What frequency evidence can tell you
Population data can identify variants that are too common for a proposed disease mechanism. It can also show that a variant is absent or very rare in a large reference population. Neither observation should be read without context.
The ClinGen FH expert panel introduced LDLR-specific population-frequency thresholds because generic cutoffs do not capture the gene and disease context on their own.
A review-ready frequency check
Match the population
Review the overall and ancestry-specific frequencies. A global aggregate can hide a population-specific signal.
Check coverage and quality
An apparent absence has less meaning when the region was not adequately covered or the call quality is weak.
Use the disease model
Interpret frequency against inheritance, penetrance, prevalence, and the expected contribution of the gene.
Keep provenance
Record the population database, version, filters, and date so another reviewer can reconstruct the evidence.
Product boundary
CardioGen AI accepts allele frequency, allele count, and allele number as model inputs. These fields support review but do not, by themselves, establish an ACMG/AMP evidence code or clinical classification.
What to carry into the next evidence view
Record whether the frequency evidence supports, challenges, or does not resolve the proposed interpretation. Then compare it with molecular consequence, computational predictions, functional evidence, and case-level observations.
Primary references
- ClinGen FH Variant Curation Expert Panel consensus guidelines for LDLR variant classification, Genetics in Medicine, 2022.
- ClinGen FH Expert Panel specifications to the ACMG/AMP guidelines, current criteria registry.
- ACMG/AMP standards and guidelines for interpretation of sequence variants, Genetics in Medicine, 2015.