When two plus four does not equal six: Combining computational and functional evidence to classify BRCA1 key domain...

Area of Science:

• Genetics
• Bioinformatics
• Molecular Biology

Background:

• Accurate classification of genetic variants is crucial for clinical genetic sequencing.
• Multiplexed assays of variant effect and computational tools are key to addressing variant classification uncertainty.
• The joint performance of these tools on novel variants requires empirical validation.

Purpose of the Study:

• To quantitatively validate the strength of evidence for genetic variant classification criteria.
• To assess the performance of functional assays and computational tools for BRCA1 variants.
• To investigate the utility of exceptionally conserved ancestral residues (ECARs) in variant classification.

Main Methods:

• Developed a maximum-likelihood estimate model to convert odds ratios to proportions pathogenic.
• Applied the model to functional assays and computational tools for BRCA1 variants.
• Defined and analyzed exceptionally conserved ancestral residues (ECARs) in BRCA1.

Main Results:

• Missense substitutions at ECARs in BRCA1 are disproportionately pathogenic.
• Functional assays and computational tools showed variable performance, particularly at non-ECAR positions.
• Concordant predictions often did not meet the additive assumptions of American College of Medical Genetics and Genomics (ACMG) guidelines.

Conclusions:

• The strength of evidence assigned by ACMG guidelines requires empirical validation.
• Performance of assays and tools is context-dependent, especially concerning ECARs.
• Current methods may overestimate the reliability of combined evidence for certain genetic variants.