Sean V. Tavtigian

The paper evaluates the performance of functional assays and computational tools in classifying BRCA1 missense variants using a Bayesian framework and empirical validation.

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

This study identifies 27 genes associated with head and neck cancer risk through exome sequencing and finds significant interactions between DNA repair gene variants and cigarette smoking.

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 assesses rare missense variants in genome sequences using PP3/BP4 evidence strength classifications and finds few variants meet strong or moderate criteria.

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

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

The paper analyzes mobile element insertions and structural variants in longitudinal breast cancer samples to assess their timing and impact on tumor progression.

The paper identifies a novel RPS20 splice donor variant linked to familial CRC and polyposis, showing aberrant splicing and supporting a haploinsufficiency mechanism.

The paper provides structured recommendations for applying the PS3/BS3 functional evidence criteria in variant interpretation using the ACMG/AMP framework, including a four-step framework and minimum control requirements.

The paper evaluates the ACMG PP3 and BP4 rules using Align-GVGD on ATM and CHEK2 data, finding that severe Align-GVGD grades exceed a moderate pathogenic threshold in a Bayesian framework.

This study tests pancreatic cancer cases for germline variants in HBOC and CRC genes, finding 8.9% and 10.5% of cases carry variants with potential clinical significance.

The paper integrates functional assay data with sequence analysis to improve BRCA1 variant classification, adding 146 variants to the Ex-UV database.