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      Associations of MTHFR Gene Polymorphisms with Hypertension and Hypertension in Pregnancy: A Meta-Analysis from 114 Studies with 15411 Cases and 21970 Controls

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          Abstract

          Background

          Several epidemiological studies have investigated the associations of methylenetetrahydrofolate reductase ( MTHFR) C677T and A1298C polymorphisms with hypertension (H) or hypertension in pregnancy (HIP). However, the results were controversial. We therefore performed a comprehensive meta-analysis to provide empirical evidences on the associations.

          Methodologies

          The English and Chinese databases were systematically searched to identify relevant studies. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated to evaluate the strength of the associations. Meta-regression, subgroup analysis, sensitivity analysis, cumulative meta-analysis and assessment of publication bias were performed in our study.

          Principal Findings

          A total of 114 studies with 15411 cases and 21970 controls were included, 111 studies with 15094 cases and 21633 controls for the C677T polymorphism and 21 with 2533 cases and 2976 controls for the A1298C polymorphism. Overall, the C677T polymorphism was significantly associated with H and HIP (H & HIP: OR = 1.26, 95% CI = 1.17–1.34; H: OR = 1.36, 95% CI = 1.20–1.53; HIP: OR = 1.21, 95% CI = 1.08–1.32). Stratified analysis by ethnicity revealed a significant association among East Asians and Caucasians, but not among Latinos, Black Africans, and Indians and Sri Lankans. In the stratified analyses according to source of controls, genotyping method, sample size and study quality, significant associations were observed in all the subgroups, with the exception of population based subgroup in H studies and large sample size and “others” genotyping method subgroups in HIP studies. For the A1298C polymorphism, no significant association was observed either in overall or subgroup analysis under all genetic models.

          Conclusions

          This meta-analysis suggests that the MTHFR C677T rather than A1298C polymorphism may be associated with H & HIP, especially among East Asians and Caucasians.

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          Most cited references97

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          Heterogeneity testing in meta-analysis of genome searches.

          Elias Zintzaras, John Ioannidis (2005)
          Genome searches for identifying susceptibility loci for the same complex disease often give inconclusive or inconsistent results. Genome Search Meta-analysis (GSMA) is an established non-parametric method to identify genetic regions that rank high on average in terms of linkage statistics (e.g., lod scores) across studies. Meta-analysis typically aims not only to obtain average estimates, but also to quantify heterogeneity. However, heterogeneity testing between studies included in GSMA has not been developed yet. Heterogeneity may be produced by differences in study designs, study populations, and chance, and the extent of heterogeneity might influence the conclusions of a meta-analysis. Here, we propose and explore metrics that indicate the extent of heterogeneity for specific loci in GSMA based on Monte Carlo permutation tests. We have also developed software that performs both the GSMA and the heterogeneity testing. To illustrate the concept, the proposed methodology was applied to published data from meta-analyses of rheumatoid arthritis (4 scans) and schizophrenia (20 scans). In the first meta-analysis, we identified 11 bins with statistically low heterogeneity and 8 with statistically high heterogeneity. The respective numbers were 9 and 6 for the schizophrenia meta-analysis. For rheumatoid arthritis, bins 6.2 (the HLA region that is a well-documented susceptibility locus for the disease) and 16.3 (16q12.2-q23.1) had both high average ranks and low between-study heterogeneity. For schizophrenia, this was seen for bin 3.2 (3p25.3-p22.1) and heterogeneity was still significantly low after adjusting for its high average rank. Concordance was high between the proposed metrics and between weighted and unweighted analyses. Data from genome searches should be synthesized and interpreted considering both average ranks and heterogeneity between studies. 2004 Wiley-Liss, Inc.
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            Explaining heterogeneity in meta-analysis: a comparison of methods.

            S G Thompson, S J Sharp (1999)
            Exploring the possible reasons for heterogeneity between studies is an important aspect of conducting a meta-analysis. This paper compares a number of methods which can be used to investigate whether a particular covariate, with a value defined for each study in the meta-analysis, explains any heterogeneity. The main example is from a meta-analysis of randomized trials of serum cholesterol reduction, in which the log-odds ratio for coronary events is related to the average extent of cholesterol reduction achieved in each trial. Different forms of weighted normal errors regression and random effects logistic regression are compared. These analyses quantify the extent to which heterogeneity is explained, as well as the effect of cholesterol reduction on the risk of coronary events. In a second example, the relationship between treatment effect estimates and their precision is examined, in order to assess the evidence for publication bias. We conclude that methods which allow for an additive component of residual heterogeneity should be used. In weighted regression, a restricted maximum likelihood estimator is appropriate, although a number of other estimators are also available. Methods which use the original form of the data explicitly, for example the binomial model for observed proportions rather than assuming normality of the log-odds ratios, are now computationally feasible. Although such methods are preferable in principle, they often give similar results in practice. Copyright 1999 John Wiley & Sons, Ltd.
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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.
              Article 0 comments Cited 207 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Saeed Dastgiri: 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, USA )
                ISSN (Electronic): 1932-6203
                Publication date Collection: 2014
                Publication date (Electronic): 5 February 2014
                Volume: 9
                Issue: 2
                Electronic Location Identifier: e87497
                Affiliations
                [1 ]Environment and Non-Communicable Diseases Research Center, School of Public Health, China Medical University, Shenyang, China
                [2 ]Department of Medical and Molecular Genetics, School of Medicine, Indiana University, Indianapolis, Indiana, United States of America
                Tabriz University of Medical Sciences, Iran (islamic Republic Of)
                Author notes

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

                Conceived and designed the experiments: BYY SJF GFS. Performed the experiments: XYZ YYL DW. Analyzed the data: BYY SJF XYZ YYH. Contributed reagents/materials/analysis tools: YFL MH QMZ. Wrote the paper: BYY GFS.

                Article
                Publisher ID: PONE-D-13-41420
                DOI: 10.1371/journal.pone.0087497
                PMC ID: 3914818
                PubMed ID: 24505291
                SO-VID: c33732dc-6a8d-462d-91ae-e35f934a77cf
                Copyright statement: Copyright @ 2014
                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 : 10 October 2013
                Date accepted : 24 December 2013
                Page count
                Pages: 1
                Funding
                This study was supported by a grant (No. 81072243) from the National Natural Science Foundation of China (NSFC). 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: Biology
                Subject: Genetics
                Subject: Population Genetics
                Subject: Genetic Polymorphism
                Subject: Genetics of Disease
                Subject: Medicine
                Subject: Cardiovascular
                Subject: Hypertension
                Subject: Clinical Genetics
                Subject: Clinical Research Design
                Subject: Meta-Analyses
                Subject: Epidemiology
                Subject: Biomarker Epidemiology
                Subject: Cardiovascular Disease Epidemiology
                Subject: Genetic Epidemiology
                Subject: Molecular Epidemiology
                Subject: Obstetrics and Gynecology
                Subject: Pregnancy
                Subject: Hypertensive Disorders in Pregnancy

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                ScienceOpen disciplines: Uncategorized

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