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      A Meta-Analysis of the Association between ESR1 Genetic Variants and the Risk of Breast Cancer

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          Abstract

          Background

          Single nucleotide polymorphisms (SNPs) in the estrogen receptor gene ( ESR1) play critical roles in breast cancer (BC) susceptibility. Genome-wide association studies have reported that SNPs in ESR1 are associated with BC susceptibility; however, the results of recent studies have been inconsistent. Therefore, we performed this meta-analysis to obtain more accurate and credible results.

          Methods

          We pooled published literature from PubMed, EMBASE, and Web of Science and calculated odds ratios (ORs) with 95% confidence intervals (CIs) to assess the strength of associations using fixed effects models and random effects models. Twenty relevant case-control and cohort studies of the 3 related SNPs were identified.

          Results

          Three SNPs of the ESR1 gene, rs2077647:T>C, rs2228480:G>A and rs3798577:T>C, were not associated with increased BC risk in our overall meta-analysis. Stratified analysis by ethnicity showed that in Caucasians, the rs2228480 AA genotype was associated with a 26% decreased risk of BC compared with the GG genotype (OR = 0.740, 95% CI: 0.555–0.987). The C allele of the rs3798577:T>C variant was associated with decreased BC risk in Asians (OR = 0.828, 95% CI: 0.730–0.939), while Caucasians with this allele were found to experience significantly increased BC risk (OR = 1.551, 95% CI: 1.037–2.321). A non-significant association between rs2077647 and BC risk was identified in all of the evaluated ethnic populations.

          Conclusion

          Rs3798577 was associated with an increased risk of BC in Caucasian populations but a decreased risk in Asians. Rs2228480 had a large protective effect in Caucasians, while rs2077647 was not associated with BC risk.

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          Most cited references38

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          Concordance between administrative claims and registry data for identifying metastasis to the bone: an exploratory analysis in prostate cancer

          Eberechukwu Onukwugha, Candice Yong, Arif Hussain … (2014)
          Background To assess concordance between Medicare claims and Surveillance, Epidemiology, and End Results (SEER) reports of incident BM among prostate cancer (PCa) patients. The prevalence and consequences of bone metastases (BM) have been examined across tumor sites using healthcare claims data however the reliability of these claims-based BM measures has not been investigated. Methods This retrospective cohort study utilized linked registry and claims (SEER-Medicare) data on men diagnosed with incident stage IV M1 PCa between 2005 and 2007. The SEER-based measure of incident BM was cross-tabulated with three separate Medicare claims approaches to assess concordance. Sensitivity, specificity and positive predictive value (PPV) were calculated to assess the concordance between registry- and claims-based measures. Results Based on 2,708 PCa patients in SEER-Medicare, there is low to moderate concordance between the SEER- and claims-based measures of incident BM. Across the three approaches, sensitivity ranged from 0.48 (0.456 – 0.504) to 0.598 (0.574 - 0.621), specificity ranged from 0.538 (0.507 - 0.569) to 0.620 (0.590 - 0.650) and PPV ranged from 0.679 (0.651 - 0.705) to 0.690 (0.665 - 0.715). A comparison of utilization patterns between SEER-based and claims-based measures suggested avenues for improving sensitivity. Conclusion Claims-based measures using BM ICD 9 coding may be insufficient to identify patients with incident BM diagnosis and should be validated against chart data to maximize their potential for population-based analyses.
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            Genome-wide association study identifies five new breast cancer susceptibility loci.

            Clare Turnbull, Shahana Ahmed, Jonathan Morrison … (2010)
            Breast cancer is the most common cancer in women in developed countries. To identify common breast cancer susceptibility alleles, we conducted a genome-wide association study in which 582,886 SNPs were genotyped in 3,659 cases with a family history of the disease and 4,897 controls. Promising associations were evaluated in a second stage, comprising 12,576 cases and 12,223 controls. We identified five new susceptibility loci, on chromosomes 9, 10 and 11 (P = 4.6 x 10(-7) to P = 3.2 x 10(-15)). We also identified SNPs in the 6q25.1 (rs3757318, P = 2.9 x 10(-6)), 8q24 (rs1562430, P = 5.8 x 10(-7)) and LSP1 (rs909116, P = 7.3 x 10(-7)) regions that showed more significant association with risk than those reported previously. Previously identified breast cancer susceptibility loci were also found to show larger effect sizes in this study of familial breast cancer cases than in previous population-based studies, consistent with polygenic susceptibility to the disease.
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              A method for meta-analysis of molecular association studies.

              Ammarin Thakkinstian, Patrick McElduff, Catherine d'Este … (2005)
              Although population-based molecular association studies are becoming increasingly popular, methodology for the meta-analysis of these studies has been neglected, particularly with regard to two issues: testing Hardy-Weinberg equilibrium (HWE), and pooling results in a manner that reflects a biological model of gene effect. We propose a process for pooling results from population-based molecular association studies which consists of the following steps: (1) checking HWE using chi-square goodness of fit; we suggest performing sensitivity analysis with and without studies that are in HWE. (2) Heterogeneity is then checked, and if present, possible causes are explored. (3) If no heterogeneity is present, regression analysis is used to pool data and to determine the gene effect. (4) If there is a significant gene effect, pairwise group differences are analysed and these data are allowed to 'dictate' the best genetic model. (5) Data may then be pooled using this model. This method is easily performed using standard software, and has the advantage of not assuming an a priori genetic model. Copyright 2004 John Wiley & Sons, Ltd.
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                Author and article information

                Contributors
                William B. Coleman: Role: Editor
                Journal
                Journal ID (nlm-ta): PLoS One
                Journal ID (iso-abbrev): PLoS ONE
                Journal ID (publisher-id): plos
                Journal ID (pmc): plosone
                Title: PLoS ONE
                Publisher: Public Library of Science (San Francisco, CA USA )
                ISSN (Electronic): 1932-6203
                Publication date (Electronic): 12 April 2016
                Publication date Collection: 2016
                Volume: 11
                Issue: 4
                Electronic Location Identifier: e0153314
                Affiliations
                [1 ]Department of Epidemiology and Biostatistics, School of Public Health, Southeast University, Nanjing, China
                [2 ]National Research Institute for Family Planning, Beijing, China
                University of North Carolina School of Medicine, UNITED STATES
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: TSL. Performed the experiments: JZ QYD. Analyzed the data: TSL JYY. Contributed reagents/materials/analysis tools: HH LNW XM. Wrote the paper: TSL PL.

                Article
                Publisher ID: PONE-D-15-46414
                DOI: 10.1371/journal.pone.0153314
                PMC ID: 4829239
                PubMed ID: 27070141
                SO-VID: ebcb8376-9ddc-41d7-bb06-74f450190ff8
                Copyright © © 2016 Li et al
                License:

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                Date received : 25 November 2015
                Date accepted : 28 March 2016
                Page count
                Figures: 7, Tables: 3, Pages: 19
                Funding
                Funded by: Program of National Scientific Data Sharing Platform for Population and Health
                Award Recipient :
                This work was supported by the Program of National Scientific Data Sharing Platform for Population and Health. Xu Ma received the funding. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Subject: Research Article
                Subject: Research and Analysis Methods
                Subject: Mathematical and Statistical Techniques
                Subject: Statistical Methods
                Subject: Meta-Analysis
                Subject: Physical Sciences
                Subject: Mathematics
                Subject: Statistics (Mathematics)
                Subject: Statistical Methods
                Subject: Meta-Analysis
                Subject: Biology and Life Sciences
                Subject: Genetics
                Subject: Genetic Loci
                Subject: Alleles
                Subject: Biology and Life Sciences
                Subject: Computational Biology
                Subject: Genome Analysis
                Subject: Genome-Wide Association Studies
                Subject: Biology and Life Sciences
                Subject: Genetics
                Subject: Genomics
                Subject: Genome Analysis
                Subject: Genome-Wide Association Studies
                Subject: Biology and Life Sciences
                Subject: Genetics
                Subject: Human Genetics
                Subject: Genome-Wide Association Studies
                Subject: Research and Analysis Methods
                Subject: Database and Informatics Methods
                Subject: Database Searching
                Subject: Medicine and Health Sciences
                Subject: Oncology
                Subject: Cancers and Neoplasms
                Subject: Breast Tumors
                Subject: Breast Cancer
                Subject: Biology and Life Sciences
                Subject: Genetics
                Subject: Heredity
                Subject: Genetic Mapping
                Subject: Variant Genotypes
                Subject: People and Places
                Subject: Population Groupings
                Subject: Ethnicities
                Subject: Biology and Life Sciences
                Subject: Biochemistry
                Subject: Hormones
                Subject: Estrogens
                Custom metadata
                Data Availability All relevant data are within the paper and its Supporting Information files.

                ScienceOpen disciplines: Uncategorized
                Data availability:
                ScienceOpen disciplines: Uncategorized

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