Functional annotation of breast cancer risk loci: current progress and future directions

Abstract

Genome-wide association studies coupled with large-scale replication and fine-scale mapping studies have identified more than 150 genomic regions that are associated with breast cancer risk. Here, we review efforts to translate these findings into a greater understanding of disease mechanism. Our review comes in the context of a recently published fine-scale mapping analysis of these regions, which reported 352 independent signals and a total of 13,367 credible causal variants. The vast majority of credible causal variants map to noncoding DNA, implicating regulation of gene expression as the mechanism by which functional variants influence risk. Accordingly, we review methods for defining candidate-regulatory sequences, methods for identifying putative target genes and methods for linking candidate-regulatory sequences to putative target genes. We provide a summary of available data resources and identify gaps in these resources. We conclude that while much work has been done, there is still much to do. There are, however, grounds for optimism; combining statistical data from fine-scale mapping with functional data that are more representative of the normal "at risk" breast, generated using new technologies, should lead to a greater understanding of the mechanisms that influence an individual woman's risk of breast cancer.

Fig. 1. Summary of data generated in breast-relevant cell lines, tissue and primary cells that are available through ENCODE and Roadmap Epigenomics.

Datasets that are available through (a) ENCODE and (b) Roadmap Epigenomics are summarised as bar plots. Different data types are colour-coded as indicated in the keys. The cell or tissue types in which the data were generated are shown on the x axis with the number of datasets available in each of these cell or tissue types on the y axis.