Seo, Ji-Heui7; Li, Qiyuan1; Fatima, Aquila7; Eklund, Aron Charles4; Szallasi, Zoltan Imre4; Polyak, Kornelia5; Richardson, Andrea L.7; Freedman, Matthew L.6
1 Department of Systems Biology, Technical University of Denmark2 Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark3 Dana-Farber Cancer Institute4 Department of Bio and Health Informatics, Technical University of Denmark5 Harvard Medical School6 Broad Institute of Harvard University and Massachusetts Institute of Technology7 Dana-Farber Cancer Institute
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 loci (eQTLs). 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 tissues.
Royal Society of London. Philosophical Transactions B. Biological Sciences, 2013, Vol 368, Issue 1620, p. 20120363-20120363
Expression quantitative trait locus,; Heterogeneous tissue; Breast cancer risk; Single nucleotide polymorphisms