Siddhartha Yadav

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

This paper presents a single-cell RNA sequencing atlas of the adult human male breast, revealing distinct transcriptional properties and composition compared to female breast tissue.

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 evaluates breast cancer risks associated with pathogenic variant type and location in BRCA2 and other genes, finding differential risks based on variant position.

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 develops a tool to estimate mutation prevalence in hereditary cancer genes based on clinical and demographic factors using data from 150,000 multi-gene panel tests.

BRCA-ML is a machine learning model trained to predict the functional impact of missense variants in BRCA1 and BRCA2 genes, outperforming existing methods.