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Deconvoluting complex tissues for expression quantitative trait locus-based analyses.

Ji-Heui Seo1, Qiyuan Li, Aquila Fatima

  • 1Department of Medical Oncology, The Center for Functional Cancer Epigenetics, Dana Farber Cancer Institute, Boston, MA 02215, USA.

Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences
|May 8, 2013
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Summary
This summary is machine-generated.

Identifying breast cancer risk genes is challenging as most variants are non-coding. This study developed a method to analyze expression quantitative trait loci (eQTLs) in heterogeneous breast tissue, linking genetic variants to target genes.

Keywords:
breast cancer risk single nucleotide polymorphismsexpression quantitative trait locusheterogeneous tissue

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Area of Science:

  • Genomics
  • Cancer Biology
  • Bioinformatics

Background:

  • Genome-wide association studies (GWAS) identify numerous breast cancer risk variants, but most are non-coding, complicating gene identification.
  • Expression quantitative trait loci (eQTLs) link genetic variants to gene expression, offering a strategy to identify target genes of risk variants.
  • Analyzing eQTLs in heterogeneous human tissues like breast tissue is methodologically challenging.

Purpose of the Study:

  • To develop and apply a computational method to infer cell type fractions in normal human breast tissues.
  • To identify candidate target genes for 13 known breast cancer risk loci using an eQTL-based strategy.
  • To demonstrate the feasibility of large-scale eQTL studies in complex, heterogeneous tissues.

Main Methods:

  • Developed a computational method to estimate cell type composition in normal human breast tissue samples.
  • Performed eQTL analysis on 13 hypothesized breast cancer risk loci within a 2 Mb window for each locus.
  • Conducted the eQTL analysis using RNA sequencing data from 100 reduction mammoplasty cases.

Main Results:

  • Identified 18 significant eQTL associations across the 13 breast cancer risk loci.
  • Discovered eight significant associations within the epithelial cell compartment.
  • Found 10 significant associations within the stromal cell compartment.

Conclusions:

  • The developed method enables robust eQTL analysis in heterogeneous human breast tissue.
  • This approach successfully linked known breast cancer risk loci to potential target genes in specific cellular compartments.
  • Highlights the potential for large-scale eQTL studies to unravel the functional impact of non-coding variants in complex diseases.