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ACMG & ACGS guidelines Part 5: Functional data

ACMG & ACGS guidelines part 5 _functional data
© St George’s, University of London

In this step, we will consider the functional data category. We discussed functional data analysis in detail early on in Week 2.

Take some time to look at the figure below, which shows the part of the ACMG evidence framework that relates to functional data.

Functional data can be used as evidence to support either a benign or a pathogenic classification. There are three criteria that might be used to support pathogenicity (PS3, PM1, and PP2) and one criterion that might be used to support a benign classification (BS3).

Table showing the Functional data row from the ACMG guidelines. The row reads as follows: 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, Moderate: Mutational hot spot or well-studied functional domain without benign variation PM1, Strong: Well-established functional studies show a deleterious effect PS3.​
Click to expand

Functional data can be divided into three types of evidence:

  • Functional studies – functional studies employ experimental assays to directly measure whether a variant has a deleterious effect on protein function in vitro.
  • Functional domains or mutational hotspots – this evidence applies to missense variants and considers the location within the gene, as well as the nature of the amino acid change.
  • Constraint score – this evidence applies to missense variants and looks at how tolerant a given gene, or region of a gene, is to missense variation.

We have created a PDF table summarising these three different types of evidence, and how they relate to the various ACMG criteria within the functional data evidence category. We suggest that you take some time now to study this table in detail, make sure you understand the circumstances in which each evidence criterion is used, and download it for future reference.

Again, whilst this provides a brief summary, those wishing for full details should refer to the relevant sections of the ACMG guidelines1 and ACGS update.2

2 Ellard S, Baple E, Callaway A, et al, ‘ACGS Best Practice Guidelines for Variant Classification in Rare Disease 2020’, AGCS. 2020.
2 Brnich, S.E., Abou Tayoun, A.N., Couch, F.J. et al, ‘Recommendations for application of the functional evidence PS3/BS3 criterion using the ACMG/AMP sequence variant interpretation framework’, Genome Med 12, 3 (2020)

For those taking part in the external course evaluation please follow this link to provide feedback for the step.

© St George’s, University of London
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