Deconvoluting complex tissues for expression quantitative trait locus-based analyse

Ji Heui Seo, Qiyuan Li, Aquila Fatima, Aron Eklund, Zoltan Szallasi, Kornelia Polyak, Andrea L. Richardson, Matthew L. Freedman

Research output: Contribution to journalArticle

Abstract

Breast cancer genome-wide association studies have pinpointed dozens of variants associated with breast cancer pathogenesis. The majority of risk variants, however, are located outside of known protein-coding regions. Therefore, identifying which genes the risk variants are acting through presents an important challenge. Variants that are associated with mRNA transcript levels are referred to as expression quantitative trait locieQTLs. Many studies have demonstrated that eQTL-based strategies provide a direct way to connect a trait-associated locus with its candidate target gene. Performing eQTL-based analyses in human samples is complicated because of the heterogeneous nature of human tissue. We addressed this issue by devising a method to computationally infer the fraction of cell types in normal human breast tissues. We then applied this method to 13 known breast cancer risk loci, which we hypothesized were eQTLs. For each risk locus, we took all known transcripts within a 2 Mb interval and performed an eQTL analysis in 100 reduction mammoplasty cases. A total of 18 significant associations were discovered eight in the epithelial compartment and 10 in the stromal compartment. This study highlights the ability to perform large-scale eQTL studies in heterogeneous tissue.

Original languageEnglish (US)
JournalPhilosophical Transactions of the Royal Society B: Biological Sciences
Volume368
Issue number1620
DOIs
StatePublished - Jun 19 2013
Externally publishedYes

Keywords

  • Breast cancer risk single nucleotide polymorphisms
  • Expression quantitative trait locus
  • Heterogeneous tissue

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)

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