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Functional Assessment of BRCA1 variants using CRISPR-Mediated Base Editors

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When Two plus Four Does Not Equal Six: Combining Computational and Functional Evidence to Classify BRCA1 Key Domain

Scott T Pew, Madison B Wiffler, Alun Thomas

Medrxiv : the Preprint Server for Health Sciences

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Summary

Multiplexed assays of variant effect (MAVEs) and computational tools help classify genetic variants. However, their performance on novel variants requires empirical validation, especially for BRCA1 mutations at exceptionally conserved ancestral residues (ECARs).

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Area of Science:

Genomics
Bioinformatics
Molecular Biology

Background:

Accurate genetic variant classification is crucial for clinical genetic sequencing.
Multiplexed assays of variant effect (MAVEs) and computational tools are promising for addressing variant classification uncertainty.
The joint performance of MAVEs and computational tools on novel variants remains unestablished.

Purpose of the Study:

To empirically validate the strength of evidence for genetic variant classification criteria.
To assess the performance of MAVEs and computational tools, individually and combined, for BRCA1 variants.
To investigate the utility of exceptionally conserved ancestral residues (ECARs) in predicting variant pathogenicity.

Main Methods:

Developed a maximum likelihood estimate (MLE) model to convert frequentist odds ratios to proportions pathogenic.
Applied the MLE model to functional assay data and computational tool predictions for BRCA1 variants.
Defined and analyzed exceptionally conserved ancestral residues (ECARs) in BRCA1.

Main Results:

Missense substitutions at ECARs in BRCA1 are disproportionately pathogenic, with effect sizes comparable to protein-truncating variants.
For non-ECAR positions, concordant predictions from assays and tools often do not meet the additive assumptions of American College of Medical Genetics and Genomics (ACMG) guidelines.
The performance of assays and tools varied significantly at ECARs versus non-ECAR positions.

Conclusions:

The strength of evidence assigned by ACMG guidelines is not universally applicable and requires empirical validation.
MAVEs and computational tools show promise but their combined application needs refinement based on empirical data.
ECARs represent a valuable feature for identifying potentially pathogenic variants in genes like BRCA1.