LRRK2) R1628P polymorphism has been associated with the risk of Parkinson's disease (PD) in Taiwan, China, and Singapore, there are conflicting findings regarding this relationship. Thus, the aim of the present meta-analysis was to evaluate the associations between the LRRK2 R1628P polymorphism (rs33949390) and PD in Asian populations. A search for eligible studies was performed in PubMed, Embase, SinoMed, and the China Knowledge Resource Integrated Database, and pooled odds ratios and 95% confidence intervals were used to evaluate the strength of the association between the R1628P polymorphism and PD. This meta-analysis assessed 19 studies from 14 papers that involved a total of 9,927 PD patients and 8,602 controls and found that the R1628P polymorphism was significantly associated with the risk of PD in Asian populations. Moreover, stratification analyses indicated that the R1628P polymorphism was significantly associated with an increased risk of PD among Chinese as well as non-Chinese Asian populations and an increased risk of PD in Chinese patients from China, Taiwan, and Singapore. In a stratified analysis conducted according to age, significant associations were found for both late-onset PD and early-onset PD. The present data indicate that the R1628P polymorphism of the LRRK2 gene contributes to PD susceptibility in Asian, especially Chinese, populations.Although the leucine-rich repeat kinase 2 ( LRRK2 gene are related to an increased presence of PD compared with control groups [Parkinson's disease (PD), which is one of the most common neurodegenerative disorders, is characterized by tremors, rigidity, bradykinesia, and postural instability . It has l groups .LRRK2 R1628P (rs33949390) polymorphism was a genetic risk factor for PD [LRRK2 R1628P polymorphism is associated with susceptibility to PD.Recently, a study from three independent centers in Taiwan and Singapore demonstrated that r for PD . Subsequr for PD . HoweverA total of 368 papers were yielded after our initial search. After removing duplicates and screening the titles and abstracts, 345 papers were removed. Therefore, 23 papers were selected for further full text review. Four papers were not case-control studies –24; one vs. G), dominant model (CC + CG vs. GG) and heterozygous model (CG vs. GG) without significant heterogeneity. Due to the deficiency of CC genotypes in included studies, we could not calculate the data of recessive model and homozygous model. As shown in Table vs. G: OR, 1.97; 95% CI, 1.70-2.28, P < 0.00001, Figure vs. GG: OR, 1.97; 95% CI, 1.70-2.28, P < 0.00001; GC vs. GG: OR, 1.93; 95% CI, 1.67-2.24, P < 0.00001). Stratification analyses were conducted according to ethnicity, age and country .A fixed effects model was conducted for allele model , a leucine-rich repeat (LRR), Ras of complex proteins , C-terminal of ROC (COR), mitogen-activated kinase kinase kinase (MAPKKK), and WD40 domains [PD is a progressive neurodegenerative disease, which is caused primarily by the loss of dopaminergic neurons in the substantia nigra compacta and the presence of intracellular inclusions known as Lewy bodies. Although the potential etiology of PD remains poorly understood, PD is thought to be caused by both genetic and environmental factors. To date, several candidate genes including sk of PD . Many stinterest . The LRR domains , 33. The domains .LRRK2 R1628P polymorphism and the risk of PD in Asian individuals [LRRK2 R1628P polymorphism was associated with an increased risk of PD among Asian populations. Previous meta-analyses [Many studies investigated the relationship between the ividuals –20, but analyses , 34 inveanalyses  analyzedLRRK2 R1628P polymorphism may be a specific risk factor of PD among Chinese individuals, which suggests the need for future larger-scale studies to validate this assumption. The stratified analysis in the present meta-analysis also indicated that the R1628P polymorphism increases the risk of PD in Chinese patients from China, Taiwan, and Singapore. In the stratified analysis that was conducted according to age, significant associations were found in both late-onset and early-onset PD cases, but this finding should be interpreted with caution due to limited sample sizes. A sensitivity analysis indicated that the present data were stable and trustworthy.The stratification analyses in this meta-analysis also indicated that the R1628P polymorphism is significantly associated with increased risks of PD among Chinese and non-Chinese individuals, which was not found by Wu et al. . HoweverLRRK2 R1628P polymorphism and PD in Caucasian populations. Second, unpublished trials may have been excluded unintentionally and could have resulted in a publication bias; however, there was no evidence of this. Third, the present findings were based on unadjusted estimates that did not consider confounding factors such as age, sex, or environmental factors, and thus, the analysis could have been more precise if individual data were available. Fourth, the results of the stratified analyses should be interpreted with caution due to limited sample sizes. Future larger-scale studies are urgently needed to confirm these results. Fifth, it is impossible to include all the languages when we performed the literature search.The present meta-analysis has several limitations that should be should be taken into consideration. First, this study did not investigate the association between the LRRK2 R1628P polymorphism is significantly associated with an increased risk of PD in Asians, especially among Chinese subjects. Future large-scale studies are necessary to characterize the association between the LRRK2 R1628P polymorphism and PD.In conclusion, this meta-analysis indicates that LRRK2 gene polymorphism and PD. The following search terms were used: “Leucine-rich repeat kinase 2,” “LRRK2,” “PARK8,” “Parkinson's disease,” “Parkinson disease,” “PD,”“SNP,” and “polymorphism”. Two independent investigators conducted the search. No language or other restrictions were placed on the search. We also performed a manual search of references cited in published papers to identify other initially omitted studies. Any disagreements were resolved by consensus. Criteria for the inclusion in this analysis were: (1) studies that evaluated the association between LRRK2 R1628P polymorphism (rs33949390) and PD in Asian populations, (2) studied on human beings, (3) study provided sufficient data to calculate the Odds ratios (ORs) and 95% confidence intervals (CIs), and P value, and (4) case-control study. Exclusion criteria were: (1) duplication of previous publications; (2) review, editorial, or other non-original studies; (3) studies without detailed genotype data; (4) studies without controls.We performed a comprehensive search in PubMed, Embase, SinoMed, and the China Knowledge Resource Integrated Database to identify studies through March 1, 2016 that were related with the LRRK2 R1628P genotype frequency in cases and controls. Extraction of the data was independently performed by two authors according to the inclusion criteria. All included studies were evaluated using theFor all eligible studies, the extracted information including: name of first author, publication year, country of origin, and Newcastle-Ottawa Scale (NOS) . They coLRRK2 R1628P polymorphism (rs33949390) and PD. Heterogeneity was evaluated by the Q statistic (significant at P < 0.1) and I2 statistic (where > 50% indicates significant heterogeneity) [2 test. Potential publication bias was investigated with the use of Begger's and Egger's linear regression test [P value < 0.05 was considered to indicate statistically significant.All statistical analyses were performed using the Stata 11.0 software  and RevMan 5.3 . Pooled ORs and 95% CIs were calculated to evaluate the strength of the association between geneity) . A fixedion test . A P val


Associations between interleukin-8 (IL-8) gene polymorphisms and periodontitis susceptibility have been investigated in many published studies, but the conclusions are still inconsistent. Therefore, we performed this systematic review and meta-analysis to review which polymorphisms have been researched and to obtain a precise result of the same polymorphism from different studies.11722T2 and C1633T, and one for rs2234671 polymorphism. Of them, IL-8 C1633T and rs1126580 polymorphisms showed positive association while the other ten polymorphisms revealed negative results.Finally 10 publications involving 1938 patients and 1569 controls were yielded, including 12 polymorphisms. Six studies investigated rs4073 polymorphism; two focused on rs2227306 and rs2227307; two referred to rs2227532 and T-738A; one detected rs2230054, rs1126579 and rs1126580; one inspected A2767T, TA comprehensive literature search from PubMed, Web of Science, and Chinese National Knowledge Infrastructure was conducted for all potentially relevant studies published before January 2, 2017. Two authors selected the studies and extracted data. The pooled analysis was conducted using the RevMan 5.1 software if a polymorphism was reported by two or more studies.11722T2, rs2234671, rs2230054, rs1126579, rs2227306, rs2227307, rs2227532, and T-738A polymorphisms were not associated with periodontitis susceptibility; the IL-8 C1633T and rs1126580 polymorphisms were associated with increased risk of periodontitis.Based on current evidence, the IL-8 rs4073, A2767T, T Interleukin (IL) plays an important role in mediating immune and inflammatory responses, and periodontitis is known as a chronic infectious disease. Hence, the genes and their variants (polymorphisms) of IL may be the important determinants of pathogenesis of periodontitis . There aThe initial search identified 61 publications and finally 10 articles –26 were 11722T2, C1633T, rs2234671, rs2230054 (C785T), rs1126579 (T1208C), rs1126580 (G1440A), rs2227306 (C781T), rs2227307 (G396T), rs2227532 (T-845C), and T-738A. Eight studies focused on chronic periodontitis (CP) [The characteristics and relevant data of the included studies are shown in Table tis (CP) , 24–26; Six studies , 24–27 rp = 0.52; TA+TT vs. AA: OR = 1.38, 95% CI = 0.59–3.25, p = 0.46).One study  on A2767Meta-analysis of two studies –22 showe11722T2 polymorphism and periodontitis susceptibility suggested that there was no significant association between them .One study  detectinTable p < 0.01; TC+TT vs. CC: OR = 3.19, 95%CI = 1.85–5.51, p < 0.01).The results from one study  on C1633p = 0.71; CG+CC vs. GG: OR = 1.12, 95%CI = 0.66–1.90, p = 0.68).One study  involvinTwo studies , 25 repoThe T-738A polymorphism was evaluated by two studies , 25 and p = 0.86; TC+TT vs. CC: OR = 1.08, 95%CI = 0.55–2.12, p = 0.82).The rs2230054 (C785T) polymorphism was assessed in one study  which rep = 0.64; CT+CC vs. TT: OR = 0.84, 95%CI = 0.47–1.51, p = 0.56).The rs1126579 (T1208C) polymorphism was reported by one study . The resp < 0.01).The results from one study  demonstr11722T2, rs2234671, rs2230054 (C785T), rs1126579 (T1208C), rs2227306 (C781T), rs2227307 (G396T), rs2227532 (T-845C), and T-738A polymorphisms were not significantly related to periodontitis susceptibility; however, there was a significant difference in the IL-8 C1633T and rs1126580 (G1440A) polymorphisms between the periodontitis patients and healthy control groups.IL-8 gene located on chromosome 4q12-21 contains four exons and three introns which possesses many functional polymorphisms . PublishCompared with published two meta-analyses –14 on thThe major limitation of our study was the numbers of included studies and sample size. Of these 12 polymorphisms, only IL-8 rs4073 polymorphism was involved in 6 studies while the others referred to one or two studies. Correspondingly, the sample size for each polymorphism was relatively small, which restrained the confidence of current results. Moreover, the subgroup analyses based on difference between ethnicity, smoking status, type of periodontitis, or other factors couldn't be conducted to investigate the source of heterogeneity, which might bias the results. Because of the limited numbers of included studies, we only assessed the publication bias of those investigating the IL-8 rs4073 polymorphism. Secondly, systematic review and meta-analysis is an observational study which was restricted by the quality of primary studies , 33–35. 11722T2, rs2234671, rs2230054, rs1126579, rs2227306, rs2227307, rs2227532, and T-738A polymorphisms are not associated with periodontitis while the IL-8 C1633T and rs1126580 polymorphisms may elevate the susceptibility of periodontitis based on the currently available evidences. However, for considering that the studies included in our meta-analysis were based on small numbers, the current results should be treated with caution, and the results may change with the larger sample size in future. Due to these limitations, more well designed, studies with larger sample size, and adjusted risk factors are required to further validate our results.In conclusion, our meta-analysis suggests that the IL-8 rs4073, A2767T, TWe included the studies which met all of the following criteria: (1) the patients were clearly diagnosed as periodontitis (CP and/or AgP) and the controls were periodontitis-free or periodontal healthy; (2) at least one of the IL-8 polymorphisms and periodontitis susceptibility were evaluated, using a case-control or cohort study design; (3) the studies reported full data for necessary genotypes in each group or contained sufficient data to calculate them. If the same institute published two or more publications, we treated them as independent ones and chose the more comprehensive one.A comprehensive literature search was performed in PubMed, Web of Science, and Chinese National Knowledge Infrastructure (CNKI) up to January 2, 2017. The following key words were used: IL-8, interleukin-8, interleukin 8, periodontal disease, periodontitis, variant, and polymorphism. Moreover, all listed references of included studies and recently reviews were retrieved for any additional relevant studies. No language restriction was applied in the search process.Study selection and data extraction were performed by two authors and any discrepancy was resolved by discussion. The following data were extracted from each included study: surname of first author, year of publication, study design, country and ethnicity of study population, demographics, periodontitis type, smoking status, number of cases and controls, polymorphism, genotype distribution, source of controls, genotyping method, and Hardy-Weinberg equilibrium (HWE) for controls.Q statistic and I2 statistic [P ≥ 0.1 and I2 < 50% indicating acceptable heterogeneity. If no significant heterogeneity existed, the fixed effect model was used; otherwise, the random-effects model was used. If the number of included studies was available, we conducted subgroup analyses on the ethnicity, smoking status, and periodontitis type. Publication bias was assessed by funnel plot [The OR and its 95% CI were calculated to estimate the relationship between the IL-8 polymorphisms and periodontitis risk. All studies used a allele genetic model and a dominant genetic model [tatistic , 36, witnel plot . All ananel plot –39.


Many studies have been conducted to investigate the association between miR-27 rs895819 A > G and miR-423 rs6505162 C > A and cancer risk; however, the results are not consistent. In order to acquire a more precise assessment of the correlation, we performed this meta-analysis. We searched PubMed, EMBASE and Web of Science databases to identify eligible studies. Pooled odds ratios (ORs) and 95% confidence intervals (CIs) were applied to evaluate the correlation of these two microRNA polymorphisms with cancer risk. Forty-five eligible studies from thirty-five articles were included in our analysis. The results showed that rs895819 was associated with a decreased cancer risk in Caucasians . When grouped by ethnicity, an increased risk was observed in colorectal cancer , while a decreased risk was found in breast cancer . For rs6505162, a significantly decreased cancer risk was observed in lung cancer under all five genetic models. To summarize, our results indicated that rs895819 was a protective factor for cancer in Caucasians and could increase colorectal cancer risk but decrease breast cancer risk. Moreover, rs6505162 was a protective factor for lung cancer. MicroRNAs (miRNAs) are a class of small, endogenous, non-coding, single-stranded, highly conserved and tissue-specific RNA molecules which take part in the regulation of target mRNAs expression at the post-transcriptional level , 2. By bAs the most common type of genetic variation in the human genome, single nucleotide polymorphisms (SNPs) occurring in miRNA genes have been found to be able to affect miRNA expression and function by interfering the interaction between miRNAs and their corresponding target mRNAs. A large number of studies have demonstrated that SNPs in the miRNA genes are associated with the occurrence of various diseases, such as cancer, so microRNA polymorphisms are considered to be a potentially important mechanism in the acquisition of cancer susceptibility , 6. In rHowever, for two common functional microRNA polymorphisms miR-27 rs895819 and miR-423 rs6505162, although many case-control studies involving various cancer types have been performed among different ethnic populations, there is currently no consensus on whether there exists an association between these two microRNA polymorphisms and cancer risk because of inconsistent results published studies reported. In addition, many relevant case-control studies on this theme have been published recently, which will expand the sample size and may have an impact on the evaluation of the association . TherefoThe selection process of eligible studies is presented in Figure The main meta-analysis results of the correlation between rs895819 A > G and cancer susceptibility are listed in Table For rs6505162 C > A, no significant risk association was observed in the overall analysis. Similarly, when grouped by ethnicity, we couldn't detect a correlation of the rs6505162 polymorphism with cancer risk in either Caucasians or Asians. However, after subgroup analysis by cancer type, the rs6505162 polymorphism was found to be associated with a decreased risk of lung cancer in all five genetic models   was observed in the overall pooled analysis or subgroup analysis, the random-effects model would be applied because it could generate wider confidence intervals. Otherwise, we would select fixed-effects model to conduct related data analysis.Q test and IP values of Begg's test and Egger's test were less than 0.05). Similarly, we could not observe a publication bias for the rs6505162 polymorphism in three of five comparison models including homozygote model, heterozygote model and recessive model. Interestingly, for both allele contrast model and recessive model, P values of Egger's test in these two genetic models were all > 0.05, while that of Begg's test were all < 0.05, which might be due to small sample size and limited number of studies included in this meta-analysis. Therefore, there might exist publication bias in our study.The Begg's funnel plot and Egger's test were performed to examine the publication bias in this meta-analysis. For rs895819, no publication bias was found in allele contrast model, homozygote model and recessive model, and the shape of the funnel plots appeared to relatively symmetrical  AND (“polymorphism” OR “SNP” OR “variation” OR “locus” OR “mutation”) AND .All eligible studies included in this meta-analysis must meet the following criteria: (1) evaluating the association of rs895819/rs6505162 with cancer risk; (2) case-control design; (3) providing sufficient genotyping data for estimating odds ratios (ORs) and 95% confidence intervals (CIs); (4) genotype frequencies of subjects in controls were in accordance with Hard-Weinberg equilibrium (HWE); (5) all cancer cases were confirmed by pathology. The exclusion criteria were as follows: (1) not case-control studies; (2) case reports, comments or review articles; (3) duplicate articles; (4) the control groups did not conform to HWE; (5) lack of enough genotyping data.P value of Hardy-Weinberg equilibrium (HWE) in controls. Any inconsistency was resolved through discussion until a consensus was reached. Moreover, we also evaluated the methodological quality of included studies based on Newcastle-Ottawa Scale (NOS), which scored studies according to three aspects: selection, comparability, and exposure. Therefore, all studies could be divided into three categories: “low quality” studies (score 0-3); “moderate quality” studies (score 4-6); “high quality” studies (score 7-9).Two independent investigators carefully collected the following information from each study included in this meta-analysis: first author, year, country, ethnicity, cancer type, genotyping method, source of controls, number of cases and controls, genotype distribution of cases and controls, and 2 test were applied to assess the between-study heterogeneity in this meta-analysis. If there was significant heterogeneity among included studies , ORs with corresponding 95% CIs would be calculated using the random-effects model; otherwise, the fixed-effects model would be selected. The subgroup analysis was undertaken according to ethnicity, cancer type (investigations consisting of only one study were merged into the “others” group) and source of controls. We conducted sensitivity analysis by sequentially omitting a single study each time to assess the influence of individual study on the pooled ORs. Moreover, we employed Begg's funnel plot and Egger's test (P < 0.05 was considered significant) to assess whether there existed publication bias. Besides, chi-square test was applied to calculate P value of Hardy-Weinberg equilibrium (HWE) in controls. All data analyses performed in the present analysis were carried out using STATA 12.0 software . All statistical tests were two sided, and P < 0.05 indicated statistical significance.The association of rs895819 and rs6505162 with cancer susceptibility was estimated by pooled odds ratios (ORs) and 95% confidence intervals (CIs) under five different genetic models including allele contrast model, homozygote model, heterozygote model, dominant model and recessive model. Both Q-statistical test and I


Bt rice expressing the insecticidal proteins derived from Bacillus thuringiensis Berliner (Bt) has been developed since 1989. Their ecological risks towards non‐target organisms have been investigated; however, these studies were conducted individually, yielding uncertainty regarding potential agroecological risks associated with large‐scale deployment of Bt rice lines. Here, we developed a meta‐analysis of the existing literature to synthesize current knowledge of the impacts of Bt rice on functional arthropod guilds, including herbivores, predators, parasitoids and detritivores in laboratory and field studies. Laboratory results indicate Bt rice did not influence survival rate and developmental duration of herbivores, although exposure to Bt rice led to reduced egg laying, which correctly predicted their reduced abundance in Bt rice agroecosystems. Similarly, consuming prey exposed to Bt protein did not influence survival, development or fecundity of predators, indicating constant abundances of predators in Bt rice fields. Compared to control agroecosystems, parasitoid populations decreased slightly in Bt rice cropping systems, while detritivores increased. We draw two inferences. One, laboratory studies of Bt rice showing effects on ecological functional groups are mainly either consistent with or more conservative than results of field studies, and two, Bt rice will pose negligible risks to the non‐target functional guilds in future large‐scale Bt rice agroecosystems in China.Transgenic  We re‐analysed the data after removing the information on thrips and aphids, which, again, revealed a significant reduction in herbivore reproduction . Populations of detritivores increased in Bt rice fields relative to controls , possibly due to higher abundance of Collembola  expressing the Cry1Ab protein in laboratory and field surveys  in previous meta‐analyses  in Hubei Province have been issued twice by the government in 2009 and 2014  seemed to be not enough. With the knowledge accumulated of risk assessment, these aspects will be gradually improved in future.Moreover, although we tried our best to collect data and conduct the analysis, there were still some limitations. Firstly, we failed to make some comparisons between the treatments of http://isiknowledge.com), PubMed (http://www.ncbi.nlm.nih.gov/pubmed) and China National Knowledge Infrastructure  using the key words ‘(Bt OR Bacillus thuringinesis) AND rice’. The following criteria were applied to screen the studies: (i) transgenic rice expresses one or more Cry proteins and targets lepidopteran pests; (ii) non‐transgenic rice was used as control plants in laboratory and field research; (iii) one or more non‐target arthropods were assessed; (iv) the papers reported data on development, survival and reproduction as response variables for non‐target taxa in laboratory studies, and species abundances in field studies; (v) each study reported means accompanied by standard deviations (SD) or standard error (SE) and sample size (n); (vi) all studies were conducted in China and published in English or Chinese up to September 2016.The database was created by searching the Web of Science , the non‐target group  and the experimental treatment with its control. We also recorded study location, cultivation, plot size, exposure method, sampling method and other methodological details. If a study reported figures without numerical data, we used ImageJ software version 1.45 to measure means and its variance  total eggs per female for reproduction, immature stage survival rate, adult emergence and pupation rate, but not egg‐hatching data for survival; (ii) we selected the separately recorded male and female development time and the entire time span of larval or nymphal development; (iii) if studies reported multiple generation effects, we used the last generation for analyses. The criteria for field studies were (i) we chose the seasonal means and, when they were not available, the peak abundance; (ii) when multiple life‐stage data was reported in one study, we chose the larval or nymphal abundance; (iii) we used the ecological functional groups, not individual species. However, when analysing by taxonomic group, we used the individual species.et al. , Hedges’d, was used to calculate the difference between an experimental (Bt) and the control (non‐Bt) mean divided by the pooled standard deviation and weighted by the reciprocal of the sampling variance  to test the results. If the interval enclosed zero, we took the effect size as not significantly different from zero. We assessed heterogeneity with the Q test and I2 statistic. When the I2 value was larger than 50% and Pheterogeneity < 0.001, we considered the data highly heterogeneous and conducted subgroup analysis to analyse the high heterogeneity. The random‐effect model was the more appropriate method to carry out all the analysis for the data included in the analysis came from different research groups (Borenstein et al., et al., A weighted mean effect size (The authors declare no conflicts of interest.Figure S1 Publication bias test for the laboratory data.Figure S2 Publication bias test for the field data.Figure S3 Trim‐and‐fill method to explain the publication bias of the abundance data of herbivores and parasitoids.Figure S4 Meta‐analysis of laboratory studies examining non‐target effects of transgenic Bt rice on herbivores reproduction.Figure S5 Meta‐analysis of laboratory studies examining non‐target effects of transgenic Bt rice on predators development.Figure S6 Meta‐analysis of laboratory studies and field studies examining non‐target effects of transgenic Bt rice on planthoppers.Table S1 Summary of meta‐database used in analysis of laboratory studies.Table S2 Summary of meta‐database used in analysis of field studies.Table S3 Functional guilds classification in analysis.Click here for additional data file.


Vn, and derive its distribution.An important question for clinicians appraising a meta‐analysis is: are the findings likely to be valid in their own practice—does the reported effect accurately represent the effect that would occur in their own clinical population? To this end we advance the concept of statistical validity—where the parameter being estimated equals the corresponding parameter for a new independent study. Using a simple (‘leave‐one‐out’) cross‐validation technique, we demonstrate how we may test meta‐analysis estimates for statistical validity using a new validation statistic, Vn and the Q statistic are compared for univariate random effects meta‐analysis and a tailored meta‐regression model, where information from the setting (included as model covariates) is used to calibrate the summary estimate to the setting of application. Their properties are found to be similar when there are 50 studies or more, but for fewer studies Vn has greater power but a higher type 1 error rate than Q. The power and type 1 error rate of Vn are also shown to depend on the within‐study variance, between‐study variance, study sample size, and the number of studies in the meta‐analysis. Finally, we apply Vn to two published meta‐analyses and conclude that it usefully augments standard methods when deciding upon the likely validity of summary meta‐analysis estimates in clinical practice. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.We compare this with the usual approach of investigating heterogeneity in meta‐analyses and demonstrate the link between statistical validity and homogeneity. Using a simulation study, the properties of  In this regard, it has achieved undoubted success; however, the blight of heterogeneity, which so often affects meta‐analyses, can potentially affect the applicability of results in individual clinical settings, such as individual practices, hospitals, regions, or even countries.cross‐validationk primary studies in the meta‐analysis provide all the data used in the validation process. The cross‐validation process involves comparing the meta‐analysis estimate from k − 1 studies with that estimate from the omitted study; this is repeated k times, each time omitting a different study.Although methods have been developed to quantify and ascertain the effects of heterogeneity, more recently, particularly in the field of predictive modeling, the focus has been on developing statistical approaches that increase the validity of meta‐analysis results when applied in new populations. When evaluating diagnostic and prognostic tests, Riley and colleagues The need to examine and improve the validity of meta‐analysis results should not be confined to the prediction modeling field. Indeed, assessing whether meta‐analysis results translate into practice should be the concern of all reviewers and statisticians producing summary results from a body of evidence, whether it is for the purpose of diagnosis, treatment, prognosis, or otherwise.ma is the parameter for the true summary effect of interest as estimated by the meta‐analysis analysis model and μsetting is the parameter for the true effect in an independent setting of interest, then does μma = μsetting? When the two are equal, we propose that the summary estimate from the meta‐analysis model can be described as having statistical validity. However, if the two are not equal, then meta‐analysis results may need to be modified (or ‘tailored’) to the setting of interest in order to ensure statistical validity. We will also examine statistical validity in the context of heterogeneity considering some of the methods used to establish heterogeneity.With this in mind, in this article we propose a general method for assessing the statistical validity of meta‐analysis results when applied in clinical practice. The predominant question we aim to address is when should we apply a summary meta‐analysis estimate to an independent setting? Specifically, if μVn, and derive its associated distribution applied to meta‐analysis and meta‐regression models. We consider how statistical validity relates to heterogeneity and compare Vn with Cochran's Q statistic. The properties of Vn and Q are examined more closely in a simulation study in We develop this in the following sections. We describe the meta‐analysis and tailored meta‐regression models in 2(i)Meta‐analysis modelSupposing there are multiple studies that each evaluate a particular effect of interest . Of interest is the summary (mean) effect across studies and, for the purposes of this paper, how to apply or tailor such summary meta‐analysis estimates to clinical practice. Given the potential variation in the true effects between primary studies, we develop methods from the view point of a random effects model.i. This will be used as the base model in the meta‐analysis, where the key result for translation to clinical practice is the summary effect estimate, y^.(ii)Tailored meta‐regression modelFor the observed mean effect, p − 1 covariates, we havep elements (the first element is 1) associated with study i, p‐vector of coefficients to be estimated (the first element being the intercept term), and Xiβ^−i where β^−i denotes the estimate of ith study omitted. Therefore, following estimation of model (2), the key result for translation to clinical practice is Xiβ^−i. The merits of using information from the setting of interest have been recently described by Willis and Hyde Heterogeneity across settings may be explained by study‐level covariates, and such covariates may be important when applying summary meta‐analysis results in clinical practice. We consider the effects of covariates on the meta‐analysis by incorporating these within a meta‐regression model. When there are MetaforModels (1) and (2) can be estimated using standard techniques, such as methods of moments and restricted maximum likelihood (REML). All meta‐analysis and meta‐regression models in this article are conducted in R using the package 33.1k independent studies, the procedure generates k different meta‐analysis estimates, and thus k different validations. As such, the primary studies selected for the meta‐analysis are themselves being used as the basis for independent validation.To evaluate whether meta‐analysis results may be translated into practice requires the development of methods that allow the cross‐validation of the derived summary estimates in new independent settings. In prognostic modeling, Royston and colleagues proposed what they called an ‘internal–external cross‐validation’ procedure to establish the generality of a prognostic model developed across different studies 3.2ith study, and y^−i be the summary meta‐analysis estimate (from either model (1) or model (2)) generated from using k − 1 studies with the ith study omitted. Therefore, following the cross‐validation exercise, we have a dataset containing k values of y^−i.In the remainder of this article, we focus on developing a statistic to test the validity of summary meta‐analysis estimates for clinical practice, within the context of the aforementioned cross‐validation approach. Let Vnvary^−i is the variance of y^−i.In this context, we propose the validation statistic, Vn statistic may be used as an overall test of the null hypothesis that i, where y^−i predicted by the meta‐analysis model, and i. By our definition, when the null hypothesis is true, the meta‐analysis/regression estimate is a statistically valid estimate for a new setting. Thus, if we define a p value <0.05 for Vn as significant, then a p < 0.05 implies that there is sufficient evidence to conclude the meta‐analysis/regression estimate is not statistically valid.Assuming Vn is derived for meta‐analysis/regression models.In 3.3Vn recognising that Vn is a quadratic form and using an approach described in previous studies Here, we give an outline of the derivation of the asymptotic distribution of ith study have observed mean effect =σi2/ni where σi2 is the variance of the patient‐level observations in each study with sample size wi*=1/ needs to be known. The distribution for a linear combination of chi‐square variables has received considerable attention over the years. In general, there is no closed form to the distribution so that it has to be obtained numerically and a number of approaches have been described. Sattherwaite in 1946 described an approximate method based on the observation that when the non‐zero eigenvalues all equal one the distribution simplifies to single chi‐squared distribution with k degrees of freedom To apply n terms had an upper bound which was dependent on a chi‐squared distribution, the coefficients of the terms in the expansion, and n—all of which could be estimated accuratelyn terms, such that n is set to make the truncation error arbitrarily small Vn. The version of Farebrother's algorithm applied below is from the package CompQuadForm in R Vn for a case example may be found in appendix Ruben made a notable development when he demonstrated that a linear combination of chi‐square variables could be written as an infinite series 3.5Vn statistic may be used as a test of the null hypothesis H0: i. For the base meta‐analysis model (1) if we define w−ij=1/σj2/nj+τ−i2 for j ≠ i, then the null hypothesis is equivalent to the following for each iThe It can be seen from this that the null hypothesis is only satisfied when Vn in matrix form is useful when considering the null hypothesis. As stated earlier, the null hypothesis equates to k‐vector of parameters; in other words, the above equations are equivalent to the kernel or null space of . Using Gauss‐Jordan elimination, Writing Vn to lead to the null hypothesis being rejected in most applications of meta‐analysis.Heterogeneity is likely to exist in most meta‐analyses, and thus, in general, individual clinical settings will have a true effect that differs from the mean (summary) effect; thus, one would expect Vn is likely to be more useful when considering the case of tailored meta‐analysis results, as derived from the tailored meta‐regression model in (2) where covariates are included. Reducing i then for p − 1 covariatesIn contrast, nullity, is p—this follows from the rank‐nullity theorem, because the rank, σi2 (variance of patient‐level observations), n , and k . The following values were used: τ2 ; σi2 ; n, ; and k . The σi2 and n were varied between meta‐analyses/regressions but not between the primary studies within the meta‐analysis/regression to allow us to study the contributions of these separately.In order to study the properties of Vn and Q are calculated using the asymptotic estimates σ^i2 and τ^2 for the parameters σi2 and τ2, respectively. To simulate these for each primary study, n simulated observations from ym,i~Nμiσi2 for m = 1,..,n. The σ^i2 was estimated as the variance of the observations τ^2 was estimated using σ^i2 to fit the meta‐analysis/regression models via the Metafor package In practice, Vn and Q using a meta‐analysis model, we simulated a single i = k − 1, k, were simulated, and the remaining k − 2 values of When evaluating the type 1 errors of Vn and Q the true effect (parameter) for each primary study, i = 1,..,k. These were checked to ensure that To evaluate the power of 2, σi2, n, k), the type I error rate and the power of Vn and Q were determined for a critical value of p = 0.05. The rate of type I error and the power were estimated based on 40 000 meta‐analysis/meta‐regression replications for each  combination.For different combinations of  are given. Here, there is no heterogeneity (τ2 = 0), that is, n, in the individual studies and the number of studies in the meta‐analysis, k. This reflects the estimates σ^i2 and τ^2 being less prone to sampling error as n and k increase, respectively.In Table k < 50, and smaller sample sizes, n, the type 1 error rate of Q is closer to the threshold of 5% than Vn. The null distribution of Q applied to meta‐analysis is a k − 1 when σi2 is known exactly n = 1000, where sampling error of σ^i2 is minimised, the type 1 error rate for Q is consistently around the 5% mark across different numbers of studies.When there are fewer studies Vn and Q when applied to the meta‐regression model in (2) where one continuous covariate was included. As in the meta‐analysis model, when compared with Vn, Q has type 1 error rates which are closer to the 5% threshold. When n = 50 and k = 5, Vn has a type 1 error rate as high as 9.3% compared 5.3% for Q. In general, for both statistics when the sample size of the individual studies is small (n = 50), the type 1 error rate increases with k.Table 4.2Vn is plotted against se=σi/n) for models (1) and (2). In both the meta‐analysis and meta‐regression models, the power of Vn increases with increasing number of studies for a given Vn.In Figure Vn are shifted slightly to the right in the meta‐regression model when there are 10 studies or fewer. In short, the probability of rejecting statistical validity when it is known to be false for a given Vn is applied to a meta‐analysis model than in a meta‐regression model with one covariate.Comparing the left and right panels, respectively, the power curves for Vn and Q are compared for each of the models when there are 5 and 25 studies, respectively. When there are 5 studies, Vn has greater power than Q for a given Vn and Q are closer, but Vn maintains greater power over Q for a wide range of . In short, Vn is more useful if we may assume heterogeneity and that estimates are more likely to be invalid.In Figure . When a meta‐analysis has 25 studies, the type 1 error rate of Vn is 0.001–0.006 higher than Q (this difference is 0.004–0.008 for meta‐regression). Although power varies with Vn is 0.002–0.014 higher than Q (difference in meta‐regression is 0.003–0.016). Essentially, the higher type 1 error rate of Vn compared with Q is compensated by a corresponding increase in power.Also of note is how the difference in type 1 error rates between the two statistics compares with the difference in power4.3Vn is its use in testing the null hypothesis of statistical validity, but its interpretation has to involve weighing up the results in the context of other evidence. Before we interpret Vn, an important consideration is to decide whether the null hypothesis  is likely to be true. We know from 3.6 this is equivalent to deciding whether the studies are homogeneous and the cumulative evidence provided by such measures as the Q statistic I2Central to proposing Vn in terms of the type 1 error rate or power . Here, the simulation study which evaluated these for both Vn and Q may be used to inform decision‐making when interpreting results.Depending on whether statistical validity seems likely, we then interpret the results of Vn is significant (p < 0.05), then either the meta‐analysis/regression estimates are invalid or there is a type 1 error. For Vn is above 85% when there are 5 studies and above 99% when there are 10 studies. Thus, if model estimates are statistically invalid and Vn will nearly always be significant. A type 1 error arises when the estimates are valid but Vn is significant. When there are few studies (k = 5) and the sample size is small (n = 50), Vn can have a type 1 error rate as high as 9% at a level of significance of 5%. Because validity is dependent on homogeneity (as we showed in 3.5), we may use the Q statistic in such instances as it maintains a type 1 error rate of around 5% even when k and n are small.If Vn is not significant but statistical invalidity seems likely, then we should consider the type 2 error rate (1‐ power). The power of Vn is dependent on k and τ^ directly from the meta‐analysis model and estimate the average standard error se¯ of studies in the meta‐analysis based on that proposed by Higgins and Thompson wi=ni/σi2. Thus, any non‐significant results should be interpreted by weighing up whether they are consistent with the null hypothesis being true or there being a type 2 error given our estimate, τ^/se¯.If τ^/se¯ that may be expected, we screened the Cochrane database of Systematic Reviews for reviews published in September 2016. Including the two cases that follow, we found 16 reviews which provided summary 2 × 2 table data and had 5 or more primary studies  = 1/tp + 1/cp – 1/(tp + tn) – 1/(cp + cn) where tp and tn are the number of TB positive and negative patients in those who were vaccinated; cp and cn are the number of TB positive and negative patients in those who were not vaccinated. The var(logRR^−i) is the model variance.The first dataset is from Berkey et al. logRR^−i. This shows directly how well the meta‐analysis and tailored meta‐regression estimate predicts that observed in the excluded study.In Table Vn  is significant, and as τ^/se¯ = 3.44, the power of Vn is likely to be close to 100% at a 5% level of significance. This strongly suggests the meta‐analysis estimate is unlikely to be valid in a new setting. The other statistics in Table From Table ith study omitted took the form of:α^−i and β^−i are estimated from fitting the model. It is of interest to know if the summary estimates from this tailored meta‐regression are valid in particular countries. Therefore, the cross‐validation approach is useful, with Vn used to test the calibration of the meta‐regression predicted effects and the actual study effects.When undertaking a meta‐regression, the reported efficacy in terms of the relative risk was associated with the latitude of the setting in which the study was conducted Q and I2 have decreased), Vn  remains significant. As noted above τ^/se¯ = 3.44, and for a tailored meta‐regression model, the power of Vn is still around 100%—this points strongly to the tailored meta‐regression estimate being invalid.Although including the latitude as a covariate in the model has helped explain some of the heterogeneity i] = 1/[(tp + fp)PPV(1‐PPV)] where tp and fp are the number of true and false positive patients. The individual study estimates for logPPV^−i are given in Table The individual study variance Q , I2 = 59.75% and the 95% prediction interval for the summary logit (PPV) is  —these all suggest the studies are heterogeneous. As might be expected, Vn  is also significant which leads to the conclusion that any summary estimates are unlikely to be valid.From Table PPV is known to depend on the disease prevalence. We implemented a tailored meta‐regression approach in which the prevalence of disease was assumed to be known for each primary study Vn statistic, each fitted tailored meta‐regression model took the form:α^−i and β^−i are estimated from fitting the model with the ith study omitted.From Bayes' theorem, the Vn  is non‐significant. Should this be considered as consistent with the null hypothesis of statistical validity or is it a type 2 error? When there are few studies (k = 7), Vn has greater power and therefore a lower type 2 error rate than Q. From Table τ^/se¯ = 1.21 and inspecting the power curves in Figure Vn will be much higher because p = 0.484. (A simulation study based on τ^/se¯ = 1.21 and an average sample size per study of 128 for the 7 studies demonstrates the power to be 90.0% giving a type 2 error rate of 10%). This would suggest that estimates are likely to be valid.In contrast to the first example, I2 = 0%, and the prediction interval has narrowed to  equivalent to a PPV of 23–36% suggesting that the heterogeneity has been explained by the addition of the covariate to the model. Based on this evidence, it would be reasonable to implement a PPV estimate from this model in an independent setting and hence practice.This is also supported by the other measures, as Both of these examples demonstrate why clinicians should not automatically assume summary meta‐analysis results are applicable to their population and that even when a summary estimate appears valid this should be judged in the context of the properties of the statistic and other evidence used to make that decision.6One of the issues facing medical research in general is determining how well the research results translate into practice. To truly address this, we need a separate evaluation of the research in practice settings which are independent from the research settings. The terms validity or external validation are often reserved for when the research findings may be generalised or translated into clinical practice. With regard to meta‐analysis results, however, it is often impractical to conduct further independent studies to assess whether the aggregate estimates are valid. Judgments on validity are therefore generally based on an assessment of quality, in which a combination of qualitative and quantitative characteristics of the comprising studies is weighed up.Yet the need for further studies is partly circumvented in meta‐analysis by making use of the existing studies in a similar way to the Jack‐knife method Vn, and its asymptotic distribution to test the viability of statistical validity.To address this, we considered the concept of statistical validity in relation to estimates generated by meta‐analysis and meta‐regression models. In particular, we defined it as when the model parameter for the effect measure of interest equates to that in an independent setting. From this, and using the previously described cross‐validation procedure, we derived a statistic, Vn, it was demonstrated that in meta‐analysis statistical validity follows only when the studies are homogenous. Furthermore, when meta‐analysis is extended to include discrete covariates in a tailored meta‐regression model, statistical valid estimates arise when the individual sub‐groups of studies are homogeneous, equivalent to the covariates being used to ‘explain’ the heterogeneity.Homogeneity plays a central role and is integral to statistical validity when evaluating meta‐analysis or meta‐regression models with discrete categorical covariates. As part of the derivation of p covariates are continuous in which case the set of statistical valid estimates spans a p + 1 dimensional sub‐space of the k‐space of parameters  when making decisions on the validity of meta‐analysis/regression estimates.In terms of the type 1 error rate and power, they are similar when then are 50 or more studies. However, when there are fewer studies, Vn not only depends on the number of studies but also on τ/se, so there is a trade‐off between the level of heterogeneity and the average precision of the studies. Thus, when the meta‐analysis consists predominantly of high precision studies, Vn may detect differences between the model estimate and those observed which may be too small to be clinical relevant but are, nonetheless, statistically significant. However, our overview of Cochrane reviews showed this not to be a large problem.The power of Q statistic. This motivated the proposing of statistics, such as the I2 statistic, that are aimed at measuring the extent of heterogeneity rather than its presence As in previous studies Vn provides little information when used in isolation; its usefulness depends upon the context in which it is applied. As statistical validity is intrinsically linked to homogeneity if homogeneity seems likely then a significant Vn result should be interpreted in terms of its potential type 1 error rate. In such an instance, the Q statistic, with its lower type 1 error rates, is likely to be the more informative of the two.Like many statistics, Vn's greater power means that it could be more useful than the Q statistic particularly for Vn as support for a recommendation which discourages the wider application of the meta‐analysis results.However, if heterogeneity is considered to be likely at the outset , then Vn be interpreted when the inclusion of covariates in meta‐regression analyses seems to ‘explain’ the between‐study heterogeneity? One of the risks in this instance is that legitimate exploratory analyses could lead to recommendations on validity. When the objective is to determine potential sources of variation and not make assertions on the ‘applicability’ of results, then the Q statistic and I2 can be used to inform such analyses. Although in any event, a non‐significant Vn should be interpreted against the likelihood of a type 2 error, it should not be used to assert statistical validity in meta‐regression analyses when the covariates were identified as part of an exploratory phase. The risks of such data fishing or dredging are well documented But how should Vn statistic, Riley et al. propose using 95% prediction intervals to quantify the potential error in the predictions of true study values from applying meta‐analysis/regression models to new settings Vn statistic, the ‘error’ in the proposed prediction interval, as estimated using Riley's method As an alternative to the hypothesis testing approach of the In this study, we confined our investigations to using study‐level data either as part of meta‐analyses or as covariates included in meta‐regression analyses. As IPD or patient‐level data become increasingly available, there is the potential to use IPD meta‐analyses to improve the summary estimates translated into practice. An important part of this process would be to evaluate their statistical validity and is worthy of future research.Vn statistic, and it is applicable whenever the meta‐analysis model (1) or the tailored meta‐regression model (2) is applied to combine effect estimates from multiple studies. It does have limitations as a hypothesis test, and these should be noted. However, as statistical validity relates to identifying homogenous sub‐groups of studies, these limitations may be partly circumvented by using it alongside other statistics such as the Q statistic. As such, Vn provides a useful summary of the likely statistical validity of results from meta‐analysis/regression models when applied to clinical practice.In conclusion, for summary estimates from meta‐analysis to be useful in practice, they need to be statistically valid. As a direct measure of statistical validity, we have proposed the Data S1. Supporting info itemClick here for additional data file.


DRD2) gene and schizophrenia has been studied though no conclusive outcomes have been attained. The aim of this study was to perform a systematic review and meta-analysis to explore the relation between three polymorphisms of the DRD2 gene  and schizophrenia.The association between the dopamine D2 receptor (The search was made in PubMed and EBSCO databases (up to February 2016). The systematic review included 34 case–control association studies . The association analysis comprised the allelic, additive, dominant, and recessive genetic models. The meta-analysis was performed following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement.The meta-analysis showed that TaqI  and C957T  exert a protective effect against developing schizophrenia. However, the sub-analysis for the C957T variant showed that this polymorphism exhibits a risk factor effect on Chinese individuals .DRD2 gene and the risk for schizophrenia, given that TaqI and C957T polymorphisms presented a protective effect against schizophrenia, and in the sub-analyses the C957T variant increased the risk for this disorder in the Chinese population.Our meta-analysis suggests an association of the The online version of this article (doi:10.1186/s12993-016-0114-z) contains supplementary material, which is available to authorized users. Schizophrenia (SZ) is a common and complex multifactorial psychiatric disorder characterized by a variety of symptoms. These symptoms involve multiple psychological domains, including inferential thinking, attention, social interaction, expression of emotions, and volition. Typically, the onset of these symptoms starts manifesting in adolescence or early adulthood , 2. SchiDRD2). This is a transmembrane G protein-linked receptor which activates intracellular signaling by the inhibition of cAMP synthesis [DRD2 gene is localized on chromosome 11 at the q22–q23 locus. This gene presents multiple polymorphisms, about 514 (http://snpper.chip.org/bio/snpper-enter/). From these, we selected three functional variants [Although the biological etiology of schizophrenia is unknown, dopamine system dysfunction has been widely implicated in the pathogenesis of this disorder, and genes involved in dopaminergic pathways are being studied as candidate genes , 6. Partynthesis . In humavariants , 9. The variants –12. Secovariants , 14. Finvariants , 16.DRD2 gene polymorphisms  has been reported by a number of authors [DRD2 gene on the overall risk for SZ. The effects of ethnicity were also evaluated in this study.To date, a significant association between SZ and these functional  authors –19. Howe authors , 20. At  authors  in 2014  authors  in 2015.DRD2 gene variants was performed according to the following assessments: (1) a meta-analysis of the TaqI polymorphism in subjects with SZ compared to healthy controls, (2) meta-analysis of the C957T polymorphism in subjects with SZ compared to healthy controls, (3) meta-analysis of the Ser311Cys polymorphism in subjects with SZ compared to healthy controls, (4) meta-analysis of the TaqI polymorphism in schizophrenics versus healthy controls in the Caucasian population, (5) meta-analysis of the C957T polymorphism in schizophrenics versus healthy controls in Caucasian and Asian populations, and a further analysis in Chinese and Japanese subjects, (6) meta-analysis of the Ser311Cys polymorphism in schizophrenics versus healthy controls by population. (7) Finally, a meta-regression method based on age including TaqI, C957T, and Ser311Cys polymorphisms was performed.The search association between SZ and The meta-analyses were reported according to the preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement , 24. Thehttp://www.crd.york.ac.uk/prospero/) with the registration number CRD42015029744.The protocol of this meta-analysis was registered in PROSPERO  to be published in peer-reviewed journals, (2) to be designed as case–control studies, (3) to contain independent data, (4) to be association studies in which the frequencies of three genotypes were clearly stated or could be calculated, (5) inclusion of SZ diagnosis in the patient study group, and (6) the articles had to be written in English. Studies were excluded when: (1) they were not case–control studies, (2) they were reviews, comments or editorial articles, (3) they provided insufficient data, and (4) they were repeated studies.All the available data were extracted from each study by two researchers (Hernández-Díaz and González-Castro) working independently and in accordance with the inclusion criteria listed above. In case of disagreement in the inclusion, a third investigator was involved (Tovilla-Zárate) to resolve the discrepancy and a final decision was reached by the majority of votes. Data such as authors, year of publication, location, ethnic group, number of cases and controls, age, gender, SZ diagnosis of the participants and genotypes were collected.The possible presence of publication bias was evaluated graphically by drawing funnel plots and statistically by the Egger’s standard regression test. In the Egger’s test p < 0.10 was considered a statistically significant publication bias. The shape of the funnel plots serve as an indication of any obvious asymmetry for the TaqI, C957T and Ser311Cys variants, which was additionally supported by the Egger’s test. Moreover, to strengthen the analysis we evaluated publication bias by using the GRADE approach  using the Newcastle–Ottawa Assessment Scale (NOS) to estimate the methodological quality  Table 11. The quDRD2 gene and SZ risk. Pooled ORs with their corresponding confidence intervals (95% CIs) were calculated for each of the models used: allelic (T vs C), additive (TT vs CC), dominant (TT + CT vs CC), and recessive (TT vs CT + TT). The estimated pooled ORs for each study were calculated using a random-effects model (Dersimonian and Laird method), though the fixed effects model was also considered (Mantel–Haenszel method). Heterogeneity of the studies was assessed with I2 and Q test statistics to identify significant outcomes. The sources of heterogeneity were also detected by sub-group analyses. Two sub-groups (Caucasian or Asian) according to different descents were analyzed for an ethnic-specific genetic comparison. Sample heterogeneity was analyzed with the Dersimonian and Laird’s Q test. Q test results were complemented with graphs to help the visualization of those studies favoring heterogeneity. The reliability of the results was assessed by sensitivity analysis performed for all outcomes to determine whether the results were driven mainly by single studies. In addition, we performed a meta-regression method based on age, to reduce the small sample size problem. We also performed a cumulative meta-analysis to provide a framework for updating the genetic effect of all studies. For the cumulative meta-analysis, studies were sorted chronologically by year of publication. The Hardy–Weinberg equilibrium (HWE) was checked using a Chi square test in each case and control group of the included studies; values of p < 0.05 were considered as showing a significant deviation from HWE. Finally, the strength of agreement between reviewers regarding study selection was evaluated by Kappa statistic.The comprehensive meta-analysis software  was used for the statistical analyses. The results are presented as odds ratios (ORs) and were used to assess the strength of the association between TaqI, C957T and Ser311Cys polymorphisms of the On-line literature search supplemented with a manual search resulted in 285 reports comprising 86 case–control studies , 26–69, DRD2 gene variants, all the genetic models: allelic , additive , recessive  and dominant  showed heterogeneity with p < 0.05. Subsequently, when we excluded the studies that favored the presence of the heterogeneity, we then observed the effect of the TaqI polymorphism in all populations using the additive genetic model  and found a protective effect in the population as a whole. However, when we analyzed the recessive model, a risk effect was encountered ; see Table Seventeen studies were included to identify the association between TaqI and SZ risk. Following the same pattern of analysis previously established for allelic , recessive  and dominant . However, in the additive model we observed a protective effect of TaqI on schizophrenia  , additive , recessive  and dominant , in which p of Q test <0.05 indicated heterogeneity. No statistical association was found between the C957T polymorphism and schizophrenia. However, when we discarded the studies favoring heterogeneity, we obtained the following outcomes of statistical association for the models: additive  and recessive   Table . In addiallelic , additive , recessive  and dominant  models. Subsequently, when heterogeneity was discarded, the outcome presented a positive association with schizophrenia in the allelic model . However, a slight possibility of an association in the additive  and recessive  models could be suggested. But since these findings were in the presence of heterogeneity and using the fixed effects model, we did not consider them for the analysis. For all the analyses in Caucasians, the p value of the Egger’s test suggested the non-existence of publication bias  and dominant models . After we excluded the studies that favored heterogeneity, the results evidenced an association between the C957T polymorphism and SZ in the four models: allelic , additive , recessive  and dominant , using the random effects method. However, we want to emphasize that the outcomes showed the same protective association between C957T and SZ in the all models when we used the fixed effects model.Finally, for the C957T polymorphism in the Asian population we followed the same pattern of analysis as in the previous sub-section. In the initial analysis the outcomes exhibited the presence of heterogeneity (p < 0.05) in the allelic , additive , recessive  and dominant , but all the results were for the fixed effects model. Nevertheless, when we used the random effects method we encountered the same pattern only in the allelic  and dominant  models. With regard to the Chinese population, we found a similar association to that of the previous sub-analysis. Interestingly, we found a risk effect in the allelic  and dominant  models, without heterogeneity using the random effects method  has been associated with some psychiatric disorders such as alcoholism and substance abuse [DRD2 polymorphism at the Taq1 A locus present lower DRD2 density than those with no A1 allele [First, we performed the analysis of the TaqI polymorphism to assess the role of this genetic variant in schizophrenia. There was a protective effect in the additive model in the population as a whole and in Caucasians. Also, we found a risk effect when using the recessive model in the combined results of the analysis for all populations. However, various studies have reported that TaqI polymorphism does not play an important role in the psychopathological symptoms of schizophrenia, whereas other researches agree with our results , 63, 66.ce abuse , 75. On 1 allele . Also, o1 allele . Due to 1 allele .For the C957T polymorphism, the comparisons performed in our study showed a significant positive association between this polymorphism and SZ in the overall population and in Caucasian and Asian sub-groups. In this sense, we recognize the existence of two previous meta-analyses , 22, in There are several limitations in this study. First, the sample size for some sub-group analyses was limited; therefore, more studies with larger samples should be included to enhance the reliability and stability of the meta-analysis. Second, a language bias may be present given that only studies published in English were included. Third, due to the limitation of the data, we did not stratify according to other potential factors which may enhance the risk for the development of SZ, such as gender, age of onset and clinical manifestations.DRD2 gene in SZ risk.The meta-analysis indicated that TaqI and C957T polymorphisms show a protective effect against SZ. In the sub-analysis of the C957T polymorphism we observed that this variant may contribute to the occurrence of schizophrenia in Chinese subjects, so the influence of ethnicity could be important in modifying the role of this polymorphism in SZ. Given the limitations of the studies included in the meta-analysis, future studies with larger samples and prospective designs are needed to fully understand the relationship between these polymorphisms and SZ. However, this meta-analysis still provides new insights into the role of the


Background and Objective: Apolipoprotein E (APOE) plays important roles in lipoprotein metabolism and cardiovascular disease. Evidence suggests the APOE gene epsilon2/epsilon3/epsilon4 (ε2/ε3/ε4) polymorphisms might be associated with the susceptibility of coronary artery disease (CAD) in patients with type 2 diabetes mellitus (T2DM). However, no clear consensus has yet been established. Therefore, the aim of this meta-analysis is to provide a precise conclusion on the potential association between APOE ε2/ε3/ε4 polymorphisms and the risk of CAD in patients with T2DM based on case-control studies.Methods: Pubmed, Embase, Chinese National Knowledge Infrastructure (CNKI), and Wanfang databases were searched for all relevant studies prior to August 2017 in English and Chinese language. The pooled odds ratios (ORs) and their corresponding 95% confidence intervals (CIs) were used to assess the strength of the relationships. The between-study heterogeneity was evaluated by Cochran's Q-test and the I2 index to adopt fixed- or random- effect models.Results: A total of 13 studies were eligible for inclusion. There was evidence for significant associations between APOE ε4 mutation and the risk of CAD in patients with T2DM . In contrast, no significant associations were found in genetic model of APOE ε2 mutation .Conclusions: The APOE gene ε4 mutation is associated with an increased risk of CAD in patients with T2DM, while the ε2 variation has null association with this disease. Type 2 diabetes mellitus (T2DM) is a long-term metabolic disease with a high incidence and prevalence in the world. T2DM is often accompanied by various complications such as hypertension, dyslipidemia and coronary artery disease (CAD) , epsilon3 (ε3), and epsilon4 (ε4). The differences between the three isoforms are the location of 112 and 158 in the amino acid chain where cysteine or arginine is present. These three APOE alleles are determined by the rs7412 and rs429358 single-nucleotide polymorphisms. The three alleles, APOE-ε2 (cys112 and cys158), APOE-ε3 (cys112 and arg158) and APOE-ε4 (arg112 and arg158), yield six different genotypes for the APOE gene: ε2/ε2, ε2/ε3, ε2/ε4, ε3/ε3, ε3/ε4, and ε4/ε4. Because the ε3 allele or ε3/ε3 genotype is the most common allele or genotype among the population, they are well accepted as the “wild-type” and used as the “reference” in the genetic models  statement  case-control design. (3) detailed data for the APOE alleles or genotype distribution in case and control groups to estimate odds ratio (OR) with 95% confidence interval (CI). Exclusion criteria: (1) duplication of previous data; (2) review, comment and editorial; (3) no sufficient genotype data. Any dispute about the eligibility of an article was resolved by discussion.The first two investigators independently accessed the eligibility of the studies by screening the title, abstract and full-text, based on the inclusion and exclusion criteria. The inclusion criteria for all studies were as follows: (1) study on the associations between The data was drawn out based on a standard protocol. The following information was carefully extracted from all eligible publications independently by two authors (JQL and HR) using a standardized form: last name of first author, year of publication, study country, sample size in cases and controls, methods of genotyping, number genotypes and alleles. If similar data sets presented in different articles by the same research group, the data would be adopted only once. The collected data were compared, and possible disagreements were discussed by the authors and resolved with consensus.APOE ε2/ε3/ε4 polymorphisms and CAD in patients with T2DM is described in the Supplementary Table The study quality was independently assessed by two reviewers. Quality assessment of genetic associations between APOE ε2/ε3/ε4 polymorphisms and risk of CAD in T2DM subjects. The pooled ORs were, respectively, performed for nine genetic models . The statistically significant level of the combined OR was determined by the Z-test with P < 0.05. Heterogeneity between studies was calculated by using the Cochran's Q-test and Higgins I2 index. In the absence of between-study heterogeneity (I2 < 50%), the fixed effect model (Mantel–Haenszel method) was chosen to calculate the pooled estimates. Otherwise, random effect model (DerSimonian and Laird method) would be adopted if the I2 > 50% . Hardy-Weinberg equilibrium was performed in control groups using the chi-square test. The combined OR and 95%CI were calculated to evaluate the strength of the association between the As depicted in Figure APOE ε2/ε3/ε4 polymorphisms and the risk of CAD in T2DM patients are presented in Table P < 0.001; for ε4/ε4 vs. ε3/ε3: OR = 2.72, 95% CI = 1.61–4.60, P < 0.001; for ε4/ε4+ε3/ε4 vs. ε3/ε3: OR = 1.83, 95% CI = 1.52–2.22, P < 0.001. In contrast, the ε2 variation had null association with this disease ; ε2/ε3 vs. ε3/ε3 ; ε2/ε4 vs. ε3/ε3 ; ε2/ε2+ε2/ε3 vs. ε3/ε3 . In addition, the genetic models of allele-based contrasts in the overall analysis also revealed a statistically significant pooled estimates for ε4 allele vs. ε3 allele  but not for ε2 allele vs. ε3 allele .The main results of this meta-analysis for the association between P = 0.026).In the subgroup analysis according to the source of patients (Chinese and non-Chinese), the pooled ORs of all genetic models except the ε2/ε4 vs. ε3/ε3 model were consistent with the results in the overall population. In the Chinese population, the ε2/ε4 genotype increased the risk of CAD in patients with T2DM  and ε2/ε2+ε2/ε3 vs. ε3/ε3  in the overall analysis. However, no significant heterogeneity was found in other genetic models . The heterogeneity analysis results indicated that the pooled results of this meta-analysis in most genetic models were statistically steady and robust. In addition, subgroup analysis indicated that there was no heterogeneity under all nine genetic models in the Chinese population.As shown in Table Pheterogeneity = 0.054, I2 = 44.70%) and ε2/ε2+ε2/ε3 vs. ε3/ε3 , so Galbraith plot analysis and sensitivity analysis were performed to detect the possible sources of heterogeneity. Under the genetic model of ε2 allele vs. ε3 allele, the Galbraith plot analysis . Thus, the study by Halim et al. may be the source of heterogeneity in the meta-analysis for the ε2 allele vs. ε3 genetic model.There was evidence of moderately large between-study heterogeneity in the genetic model of ε2 allele vs. ε3 allele . Therefore, the studies of Halim et al. and Chaudhary et al. may be the main contributors to the source of heterogeneity in the meta-analysis for the ε2/ε2+ε2/ε3 vs. ε3/ε3 genetic model.Similarly, under the genetic model of ε2/ε2+ε2/ε3 vs. ε3/ε3, the Galbraith plot analysis Figure  and sensPBegg = 0.251 and PEgger = 0.08 for ε2/ε2 vs. ε3/ε3, PBegg = 0.373 and PEgger = 0.320 for ε2/ε3 vs. ε3/ε3, PBegg = 0.283 and PEgger = 0.403 for ε2/ε4 vs. ε3/ε3, PBegg = 0.466 and PEgger = 0.988 for ε4/ε4 vs. ε3/ε3, PBegg = 0.119 and PEgger = 0.053 for ε2 allele vs. ε3 allele, PBegg = 0.300 and PEgger = 0.331 for ε2/ε2+ε2/ε3 vs. ε3/ε3).No obvious asymmetry was observed in the shape of the funnel plot for the following genetic models: ε2/ε2 vs. ε3/ε3 Figure ; ε2/ε3 vPBegg = 0.213, PBegg = 0.033, and PBegg = 0.043, respectively) or Egger's test  revealed publication bias. Nevertheless, by using the trim and fill method, the recalculated estimates  remained statistically significant, which indicated that our meta-analysis results were steady and not influenced by publication bias. Figure The results from the following three genetic models ε3/ε4 vs. ε3/ε3; ε3/ε4+ε4/ε4 vs. ε3/ε3 and ε4 allele vs. ε3 allele performed by Begg's test  and an elevated risk of CAD in patients with T2DM. In contrast, no significant association was found in genetic model of APOE ε2 variation . However, CAD in patients with T2DM is believed to be multifactorial and involved in many susceptibility genes with small individual effects. Therefore, the integration of information derived from several polymorphisms in multiple susceptibility genes may become clinically useful.T2DM is a well-established risk factor for the development of CAD. The management of CAD in patients with T2DM poses great challenges to the medical profession  differ from each other by only one or two amino acids at positions 112 and 158, but these slight differences alter the structure and function of APOE. In general, the APOE-ε4 allele is associated with higher and the APOE-ε2 allele with lower total plasma cholesterol and LDL-C concentrations compared with the APOE-ε3 allele  compared with the ε3/ε3 genotypes (Song et al., APOE-ε2 allele in the risk of CAD may be dependent on the patient ethnicity (Xu et al., APOE ε2/ε3/ε4 polymorphisms and the risk of T2DM in the general population was also well explored in previous meta-analysis (Anthopoulos et al., APOE ε2 and ε4 alleles were associated with an increased risk of T2DM in the general population. In 2015, Wu et al. performed a meta-analysis on the association between APOE ε2/ε3/ε4 polymorphisms and T2DM patients with CAD among Chinese Han population. They found that APOE-ε4 allele resulted in an increased risk of T2DM patients with CAD in China (Wu et al., APOE ε2/ε3/ε4 polymorphisms and risk of CAD in the T2DM patients.Several meta-analysis studies have been conducted to assess the association between APOE-ε3 allele was nearly 95% in Halim's study, whereas lower than 90% in other studies (Zhang et al., APOE ε2/ε3/ε4 polymorphisms among the included studies. Although Halim's study caused the substantial heterogeneity in the genetic model of ε2 allele vs. ε3 allele, the pooled effect was still insignificant after removing it.Heterogeneity across studies is common in meta-analysis of genetic association study (Munafo and Flint, APOE-environment interactions should be considered. For example, the study by Talmud et al. has found that the impact of the APOE-ε4 on the risk of CAD appeared to be restricted to smokers (Talmud et al., There are several limitations in this meta-analysis that should be noted. First, the included studies were limited to only English or Chinese languages in our research and some eligible studies may be published in other languages, which would cause bias of the results. Second, all the included studies in this meta-analysis were the type of retrospective case-control studies, which may result in some selection bias. Third, publication bias existed in the following three genetic models: ε3/ε4 vs. ε3/ε3; ε3/ε4+ε4/ε4 vs. ε3/ε3; ε4 allele vs. ε3 allele. However, by using the trim and fill method, the recalculated ORs and their 95% CIs did not change, which indicated the stability and robustness of meta-analysis results. Last but not the least, T2DM complicated with CAD is a multifactorial disease caused by both genetic and environmental factors. The APOE gene ε4 mutation and an increased risk of CAD in patients with T2DM, while the ε2 variation had null association with this disease. Taking into account the above limitations, more studies with larger sample size and incorporated with gene-environment interactions are needed to definitively determine the association between the APOE gene ε2/ε3/ε4 polymorphisms and the risk of CAD in patients with T2DM.In conclusion, we observed a significant association between the Conceived and designed the study: J-QL and HR. Performed the search: J-QL, HR, M-ZL. Analyzed the data: J-QL and HR. Contributed reagents/material/analysis tools: J-QL, HR, M-ZL, PX, P-FF, and D-XX. Wrote and review the manuscript: J-QL, HR and HB. Reference collection, data management, statistical analyses, paper writing, and study design: J-QL and HR.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The subgroup analyses stratified by geographic area(s) and source of controls revealed significant results in South China, in hospital-based studies and population-based studies. In summary, this meta-analysis suggested that PADI4 -92C/G polymorphism may be associated with the RA incidence in the Chinese population, especially for South China. Further studies conducted on other ethnic groups are required for definite conclusions.Many studies have evaluated the correlation between peptidylarginine deiminase 4 (PADI4) -92C/G polymorphism and rheumatoid arthritis (RA), but the results remain inconclusive. Therefore, we performed a meta-analysis in the Chinese population to provide comprehensive data on the association between PADI4 -92C/G polymorphism and RA. Eligible studies published before May 2016 were identified in PubMed and Chinese databases. The strengths of these associations were assessed by pooled odds ratios (OR) and 95% confidence interval (CI). Eight studies documenting a total of 1351 RA cases and 1585 controls were included in this meta-analysis. In the overall analysis, a significant association between the PADI4 -92C/G polymorphism and RA was found in the Chinese population (G  Rheumatoid arthritis (RA) is an autoimmune disease characterized by chronic inflammation of the joints, which may lead to joint destruction and disability . It is aEligible studies were identified by searching PubMed and Chinese databases for relevant reports published before May 2016 using the following search terms: PADI4, peptidylarginine deiminase 4, -92C/G, rheumatoid arthritis, Chinese, China and Taiwan. No restriction was imposed on language. Furthermore, references cited in the retrieved articles were screened to trace additional relevant studies.Inclusion criteria were: 1) case-control or cohort studies describing the association of the PADI4 -92C/G polymorphism and RA; 2) provided the genotypes in cases and controls; 3) all patients diagnosed according to the classification criteria proposed by the American College of Rheumatology for RA in 1987; 4) participants were Chinese. Exclusion criteria were: 1) repeated literature; 2) incomplete data; 3) case-only articles; 4) review articles and abstracts.http://www.ohri.ca/programs/clinical_epidemiology/oxford.asp).We conducted a systematic review and meta-analysis in accordance with the guidelines provided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. All studies were reviewed twice and the data was extracted using a standardized form. Disagreements were resolved by discussion. All the information was collected from each included article: name of the first author, publication year, source of controls, geographic area(s), sample size, and PADI4 -92C/G genotypes data. Hardy-Weinberg equilibrium (HWE) in controls was calculated from corresponding genotype distributions. In this meta-analysis, the quality of individual studies was assessed according to the nine-star Newcastle-Ottawa Scale  with their 95% confidence intervals (CI). The Z-test was used to determine the significance of the pooled OR and 95%CI. The between-study heterogeneity was assessed using the chi-square based Q-statistic . The ranA total of 52 articles that examined the association between PADI4 polymorphisms and risk of RA were identified after duplicates were removed in different databases. After the first screening of titles and abstracts, 42 articles were excluded due to the exclusion criteria. Of the 10 potentially relevant articles –18 identvs C: OR=1.32, 95%CI=1.02–1.71; for GG+CG vs CC: OR=1.75, 95%CI=1.20–2.53; vs C: OR=1.62, 95%CI=1.16–2.26; for GG vs CC: OR=1.63, 95%CI=1.25–2.14; for GG vs CC+CG: OR=1.47, 95%CI=1.16–1.86; for GG+CG vs CC: OR=2.22, 95%CI=1.21–4.10; In the total analyses, the combined results revealed that the G variant of the PADI4 -92C/G gene polymorphism was significantly associated with an increased risk of RA in the Chinese population  and the hospital-based analysis . In the subgroup analysis stratified by geographical areas, there were significantly increased risks in South China , but not in North China , suggesting that the same gene polymorphism may have different roles in RA susceptibility among different controls. By dividing the samples into subgroups according to geographic areas, a significant association was found in South China, but not in North China, showing that there is a higher susceptibility of PADI4 -92C/G variant in southerners than in northerners of China. This may suggest a possible role of geographic differences in genetic backgrounds and the environment people live in. Because of insufficient data, however, we were unable to perform subgroup analysis for ethnic and other environmental factors. Therefore, studies of RA-related genes should be based on region and nationality, and the control subjects should be comparable with the cases in all relevant aspects.Heterogeneity is a potential problem in understanding the results of meta-analyses. In this study, significant heterogeneity between different studies was observed in the overall population. To clarify the source of heterogeneity, geographic areas and control source were used to stratify the studies, finding that part of this heterogeneity can be effectively attenuated or removed when stratifying by geographic areas. This indicates that it is important for meta-analyses of genetic association studies to perform subgroup analyses by geographic areas. After subgroup analysis by source of controls, the heterogeneity was also decreased; therefore, it can be assumed that the heterogeneity partly results from differences in source of controls. That may be because potential confounding factors in many epidemiologic studies result from the difference in control types . FurtherSeveral limitations of this study should be noted. First, this ethnic-specific meta-analysis only included data from Chinese patients, and thus our results are only applicable to this ethnic group. Second, in the subgroup analyses by geographical areas and source of controls, the sample size was relatively small and the statistical power might be insufficient. Third, due to the limitations of funnel plotting, which requires a range of studies, we did not evaluate publication bias in this meta-analysis.In conclusion, this meta-analysis suggested that PADI4 -92C/G polymorphism may be associated with the RA incidence in the Chinese population, and may play a stronger role in southerners than in northerners of China. More studies are warranted to assess this association in other ethnic groups.