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Breast Cancer Research and Treatment

, Volume 126, Issue 3, pp 717–727 | Cite as

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

  • Jingmei Li
  • Keith Humphreys
  • Tuomas Heikkinen
  • Kristiina Aittomäki
  • Carl Blomqvist
  • Paul D. P. Pharoah
  • Alison M. Dunning
  • Shahana Ahmed
  • Maartje J. Hooning
  • John W. M. Martens
  • Ans M. W. van den Ouweland
  • Lars Alfredsson
  • Aarno Palotie
  • Leena Peltonen-Palotie
  • Astrid Irwanto
  • Hui Qi Low
  • Garrett H. K. Teoh
  • Anbupalam Thalamuthu
  • Douglas F. Easton
  • Heli Nevanlinna
  • Jianjun Liu
  • Kamila Czene
  • Per Hall
Epidemiology

Abstract

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.

Keywords

Breast neoplasms Genetic association studies Genetic epidemiology Genetic susceptibility Genetic predisposition to disease/genetics Case–control studies 

Abbreviations

CGEMS

Cancer Genetic Markers of Susceptibility

CI

Confidence interval

EIRA

Epidemiological Investigation of Rheumatoid Arthritis

FPRP

False positive report probability

GWAS

Genome-wide association study

OR

Odds ratio

RBCS

Rotterdam Breast Cancer Study

SEARCH

Study of Epidemiology and Risk factors in Cancer Heredity

SNP

Single nucleotide polymorphism

λGC

Genomic inflation factor λ

Notes

Acknowledgments

This work was supported by National Institutes of Health [RO1 CA58427], the Agency for Science, Technology and Research (A*STAR; Singapore), the Nordic Cancer Society, and Märit and Hans Rausing’s Initiative against Breast Cancer. J Li is a recipient of an A*STAR Graduate Scholarship (Overseas) award. KH was supported by the Swedish Research Council [523-2006-972]. KC was supported by the Swedish Cancer Society [5128-B07-01PAF]. The Helsinki study was supported by the Helsinki University Central Hospital Research Fund, Academy of Finland, the Finnish Cancer Society and the Sigrid Juselius Foundation. We thank Dr. Kirsimari Aaltonen and RN Hanna Jäntti for their help with the patient data. The SEARCH study was funded by Cancer Research UK. DFE is a Principal Research Fellow of Cancer Research UK. We thank the SEARCH team and Eastern Cancer Registry and Information Centre (ECRIC) for recruitment of the SEARCH cases. The RBCS was supported by the Dutch Cancer Society (Grant DDHK 2004-3124). We acknowledge the clinicians from the Rotterdam Family Cancer Clinic who were involved in collecting the RBCS samples: C. Seynaeve, J. Klijn, J. Collee, and R. Oldenburg. The sponsors took no role in the study design, the collection or analysis of the data, the interpretation of the results, the preparation of the manuscript, or the decision to submit the manuscript for publication.

Conflict of interest

The authors declare that they have no competing interests.

Supplementary material

10549_2010_1172_MOESM1_ESM.docx (46 kb)
Supplementary material 1 (DOCX 45 kb)
10549_2010_1172_MOESM2_ESM.doc (148 kb)
Supplementary File 1 Full description of methods (DOC 147 kb)
10549_2010_1172_MOESM3_ESM.xls (294 kb)
Supplementary File 2 Full description of combined results, including results of analysis carried out under recessive and dominant models. Results of corresponding meta-analyses of the three sample populations (Swedish, Finnish, CGEMS) are also presented (XLS 294 kb)
10549_2010_1172_MOESM4_ESM.pdf (602 kb)
Supplementary File 3 Scatter plots of principal component analysis (PCA) within the Swedish, Finnish and CGEMS populations (PDF 601 kb)
10549_2010_1172_MOESM5_ESM.xls (40 kb)
Supplementary File 4 Full description of results for previously known SNPs, including analyses performed for dominant and recessive models (XLS 39 kb)
10549_2010_1172_MOESM6_ESM.xls (1.2 mb)
Supplementary File 5 Full description of results for single SNP association results for SNPs within top-ranking pathways (XLS 1210 kb)
10549_2010_1172_MOESM7_ESM.xls (36 kb)
Supplementary File 6 Full description of results for pathway analysis. Empirical p-values for 212 KEGG pathways are presented (XLS 36 kb)

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Copyright information

© Springer Science+Business Media, LLC. 2010

Authors and Affiliations

  • Jingmei Li
    • 1
    • 2
  • Keith Humphreys
    • 1
  • Tuomas Heikkinen
    • 3
  • Kristiina Aittomäki
    • 4
  • Carl Blomqvist
    • 5
  • Paul D. P. Pharoah
    • 6
    • 7
  • Alison M. Dunning
    • 7
  • Shahana Ahmed
    • 7
  • Maartje J. Hooning
    • 8
  • John W. M. Martens
    • 8
  • Ans M. W. van den Ouweland
    • 9
  • Lars Alfredsson
    • 10
  • Aarno Palotie
    • 11
    • 12
    • 13
    • 14
  • Leena Peltonen-Palotie
    • 11
    • 12
    • 13
    • 14
  • Astrid Irwanto
    • 2
  • Hui Qi Low
    • 2
  • Garrett H. K. Teoh
    • 2
  • Anbupalam Thalamuthu
    • 2
  • Douglas F. Easton
    • 6
    • 7
  • Heli Nevanlinna
    • 3
  • Jianjun Liu
    • 2
  • Kamila Czene
    • 1
  • Per Hall
    • 1
  1. 1.Department of Medical Epidemiology and BiostatisticsKarolinska InstitutetStockholmSweden
  2. 2.Human GeneticsGenome Institute of SingaporeSingaporeSingapore
  3. 3.Department of Obstetrics and GynecologyHelsinki University Central HospitalHelsinkiFinland
  4. 4.Department of Clinical GeneticsHelsinki University Central HospitalHelsinkiFinland
  5. 5.Department of OncologyHelsinki University Central HospitalHelsinkiFinland
  6. 6.Department of Public Health and Primary CareUniversity of CambridgeCambridgeUK
  7. 7.Department of OncologyUniversity of CambridgeCambridgeUK
  8. 8.Department of Medical Oncology, Rotterdam Family Cancer ClinicErasmus University Medical CenterRotterdamNetherlands
  9. 9.Department of Clinical Genetics, Rotterdam Family Cancer ClinicErasmus University Medical CenterRotterdamNetherlands
  10. 10.Institute of Environmental MedicineKarolinska InstitutetStockholmSweden
  11. 11.Institute for Molecular Medicine FinlandFIMM, University of HelsinkiHelsinkiFinland
  12. 12.Public Health Genomics UnitNational Institute for Health and WelfareHelsinkiFinland
  13. 13.Wellcome Trust Sanger InstituteCambridgeUK
  14. 14.Program in Medical and Population GeneticsBroad Institute of Harvard and Massachusetts Institute of TechnologyCambridgeUSA

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