Miguel de la Hoya

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.

This study identifies seven novel genetic loci associated with high-grade serous ovarian cancer risk using a large-scale GWAS and develops polygenic scores for improved risk prediction.

The paper functionally characterizes BRCA2 variants using saturation genome editing and classifies them clinically, finding functionally abnormal variants are associated with increased cancer risk.

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.

This study fine-maps 150 breast cancer risk regions to identify 178 high confidence target genes using genomic and functional annotations.