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Guidelines for variant classification and interpretation

An introduction to the guidelines for genetic variant interpretation

ACMG/AMP guidelines

The current guidelines for variant classification and interpretation stem from the expert opinion of the workgroup convened by the American College of Medical Genetics and Genomics (ACMG) in 2013 that comprised representatives from the ACMG, the Association for Molecular Pathology (AMP) and the College of American Pathologists (CAP). The latest guidelines were published in 2015. You will learn more about how to apply these guidelines throughout the course.

These guidelines are based on expert opinion and empirical data, though some criteria use published evidence.

When do you apply these criteria?

These guidelines are to be used for the interpretation of variants observed in patients with suspected inherited (primarily Mendelian) disorders in a clinical diagnostic laboratory setting. Please refrain from using them to interpret somatic variation, pharmacogenomic (PGx) variants, or variants in genes associated with multigenic non-Mendelian complex disorders. Ensure you apply these criteria for variants with an established gene-disease relationship. These rules should be applied to the gene and the specific disorder pair and not to all the Mendelian disorders in which the gene might have a role.

Variant nomenclature

In addition to what you have learnt in the step Genetic variations, take care to use the appropriate HGVS Nomenclature for the variant and use the Matched Annotation from NCBI and EBI (MANE) or the most clinically relevant transcript, aiming to cover a high-confidence, genome-wide transcript set. You will learn more about MANE in the step What is reference human gene annotation? and you can also check if the variant is appropriately described using Mutalyzer.

Terminology and criteria

Although the basic premise is that the clinical significance of any given sequence falls along a gradient (continuous variable), the terminology uses a categorical approach: pathogenic, likely pathogenic, variant of uncertain significance, likely benign and benign.

There are two sets of criteria, one for the classification of pathogenic and likely pathogenic variants and the other for the classification of benign and likely benign variants. If a variant does not fit the criteria of pathogenic or benign, or the evidence for benign and pathogenic is conflicting, the variant defaults to being of uncertain significance. ‘Likely’ refers to a greater than 90% certainty that a variant is either disease-causing or benign.

What evidence is used for variant classification?

The variant classification process involves collecting and evaluating the data on several qualities of a variant. Most importantly, we look at the following:

  • Allele frequency of the variant in the healthy population. A disease-causing variant is very rare and a benign variant is frequent. Therefore, prioritise local databases to assess population data.
  • A variety of software programmes, both publicly and commercially available, can help in the interpretation of sequence variants by predicting their consequences on a gene or protein function.
  • Published functional data can provide evidence of the functional consequences of the variant in human cells and animal models.
  • Segregation data and de novo data provide evidence of how the variant segregates in the family and other affected and unaffected individuals.
  • Always search for information in mutation databases from reputable sources.

You will find several sections in the course addressing these issues in more detail.

Each criterion is weighted but is flexible

The criteria for determining the pathogenicity of genetic variants are weighted as very strong, strong, moderate, or supporting for pathogenic criteria, and stand-alone, strong, or supporting for benign criteria. To enable some flexibility, these criteria can be adjusted between weights based on professional judgment and the evidence available. However, expert judgment needs to be applied when evaluating the full body of evidence to account for differences in the strength of variant evidence.

Evidence framework table. Line A = ‘Population data’: ‘Bening Strong’ = MAF is too high for disorder BA1/BS1 or observation in controls inconsistent with disease; ‘Pathogenic Moderate’ = Absent in population databases PM2; ‘Pathogenic Strong’ = Prevalence in affecteds statistically increased over controls PS4. Line B = ‘Computational and predictive data’: ‘Benign Supporting’ = Multiple lines of computational evidence suggest no impact on gene/ gene product BP4. Minissense in a gene where only truncating causes disease BP1. Silent variant with non-predicted splice impact BP7. In-frame indels in repeat w/o known function BP3; ‘Pathogenic Supporting’ = Multiple lines of computational evidence support a deleterious effect on the gene/gene product PP3; ‘Pathogenic Moderate’ = Novel missense change at an amino acid residue where a different pathogenic missense change has been seen before PM5. Protein length changing variant PM4; ‘Pathogenic Strong’ = Same amino acid change as an established pathogenic variant PS1; ‘Pathogenic Very Strong’ = Predicted null variant in a gene where LOF is a known mechanism of disease PVS1. Line C = ‘Functional data’: ‘Benign Strong’ = Well-established functional studies show no deleterious effect BS3; ‘Pathogenic Supporting’ = Missense in gene with low rate of benign missense variants and path. Missenses common PP2; ‘Pathogenic Moderate’ = Mutational hot spot or well-studied functional domain without benign variation PM1; ‘Pathogenic Strong’ = Well-established functional studies show a deleterious effect PP3. Line D = ‘Segregation data’: ‘Benign Strong’ = Nonsegregation with disease BS4; ‘Pathogenic Supporting’ = Cosegregation with disease in multiple affected family members PP1; Increased segregation data from ‘Pathogenic Supporting’ to ‘Pathogenic Strong’. Line E = ‘De novo data’: ‘Pathogenic Moderate’ = De novo (without paternity & maternity confirmed) PM6; ‘Pathogenic Strong’ = De novo (paternity & maternity confirmed) PS2. Line F = ‘Allelic data’: ‘Benign Supporting’ = Observed trans with a dominant variant BP2. Observed in cis with a pathogenic variant BP2; ‘Pathogenic Moderate’ = For recessive disorders, detected in trans with a pathogenic variant PM3. Line G = ‘Other database’: ‘Benign Supporting’ = Reputable source w/o shared data = benign BP6; ‘Pathogenic Supporting’ = Reputable source = pathogenic PP5. Line H = ‘Other data’: ‘Benign Supporting’ = Found in case with an alternate cause BP5; ‘Pathogenic Supporting’ = Patient’s phenotype of FH highly specific for gene PP4 Click to expand

Figure 1. Evidence Framework. This chart organises each of the criteria by the type of evidence as well as the strength of the criteria for a benign (left side) or pathogenic (right side) assertion. BS, benign strong; BP, benign supporting; FH, family history; LOF, loss of function; MAF, minor allele frequency; path., pathogenic; PM, pathogenic moderate; PP, pathogenic supporting; PS, pathogenic strong; PVS, pathogenic very strong. Source: Genetics in Medicine The original table can be downloaded from the manuscript.

How are these guidelines used in clinical management?

These guidelines assist in the clinical management of patients by informing decisions. They provide guidance on whether Pathogenic and Likely Pathogenic variants are likely to cause disease. It’s important that variants of uncertain significance (VUS) should not be used for clinical decision-making. A VUS represents a 10-90% chance of being pathogenic, and not all VUSs are the same. However, variant classification is just one of several pieces of evidence required for clinical decision-making.

Other critical factors include clinical information (external phenotype, results from routine and special clinical and laboratory investigations, and imaging findings); and family history (information about the patient’s family health history).

Although the ACMG/AMP framework has been central to the harmonisation of variant pathogenicity classification, there is still discordance in the use of criteria. Recommendations and guidance will continue to change due to the refinement of the framework and new/updated resources and datasets. Since the publication of these guidelines, several additional recommendations have been made to improve them, interested readers can view these in the links below.

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© Wellcome Connecting Science
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