Clare Turnbull

CanVIG-UK evaluates the impact of its activities on variant interpretation in cancer susceptibility genes through a survey, showing high utility and usage of its consensus recommendations.

The paper evaluates how different truthset strategies affect evidence points (EPs) in clinical validation of functional assays across 2,120 gene-truthset-assay combinations.

The paper establishes consensus guidelines for constructing truthsets to validate functional assays in cancer variant interpretation, focusing on missense variants and clinical validation frameworks.

This paper evaluates a streamlined germline genetic testing pathway for breast cancer patients, showing high satisfaction and feasibility while highlighting the need for simplified criteria to improve detection of pathogenic variants.

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.

CanVar-UK is a web platform that supports collaborative interpretation of germline variants in cancer susceptibility genes by integrating variant data, clinical information, and expert discussions.

The paper evaluates the limitations of existing benign missense variant truthsets for MAVEs and proposes a proactive-systematic framework to improve their construction for better functional evidence classification.

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

The paper analyzes UK Biobank data to assess the association of APC I1307K variant with colorectal cancer risk in Ashkenazi and non-Ashkenazi whites, finding no significant association and highlighting the need for larger studies.

The paper surveys NHS clinical scientists on their acceptance of MAVE data validation, finding that trusted central bodies are needed for clinical adoption.

The paper recommends a standardized multigene germline panel for breast cancer patients based on cancer mortality benefit, including specific genes and excluding others.

The paper develops a framework for classifying variants of reduced penetrance in cancer susceptibility genes to improve variant interpretation consistency.

This study compares digital and in-person genetic counseling for germline testing in breast cancer patients, finding higher uptake with digital information without compromising other outcomes.

The paper introduces PS4-LRCalc, a tool that quantitatively assigns evidence for variant classification using case-control data, enabling continuous scoring instead of dichotomous PS4 scoring.

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 identifies 88 novel rare disease-gene associations using a burden analysis framework on whole genome sequencing data from the 100,000 Genomes Project, with potential clinical impact for undiagnosed cases.

This study assesses polygenicity and risk prediction using common variants across 14 cancers, finding varying numbers of susceptibility variants and risk score effectiveness.

This study combines genome-wide association data from four cancers to identify shared and novel genetic associations and pleiotropic effects.

The paper identifies genetic determinants of mosaic Y chromosome loss in blood and links it to genomic instability and non-haematological cancer risk.