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The Clinical Significance tab provides essential variant-level and gene-level details to help you evaluate a variant’s potential pathogenicity. It combines AI-driven insights with curated public data for informed interpretation.
Information is organized into the following cards:
Variant info: Displays key details such as HGVS nomenclature, predicted effect, variant type, zygosity, and links to external databases.
In silico predictions: Shows individual tool results and algorithmic predictions for missense, conservation, and splicing effects.
: Summarizes gene’s level of intolerance to different types of variation.
: Highlights previous pathogenicity classifications from your lab and public sources.
: Lists known gene–disease associations with inheritance patterns and source links.
The Variant info card provides key details about the variant.
Variant type
Main effect
Zygosity for each sequenced family member
Gene symbol
: Variant description according to the HGVS nomenclature based on the selected transcript
Exon information: Exon number and total number of exons for the selected transcript
dbSNP ID (SNV/Indel only)
: Links to external databases with pre-filled search queries
CNV/SV-specific data:
SV Type (e.g., DEL, DUP)
SV Length
Variant badges are quick visual indicators that highlight special characteristics or potential interpretation challenges of a variant. They help identify variants that may require extra attention during review.
Homology Region
In Emedgene, variants located in regions of high sequence similarity are labeled with the Homology Region tag, reflecting potential mapping ambiguity due to paralogous sequences. This tag is automatically applied when High Sensitivity Mode is enabled (default), helping users identify variants that may require orthogonal validation. Users can also create preset filters using this tag to streamline variant triage in complex genomic regions.
ISCN notation
Cytoband location
Emedgene now applies the Potential Mosaic tag to variants identified by its improved mosaic calling model, which delivers 4× fewer false positives than DRAGEN 4.2 High Sensitivity Mode and outperforms other mosaic callers in both speed and accuracy. Mosaic detection is enabled by default when running DRAGEN through Emedgene, with an allele frequency threshold of 0.2, and users can streamline interpretation by creating preset filters using this tag.
Ambiguous Calling
Emedgene now tags variants from segmental duplication regions with Ambiguous Calling, reflecting their uncertain placement due to poor mappability across ~5% of the genome. These variants are detected using the MRJD caller, which performs haplotype-based de novo small variant calling across paralogous regions, and are linked via a unique identifier (JIDS) in the new Connected Variants component. When DRAGEN is run through Emedgene, MRJD High Sensitivity Mode is enabled by default, and users can filter these variants using the Calling Methodology filter.
Suspected MNP
Repeat
Recombinant
The Resources field provides links to external databases with pre-filled search queries, helping users efficiently gather supporting evidence for variant interpretation.
UCSC Genome Browser
GeneCards (retired in v100.39.0)
PubMed
WikiGenes (retired in v100.39.0)
Google Scholar (v100.39.0+)
LitVar2 (v100.39.0+)
Genomenon
Search queries are built using detailed variant information whenever possible.
The smart query builder:
Supports variant aliases
Takes variant type into account for more precise and context-aware results
The query logic varies by variant type:
SNVs/indels (including mtDNA): Includes gene symbol, dbSNP identifier, HGVS coding change, and HGVS protein change across affected transcripts
CNVs: Includes gene symbol, ISCN notation, cytoband, genomic location, and standard terms like «CNV», «deletion» and similar
SV insertions: Includes gene symbol, cytoband, genomic location, and standard SV descriptors, and standard terms like «SV», «rearrangement» and similar
Search queries are based on gene information only.
The Gene metrics card helps you assess whether a gene is tolerant or intolerant to variation. It includes:
Intolerance metrics from ExAC and gnomAD that relate to gene constraint and variant burden
Direct links to ExAC and gnomAD for quick access
Resources field (v100.39.0+) with links to external gene databases offering detailed information on gene function and clinical relevance:
ClinGen gene
DECIPHER gene
GenCC
OMIM gene
pLI = p(LoF intolerant) is a probability of being loss-of-function intolerant to heterozygous and homozygous LoF variants.
Scale:
🔴 pLI ≥ 0.9: extremely LoF intolerant,
🟠 pLI > 0.1 & < 0.9: intermediate value,
Note: Gene metrics card is not available for CNVs or mtDNA variants.
The missense Z-score indicates intolerance to missense variants based on the deviation of observed missense variants versus the expected number.
Scale:
🔴 Missense Z ≥ 3: missense intolerant,
🟠 Missense Z > 2.5 & < 3: intermediate value,
🟢 Missense Z ≤ 2.5: missense tolerant.
p(REC) is a probability of being intolerant to homozygous, but not heterozygous LoF.
Scale:
🔴 p(REC) ≥ 0.8: Hom LoF intolerant,
🟠 p(REC) > 0.2 & < 0.8: intermediate value,
🟢 p(REC) ≤ 0.2: Hom LoF tolerant.
RVIS = Residual Variation Intolerance Score is indicative of a gene's intolerance to functional variation based on comparing the overall number of observed variants in a gene to the observed common functional variants.
Scale:
🔴 RVIS ≤ 30: functional variation intolerant,
🟠 RVIS > 30 & < 50: intermediate value,
🟢 RVIS ≥ 50: functional variation tolerant.
O/E Score is the ratio of the observed/expected number of LoF variants. It is a continuous measure of gene tolerance to LoF variation that incorporates a 90% confidence interval. The closer the O/E is to zero, the more likely the gene is LoF-constrained. If a hard threshold is needed for the interpretation of Mendelian disease cases, use the upper bound of the O/E confidence interval < 0.35.
The Gene-related diseases card lists known disease associations for the gene or genes affected by the variant. If the variant affects multiple genes, the list shows associations for each gene.
Disease name
As defined in gene-disease connection source(s)
Source links:
OMIM
Monarch link (v100.39.0+) Redirects to the disease entry in the Mondo Disease Ontology supported by The Monarch Initiative.
GenCC badge (v100.39.0+) Diseases with one or more entries in the GenCC (Gene Curation Coalition) database display a badge next to the disease name. This badge represents the highest-level gene–disease connection validity classification. If multiple GenCC entries exist for the same gene–disease connection, a “+n” indicator appears beside the badge. Hovering over this indicator reveals the additional classifications and their sources.
Note:
GenCC is included as a gene–disease source starting with .
To view GenCC annotations in the UI, you must use or later.
On older platform versions, the GenCC validity badge and submitter details are not displayed, and GenCC links are inactive.
Once a variant is tagged, a default disease is automatically selected. Users have the following options:
Leave the default disease
Select a different disease from the available list of diseases
Add a custom disease name
The selected disease appears in the Gene-related diseases card of the , as well as on the , and can be included in the .
Verify your disease selection before completing the report to ensure accurate evidence graph, phenotype matching, and consistent Curate data.
Click Edit.
Select a different disease.
Click Save.
The evidence graph will update automatically to reflect the new disease association.
The change will be recorded in the Activity Log.
Click Edit.
Click the button on the top right of the card.
Enter your custom disease name.
Custom disease name will be stored in the case only. If you export the variant to Curate, no disease will be saved in Curate for that variant.
Academic papers included in the Emedgene knowledge graph
CGD
Orphanet
GenCC (v100.39.0+)
Inheritance modes
Displayed per source when available. Not shown for Orphanet
Click Save.
The Clinical significance card provides insights into how a variant has been classified previously—both within your organization and across public databases. This helps you quickly assess existing interpretations and decide whether additional evidence is needed.
Manually classified
Indicates if the variant was classified in any previous case in the account
Networks classified
Shows if the variant was classified by partner organizations in your
Known issue: The Networks classified tab may occasionally appear empty in error. Until the fix is implemented, please rely on the , which displays the relevant information.
Curate Displays whether the variant exists in your variant database
ClinVar
Lists ClinVar submissions for the variant
ClinGen Regions (CNVs) Indicates whether a variant overlaps a dosage-sensitive region defined by ClinGen
Organization database Shows classifications from maintained by your organization.
MITOMAP (mtDNA variants) Shows a variant's status in . By clicking on the MITOMAP interactive link, you will be taken to .
The Transcript field displays variant description according to the Human Genome Variation Society (HGVS) standard for coding DNA and protein changes.
Some transcripts are marked with one or both indicators:
Checkmark : Canonical transcript
Curate icon: Transcript selected in your database.
Emedgene automatically selects a reference transcript according to predefined . The reference transcript determines:
Main affected gene
Main variant effect
Coding DNA change and resulting protein change
If the automatically selected transcript does not match your reporting preference, you can:
Select a different transcript* from the Transcript dropdown menu
Add a custom transcript as part of a custom HGVS variant description (see below). You may need to add a custom HGVS variant description in the following cases:
The required transcript is not available in the suggested options
When a variant occurs in a region shared by transcripts from different genes, Emedgene enforces a rule:
You cannot select a transcript that belongs to a different gene than the variant's primary gene.
Click the plus icon next to the HGVS description box.
In the field, enter the notation in the correct format. Do not include spaces.
Coding change only: GENE,NM_123456:c.-123A>C
Once added, a custom description becomes available for .
Certain variants, such as upstream or downstream gene variants, may be lacking an automatic HGVS description
This restriction prevents mismatches between the variant and its gene context, which could lead to incorrect interpretation. If you try to select a transcript from a non-primary gene, the option is blocked.
Coding and protein change: GENE,NM_123456:c.123G>A,p.123Arg>His
The In silico predictions card highlights aggregated scores for missense prediction, conservation, and splicing prediction. These are algorithmic assessments of variant effects based on known biological features, protein structures, evolutionary conservation, or machine learning models trained on large-scale data. The scores are calculated by proprietary algorithms that integrate outputs from individual in silico variant pathogenicity predictors.
These scores support ACMG classification, particularly PVS1/PP3 (pathogenic evidence) and BP4/BP7 (benign evidence), and are especially useful when experimental data is lacking.
Note on multiple in silico prediction scores
When a variant has more than one in silico prediction score (for example, due to multiple transcripts), Emedgene automatically displays the most severe score available, regardless of which transcript is selected in the case. This logic applies to all individual in silico prediction tools.
Each prediction type is grouped into three key categories: missense prediction, conservation, splicing prediction.
The tools in this category evaluate the potential functional impact of genetic variants, with a primary focus on assessing how missense substitutions affect protein structure and function.
REVEL scores are used for automated tagging of ACMG PP3 and BP4 criteria.
LRT: Identifies deleterious missense variants via selective constraint (Chun & Fay, 2009).
PolyPhen-2 HDIV and HVAR: Predict the possible impact of a missense variant on protein stability and function (Adzhubei et al., 2010).
CADD (v38.0+):
Ensemble predictor integrating over 60 genomic annotations (Kircher et al., 2014).
Assesses deleteriousness of SNVs, MNVs, and small indels in both coding and non‑coding regions.
APOGEE: Predicts pathogenicity of mitochondrial missense variants (Castellana et al., 2017).
MitoTIP: Evaluates the likelihood that novel SNVs or indels in mitochondrial tRNA genes lead to disease (Sonney et al., 2017).
REVEL score check
If REVEL score is greater than 0.75, missense prediction is Damaging.
If REVEL score is less than or equal to 0.75, missense prediction is Neutral.
Conservation tools assess how strongly a nucleotide or amino acid position is conserved across species, helping to determine whether a variant is located within a biologically critical region.
The individual conservation scores are considered for automated tagging of ACMG PP3 criterion.
SiPhy 29 Mammals: Estimates the rate of evolution at each nucleotide based on 29 mammalian genomes to identify regions under selective constraint (Garber et al., 2009).
GERP RS: Quantifies the difference between the neutral substitution rate and the observed rate at a specific site (Davydov et al., 2010).
Score availability check
If no conservation prediction scores are available, conservation prediction is Unknown.
Splicing prediction tools evaluate whether a variant disrupts normal RNA splicing, potentially altering transcript structure or gene expression. They are especially critical for flagging intronic, synonymous, and non‑canonical splice‑region variants with potentially high splicing impact. These variants should be prioritized for transcript‑level review or laboratory RNA testing to verify the predicted effects.
SpliceAI supports tagging of PP3 and BP7 ACMG tags. SpliceAI-10K supports tagging of PVS1 and PP3 ACMG tags.
dbscSNV (AdaBoost and RandomForest): Machine learning ensemble models trained to predict splice‑site disruption from sequence context (Jian et al., 2014).
Score availability check
If no splicing prediction scores are available, splicing prediction is Unknown.
Note: Variants of types CNV, SV and STR are not annotated with in silico predictions.
PrimateAI-3D: Deep learning model developed by Illumina to quantify missense variant pathogenicity (Sundaram et al., 2018).
REVEL:
Ensemble predictor combining 18 individual scores to identify rare pathogenic missense variants (Ioannidis et al., 2016).
REVEL scores are used for automated tagging of ACMG PP3 and BP4 criteria.
SIFT: Evaluates whether an amino acid substitution affects protein function based on sequence homology (Sim et al., 2012).
DANN:
Deep neural network model trained on the same annotations as CADD, designed to improve upon CADD’s methodology, particularly for non‑coding variants (Quang et al., 2015).
Evaluates SNVs, MNVs, and small indels, both coding and non-coding.
MutationTaster:
Ensemble predictor incorporating conservation, splice‑site changes, protein features, and population frequency (Schwarz et al., 2014).
Assesses SNVs and small indels in both coding and non‑coding regions.
If REVEL score is not available, proceed to step 2.
PrimateAI-3D prediction check
If PrimateAI-3D prediction is D, missense prediction is Damaging.
If PrimateAI-3D prediction is not available or is B, proceed to step 3.
If any of the following is true, missense prediction is Damaging:
LRT prediction is D
SIFT prediction is D
MutationTaster prediction is A or D
Polyphen2 HDIV or HVAR prediction is P or D
DANN score is greater than 0.96
CADD Phred score is greater than or equal to 20
If none of the above conditions are met, proceed to step 4.
If any of the following is true, missense prediction is Neutral:
LRT prediction is N or U
SIFT prediction is T
MutationTaster prediction is N or P
Polyphen2 HDIV or HVAR prediction is B
If none of the above conditions are met, proceed to step 5.
If none of the conditions in steps 1-4 are met, missense prediction is Unknown.
If any of the following is true, conservation prediction is High:
GERP RS score is greater than 3
phastCons 100 Vertebrates score is greater than 0.9
If none of the above conditions are met, proceed to step 3.
If any of the following is true, conservation prediction is Moderate:
GERP RS score is greater than 1
phastCons 100 Vertebrates score is greater than 0.2
If none of the above conditions are met, proceed to step 4.
If none of the above conditions in steps 1-3 are met, conservation prediction is Low.
Deep neural network trained on large-scale human splicing data (Jaganathan et al., 2019).
Provides directional delta scores:
DS_AG (Acceptor Gain)
DS_AL (Acceptor Loss)
DS_DG (Donor Gain)
DS_DL (Donor Loss)
SpliceAI supports automated tagging of PP3 and BP7 ACMG tags.
SpliceAI-10K:
Extends SpliceAI’s window to detect broader effects such as pseudoexonization, partial intron retention, and exon skipping.
Scores are provided for donor and acceptor gain/loss, and high values may indicate cryptic splice site activation.
These annotations guide PVS1 and PP3 ACMG tag evaluation to enhance interpretation at the transcript level, especially for deep intronic variants or potential splicing disruptions.
If any of the following is true, splicing prediction is High:
Both dbscSNV scores are greater than 0.6
Any SpliceAI score is greater than 0.8
If none of the above conditions are met, proceed to step 3.
If any of the following is true, splicing prediction is Low:
Both dbscSNV scores are lower than 0.5
Any SpliceAI score is less than or equals 0.2
If none of the above conditions are met, proceed to step 4.
If none of the conditions in steps 1-3 are met, splicing prediction is Moderate.
Missense Prediction
+ Polyphen2 HDIV Polyphen2 HVAR SIFT MutationTaster LRT DANN REVEL PrimateAI-3D
CADD Phred
+
CADD Phred
+ APOGEE MitoTIP
Conservation
+ SiPhy 29 Mammals GERP RS phastCons 100 vertebrate
+
GERP RS
-
Splicing Prediction
+ dbscSNV-RF dbscSNV-Ada SpliceAI DS AG SpliceAI DS AL SpliceAI DS DG SpliceAI DS DL
-
-