Quantitative Interpretation of ACMG/AMP Variant Classification [Part 1]

The ACMG/AMP 2015 guidelines remain the standard framework for interpreting clinical genetic test results.
However, in real-world variant curation, situations frequently arise that cannot be fully explained by the predefined combinations in the guidelines. Typical examples include cases where both pathogenic and benign evidence coexist, or where evidence combinations do not clearly fit into the rule-based categories.

To address these limitations, Tavtigian et al. proposed an alternative approach in their 2020 paper, Fitting a naturally scaled point system to the ACMG/AMP variant classification guidelines.

This work should not be understood as a replacement of the ACMG/AMP system, but rather as a way of reorganizing the previously proposed Bayesian classification framework into a more intuitive form that can be more easily applied in practice.

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Why was a point system needed?

The ACMG/AMP guidelines are built on evidence strength categories: Supporting, Moderate, Strong, and Very Strong.
However, the relative differences between these levels are not quantitatively defined, which makes it difficult to directly compare or combine different types of evidence.

In 2018, Tavtigian and colleagues demonstrated that these evidence strengths could be modeled using Bayesian odds of pathogenicity, and that consistent intervals exist between the different levels of evidence strength.
The 2020 study builds on this concept by expressing these intervals as a point-based system that allows intuitive addition and subtraction of evidence.

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Core concept: a naturally scaled point system

The point system proposed in the paper is as follows:

• Supporting: +1
• Moderate: +2
• Strong: +4
• Very Strong: +8

Benign evidence is assigned negative values:

• Supporting benign: -1
• Moderate benign: -2
• Strong benign: -4

Importantly, these values are not arbitrarily assigned.
They are derived from a logarithmic transformation of Bayesian odds, meaning that the simplicity of the scoring system preserves the underlying probabilistic structure.

By summing these points, variants can be classified as follows:

• ≥ 10 points: Pathogenic
• 6–9 points: Likely Pathogenic
• 0–5 points: VUS
• -1 to -5 points: Likely Benign
• ≤ -6 points: Benign

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Implications for clinical interpretation

The key advantage of this framework is that it allows all evidence to be evaluated on a single quantitative scale.

First, it enables clearer interpretation of conflicting evidence.
For example, when both pathogenic and benign evidence are present, rule-based approaches may lead to ambiguity. A point system allows these pieces of evidence to be quantitatively balanced to assess the overall direction.

Second, it provides flexibility for combinations not explicitly defined in the guidelines.
While ACMG/AMP specifies representative combinations, real-world data are far more complex. The point system allows consistent interpretation even in non-standard scenarios.

Third, it offers a more refined understanding of VUS.
Not all VUS represent the same level of uncertainty—some are closer to pathogenic, while others are closer to benign. The point system makes this continuum more explicit.

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Considerations for interpretation

Despite its advantages, several important considerations remain.

First, this framework does not redefine the strength of individual ACMG/AMP criteria.
The calibration of specific criteria continues to be refined through subsequent work, particularly by efforts such as those from ClinGen SVI.

Second, real-world clinical interpretation still requires integration with:

• Gene-specific guidelines
• Disease-specific specifications
• Laboratory-specific standard operating procedures

The point system should therefore be viewed not as a replacement, but as a quantitative layer built on top of the existing framework.

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Summary

The 2020 study by Tavtigian et al. does not introduce a new classification system, but rather clarifies and operationalizes the quantitative structure underlying the ACMG/AMP framework.

Its key contribution is not simply the introduction of a scoring system, but the ability to consistently combine and explain evidence strength in a quantitative manner.

As subsequent calibration efforts—particularly from ClinGen—continue to build on this framework, this work can be seen as an important step toward a more quantitative and transparent approach to variant interpretation.

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Reference

Tavtigian SV, Harrison SM, Boucher KM, Biesecker LG. Fitting a naturally scaled point system to the ACMG/AMP variant classification guidelines. Hum Mutat. 2020 Oct;41(10):1734-1737. doi: 10.1002/humu.24088. Epub 2020 Aug 30. PMID: 32720330; PMCID: PMC8011844.