Tina F. Pesaran

The paper combines enriched case data with population-based case-control analyses using a likelihood-ratio methodology to classify variants in breast cancer susceptibility genes.

The paper describes the development of variant classification specifications for the VHL gene using ACMG/AMP guidelines, resulting in updated evidence codes and improved classification consistency.

The paper quantifies phenotypic specificity of rare FH variants in HLRCC and renal cancer using likelihood ratios from clinical and population data.

The paper identifies characteristics of TP53 variants with reduced penetrance using ClinVar data and a random forest model, finding they have intermediate function, higher frequency, and delayed disease onset.

This study evaluates breast cancer risks associated with pathogenic variant type and location in BRCA2 and other genes, finding differential risks based on variant position.

The paper calibrates three new computational tools for clinical variant classification, finding they provide evidence strength comparable to previously recommended tools.

The paper introduces an information content score to quantify the clinical value of MAVEs, showing it captures more information than just reclassifying variants.

The paper refines ACMG/AMP variant classification criteria for BRCA1 and BRCA2 by calibrating evidence strength and improving classification consistency through expert panel review.

The paper specifies gene-specific rules for interpreting ATM variants using ACMG/AMP guidelines, improving classification accuracy in a pilot study.

The paper functionally characterizes BRCA2 variants using saturation genome editing and classifies them clinically, finding functionally abnormal variants are associated with increased cancer risk.

The paper analyzes VUS rates in genetic testing and finds lower rates in exome/genome sequencing compared to multi-gene panels, suggesting improvements in variant reporting practices.

The paper refines ACMG/AMP splicing variant classification codes using empirical evidence to improve consistency in variant pathogenicity classification.

The paper calibrates computational tools for missense variant pathogenicity classification and provides evidence-based revisions to PP3/BP4 ACMG/AMP criteria.

The paper updates CDH1 variant curation guidelines to resolve conflicting interpretations and reduce variants of uncertain significance for clinical use.

The paper specifies TP53-specific ACMG/AMP variant classification guidelines, reducing variants of uncertain significance from 28% to 12%.

The paper evaluates how PMSA-based computational tools predict pathogenicity of APC missense variants, finding wide variation in predictions and highlighting challenges in alignment-based methods.

The paper provides refined recommendations for applying the PVS1 ACMG/AMP variant criteria to loss of function variants, addressing gaps in the original guideline through evaluation with curated variants.