Poster Presentation GENEMAPPERS 2026

From Variants to Cell Types: CLEAR Mapping of Breast Cancer Genetic Risk (#111)

Lefei Wang 1 , Drew Neavin 2 , Blake Bowen 3 , Joseph Powell 3 , Georgia Chenevix-Trench 1 , Jonathan Beesley 1
  1. Cancer Program, QIMR, Brisbane, QLD, Australia
  2. IMB, The University of Queensland, Brisbane, QLD, Australia
  3. Translational Genomics, The Garvan Institute , Sydney, NSW, Australia

Breast cancers exhibit marked cellular heterogeneity arising from distinct histological and molecular subtypes. Genome-wide association studies (GWAS) have identified over 230 germline risk loci, yet the target genes and cell types in which they act remain largely unknown. Breast cancer post-GWAS research has largely focused on mechanisms acting in epithelial cells, while the contributions of stromal and immune components remain underexplored. Single-cell technologies, particularly single-nucleus ATAC-seq, offer an opportunity to link GWAS to cell-type-specific regulatory mechanisms to highlight such understudied compartments.

Here, we profile regulatory landscapes from healthy breast tissue from 18 donors who underwent prophylactic mastectomy for risk reduction, capturing candidate regulatory elements across epithelial, stromal, and immune cell types. We introduce CLEAR (Cell-type-resolved Locus-specific Enrichment Analysis by Ranking), a framework that integrates GWAS with regulatory annotations from snATAC-seq, enabling quantification of regulatory specificity for each GWAS locus across cell types. Applying CLEAR to breast cancer GWAS data, we identified ~150 loci with regulatory activity in less than three cell types. As expected, ~120 loci showed core epithelial contributions; however, ~30 loci exhibited non-epithelial-specific chromatin accessibility, what we call as the residual contributions. For example, rs13066793 lies within an intron of VGLL3 with fibroblast-specific accessibility, and VGLL3 also demonstrates expression in fibroblasts in human breast scRNA-seq data. These findings refine functional hypotheses, prioritise variants for experimental validation, and expand discovery beyond canonical epithelial drivers. CLEAR is broadly applicable to other complex traits with diverse cellular interactions, providing a general framework to map genetic risk to its cellular context.