Dezheng Huo

Genetic and Cellular Architecture of Breast Cancer Risk Across Ancestries

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

Contextualizing the Utility of Polygenic Risk Scores using Absolute Risk Models in Diverse Ancestry Populations

This paper evaluates polygenic risk scores (PRSs) in diverse populations using absolute risk models, finding that PRSs can provide greater utility in risk stratification for certain diseases in non-European populations.

A Breast Cancer Polygenic Risk Score Validation in 15,490 Brazilians using Exome Sequencing

The paper validates a breast cancer polygenic risk score in a Brazilian cohort using exome sequencing, finding PRS3820 performs best across different ancestry groups.

A splicing-based multi-tissue joint transcriptome-wide association study identifies susceptibility genes for breast cancer

This study performs a multi-tissue splicing-TWAS to identify breast cancer susceptibility genes, finding 240 significant genes and highlighting the importance of splicing over expression.

A joint transcriptome-wide association study across multiple tissues identifies new candidate susceptibility genes for breast cancer

This study performs a joint transcriptome-wide association study across multiple tissues to identify new candidate susceptibility genes for breast cancer, finding 380 genes at 129 loci with 22 remaining significant after conditioning on known variants.

Polygenic Risk Scores for Prediction of Breast Cancer Risk in Women of African Ancestry: a Cross-Ancestry Approach

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.

Whole-genome analysis of Nigerian patients with breast cancer reveals ethnic-driven somatic evolution and distinct genomic subtypes

The paper analyzes whole-genome data from Nigerian breast cancer patients, identifying ethnic-driven somatic evolution and distinct genomic subtypes associated with African ancestry.

The Impact of Digital Histopathology Batch Effect on Deep Learning Model Accuracy and Bias

This paper shows that digital histopathology batch effects in TCGA data affect DL model accuracy and bias, and proposes a method to address this issue.

Identification of Novel Common Breast Cancer Risk Variants in Latinas at the 6q25 Locus

The paper identifies three novel common breast cancer risk variants at the 6q25 locus in Latinas and replicates their association in diverse populations.