Nicola Whiffin

The paper introduces UTRannotator, a tool to annotate 5'UTR variants that create or disrupt uORFs, annotating 30.8% of ClinVar 5UTR pathogenic variants.

The paper evaluates potential drug targets using loss-of-function genetic variants from gnomAD, highlighting the importance of deep curation and targeted recall studies for drug discovery.

From 125,748 exomes and 15,708 genomes, 443,769 high-confidence predicted loss-of-function variants yield LOEUF, a continuous gene intolerance score tracking mouse lethality, cell essentiality, and disease associations.

The paper develops an expression-based annotation metric to improve rare variant interpretation by filtering false-positive loss-of-function variants and identifying pathogenic variants in highly expressed exons.

Short-read genome sequencing of 14,891 diverse genomes yields 433,371 structural variants (SVs) for gnomAD; loss-constrained genes also resist duplication, and clinical SV carrier rates are benchmarked.

The paper analyzes loss-of-function variants in LRRK2 across large genomic datasets to assess the safety of partial LRRK2 inhibition as a Parkinson's disease therapy.

The paper systematically studies uORF variants in 15,708 genomes, showing they are under strong negative selection and contribute to disease.

The paper analyzes multi-nucleotide variants (MNVs) in 125,748 exomes and 15,708 genomes, identifying their distribution, mutational origins, and functional implications.

The paper quantifies variant pathogenicity in hypertrophic cardiomyopathy using etiological fraction, increasing actionable variant detection by 14-20%.

The paper responds to a critique of their variant filtering method, demonstrating its robustness and highlighting the benefits of using high-resolution variant frequencies for clinical genome interpretation.

CardioClassifier is a decision-support tool for inherited cardiac conditions that improves variant interpretation by integrating disease-specific annotations and case data.

The paper presents a statistical framework to filter candidate disease-causing variants using high-resolution variant frequencies, reducing the number of variants under consideration and reclassifying some as pathogenic.