Montserrat García‐Closas

This study analyzes breast cancer GWAS across multiple ancestries, finding similar heritability and genetic correlations, and identifies shared regulatory and cellular contexts.

PRANA adapts polygenic risk scores to non-European populations, improving predictive accuracy by 5%-20% in South Asian, East Asian, and Ashkenazi Jewish populations.

The paper uses case-control data from over 400,000 women to classify BRCA1 and BRCA2 variants, finding high consistency with ClinVar annotations for 785 previously unclassified variants.

This study identifies genetic signals associated with age at menarche, highlights genes involved in pubertal development, and links reproductive timing to later life disease risk.

The paper introduces CT-SLEB, a method for multi-ancestry polygenic prediction that improves performance in non-European populations using GWAS summary statistics and simulation studies.

The paper assesses breast cancer risks linked to rare missense variants in five susceptibility genes using logistic and mixture models, finding varying risk contributions across genes.

This paper develops polygenic risk scores for breast cancer prediction in women of African ancestry by combining African and European ancestry datasets, improving prediction accuracy.

The paper investigates race-specific germline genetic associations with breast cancer recurrence scores using GReX and finds differential associations between germline variants and CRS in Black and White patients.

The paper analyzes rare copy number variants (CNVs) in breast cancer cases and controls, identifying associations with known and novel susceptibility loci.

The paper uses a multi-layered approach combining GWAS, spatial autocorrelation, and Hi-C to identify novel genetic variants associated with pancreatic cancer susceptibility.

iCARE is an R package for building, validating, and applying absolute risk models using user-defined inputs and SNP data, demonstrated with breast cancer.

The paper introduces a mixed-model approach to test genetic associations with cancer risk, incorporating tumor characteristics, and shows it outperforms existing methods in identifying heterogeneous associations.

The paper identifies three immune subtypes in luminal breast cancer through immune gene expression profiling and links them to distinct genomic and clinical features.

The paper shows oncologic therapy increases clonal hematopoiesis with DDR mutations, which predicts risk of therapy-related myeloid neoplasms.