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Updated: Jun 1, 2026

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
Published on: May 17, 2019
Multi-ancestry transcriptome-wide association studies uncover insights into breast cancer genetics and biology
Jie Ping1,2, Guochong Jia1, Qiuyin Cai1
1Department of Medicine, Division of Epidemiology, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA.
Abstract:
Genome-wide association studies (GWAS) have identified over 200 genetic risk loci for breast cancer, yet their target genes remain largely unknown. We conduct multi-ancestry transcriptome-wide association studies (TWAS) to discover potential breast cancer susceptibility genes. We develop ancestry-specific genetic models to predict levels of gene expression, alternative splicing, and 3' UTR alternative polyadenylation using genomic and transcriptomic data from 652 normal female tissue samples and apply these models to GWAS data of 178,534 cases and 248,300 controls for association analyses. We identify 290 genes associated with breast cancer risk, including 103 previously unreported and 46 not located at known GWAS loci, and 39 genes show distinct associations with breast cancer risk by estrogen-receptor status. Single-cell RNA sequencing and in vitro experiment data provide additional functional evidence for 169 genes. These genes are enriched in pathways implicated in breast carcinogenesis. Our study uncovers insights into breast cancer genetics and biology.
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