Abstract
Background Genome-wide association studies (GWAS) have achieved great success in identifying common genetics variants associated with increased risk for developing breast cancer. More recently, advances in next-generation sequencing (NGS) have made possible identification of mutations associated with breast cancer. However, to date, the information generated by GWAS and NGS has not been maximally leveraged and integrated with gene expression data to identify biomarkers associated with the most aggressive subset of breast cancer: the triple-negative breast cancer (TNBC). Here we present results from an integrative genomics approach that combines GWAS and sequence information with gene expression data to identify functionally related genes and biological pathways enriched for expression-associated genetic loci and mutations associated with TNBC using publicly available data.
Highlights
Genome-wide association studies (GWAS) have achieved great success in identifying common genetics variants associated with increased risk for developing breast cancer
To date, the information generated by GWAS and next-generation sequencing (NGS) has not been maximally leveraged and integrated with gene expression data to identify biomarkers associated with the most aggressive subset of breast cancer: the triple-negative breast cancer (TNBC)
We present results from an integrative genomics approach that combines GWAS and sequence information with gene expression data to identify functionally related genes and biological pathways enriched for expression-associated genetic loci and mutations associated with TNBC using publicly available data
Summary
Genome-wide association studies (GWAS) have achieved great success in identifying common genetics variants associated with increased risk for developing breast cancer. Advances in next-generation sequencing (NGS) have made possible identification of mutations associated with breast cancer. To date, the information generated by GWAS and NGS has not been maximally leveraged and integrated with gene expression data to identify biomarkers associated with the most aggressive subset of breast cancer: the triple-negative breast cancer (TNBC). We present results from an integrative genomics approach that combines GWAS and sequence information with gene expression data to identify functionally related genes and biological pathways enriched for expression-associated genetic loci and mutations associated with TNBC using publicly available data
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