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      Pooled analysis of genome-wide association studies of cervical intraepithelial neoplasia 3 (CIN3) identifies a new susceptibility locus

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          Abstract

          Recent genome-wide association studies (GWASs) in subjects of European descent have identified associations between cervical cancer risk and three independent loci as well as multiple classical human leukocyte antigen (HLA) alleles at 6p21.3. To search for novel loci associated with development of cervical cancer, we performed a pooled analysis of data from two GWASs by imputing over 10 million genetic variants and 424 classical HLA alleles, for 1,553 intraepithelial neoplasia 3 (CIN3), 81 cervical cancer and 4,442 controls from the Swedish population. Notable findings were validated in an independent study of 961 patients (827 with CIN3 and 123 with cervical cancer) and 1,725 controls. Our data provided increased support for previously identified loci at 6p21.3 (rs9271898, P = 1.2 × 10 −24; rs2516448, 1.1 × 10 −15; and rs3130196, 2.3 × 10 −9, respectively) and also confirmed associations with reported classical HLA alleles including HLA-B*07:02, -B*15:01, -DRB1*13:01, -DRB1*15:01, -DQA1*01:03, -DQB1*06:03 and -DQB1*06:02. In addition, we identified and subsequently replicated an independent signal at rs73730372 at 6p21.3 (odds ratio = 0.60, 95% confidence interval = 0.54–0.67, P = 3.0 × 10 −19), which was found to be an expression quantitative trait locus (eQTL) of both HLA-DQA1 and HLA-DQB1. This is one of the strongest common genetic protective variants identified so far for CIN3. We also found HLA-C*07:02 to be associated with risk of CIN3. The present study provides new insights into pathogenesis of CIN3.

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          Most cited references 25

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          PLINK: a tool set for whole-genome association and population-based linkage analyses.

          Whole-genome association studies (WGAS) bring new computational, as well as analytic, challenges to researchers. Many existing genetic-analysis tools are not designed to handle such large data sets in a convenient manner and do not necessarily exploit the new opportunities that whole-genome data bring. To address these issues, we developed PLINK, an open-source C/C++ WGAS tool set. With PLINK, large data sets comprising hundreds of thousands of markers genotyped for thousands of individuals can be rapidly manipulated and analyzed in their entirety. As well as providing tools to make the basic analytic steps computationally efficient, PLINK also supports some novel approaches to whole-genome data that take advantage of whole-genome coverage. We introduce PLINK and describe the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation. In particular, we focus on the estimation and use of identity-by-state and identity-by-descent information in the context of population-based whole-genome studies. This information can be used to detect and correct for population stratification and to identify extended chromosomal segments that are shared identical by descent between very distantly related individuals. Analysis of the patterns of segmental sharing has the potential to map disease loci that contain multiple rare variants in a population-based linkage analysis.
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            Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012.

            Estimates of the worldwide incidence and mortality from 27 major cancers and for all cancers combined for 2012 are now available in the GLOBOCAN series of the International Agency for Research on Cancer. We review the sources and methods used in compiling the national cancer incidence and mortality estimates, and briefly describe the key results by cancer site and in 20 large "areas" of the world. Overall, there were 14.1 million new cases and 8.2 million deaths in 2012. The most commonly diagnosed cancers were lung (1.82 million), breast (1.67 million), and colorectal (1.36 million); the most common causes of cancer death were lung cancer (1.6 million deaths), liver cancer (745,000 deaths), and stomach cancer (723,000 deaths). © 2014 UICC.
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              Principal components analysis corrects for stratification in genome-wide association studies.

              Population stratification--allele frequency differences between cases and controls due to systematic ancestry differences-can cause spurious associations in disease studies. We describe a method that enables explicit detection and correction of population stratification on a genome-wide scale. Our method uses principal components analysis to explicitly model ancestry differences between cases and controls. The resulting correction is specific to a candidate marker's variation in frequency across ancestral populations, minimizing spurious associations while maximizing power to detect true associations. Our simple, efficient approach can easily be applied to disease studies with hundreds of thousands of markers.
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                Author and article information

                Affiliations
                1 Ministry of Education and Shanghai Key Laboratory of Children's Environmental Health, Xin Hua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
                2 Department of Immunology, Genetics and Pathology, Science for Life Laboratory Uppsala, Uppsala University, Uppsala, Sweden
                3 Laboratory of Biochemistry and Molecular Biology, School of Life Science,Yunnan University, Kunming, China
                4 Department of Neurosurgery, First Affiliated Hospital of Nanjing Medical University, Nanjing, China
                5 Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institutes for Biological Sciences, CAS, Shanghai, China
                6 University of Chinese Academy of Sciences, Beijing, China
                7 School of Life Science and Technology, Shanghai Tech University, Shanghai, China
                8 Collaborative Innovation Center of Genetics and Development, Shanghai, China
                Author notes
                Journal
                Oncotarget
                Oncotarget
                Oncotarget
                ImpactJ
                Oncotarget
                Impact Journals LLC
                1949-2553
                5 July 2016
                7 June 2016
                : 7
                : 27
                : 42216-42224
                9916
                10.18632/oncotarget.9916
                5173129
                27285765
                Copyright: © 2016 Chen et al.

                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.

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                Research Paper

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