Sarah E. A. Leigh

The paper curates 111 gene-disease relationships for syndromic disorders, classifying their clinical validity and identifying gaps in knowledge.

GenePy identifies potential missed biallelic diagnoses in the 100KGP by scoring variants and analyzing phenotypic overlaps, finding 14% of undiagnosed participants may have a missed diagnosis.

This paper standardizes terminology for describing disease-gene relationships to improve variant classification and genetic test interpretation.

This study identifies genetic differences between pediatric and adult cardiomyopathy, finding more constrained variants in childhood cases and female patients.

The paper introduces MuAt, a deep learning model that learns representations of somatic mutations to predict tumour types and subtypes with high accuracy.

This study identifies TMEM63B variants in patients with developmental and epileptic encephalopathy and neurodegeneration, showing altered cation currents and hematological abnormalities.

The paper introduces DeNovoLOEUF, a method to identify diagnostic variants in genome sequencing data, achieving 96% positive predictive value and uncovering 39 missed diagnoses in the 100,000 Genomes Project.

The paper identifies novel Mendelian genes by analyzing mouse embryo lethality and linking them to human disease phenotypes and genetic variants.

The paper identifies an intermediate-effect variant in UMOD associated with chronic kidney disease risk and shows it has reduced disease severity compared to canonical ADTKD mutations.

The paper describes the formation of the Gene Curation Coalition to standardize gene-disease validity terminology and create a unified database for curated gene-disease assertions.

This study uses whole-genome sequencing to identify regulatory and novel genic variants contributing to childhood cardiomyopathy.

The paper provides consensus guidelines for classifying LDLR variants in Familial Hypercholesterolemia using modified ACMG/AMP criteria.

The paper identifies gene-specific mutational signatures caused by replication errors and endogenous DNA damage using CRISPR-Cas9 knockouts and whole-genome sequencing.

The paper integrates gene essentiality data from human and mouse screens to classify genes into FUSIL categories, identifying developmental lethal genes associated with rare diseases.