Amanda B. Spurdle

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.

The paper develops machine learning models to predict pathogenicity of BRCA1 and BRCA2 variants using cancer genomic profiles, improving classification of variants of uncertain significance.

The paper presents a workshop-based approach to improve confidence and standardize the use of functional evidence in variant classification through community discussion and consultation.

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 paper integrates tumor homologous recombination deficiency status with germline BRCA1 and BRCA2 variant classification to improve pathogenicity prediction.

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 surveys genetic professionals on functional evidence use in variant classification and identifies the need for expert recommendations and guidelines.

The paper analyzes TP53 minigenes to show that splicing regulatory elements and their disruption by variants affect splicing outcomes, with deletions having stronger impacts than SNVs.

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 specifies MMR gene-specific criteria to the ACMG/AMP classification system, reclassifying some variants and providing a framework for improved pathogenicity assessment.

The paper calibrates bioinformatic tools to classify 5' cis-regulatory variants in Mendelian disease using clinical and population datasets, finding optimal thresholds differ from coding variant standards.

The paper introduces SAI-10k-calc, a tool that extends SpliceAI to predict splicing aberrations and their effects, achieving high sensitivity and specificity for variant impact prediction.

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

The paper demonstrates federated analysis of BRCA1 and BRCA2 variants in a Japanese cohort to interpret variants of uncertain significance without transferring sensitive data.

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

This study uses germline and tumor whole genome sequencing to identify the cause of mismatch repair deficiency in suspected Lynch syndrome cases, finding that most cases are due to somatic mutations rather than inherited variants.

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

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.

This paper evaluates how sample size affects polygenic hazard score models for prostate cancer, finding that 20-30k samples are needed for discovery models, but 10k suffice if SNPs are pre-established.

The paper identifies candidate target genes at endometrial cancer GWAS risk loci using HiChIP chromatin looping analysis and finds associations between risk variation and gene expression.

The paper evaluates a polygenic hazard score for prostate cancer screening, showing it improves positive predictive value by adjusting screening age based on genetic risk.

The paper uses nanopore sequencing to analyze full-length BRCA1 mRNA transcripts, identifying novel isoforms and co-occurring exon skipping events.

This paper develops a genetic risk score to personalize prostate cancer screening age based on polygenic hazard scores and validates its predictive power in a separate dataset.