Daffodil M. Canson

This study identifies 52 genetic risk loci for female genital tract polyps, highlighting shared susceptibility with endometriosis, fibroids, and endometrial cancer through genome-wide association and Mendelian randomisation.

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 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 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.