A combined analysis of genome-wide association studies in breast cancer

Breast Cancer Res Treat. 2011 Apr;126(3):717-27. doi: 10.1007/s10549-010-1172-9. Epub 2010 Sep 26.


In an attempt to identify common disease susceptibility alleles for breast cancer, we performed a combined analysis of three genome-wide association studies (GWAS), involving 2,702 women of European ancestry with invasive breast cancer and 5,726 controls. Tests for association were performed for 285,984 SNPs. Evidence for association with SNPs in genes in specific pathways was assessed using a permutation-based approach. We confirmed associations with loci reported by previous GWAS on 1p11.2, 2q35, 3p, 5p12, 8q24, 10q23.13, 14q24.1 and 16q. Six SNPs with the strongest signals of association with breast cancer, and which have not been reported previously, were typed in two further studies; however, none of the associations could be confirmed. Suggestive evidence for an excess of associations was found for genes involved in the regulation of actin cytoskeleton, glycan degradation, alpha-linolenic acid metabolism, circadian rhythm, hematopoietic cell lineage and drug metabolism. Androgen and oestrogen metabolism, a pathway previously found to be associated with the development of postmenopausal breast cancer, was marginally significant (P = 0.051 [unadjusted]). These results suggest that further analysis of SNPs in these pathways may identify associations that would be difficult to detect through agnostic single SNP analyses. More effort focused in these aspects of oncology can potentially open up promising avenues for the understanding of breast cancer and its prevention.

Publication types

  • Meta-Analysis
  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Alleles
  • Breast Neoplasms / genetics*
  • Case-Control Studies
  • Computational Biology
  • Data Interpretation, Statistical
  • Female
  • Genetic Markers
  • Genetic Predisposition to Disease*
  • Genome-Wide Association Study*
  • Genotype
  • Humans
  • Models, Statistical
  • Molecular Epidemiology
  • Odds Ratio
  • Polymorphism, Single Nucleotide*


  • Genetic Markers