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      Association between Common Polymorphism near the MC4R Gene and Obesity Risk: A Systematic Review and Meta-Analysis

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          Abstract

          Background

          Genome-wide association studies on Europeans have shown that two polymorphisms (rs17782313, rs12970134) near the melanocortin 4 receptor ( MC4R) gene were associated with increased risk of obesity. Subsequently studies among different ethnic populations have shown mixed results with some confirming and others showing inconsistent results, especially among East Asians and Africans. We performed a comprehensive meta-analysis of various studies from different ethnic populations to assess the association of the MC4R polymorphism with obesity risk.

          Methods

          We retrieved all published literature that investigated association of MC4R variants with obesity from PubMed and Embase. Pooled odds ratio (OR) with 95% confidence interval (CI) was calculated using fixed- or random-effects model.

          Results

          A total of 61 studies (80,957 cases/220,223 controls) for rs17782313 polymorphism (or proxy) were included in the meta-analysis. The results suggested that rs17782313 polymorphism was significantly associated with obesity risk (OR = 1.18, 95%CI = 1.15–1.21, p<0.001). Similar trends were observed among subgroups of Europeans and East Asians, adults and children, studies with high quality score, and for each five MC4R polymorphisms independently.

          Conclusions

          The present meta-analysis confirms the significant association of MC4R polymorphism with risk of obesity. Further studies should be conducted to identify the causal variant and the underlying mechanisms of the identified association.

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          Most cited references39

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          Six new loci associated with body mass index highlight a neuronal influence on body weight regulation.

          Common variants at only two loci, FTO and MC4R, have been reproducibly associated with body mass index (BMI) in humans. To identify additional loci, we conducted meta-analysis of 15 genome-wide association studies for BMI (n > 32,000) and followed up top signals in 14 additional cohorts (n > 59,000). We strongly confirm FTO and MC4R and identify six additional loci (P < 5 x 10(-8)): TMEM18, KCTD15, GNPDA2, SH2B1, MTCH2 and NEGR1 (where a 45-kb deletion polymorphism is a candidate causal variant). Several of the likely causal genes are highly expressed or known to act in the central nervous system (CNS), emphasizing, as in rare monogenic forms of obesity, the role of the CNS in predisposition to obesity.
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            Genome-wide association study for early-onset and morbid adult obesity identifies three new risk loci in European populations.

            We analyzed genome-wide association data from 1,380 Europeans with early-onset and morbid adult obesity and 1,416 age-matched normal-weight controls. Thirty-eight markers showing strong association were further evaluated in 14,186 European subjects. In addition to FTO and MC4R, we detected significant association of obesity with three new risk loci in NPC1 (endosomal/lysosomal Niemann-Pick C1 gene, P = 2.9 x 10(-7)), near MAF (encoding the transcription factor c-MAF, P = 3.8 x 10(-13)) and near PTER (phosphotriesterase-related gene, P = 2.1 x 10(-7)).
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              Meta-analysis of genetic association studies.

              Meta-analysis, a statistical tool for combining results across studies, is becoming popular as a method for resolving discrepancies in genetic association studies. Persistent difficulties in obtaining robust, replicable results in genetic association studies are almost certainly because genetic effects are small, requiring studies with many thousands of subjects to be detected. In this article, we describe how meta-analysis works and consider whether it will solve the problem of underpowered studies or whether it is another affliction visited by statisticians on geneticists. We show that meta-analysis has been successful in revealing unexpected sources of heterogeneity, such as publication bias. If heterogeneity is adequately recognized and taken into account, meta-analysis can confirm the involvement of a genetic variant, but it is not a substitute for an adequately powered primary study.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2012
                25 September 2012
                : 7
                : 9
                : e45731
                Affiliations
                [1 ]Department of Maternal and Child Health Care, School of Public Health, Shandong University, Jinan, Shandong, People's Republic of China
                [2 ]Centre for Cellular and Molecular Biology, Council of Scientific and Industrial Research, Hyderabad, Andhra Pradesh, India
                [3 ]Department of Epidemiology, Capital Institute of Pediatrics, Beijing, People's Republic of China
                [4 ]Department of Endocrinology, Linyi People's Hospital, Linyi, Shandong, People's Republic of China
                Sanjay Gandhi Medical Institute, India
                Author notes

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

                Conceived and designed the experiments: BX DHZ. Performed the experiments: YS QJW DHZ. Analyzed the data: GRC YS QJW. Contributed reagents/materials/analysis tools: QJW DHZ. Wrote the paper: BX GRC.

                Article
                PONE-D-12-20002
                10.1371/journal.pone.0045731
                3458070
                23049848
                95d327df-cc63-42ea-a81e-5237becb7be6
                Copyright @ 2012

                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
                : 5 July 2012
                : 21 August 2012
                Page count
                Pages: 7
                Funding
                This study was supported by the National “Twelfth Five-Year” Plan for Science & Technology Support Program (2012BAI03B03), the Independent Innovation Foundation of Shandong University (2010GN046), and the Foundation for Outstanding Young Scientist in Shandong Province (BS2011YY026). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology
                Genetics
                Population Genetics
                Genetic Polymorphism
                Population Biology
                Population Genetics
                Genetic Polymorphism
                Medicine
                Clinical Research Design
                Meta-Analyses
                Epidemiology
                Genetic Epidemiology
                Nutrition
                Obesity

                Uncategorized
                Uncategorized

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