Article

Common genetic determinants of breast-cancer risk in East Asian women: a collaborative study of 23 637 breast cancer cases and 25 579 controls.

Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center and.
Human Molecular Genetics (Impact Factor: 6.68). 03/2013; DOI: 10.1093/hmg/ddt089
Source: PubMed

ABSTRACT In a consortium including 23 637 breast cancer patients and 25 579 controls of East Asian ancestry, we investigated 70 single-nucleotide polymorphisms (SNPs) in 67 independent breast cancer susceptibility loci recently identified by genome-wide association studies (GWASs) conducted primarily in European-ancestry populations. SNPs in 31 loci showed an association with breast cancer risk at P < 0.05 in a direction consistent with that reported previously. Twenty-one of them remained statistically significant after adjusting for multiple comparisons with the Bonferroni-corrected significance level of <0.0015. Eight of the 70 SNPs showed a significantly different association with breast cancer risk by estrogen receptor (ER) status at P < 0.05. With the exception of rs2046210 at 6q25.1, the seven other SNPs showed a stronger association with ER-positive than ER-negative cancer. This study replicated all five genetic risk variants initially identified in Asians and provided evidence for associations of breast cancer risk in the East Asian population with nearly half of the genetic risk variants initially reported in GWASs conducted in European descendants. Taken together, these common genetic risk variants explain ∼10% of excess familial risk of breast cancer in Asian populations.

0 Bookmarks
 · 
270 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: IntroductionEstrogen forms a complex with the estrogen receptor (ER) that binds to estrogen response elements (EREs) in the regulatory region of estrogen-responsive genes, and regulates their transcription. Since sequence variants in the regulatory regions have the potential to affect the transcription factor-regulatory sequence interaction, resulting in altered expression of target genes, this study explored the association between single-nucleotide-polymorphisms (SNPs) within the ERE-associated sequences and breast cancer progression.Methods The ERE-associated sequences throughout the whole genome, demonstrated to bind ER¿ in vivo, were blasted against online information from SNP datasets, and 54 SNPs located adjacent to estrogen-responsive genes were selected for genotyping in two independent cohorts of breast cancer patients, 779 in the initial screening stage and another 888 in the validation stage. The death from breast cancer or recurrence of breast cancer were defined as the respective event of interest, and the hazard ratios of individual SNPs were estimated based on the Cox proportional hazard model. Furthermore, functional assays were performed and information from publicly available genomic data and bioinformatic platforms were used to provide additional evidence for the association identified in the association analysis.ResultsThe SNPs at 21q22.3 ERE were significantly associated with overall survival and disease-free survival of patients. Furthermore, these 21q22.3 SNPs (rs2839494 and rs1078272) could affect the binding of this ERE-associated sequence to ER¿ or Rad21 (an ER¿ coactivator), respectively, resulting in a difference in ER¿-activated expression of the reporter gene.Conclusion These findings support the idea that functional variants in the ER¿-regulating sequence at 21q22.3 is important in determining breast cancer progression.
    Breast cancer research: BCR 10/2014; 16(5):455. DOI:10.1186/s13058-014-0455-1 · 5.88 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: IntroductionSeveral genome-wide association studies (GWASs) have identified single nucleotide polymorphisms (SNPs) at 6q25.1 that are associated with breast cancer susceptibility. However, the exact causal variant(s) in this region has not been clarified.Methods In the present study, we genotyped six potentially functional SNPs within CCDC170 and ESR1 gene regions at 6q25.1 and accessed their associations with risk of breast cancer in a study of 1,064 cases and 1,073 cancer-free controls in Chinese women. Biological function of the risk variant was further evaluated by laboratory experiments.ResultsBreast cancer risk was significantly associated with three SNPs located at 6q25.1: rs9383935 in CCDC170 and rs2228480 and rs3798758 in ESR1, with variant-allele attributed odds ratio (OR) of 1.38 (95% confidence interval (CI): 1.20 - 1.57, P¿=¿2.21¿×¿10¿6), 0.84 (95% CI: 0.72 - 0.98, P¿=¿0.025) and 1.19 (95% CI: 1.04-1.37, P¿=¿0.013), respectively. The functional variant rs9383935 is in high linkage disequilibrium (LD) with GWAS-reported top-hit SNP (rs2046210), but only rs9383935 showed a strong independent effect in conditional regression analysis. The rs9383935 risk allele A showed a decreased activity of reporter gene in both MCF-7 and BT-474 breast cancer cell lines, which might be due to an altered binding capacity of miR-27a to the 3¿untranslated region (3¿UTR) sequence of CCDC170. Real-time quantitative reverse transcription PCR confirmed the correlation between rs9383935 genotypes and CCDC170 expression levels.Conclusions This study suggests that the functional variant rs9383935, located at the 3¿UTR of CCDC170, may be one candidate of the causal variants at 6q25.1 that modulate risk of breast cancer.
    Breast cancer research: BCR 08/2014; 16(4):422. DOI:10.1186/s13058-014-0422-x · 5.88 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Recent technological advances have expanded the breadth of available omic data, from whole-genome sequencing data, to extensive transcriptomic, methylomic and metabolomic data. A key goal of analyses of these data is the identification of effective models that predict phenotypic traits and outcomes, elucidating important biomarkers and generating important insights into the genetic underpinnings of the heritability of complex traits. There is still a need for powerful and advanced analysis strategies to fully harness the utility of these comprehensive high-throughput data, identifying true associations and reducing the number of false associations. In this Review, we explore the emerging approaches for data integration - including meta-dimensional and multi-staged analyses - which aim to deepen our understanding of the role of genetics and genomics in complex outcomes. With the use and further development of these approaches, an improved understanding of the relationship between genomic variation and human phenotypes may be revealed.
    Nature Reviews Genetics 02/2015; 16(2):85-97. DOI:10.1038/nrg3868 · 39.79 Impact Factor

Full-text

Download
55 Downloads
Available from
Jun 4, 2014