The American College of Medical Genetics and Genomics (ACMG) variant classification system is a standardized framework used to interpret the clinical significance of genetic changes. Developed to provide consistency and clarity in genetic testing results, it offers a unified, evidence-based approach. This ensures different laboratories and clinicians interpret findings with a shared understanding, improving the reliability of genetic information for patient care.
The Need for Variant Classification
Human genetic variation is complex, with millions of differences between individuals. Before a standardized system, interpreting genetic changes presented significant challenges. Different laboratories or clinicians might have interpreted the same variant differently, leading to inconsistent diagnoses and varied treatment plans.
The ACMG guidelines address this by providing a common framework for evaluating genetic variants. This approach reduces ambiguity in genetic test results, ensuring interpretations are based on consistent criteria. It establishes an industry standard for clinical genetic diagnostics, improving the reliability of genetic information for patient care, especially for Mendelian diseases caused by single variants in single genes.
The Five Classification Categories
The ACMG guidelines classify genetic variants into five main categories, each with distinct clinical implications. These categories communicate the likelihood that a genetic change contributes to a disease.
Pathogenic variants are known to cause disease, with strong evidence linking them directly to a specific condition. This classification indicates the variant is responsible for observed health issues.
Likely Pathogenic variants are highly probable to cause disease, with substantial evidence supporting their role, though less definitive than pathogenic. These variants inform clinical management.
A Variant of Uncertain Significance (VUS) indicates insufficient evidence to definitively classify the variant as benign or pathogenic. Clinicians cannot yet confirm if the variant causes disease or is harmless. VUS findings often require further research or additional family studies.
Likely Benign variants are very likely to be harmless and not cause disease. Evidence suggests these genetic changes do not contribute to a patient’s condition, though not definitively proven harmless.
Benign variants are known to be harmless and do not cause disease. These are common variations within the population that do not impact health, typically found at a higher frequency than disease-causing variants.
How Variants Are Classified
Genetic experts classify variants using a weighted process that considers multiple lines of evidence, rather than a simple checklist. This systematic approach ensures that the interpretation is robust and evidence-based. The 2015 ACMG/AMP guidelines established 28 criteria, categorized by the weight and type of evidence, to guide this classification.
Population Data
This assesses how common or rare a variant is in different populations. A variant found at a much higher frequency in the general population than expected for a disease strongly suggests a benign impact. Conversely, a very rare variant, especially if absent from healthy control groups, might be suspicious for pathogenicity.
Computational Predictions
These involve specialized software to predict a variant’s potential impact on protein function. These “in silico” tools analyze how a genetic change might alter a protein’s structure or function, offering insights into its likely effect. These predictions are supporting evidence, typically combined with other data.
Functional Studies
Laboratory experiments test the actual effect of a variant on gene or protein function. These experiments can directly demonstrate if a variant disrupts normal biological processes, providing strong evidence for its pathogenicity.
Segregation Analysis
This involves tracking the variant within families to see if it co-segregates with the disease. If a variant is consistently present in affected family members but absent in unaffected ones, it supports a pathogenic classification. This evidence is valuable in families with multiple affected individuals.
De Novo Occurrence
This refers to a variant appearing spontaneously in an individual without being inherited from either parent. If a de novo variant is found in a patient with a severe genetic condition, and neither parent carries it, it can be strong evidence for pathogenicity, especially if the disease is caused by new mutations.
Allelic Data
This considers whether a variant is found on the same or different chromosomes as another known pathogenic variant. For recessive conditions, knowing if two different pathogenic variants are on the same chromosome (in cis) or different chromosomes (in trans) is important for assessing disease risk.
Applying Classification in Patient Care
ACMG variant classifications guide decisions in clinical settings, directly impacting patient care. They are not just academic exercises; they are practical tools that inform personalized medical strategies.
Diagnosis
Classifications help clinicians confirm or rule out a genetic condition. A pathogenic or likely pathogenic classification provides a definitive diagnosis, allowing for appropriate medical management and surveillance. A benign classification helps exclude a genetic cause for symptoms.
Treatment Decisions
Variant classification informs personalized therapeutic strategies. Knowing the specific genetic cause can guide the selection of targeted therapies or avoidance of ineffective treatments, allowing for more precise interventions.
Genetic Counseling
Counseling relies on these classifications to provide accurate risk assessment and discuss reproductive options. Counselors explain complex genetic test results, helping families comprehend implications for their health and future, including recurrence risks.
Family Planning
Classifications guide decisions for future pregnancies by identifying carrier status or risks for specific genetic conditions. For individuals with a family history, classifications help assess the likelihood of passing on a variant, empowering informed reproductive choices.
Preventive Measures
These can be identified for individuals at risk based on variant classifications. For example, a pathogenic variant linked to increased cancer risk might prompt recommendations for earlier or more frequent screening. Classifications can be updated as new evidence emerges, particularly for variants of uncertain significance.