Panels
Gene panels are first-class objects in the Screening Service. They scope the analysis to a clinically meaningful set of genes, reduce noise, and let panel metadata (disease association, age relevance, ClinGen evidence level, display labels) drive scoring decisions.
Built-in Panels
Built-in panels ship with the platform and are maintained by Helena. The neurology panels are derived from a doctoral validation study of WGS/WES in 76 patients with rare neurological diseases (51.3% diagnostic yield, 18 novel pathogenic variants reported to ClinVar).
Diabetes
MODY 1-14 (monogenic diabetes subtypes)
KATP channel (neonatal diabetes and congenital hyperinsulinism)
Neonatal diabetes (non-MODY, non-KATP)
Wolfram syndrome
Insulin resistance and lipodystrophy
Syndromic diabetes
T2D common variants
Comprehensive diabetes (all subtypes)
Neonatal Screening
Early-onset, treatable, time-critical conditions
Neurology (validation study, 76 patients, 51.3% diagnostic yield)
Intellectual disability
Epilepsy
Neuromuscular disease
Neurometabolic disease
Mitochondrial disease
Movement disorders
Custom Panels
Organisation-specific. Laboratory administrators create, edit, and assign panels for their own organisation without affecting other organisations. Gene symbols are validated against HGNC at entry time, aliases normalised automatically.
Panel Suggestions
Computed from the patient age group. The system pre-selects high-relevance built-in panels and explains why each suggestion was made, with an explicit auto-select recommendation that the geneticist can override.
Custom Genes
Can be added on top of any panel for ad-hoc, case-specific screening, with user-defined priority and age relevance.
Inputs and Outputs
What the service consumes from the upstream pipeline and from the geneticist, and what it produces for review and downstream use.
Inputs from the Pipeline
Completed variant analysis session
ACMG/AMP classification and applied criteria
Gene constraint, dosage sensitivity, inheritance mode
In silico predictors, conservation scores, splice predictions
Population frequencies (gnomAD), ClinVar context
Optional phenotype matching results
Inputs from the Geneticist
Patient demographics: age (in days or years), sex
Recommended context: ethnicity, indication for testing, family history, consanguinity
Optional context: HPO terms, free-text notes, sample type (singleton/duo/trio/quad), parental samples, reproductive context, secondary-findings preferences
Gene panel selection: built-in panel IDs, custom panels, ad-hoc custom genes
Filtering preferences: maximum Tier 1 results, minimum total score, inclusion of VUS / Pathogenic / Tier 4
Outputs for the Geneticist
Four-tier ranking with summary counts (Pathogenic, Likely Pathogenic, VUS, per-tier)
Per-variant: total score, per-component breakdown, all applied boosts, tier, clinical actionability, justification, panel context
Gene-level summaries with best tier and best actionability per gene
Panel header metadata for reporting (panel names, ClinGen status, full gene list)
Outputs for Downstream Services
Persistent screening results consumed by the AI Service for the screening report
Per-case data available to cohort-level analytics for population work
Standards and Boundaries
The service operates against published standards and within explicit clinical boundaries.
ACMG/AMP
Variant classification follows ACMG/AMP 2015 with subsequent ClinGen specifications. Performed upstream by the Variant Analysis Service. The Screening Service consumes that classification, it does not reclassify.
Reference: Richards et al., Genetics in Medicine, 2015, PMID: 25741868
ACMG SF v3.2
Secondary findings handling aligns with the current ACMG recommendations on reportable secondary findings. The proactive adult mode uses the ACMG SF gene set as a primary signal source.
Reference: Miller et al., ACMG SF v3.2, Genet Med. 2023, PMID: 37347242
ClinGen Gene-Disease Validity
Gene-disease validity is encoded as panel metadata and modulates the panel boost. Genes with Definitive or Strong evidence are favoured over Limited or Disputed within the same panel.
HGNC
Every gene symbol added to a panel is validated against the HGNC approved-symbols set, with automatic normalisation of aliases.
HPO
Phenotype input uses the Human Phenotype Ontology. Gene-disease burden in screening mode is also derived from HPO-mapped associations.
Reference: Kohler et al., Nucleic Acids Research, 2021, PMID: 33264411
Reporting Boundary
The service produces tiered, justified prioritisation data. 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 Clinical Screening distinct from generic variant prioritisation tools.
Context-aware scoring, not flat scoring
The same variant in different patients, or different clinical questions, receives different priorities. Clinical relevance is a function of the question being asked, not a property of the variant alone.
Two clinically distinct modes in one engine
Diagnostic (phenotype-driven) and screening (proactive, with five sub-modes covering neonatal, pediatric, adult, carrier, and pharmacogenomic use cases).
Seven independent scoring components
Transparent per-component breakdown for every variant. The geneticist always sees why a variant ranked where it did, not just the final number.
Nine clinical-context boosts
Layered on top of the biological evidence, each bounded so no single boost can override the underlying signal.
Inheritance-aware tier assignment
Heterozygous carriers in recessive genes are not surfaced as diagnoses. PVS1 safety net preserved for confirmed dominant loss-of-function mechanisms.
First-class gene panel support
Built-in clinical panels (diabetes, neonatal, neurology), organisation-specific custom panels, ClinGen-modulated scoring, age-aware panel suggestions, HGNC validation.
Two independent output axes
Tier (how strong is the evidence) and actionability (what should be done, on what timeline). The combination drives the clinical conversation, not either alone.
Designed for diagnostic and proactive workflows
Pre-conceptional carrier screening, neonatal genomic screening, adult ACMG SF screening, and trio/family analysis all supported by the same engine with different mode selections.
See Clinical Screening in Practice
Request a demo to see Helena rank a real case across diagnostic and proactive modes, with full per-component breakdowns and clinical-context boosts visible alongside every variant.