Jingmei Li

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 paper evaluates differences in a breast cancer polygenic risk score across genomic platforms and finds that calibration improves consistency in high-risk classification.

This study explores preferences of Asian women for breast cancer risk reports, finding a preference for comprehensive, simplified, and visually clear reports with follow-up recommendations.

The paper introduces the GestaltMatcher Database (GMDB), a global facial phenotypic reference for rare diseases, showing improved NGP performance with diverse ancestry data.

This paper evaluates polygenic risk scores for predicting common cancers in East Asians using a large cohort, finding significant risk stratification for breast, prostate, and colorectal cancers.

RAPTOR provides a secure, cloud-native genomics repository with a five-safes framework to ensure compliance and data protection for genomic research.

The paper uses whole-exome sequencing data to identify novel breast cancer susceptibility genes and assess the contribution of coding variants to breast cancer risk.

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 study improves transcriptome-wide association studies by integrating pleiotropy-guided methods with GWAS data to identify new cancer susceptibility genes.