CACNA1C gene was defined as a risk gene for schizophrenia in a large genome-wide association study of European ancestry performed by the Psychiatric Genomics Consortium. Previous meta-analyses focused on the association between the CACNA1C gene rs1006737 and schizophrenia. The present study focused on whether there was an ancestral difference in the effect of the CACNA1C gene rs1006737 on schizophrenia. rs2007044 and rs4765905 were analyzed for their effect on the risk of schizophrenia.The Pooled, subgroup, sensitivity, and publication bias analysis were conducted.A total of 18 studies met the inclusion criteria, including fourteen rs1006737 studies , three rs2007044 studies , and two rs4765905 studies . An allele model study also related rs2007044 and rs4765905 to schizophrenia. The overall meta-analysis for rs1006737, which included the allele contrast, dominant, recessive, codominance, and complete overdominance models, showed significant differences between rs1006737 and schizophrenia. However, the ancestral-based subgroup analysis for rs1006737 found that the genotypes GG and GG + GA were only protective factors for schizophrenia in Europeans. In contrast, the rs1006737 GA genotype only reduced the risk of schizophrenia in Asians.CACNA1C gene were associated with susceptibility to schizophrenia. However, the influence model for rs1006737 on schizophrenia in Asians and Europeans demonstrated both similarities and differences between the two ancestors.Rs1006737, rs2007044, and rs4765905 of the  CACNA1C gene, which is considered a risk factor for schizophrenia based on a large genome-wide association study (GWAS) of European ancestry performed by the Psychiatric Genomics Consortium (PGC) . English studies were obtained by PubMed (2011-Present) database, and Chinese studies were obtained by CNKI (2013-Present) database. Only completed peer-review studies have the potential to be included in the present meta-analysis. The last search update was in November 2019. Rs1006737, rs2007044, rs4765905, CACNA1C may be a potential gene of schizophrenia; e. the type of studies were case-control studies. The current exclusion criteria for meta-analysis were: a. no schizophrenic patients; b. no detailed genotype frequency data; c. no controls; d. abstracts, meta-analysis or reviews; e. not case-control studies; f. including repeated sample; g. containing 2014 PGC GWAS data [The inclusion criteria for the present study were: a. including patients with schizophrenia; b. containing detailed genotypes and allele frequencies; c. including healthy control population; d. stating WAS data .Two independent authors conduct data extraction according to the inclusion and exclusion criteria. If there was inconsistency between the two authors, they will hold discussions until an agreement was reached. Table P-value greater than 0.05 indicates a publication bias.Sensitivity analysis was used to evaluate whether the combined results were stable and reliable. Funnel plots  were used to determine whether the included studies had publication bias. Egger’s test  was used2 statistics [P-value was greater than 0.1, it means a lack of heterogeneity, and the fixed effect model (Mantel-Haenszel) was selected. Conversely, a p-value was less than 0.1, indicating the existence of heterogeneity. The random effect model (M-H heterogeneity) was selected [2 is quantitative statistics, which refers to the ratio of the variation between studies to the total variation. It was divided into three groups according to heterogeneity level: low (less than 25%), moderate (25 to 75%), and high (greater than 75%). The “a” was marked as the risk allele. We use allelic: a vs. A, dominant: Aa + AA vs. AA, recessive: aa vs. AA, and codominant: aa vs. AA and Aa vs. AA, and complete overdominance: AA + aa vs. Aa models to calculate the pooled ORs. Besides, a subgroup analysis based on ancestry was conducted.We evaluated the Hardy-Weinberg equilibrium (HWE) in the control group of each study using Pearson’s chi-square test. The odds ratios (ORs) and 95% confidence intervals (CIs) were used to evaluate the correlation between polymorphisms rs1006737, rs4765905, rs2007044 and schizophrenia risk. The Cochran’s Q-test  and I2 satistics  were selselected . I2 is qP-value less than 0.05 indicates statistical difference (two tails).Meta-regression analysis was performed to assess the impact of different variables (mean age of control group and sex indexes) on the analysis. Statistical calculations were performed using Stata version 12.0  software. We investigated 67 related articles from PubMed and CNKI electronic databases. Studies that did not conform to the inclusion criteria were excluded, and 18 studies were available for meta-analysis .ORs were estimated in allelic (A vs. G), dominant (GA + AA vs. GG), recessive (AA vs. GG), codominance (AA vs. GG and GA vs. GG), and complete overdominance (GG + AA vs. GA) models . All models except for the codominance model (GA vs. GG) were performed using the fixed effects model (M-H) due to the low heterogeneity. In contrast, the codominance model (GA vs. GG) was performed using the random effects model (M-H) due to its high heterogeneity ; dominant model ; recessive model ; codominance models ; codominance model , and complete overdominance model .The overall meta-analysis Table  showed tP = 0.000, OR = 1.121, 95% CI = 1.060–1.186; GA + AA vs. GG, P = 0.001, OR = 1.127, 95% CI = 1.047–1.213; AA vs. GG + GA, P = 0.003, OR = 1.203, 95% CI = 1.067–1.357; AA vs. GG, P = 0.000, OR = 1.284, 95% CI = 1.131–1.456; GA vs. GG, P = 0.001, OR = 0.279, 95% CI = 0.132–0.587). The complete overdominance model (GG + AA vs. GA) resulted in an OR = 0.959, 95% CI = 0.891–1.033, and P = 0.272.Subsequently, subgroup analysis based on ancestor was performed for rs1006737. For the Caucasians, there were seven studies that included a total of 5546 patients with schizophrenia and 8305 controls. Rs1006737 was associated with schizophrenia using all but one genetic model ; dominant model ; codominance model ; complete overdominance model . There was no association observed using the recessive  or codominance model  models (Table P = 0.265) nor the sex indexes  had any significant impact on the results.The subgroup analysis also included six studies involving Asians with a total of 9604 patients with schizophrenia and 10,983 controls. Rs1006737 was associated with schizophrenia using the following models: allele contrast model  and rs4765905 . The main results are presented in Table Allele G of rs2007044 and allele C of rs4765905 were defined as risk alleles. Because relatively few studies related to rs2007044 and rs4765905 were included in the meta-analysis, only the allele model for these two polymorphisms was analyzed. Significant differences between the patients and controls were observed for both rs2007044  or rs4765905  allele model. For rs1006737, there were no publication biases in the dominant , recessive , codominant  and complete overdominance  models. However, there was a publication bias for the rs1006737 polymorphism with the codominance model .Delete each study item by item, and then calculate the significance of the new meta-analysis results consisting of the remaining studies . We did CACNA1C is associated with bipolar disorder [CACNA1C gene and schizophrenia has not been determined. It is also unclear whether the CACNA1C gene has the same effect on schizophrenia in both Asians and Europeans. Therefore, we conducted a comprehensive meta-analysis on the association between the CACNA1C rs1006737, rs2007044, and rs4765905 polymorphisms and schizophrenia. In the overall analysis, rs1006737 was associated with the risk of schizophrenia in all five genetic models, and rs2007044 and rs4765905 were also related to schizophrenia in the allele model, implying that the CACNA1C gene may influence the risk of schizophrenia. This view is consistent with the results of previous meta-analyses [disorder , autism disorder , major ddisorder , and othdisorder . Howeveranalyses , 40, 41.When we conducted a race-based subgroup analysis of rs1006737, we found that the effects of rs1006737 on schizophrenia in the Asians and Europeans had both similarities and differences. According to the results obtained with the allele (A vs. G) and dominant (GA + AA vs. GG) models, the effect of rs1006737 on the risk of schizophrenia in the Europeans and Asians was consistent . However, analysis by the recessive (AA vs. GG + GA) and codominant (AA vs. GG) models showed that the genotype GG + GA was only a risk factor for schizophrenia in the European sample. In contrast, according to the complete overdominance model (GG + AA vs. GA), the GA genotype of rs1006737 only reduced the risk of schizophrenia in the Asian sample. These data suggest that the effect of rs1006737 on schizophrenia is ancestrally diverse.2 = 99.0%) and publication bias (Egger’s test P = 0.002), the codominant model (GA vs. GG) was not reliable and, therefore, was not a valid gene model for evaluating the rs1006737 polymorphism. In addition, there were few studies on the association between rs2007044 or rs4765905 and schizophrenia. Thus, additional high-quality studies are needed to support our analysis.The current study has two limitations. Due to significant heterogeneity  and schizophrenia. Although the study of Xiao et al. [This meta-analysis study advanced our understanding of the relationship between ophrenia  containeophrenia , 26, 28 ophrenia  along wiophrenia , 24, 27.o et al.  also incCACNA1C rs1006737, rs2007044, and rs4765905 gene polymorphisms were associated with the susceptibility to schizophrenia. However, the influence model for rs1006737 on schizophrenia in Asians and Europeans demonstrated both similarities and differences between the two ancestors.The


Overall, this led to small discrepancies in the computation of mean effect sizes, confidence intervals and heterogeneity estimates in 13 out of 33 meta-analyses. We provide recommendations to improve transparency in the reporting of the entire meta-analytic process, including the use of preregistration, data and workflow sharing, and explicit coding practices.To determine the reproducibility of psychological meta-analyses, we investigated whether we could reproduce 500 primary study effect sizes drawn from 33 published meta-analyses based on the information given in the meta-analyses, and whether recomputations of primary study effect sizes altered the overall results of the meta-analysis. Results showed that almost half ( The ever-increasing growth of scientific publication output  has incrResearch from various fields has demonstrated issues arising from substandard reporting practices. Transformations from primary study effect sizes to the standardized mean difference (SMD) used in 27 biomedical meta-analyses were found to be commonly inaccurate , leadingprimary study effect size reproducibility. In the second part, we considered if correcting any errors in these primary study effect sizes affected main meta-analytic outcomes. Specifically, we looked at the estimate of the average effect size and its confidence interval, and heterogeneity parameter τ2. We refer to this as meta-analysis reproducibility. Although we acknowledge that more aspects in a meta-analysis could be checked for reproducibility , we focus here on the effect sizes and analyses thereof. While primary study effect size reproducibility is important for assessing the accuracy of the specific effect size calculations, meta-analytic reproducibility bears on the overall conclusions drawn from the meta-analysis. Without appropriate reporting on what was done and what results were found it is untenable to determine the validity of the meta-analytic results and conclusion , moderate  and large [≥ 0.125] and transformed them to similar classifications for the other effect sizes based on N = 64, relating to the 50th percentile of the degrees of freedom of reported test statistics in eight major psychology journals , moderate , and large [≥ .250]. For Cohen’s d, classifications were small , moderate  and large [≥ .254]. For Fisher’s r-to-z transformed correlation, classifications were the same as r.Next to checking whether certain primary study effect sizes were irreproducible due to incomplete, erroneous, or ambiguous reporting, we were also interested in quantifying whether the discrepancy between the reported and reproduced effect size estimates were small, moderate, or large. Effect sizes in various psychological fields  show small, moderate, and large effect sizes corresponding approximately to and 0.35 ,24. Basereproducible: 55%). For 54 effect sizes, the primary study paper did not contain enough information to reproduce the effect (incomplete: 11%), so the original effect size was copied. For 74 effect sizes, a different effect than originally stated in the meta-analytic article was calculated (incorrect: 15%), whereas for 96 effect sizes it was unclear what procedure was followed by the meta-analysts (ambiguous: 19%), and the effect size which was most relevant and closest to the reported effect size was chosen.Out of 500 sampled primary study effect sizes we could reproduce 276 without any issues , where all primary study effect sizes in Cohen’s d were transformed to Hedges’ g. The horizontal axis displays the original reported effect sizes and the vertical axis the reproduced effects; all data points on the diagonal line indicate no discrepancy between reported and reproduced primary study effect sizes. Likewise, r or Fisher’s r-to-z transformed correlation (k = 253), where all primary study effect sizes using a product-moment correlation r were transformed to Fisher’s z correlation. The results as illustrated in Figs https://osf.io/65b8z/.size k = 47, whereon k = 25, where ak = 162) was approximately equal to those that we calculated that were smaller than reported (k = 165), which indicates no systematic bias in either direction.In total, 114 out of 500 recalculated primary study effect sizes (23%) showed effect size discrepancies compared to the primary study effect sizes as reported in the meta-analytic articles. Of those 114 discrepancies, 62 were small (54%), 21 were moderate (18%), and 31 were large (27%). We note that it is possible for a primary study effect size to be classified as irreproducible, even if we found no discrepancies between the reported and recalculated primary study effect size estimate. This happened for instance for all primary studies that did not contain enough information to reproduce the effect size, for which we copied the reported effect size. The number of effect sizes we calculated that were larger than reported . More specifically, it was often unclear which specific effect was extracted from the primary study because multiple effects were relevant to the research question, and we did not know if and how the included effect was constructed from a combination of multiple effects. Other prevalent issues pertaining to potential data errors or lack of clarity in the meta-analytic process were inconsistencies and unclear reporting in inclusion criteria within a meta-analytic article (k = 67), uncertainty about which samples or time points were included (k = 50), reporting formulas or corrections that were incorrect or not used (k = 23), including the same sample of respondents for multiple effects without correction (k = 7), lacking information on how the primary study effect was transformed to the effect size included in the meta-analysis (k = 3), and mistaking the standard error for the standard deviation when calculating effects (k = 2).The most common reason for not being able to reproduce a primary study effect size was missing or unclear information in the meta-analysis  = 45.78, p < .0001, ΦCramer = 0.30, showing that combining multiple effect sizes into one overall estimate is moderately associated with irreproducibility of effect size estimates.Within over a quarter of primary studies . We calculated correction weights using type of effect size (outlier or non-outlier) as the auxiliary variable, and used the sample proportions of outliers and non-outliers to determine the probability of finding a potential error  for each meta-analysis and across all 33 meta-analyses in total. The computed primary study error probability for the 33 meta-analyses varied from 0 to 1. Across all meta-analyses we estimated the chance of any randomly chosen primary study effect size being irreproducible to be 37%.Because we oversampled primary study effect sizes classified as outliers, our sample of 500 primary study effect sizes is not representative for the 1,978 effect sizes we sampled from. In the sample of 1,978 effect sizes, we classified 30% as an outlier, compared to 39% in our sample of 500. This means our sample contains too many outlier primary study effect sizes, and too few non-outliers. To calculate the probability that any given effect size in a certain meta-analysis is irreproducible, we needed to correct for this overrepresentation of outliers by design (k = 11). The percentage of incorrect effect sizes (category 1) ranged from 0% to 91% across meta-analyses, ; incomplete effect sizes ranged from 0% to 67% , and ambiguous effect sizes (category 3) ranged from 0% to 91% . Note that the reporting within meta-analyses is often at least partly ambiguous .The previously reported results can be considered confirmatory because we preregistered our hypotheses and procedures. Next to confirmatory analyses we also performed one exploratory analysis, in which we compared the reproducibility of outlier and non-outlier primary study effect sizes. We found that 39% of all outlier effect sizes were irreproducible, whereas for non-outlier effect sizes it was 49% see also .In Part 1 we set out to investigate the reproducibility of 500 primary study effect sizes as reported in 33 psychological meta-analyses. Of the 500 reported primary study effect sizes, almost half (224) could not be reproduced, and 30 out of 33 meta-analyses contained effect sizes that could not be reproduced. Poor reproducibility at the primary study level might affect meta-analytic outcomes, which is worrisome because it could bias the meta-analytic evidence and lead to substantial changes in conclusions. We investigate this in Part 2 of this study.τ2. We hypothesized to find discrepancies between the reported and reproduced primary studies, and subsequently also expected several meta-analytic pooled effect size estimates to be irreproducible. Discrepancies in primary study effect sizes we found in Part 1 can be either systematic or random. Systematic errors in primary study effect sizes will bias results and thus have a larger impact on the meta-analytic mean estimate, whereas random primary study errors can be expected to increase the estimate of heterogeneity in the meta-analysis. Because we expected most primary study errors to be random instead of systematic, we hypothesized corrected primary study effect sizes would have a larger impact on the boundaries of the confidence interval  than the meta-analytic effect size estimate.In Part 2, we examined whether irreproducible primary study effect sizes affect three meta-analytic outcomes; the overall effect size estimates, its confidence interval, and the estimate of heterogeneity parameter https://osf.io/pqt9n/. For each of the 33 meta-analyses, we conducted two analyses: one with the primary study effect sizes as reported in the meta-analysis, and one with the primary study effect sizes as we recalculated them. We then documented the pooled meta-analytic effect size estimate, its confidence interval, and the estimated τ2 parameter for both the reported and reproduced meta-analysis, and compared these outcomes for discrepancies. For Part 2 we upheld the same discrepancy measures as in Part 1.To investigate meta-analysis reproducibility, we first documented for each of the 33 meta-analyses which software was used to run the meta-analysis, what type of meta-analysis was conducted , and what kind of estimator was originally used. The information we extracted to reproduce the meta-analytic effects can be found for each meta-analysis in https://osf.io/pf4x9/.The results presented next are based on the subset of primary study effect sizes that were sampled for checking, instead of complete meta-analyses including all primary studies. We decided to report on subsets of the meta-analysis containing only sampled studies, because the effect of corrected primary study effect sizes on meta-analytic outcomes can best be shown when only the corrected primary study effect sizes are included in the meta-analysis. We also conducted analyses on all 33 complete meta-analyses, for which results are reported in m = 25, 76%) used either a random-effects or both fixed effect and random-effects models.We first documented which procedures the meta-analysts used for their analyses. We found the same level of imprecise reporting here as in Part 1: most meta-analytic articles reported scarce information related to estimation methods. Many meta-analyses simply referred to well-known meta-analysis books without mentioning specifically which method was used. Out of 33 meta-analyses, only two explicitly reported which models, software and estimator they used for their estimation. For the other meta-analyses, we were forced to guess the used estimation method. Most meta-analytic authors  primary study effect sizes showed large discrepancies between the reported and recalculated results. Note that even though the number of primary study effect sizes that were irreproducible was large, the number of discrepancies between the reported and reproduced primary study effect sizes was relatively small, mostly due to our decisions to copy the effect size if not enough information was available, or choosing the estimate that most closely resembled the reported one when the effect was ambiguous. We found small discrepancies in the pooled effect size estimates for nine out of 33 meta-analyses (27%), displayed in panel a of b). We found 13 meta-analyses with discrepancies in the confidence intervals (39%), of which nine were small  and three were moderate . In line with our hypothesis, this result shows that corrected primary study effect sizes have a larger impact on the boundaries of the confidence interval than the pooled effect size estimate. In none of the meta-analyses was the statistical significance of the average effect size affected by using the recalculated primary study effect sizes.For meta-analytic outcomes, 13 out of 33 meta-analyses (39%) showed discrepancies in either the pooled effect size estimate, its confidence interval, or We did not find any evidence for systematic bias in meta-analytic results; we estimated 19 pooled effect sizes to be larger than originally reported and 14 to be smaller than originally reported. We estimated wider pooled effect size CIs in 18 cases and smaller CIs in 15. This latter result is contrary our expectations that CI estimates would be smaller after adjustment; we think this is due to the large number of primary study effect sizes that we found were ambiguous.τ2. The discrepancies in τ2 estimates are displayed in panel c of τ2 parameter estimates were larger after recalculating the primary study effect sizes, 12 were smaller, and four showed no difference.We extended our preregistration by checking whether irreproducible primary study effect sizes affected the meta-analytic estimate of heterogeneity incomplete effect sizes in tests of meta-analytic reproducibility, since these effects could not be reproduced due to a lack of information. Including these incomplete effect sizes might deflate the differences between the reported and reproduced meta-analyses. As such, we decided to compare the reported and reproduced meta-analyses again, only including effect sizes we could calculate: correct, incorrect, and ambiguous effect sizes. In total, 14 out of 33 meta-analyses (42%) showed discrepancies in either the pooled effect size estimate, its confidence interval, or τ2 parameter, which is one meta-analysis more than in the previous analyses when all four effects were included. The discrepancies between the original and reproduced meta-analyses became larger in this analysis, but the statistical significance of the overall pooled effect sizes did not change; detailed results are displayed in A reviewer to a previous version of this paper rightfully claimed that it is problematic to include the χ2 test showed that it is unrealistic to assume that the probability of a reproducible individual effect size is equal across all meta-analyses (χ (32) = 156.12, p < .001, χ2 test of independence). We performed a multilevel logistic regression analysis with (in)correctly reproduced individual effect sizes as the dependent variable, and the 33 meta-analyses as the grouping variable. We used the estimates from this model  to approximate the distribution of the probability of a reproducible effect size with 1,000 points, and used this approximated distribution to calculate the probability of reproducing a meta-analysis with a given number of effect sizes. The analysis script is located at https://osf.io/5k4as/. Using this model, we predict meta-analyses of size 1, 5, 10, 16 or larger to be fully reproducible with respective probabilities .538, .170 .082, and < .050.We acknowledge that it is impossible to formulate definite conclusions regarding the reproducibility of full meta-analyses because we took samples of individual effect sizes from each. However, under some strong assumptions we can predict the probability that a full meta-analysis is reproducible. A In Part 1 we investigated the reproducibility of 500 primary study effect sizes from 33 meta-analyses. In Part 2, we then looked at the effect of irreproducible effect sizes on meta-analytic pooled effect size estimates, confidence intervals and heterogeneity estimates. Almost half of the primary study effect sizes could not be reproduced based on the reported information in the meta-analysis, due to incomplete, incorrect, or ambiguous reporting. However, overall, the consequences were limited for the main meta-analytic outcomes. We found small and a few moderate discrepancies in meta-analytic outcomes in 39% of meta-analyses, but most discrepancies were negligible. In none of the meta-analyses did the use of recalculated primary study effect sizes change the statistical significance of the pooled effect size, whereas the statistical significance of the heterogeneity estimate changed in two meta-analyses. These two meta-analyses were characterized by many ambiguous effect size computations  effect on meta-analytic mean and heterogeneity estimates, we expect the errors to also have a  effect on methods that correct for publication bias. However, as primary study errors seemed mostly random rather than systematic, we expect an increase in variance of estimates when correcting for publication bias. An increase in variation might affect the results by obscuring true patterns of bias, because of diminished power in some analyses of bias. For instance, in Egger’s test, the added random variation might lower the power to detect asymmetry in the funnel plot that is indicative of publication bias.We should note that we were conservative in our estimations for primary study effect sizes for which we did not have enough information from the original papers to recompute them ourselves. We decided to copy those effect sizes as they were reported, meaning they did not count towards any discrepancies when rerunning the meta-analyses in Part 2. Similarly, for ambiguous effects, we chose the estimate that was closest to the reported one, leading to conservative estimates of the discrepancies. We acknowledge that results of our study could have been (very) different if we had decided to include either the minimum, maximum, or a random effect size with these ambiguous effect sizes. Moreover, we took samples from each meta-analysis to keep our coding time manageable, and so we leave it to the interested (and industrious) reader to recompute specific meta-analytic outcomes after checking all effect size computations featured in it. Based on previous research on the reproducibility of meta-analyses in medicine , we expeSurprisingly, we found unpublished and non-outlier primary studies to be more reproducible than published or outlier primary studies. It could be that meta-analytic authors are more cautious when calculating effect sizes from unpublished articles because the article is perhaps not peer-reviewed. Similarly, perhaps many meta-analysts do pay more attention to effect sizes that are relatively large.We recognize some limitations in our study. Our primary study effect size sample is not completely random because we first identified which primary study effect sizes were outliers and oversampled these, before we took a random sample from both outlier and non-outlier primary studies. However, we believe our estimate of the average probability of being able to reproduce a random primary study effect size from a meta-analysis (37%) is accurate, as we corrected for our planned oversampling of outlier primary effect sizes and included a large and systematically drawn sample. Additionally, although our selection of meta-analyses was random and based on a large and fairly comprehensive database, we only included meta-analyses that contained a data table with a minimum amount of information. These meta-analyses can be considered to be relatively well reported compared to meta-analyses lacking any data table. Based on the finding that reluctance to share data is associated with more reporting errors in primary studies  we attempted to reproduce at least half of all included primary study effect sizes. Moreover, the coding process for the primary study effect sizes and meta-analyses proved to be very labor intensive, due to the substandard reporting practices that hindered the reproducibility and verifiability of many results. Our study revealed that most meta-analysts do not adhere to the Meta-Analysis Reporting Standards  and PreA final limitation is that our sampled meta-analyses were from 2011 and 2012. It could be that the increasing emphasis on reproducibility in psychology of the last years has incentivized meta-analysts to make reporting more extensive and result checking more diligent. However, results from , who chehttps://osf.io/pqt9n), where we documented the relevant text, references, and formulas from all 33 meta-analyses to indicate which specific transformations we made to the effect sizes within meta-analyses. Moreover, in our codebook (https://osf.io/7abwu) we specified the names of the groups and variables that were compared for each primary study effect size, exactly as they were reported in the primary study. If certain groups or effects were combined, it was also necessary to add a comment on how and in which order these were combined. We acknowledge it is hard to document all relevant information related to effect size computation in meta-analyses, and emphasize that sharing all data, code, and documentation used in the process would benefit reproducibility of meta-analyses tremendously. For more information on best practices in systematic reviewing, we refer to Reviewers' comments:Reviewer's Responses to QuestionsComments to the Author1. Is the manuscript technically sound, and do the data support the conclusions?The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: YesReviewer #2: Yes**********2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: YesReviewer #2: Yes**********3. Have the authors made all data underlying the findings in their manuscript fully available?PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.The Reviewer #1: YesReviewer #2: Yes**********4. Is the manuscript presented in an intelligible fashion and written in standard English?PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #1: YesReviewer #2: Yes**********5. Review Comments to the AuthorPlease use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #1: Thank you for the opportunity to review this manuscript. This manuscript is an important contribution to the methodological literature on the conduct and reporting of systematic reviews that include meta-analyses. The manuscript focuses on one of many ways that meta-analyses can fail to be reproducible – the computation of effect sizes. The manuscript makes a clear and compelling case for transparency and the use of open science practices in the conduct and reporting of meta-analysis particularly with regard to the computation of effect sizes. The associated registry of their own conduct of this research project provides an example of how to implement transparency in research.The manuscript also points out issues that often encountered in reviewing meta-analyses and that reflect issues with current meta-analysis practice. Few reviews adhere to the published standards in MARS or PRISMA. Few published meta-analyses provide complete data tables, much less information on how effect sizes were computed from primary studies. This manuscript should provide more evidence that meta-analyses need to follow current reporting standards and use open science practices.One hypothesis about why some effect sizes might be irreproducible or ambiguous relates to how the meta-analysis handles multiple effect sizes from primary studies. Since the publication date of the meta-analyses used in this manuscript, methods for handling dependent effect sizes are being used more widely. Prior to the publication of Hedges, Tipton & Johnson (2010), researchers either chose a single effect size from a study or combined effect sizes measuring a similar construct in a non-transparent manner. Though this may be one contributing factor to the irreproducibility of effect sizes in this manuscript, the general message still holds today that more transparency is needed when reporting effect size computations in a meta-analysis. As a minor point, I wondered how many of the irreproducible and ambiguous effect sizes were related to the issue of dependent effect size.In sum, the manuscript will add to the growing evidence that meta-analyses need to embrace fully open science practices given the dependence of many policy decisions on meta-analytic results.Reviewer #2: I think that this work is of great interest given the important and lofty role meta-analysis plays in scientists' and practitioners' evaluation of research results. It is useful to finally have some data on the reproducibility of these research results. If I understand the results correctly, the reproducibility of individual effect sizes is poor, but this does not seem to have a substantial or predictable effect on the reproducibility of meta-analytic means and heterogeneity. This is useful to know as we continue to practice meta-analysis, calibrate the degree of trust we put in meta-analysis, and consider which transparency guidelines are most important in meta-analysis.I can't go through all the data and code, although I appreciate its presence in the Github repository. I loaded up codebook-primary-studies-complete.xlsx just to check on those d > 3 estimates. I guess the SIRS is a very powerful instrument.This looks like a complete piece of work that has already been through several rounds of peer review. I only have some minor suggestions:It's not clear to me that "ambiguous" is necessarily worse than "incorrect". As introduced in line 190 I thought this was going to be the basis for some sort of ordinal regression but was relieved to see it is merely descriptive and the badness of these categories merely a statement of the authors' beliefs.DiscussionWhen you say "For a meta-analysis to be completely reproducible, we would add to these guidelines the requirement that effect size computations should be specified per effect size in supplementary materials. It should be clear which decisions were made and when, and each primary study effect size should be able to be uniquely identified." Can you point to examples in your own codebook to provide a concrete demonstration of how this should look?Future directions:Authors will sometimes confuse SE and SD in their own writing, leading to meta-analysts using the SE instead of the SD when calculating effect sizes, yielding effects that are too big. Did you observe this with any frequency in this data set? This could yield an effect size that is reproducible given the primary study and yet incorrect.These errors seem to have little influence over the mean and heterogeneity. How might these errors influence less robust statistics like PET/PEESE, p-curve, etc?I always sign my reviews,Joe Hilgard**********what does this mean?). If published, this will include your full peer review and any attached files.6. PLOS authors have the option to publish the peer review history of their article  digital diagnostic tool,  26 Apr 2020April 26, 2020Dear Dr. Gnambs,We hereby resubmit our manuscript ‘Reproducibility of Individual Effect Sizes in Meta-Analyses in Psychology’. We have responded to all points raised by you and the reviewers. Below, we respond to all the points in italics, and we have added the specific edits we made to the manuscript after in red.We hope our revision has addressed the comments of both you and the reviewers sufficiently. We look forward to your reply.Kind regards, Esther Maassen also on behalf of, Marcel A.L.M. van Assen, Michèle B. Nuijten, Anton Olsson-Collentine, and Jelte M. Wicherts. emaassen@protonmail.com------ 1. If I understood it correctly, “reproducible” effect sizes were classified as such if the difference between the original and recalculated effect size was smaller than r = .025 (page 10); so, you were trying to recalculate the effect size with some margin of error. I would recommend clarifying this point when introducing your classification scheme at the end of page 9.As per your recommendation, we have clarified there is a margin of error when calculating {ir}reproducible effect sizes. We have now explicitly stated this margin for the two most common effect sizes (correlation r and standardized mean difference Hedges' g) on page 9 and 10: We categorized reproduced primary study effects into one of four categories we considered, ranging from best to worst outcome: (0) reproducible: we could reproduce the effect size as reported in the meta-analytic article or with a margin of error of correlation r < .025 or Hedges’ g < .049; (1) incomplete: not enough information was available to reproduce the effect size . In this case we copied the original effect size as reported in the meta-analysis because we were not sure what computations the meta-analysts performed, or whether they had contacted the authors for necessary statistics; (2) incorrect: our recalculation resulted in a different effect size of at least r ≥ .025 or Hedges’ g ≥ .049 ;2. It would be informative to describe Figure 4 in more detail. In particular, what was the distribution  of the proportion of reproducible effect sizes in the meta-analyses? Is the reported proportion of 37% (page 14) an adequate estimate for all meta-analyses or is the distribution rather skewed; for example, it might be the case that in many meta-analyses most effect sizes are irreproducible, whereas few meta-analyses primarily reported irreproducible effect sizes.We have described the distribution of effect size errors in more detail on page 15. We added percentages per category per meta-analysis to Figure 4. We added the following:Fig 4 displays a bar plot with the frequency of irreproducible effect sizes per meta-analysis. The distribution of reproducible effect sizes (category 0) ranged from 0% to 100% with a mean of 53% and median of 56%. Only three of the 33 samples of primary studies were completely reproducible, and one was completely irreproducible (k = 11). The percentage of incorrect effect sizes (category 1) ranged from 0% to 91% across meta-analyses, ; incomplete effect sizes ranged from 0% to 67% , and ambiguous effect sizes (category 3) ranged from 0% to 91% . Note that the reporting within meta-analyses is often at least partly ambiguous .3. As a minor point, I wondered how many of the irreproducible and ambiguous effect sizes were related to the issue of dependent effect size.Reviewer 1 recommended to add more details on the reproducibility rates when using dependent effect sizes . We did so on page 13, and added a new table (Table 1) containing percentages of effect size reproducibility for single and multiple effect sizes. More specifically, we added the following paragraph:Within over a quarter of primary studies  we combined multiple effects into one overall effect size estimate for that primary study. The percentage of irreproducible effect sizes is relatively large within this group. Within this subset, 18% was classified as incorrect (single effect sizes: 13%), 34% as ambiguous (single effect sizes: 13%), 1% as incomplete (single effect sizes: 15%), and 46% as reproducible (single effect sizes: 59%); Χ^2 = 45.78, p < .0001, ΦCramer = 0.30, showing that combining multiple effect sizes into one overall estimate is moderately associated with irreproducibility of effect size estimates. Table 1. Reproducibility frequencies separated by primary study effect sizes consisting of one or multiple combined effect sizesSingle effect size Combined effect sizesReproducible 208 (59%) 68 (46%)Incorrect 47 (13%) 27 (18%)Incomplete 52 (15%) 2 (1%)Ambiguous 46 (13%) 50 (34%)Total 353 (100%) 147 (100%)4. It's not clear to me that "ambiguous" is necessarily worse than "incorrect". As introduced in line 190 I thought this was going to be the basis for some sort of ordinal regression but was relieved to see it is merely descriptive and the badness of these categories merely a statement of the authors' beliefs.On page 10 we clarified that the categorization of effect sizes is our own, and is only used descriptively. More specifically:We consider ambiguous effects (category 3) to be more problematic than incorrect effects (category 2), as for the incorrect effects it was clear which effect size the meta-analysts had chosen, whereas for the ambiguous effects it was impossible to ascertain if we had selected the correct effect size, and what steps were taken to get to the effect size as reported. We acknowledge this categorization is open to discussion, and only used it descriptively throughout this study.5. When you say "For a meta-analysis to be completely reproducible, we would add to these guidelines the requirement that effect size computations should be specified per effect size in supplementary materials. It should be clear which decisions were made and when, and each primary study effect size should be able to be uniquely identified." Can you point to examples in your own codebook to provide a concrete demonstration of how this should look?Reviewer 2 asked us to point to an example in our own codebook to provide a concrete demonstration on how specific effect size computation should look like. We added an extra reference to our supplemental materials in text, which indicate all inclusion criteria and formulas used per meta-analysis, as well as our codebook that documents the relevant information per primary study. More specifically: https://osf.io/pqt9n/), where we documented the relevant text, references, and formulas from all 33 meta-analyses to indicate which specific transformations we made to the effect sizes within meta-analyses. Moreover, in our codebook (https://osf.io/7abwu/) we specified the names of the groups and variables that were compared for each primary study effect size, exactly as they were reported in the primary study. If certain groups or effects were combined, it was also necessary to add a comment on how and in which order these were combined. We acknowledge it is hard to document all relevant information related to effect size computation in meta-analyses, and emphasize that sharing all data, code, and documentation used in the process would benefit reproducibility of meta-analyses tremendously. For more information on best practices in systematic reviewing, we refer to [28].For a meta-analysis to be completely reproducible, we would add to the existing guidelines the requirement that effect size computations should be specified per effect size in supplementary materials. It should be clear which decisions were made and when, and each primary study effect size should be able to be uniquely identified. For an example, we refer to S3 . More specifically:The most common reason for not being able to reproduce a primary study effect size was missing or unclear information in the meta-analysis . More specifically, it was often unclear which specific effect was extracted from the primary study because multiple effects were relevant to the research question, and we did not know if and how the included effect was constructed from a combination of multiple effects. Other prevalent issues pertaining to potential data errors or lack of clarity in the meta-analytic process were inconsistencies and unclear reporting in inclusion criteria within a meta-analytic article (k = 67), uncertainty about which samples or time points were included (k = 50), reporting formulas or corrections that were incorrect or not used (k = 23), including the same sample of respondents for multiple effects without correction (k = 7), lacking information on how the primary study effect was transformed to the effect size included in the meta-analysis (k =3), and mistaking the standard error for the standard deviation when calculating effects (k = 2). 7. These errors seem to have little influence over the mean and heterogeneity. How might these errors influence less robust statistics like PET/PEESE, p-curve, etc?Reviewer 2 asked how the reproducibility errors might influence less robust statistics like PET/PEESE and p-curve. Since our focus in this study was based on meta-analytic outcomes, we decided not to check for each of the 33 meta-analyses separately what the effects of these errors on publication bias methods were. We added a paragraph to our discussion section on page 22 on what we expect the effect on publication bias methods to be. More specifically:In this study, we focused only on the effect of primary study errors on meta-analytic estimates. Since we found errors in primary studies to have a  effect on meta-analytic mean and heterogeneity estimates, we expect the errors to also have a  effect on methods that correct for publication bias. However, as primary study errors seemed mostly random rather than systematic, we expect an increase in variance of estimates when correcting for publication bias. An increase in variation might affect the results by obscuring true patterns of bias, because of diminished power in some analyses of bias. For instance, in Egger’s test, the added random variation might lower the power to detect asymmetry in the funnel plot that is indicative of publication bias.Attachmentresponse-to-reviewers.pdfSubmitted filename: Click here for additional data file. 29 Apr 2020Reproducibility of individual effect sizes in meta-analyses in psychologyPONE-D-20-01919R1Dear Dr. Maassen,We are pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it complies with all outstanding technical requirements.Within one week, you will receive an e-mail containing information on the amendments required prior to publication. When all required modifications have been addressed, you will receive a formal acceptance letter and your manuscript will proceed to our production department and be scheduled for publication.https://www.editorialmanager.com/pone/, click the "Update My Information" link at the top of the page, and update your user information. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.Shortly after the formal acceptance letter is sent, an invoice for payment will follow. To ensure an efficient production and billing process, please log into Editorial Manager at onepress@plos.org.If your institution or institutions have a press office, please notify them about your upcoming paper to enable them to help maximize its impact. If they will be preparing press materials for this manuscript, you must inform our press team as soon as possible and no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact With kind regards,Timo GnambsAcademic EditorPLOS ONEAdditional Editor Comments :Reviewers' comments: 4 May 2020PONE-D-20-01919R1 Reproducibility of individual effect sizes in meta-analyses in psychology Dear Dr. Maassen:I am pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. onepress@plos.org.If your institution or institutions have a press office, please notify them about your upcoming paper at this point, to enable them to help maximize its impact. If they will be preparing press materials for this manuscript, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact plosone@plos.org. 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Multiple sclerosis (MS) is a chronic progressive neurological disorder. Several environmental factors have been discussed as possible causing agents, e.g. organic solvents, whose impact on the disease is analysed in this review.Systematic search strategies were used to identify high-quality studies of workers exposed to organic solvents, published up to September 30, 2019, in databases, such as PubMed, Cochrane library and Scopus. The exposure was in most studies obtained by questionnaires, supplemented with telephone interviews. The diagnosis MS was mainly detemined following a thorough neurological examination. Finally, fourteen case–control studies and two cohort studies met the inclusion criteria and were included in the meta-analysis. Random effects models were used to pool the results of the studies.The odds ratios from the 14 case–control studies included in the meta-analysis ranged from 0.12–4.0. Five case–control studies and one cohort study showed a significant association between the development of multiple sclerosis and exposure to organic solvents. The results from the other nine case–control studies and from one of the two cohort studies did not reach statistical significance. The pooled data from the 14 case–control studies gave an OR of 1.44 (95% CI 1.03–1.99), which shows a moderately increased risk of developing MS after exposure to organic solvents.The final interpretation of the result is that organic solvents may be slightly associated with an increased risk to develop MS. In addition, other factors, e.g. genetic markers and smoking, may contribute to the development of the disease. Multiple sclerosis (MS) is a demyelinating disease, which is found in all parts of the world. The prevalence varies greatly, from high levels in North America and Europe  to low rates in Eastern Asia and the sub-Saharan Africa   and tetrachloroethylene (dry cleaning and degreasing). Time-weighted average levels of tetrachloroethylene in dry cleaning during the 1970s could range between 20 and 150 mg/mAmong aromatic compounds, benzene (solvent in glues), styrene (lamination of reinforced plastics) and toluene  were common. Toluene, xylene, white spirits and other solvents in mixtures could be used in furniture and cabinet making, paint manufacture and painting, shoe manufacture, graphics industry and chemical industries.3 during 1950–1980. A European study from reinforced plastic industries showed styrene exposures around 850 mg/m3 in the 1950s and 85 mg/m3 in the 1980s, that is a tenfold reduction .Human subjects with diagnosis of MS. Studies were excluded from the analysis if they did not list multiple sclerosis as an outcome.Studies listing organic solvents as the dominating exposure source.For inclusion in the meta-analysis, the studies had to supply sufficient data to calculate an odds ratio and an associated confidence interval.All papers were reviewed independently by two physicians (LH and LG). If the classification differed, a consensus discussion followed to get a mutual agreement.After the removal of duplicates 406 articles remained. All titles and abstracts were scrutinized. The eligible criteria for being included in the meta-analysis were as follows:The online search identified 406 articles Fig. . None ofThe meta-analysis calculates a weighed risk estimate for studies included. The weighed estimate takes into account the precision of the risk estimation for the individual studies.I2-statistics with recommended cut-offs of 25, 50 and 75% showed that the heterogeneity among the studies was greater than if it had been due to chance. Accordingly, a random effects model was used.A small study with a low precision  gets a lower weight, while a large study with higher precision gets a higher weight. The summary risk estimate is presented as an OR with a 95% confidence interval. The case–control studies included in this meta-analysis with total number of cases and referents as well as number of solvent-exposed cases and referents are presented in Table Funnel plots were used to investigate a possible publication bias. If there is no publication bias, the OR estimates should be distributed symmetrically around the weighted OR. The Egger test and the Begg–Mazumdar test were used to further evaluate the funnel plot.https://www.R-pmroject.org/ : R: A language and environment for statistical computing; R Foundation for Statistical Computing, Vienna, Austria . The test for heterogeneity showed a Q-value (df = 14) of 34.64 with a p value of 0.001.The total random effects model results gave an Odds Ratio of 1.44 (95% CI 1.03–1.99) for the meta-analysis of the 14 case–control studies, which is a significant outcome . A similar result was obtained with the Egger’s test for funnel plot asymmetry showing a p value of 0.80. Accordingly, the outcome does not support the assumption of publication bias. Of the total model variation, 72.3% was attributable to the variation between the case–control studies and 27.7% was due to the variation within each study.When calculating Kendall’s tau as a measure of rank correlation for funnel plot asymmetry for the 14 case-controls studies, the tau-value from the Begg–Mazumdar’s test was 0.08 . The test of heterogeneity shows a Q-value (df = 12) of 29.95 and a p value of 0.003. The test of funnel plot asymmetry with the Begg–Mazumdar’s test gave a tau-value of 0.23 . A similar outcome was shown by the Egger’s test giving a p value of 0.88. Thus, the outcome OR figures of the meta-analysis and the test results of the funnel plot asymmetry are more or less the same if the study by Juntunen et al. , which was marginally significant.In the last decades there have been several systematic literature reviews trying to analyse the association between exposure to organic solvents and the development of MS. Landtblom et al. . This meta-analysis is focused on work-related exposure to organic solvents. Exposure to organic solvents in leisure time is considered negligible in relation to the work-related exposure.p value 0.001) showed that the variability among these studies is greater than would be expected by chance. Four out of five larger studies published after 2003 did not reach statistical significance. Moreover, one of two cohort studies evaluated was negative, while the other showed a tendency to an increased risk of developing MS. The final interpretation of the result is that organic solvents may be slightly associated with an increased risk to develop MS. However, the relationship between MS and organic solvents is still not fully clarified. Also, other factors, such as smoking and genetic markers, may contribute and further increase the risk to develop the disease.The meta-analysis of the 14 case–control studies selected gave a summary OR of 1.44 (95% CI 1.03–1.99). The test of heterogeneity (


Aurora kinase A (AURKA) is a cell cycle regulatory serine/threonine kinase that promotes cell cycle progression. It plays an important role in regulating the transition from G2 to M phase during mitosis. The association between the AURKA rs2273535 T>A polymorphism and cancer risk has been investigated, but the results remain inconsistent. To get a more accurate conclusion, we conducted a comprehensive meta-analysis of 36 case-control studies, involving 22,884 cancer cases and 30,497 healthy controls. Crude odds ratios (ORs) and 95% confidence intervals (CIs) were calculated to determine the association of interest. Pooled analysis indicated that the AURKA rs2273535 T>A polymorphism increased the overall risk of cancer . Stratification analysis by cancer type further showed that this polymorphism was associated with an increased breast cancer risk. This meta-analysis indicated that the AURKA rs2273535 T>A polymorphism was associated with an overall increased cancer risk, especially breast cancer. Further validation experiments are needed to strengthen our conclusion. Aurora kinase A (AURKA) is a cell-cycle regulatory serine/threonine kinase that promotes cell cycle progression . AURKA iAURKA rs2273535 T>A, also known as F31I or Phe31Ile, is caused by a T-to-A transversion at position 91 in the AURKA coding sequence. This single nucleotide polymorphism is located in the Aurora Box1 (aa 5-40) motif, which belongs to the NH2-terminal region of aurora-A. This motif is thought to act on ubiquitin-based proteolysis .Up until now, there have been many studies that have investigated the association between the AURKA F31I polymorphism and the risk of different types of cancer in different populations. However, the results are inconsistent, most likely because the sample size is relatively small in each published study, and the impact of the polymorphism on cancer risk might be small. Therefore, we conducted a comprehensive meta-analysis by identifying as many relevant articles as possible to identify evidence for the link between the AURKA F31I polymorphism and cancer risk.Two authors (JQ and SW) conducted the search for relevant articles before December 2018, using the following terms, “AURKA or Aurora Kinase A,” “tumor or cancer or carcinoma or neoplasm,” and “polymorphism or single-nucleotide polymorphism (SNP) or variant,” in the PubMed, EMBASE, CNKI, WANFANG, and Vip databases. We also examined the references of the retrieved publications for additional eligible studies.Publications retrieved from the various databases were assessed for eligibility according to the following criteria: (1) the publication was published in English or Chinese; (2) the publication evaluated the association between the AURKA gene rs2273535 polymorphism and cancer risk; (3) the publication was a case-control study; the publication with the largest number of individuals was selected if studies had duplicate subjects. In addition, publications were excluded according to the following criteria: (1) genotype data included were not sufficient to calculate an odds ratio (OR) and 95% confidence interval (CI); (2) only survival data was included; (3) the genotype frequency distribution departed from Hardy-Weinberg equilibrium (HWE) in the controls.The following information was independently extracted by two authors (SW and JQ): first author name, year of publication, cancer type, region, ethnicity, genotyping method, source of controls , the genotype counts of cases, and controls for the investigated polymorphism.Two authors (SW and JQ) independently assessed the quality of the included studies according to the Strengthening the Reporting of Observational studies in Epidemiology (STROBE) quality scoring system . Forty eP < 0.1 (heterogeneity). Otherwise, a fixed-effect model was adopted , homozygous (AA vs. TT), dominant (AT+AA vs. TT), recessive (AA vs. AT+TT), and allele contrast (A vs. T) models. We conducted stratification analyses by ethnicity, cancer type (“others”: one cancer type was investigated in less than 3 studies), study design  and risk of bias. The Chi square-based Q-test was used to calculate the heterogeneity among studies. A random-effect model was adopted when  adopted . Funnel  adopted . All datAs shown in The 34 publications actually consisted of 36 case-control studies, because 2 publications included 2 individual studies. The characteristics of these studies were shown in As evident in Symmetry in the funnel plots , 8 suggeAURKA is a key factor in regulating thetransition from G2 to M phase during mitosis. The AURKA protein includes a 129-amino acid N-terminal domain that facilitates AURKA nuclear-translocation during mitosis and a 274-amino acid C-terminal kinase catalytic domain . AURKA hHowever, there were some limitations in this meta-analysis. First, only publications written in Chinese or English were selected. Second, the number of studies for certain cancer types was inadequate, such as colorectal cancer (<5 studies). There were 3 included studies having high risk of bias (0 ≤ STROBE score ≤ 19); further studies with low risk of bias are needed to validate the true association. In addition, other factors may also influence cancer risk, such as age and living habits. Our findings might suffer from potential confounding bias due to the lack of original data. Taken together, the results should be interpreted with caution.To conclude, this meta-analysis suggests that the AURKA gene rs2273535 T>A polymorphism is significantly associated with an overall increased cancer risk, especially breast cancer. In the research, most of the included studies had low to moderate risk of bias. Future well-designed, large-scale studies that report upon the association of the rs2273535 polymorphism and cancer, in multiple cancer types, are required to validate the findings of this study.The datasets generated for this study can be found in the article/SW, MW, MZ and JN conceived and designed the study. SW, JQ, and MW conducted the literature searches, extracted the data, analyzed the data and prepared the figures and tables. SW, MW, and JQ wrote the draft of manuscript. SW, MW, and JN revised the manuscript. All authors contributed to the article and approved the submitted version.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 pooled meta‐analyses results showed that eNOS rs2070744 polymorphism, but not rs1799983 and rs869109213 polymorphisms, might influence predisposition to hemorrhagic cerebral vascular diseases in East Asians.This meta‐analysis suggested that  eNOS polymorphisms and hemorrhagic cerebral vascular diseases. We found that eNOS rs2070744 polymorphism might influence predisposition to hemorrhagic cerebral vascular diseases in East Asians. Our findings indicated that eNOS may involve in the development of hemorrhagic cerebral vascular diseases.This is a comprehensive meta‐analysis about  Thus, we performed this meta‐analysis to get a more statistically reliable conclusion regarding relationship between eNOS polymorphisms and hemorrhagic cerebral vascular diseases by pooling the results of already published relevant studies.In the last two decades, investigators across the world extensively explored relationship between 2The PRISMA guideline was followed by the authors when conducting this meta‐analysis  and  and . When searching CNKI, the authors translated searching terms into Chinese. The authors also checked the references of obtained articles manually for additional related studies.eNOS polymorphisms and hemorrhagic cerebral vascular diseases; II. provide sufficient genotypic data of eNOS polymorphisms in patients with hemorrhagic cerebral vascular diseases and controls; III. the whole manuscript is available in English or Chinese. Articles were excluded when at least one of the following three conditions was fulfilled: I. studies not concerning eNOS polymorphisms and hemorrhagic cerebral vascular diseases; II. reviews or expert comments; III. case series only involved participants with hemorrhagic cerebral vascular diseases. When duplicate reports were observed during literature searching, only the most recent one was included for pooled meta‐analyses. The literature searching was performed by the first author and the second author, and the latest literature searching update was performed on 10 November 2019.Eligible studies must meet all of three inclusion criteria: I. formally published case–control studies evaluating relationship between 2.2eNOS polymorphisms in patients and control subjects. We also calculated p values of Hardy–Weinberg equilibrium (HWE) based on genotypic distributions of eNOS polymorphisms using chi‐square test.We extracted following items from included studies: I. surname of the first author; II. year of online publication; III. country and ethnicity of involved participants; IV. number of patients and controls; V. genotypic distributions of The authors used Newcastle–Ottawa scale (NOS) to assess the quality of included studies Stang, . The NOSData extraction and quality assessment of included studies were performed by the first author and the second author separately. The authors wrote to the corresponding authors for additional data when they failed to extract necessary information from included studies. In case of disagreement between the first author and the second author, the two authors would consult the corresponding author for suggestions, and a consensus must be reached among three authors.2.3Z test to evaluate relationship between eNOS polymorphisms and predisposition to hemorrhagic cerebral vascular diseases in dominant, recessive, overdominant, and allele models  to pool the results if I2 is larger than 50%. Otherwise, the authors used Mantel–Haenszel method (fixed‐effect model) to pool the results. The authors also conducted subgroup analyses by ethnicity. The authors examined stabilities of pooled results through omitting one study each time and pooling the results of the other studies. The authors examined publication biases through funnel plots.The authors used Review Manager to pool meta‐analyses results . The authors calculated ORs and 95% CIs of 33.1One hundred and sixty articles were retrieved by the authors through our literature searching strategy. The authors assessed thirty‐six articles for eligibility after omitting unrelated or repeated reports. Twelve reviews and six case series were further excluded by the authors. Totally eighteen studies were finally pooled in our meta‐analyses Figure . Extract3.2eNOS rs1799983 polymorphism and hemorrhagic cerebral vascular diseases. The pooled meta‐analyses did not reveal any significant associations for eNOS rs1799983 polymorphism and hemorrhagic cerebral vascular diseases   in intron 4, is also associated with altered eNOS enzyme activity. Thus, it is likely that these eNOS variations might influence normal functioning of eNOS, lead to vascular endothelial dysfunction, and influence predisposition to hemorrhagic cerebral vascular diseases. In this meta‐analysis, only rs2070744 polymorphism was found to be significantly associated with predisposition to hemorrhagic cerebral vascular diseases. Nevertheless, we noticed that the trends of associations for rs1799983 and rs869109213 polymorphisms were actually similar to rs2070744 polymorphism. Therefore, maybe our pooled meta‐analyses were still not statistically sufficient to detect the real associations between eNOS polymorphisms and hemorrhagic cerebral vascular diseases, and future studies in larger populations are still needed so as to get a more statistically robust conclusion. Second, the etiologies and pathogenesis mechanisms of hemorrhagic cerebral vascular diseases are extremely sophisticated. In this meta‐analysis, we noticed that almost all eligible studies were about IA and its associated aSAH, whereas only a few studies were about hypertensive cerebral hemorrhage. Nevertheless, considering that the majority of hemorrhagic cerebral vascular diseases should be related to hypertensive disorders. Future studies should concentrate more on elucidating the relationship between eNOS polymorphisms and hemorrhagic cerebral vascular diseases of other etiologies, particularly hypertension. Third, we aimed to analyze all eNOS polymorphisms at the beginning. However, we did not find sufficient eligible studies to support meta‐analyses of other eNOS polymorphisms, so we have to focus on only three polymorphisms in this meta‐analysis.Few points need to be considered when interpreting our findings. First, previous experimental studies demonstrated that all investigated polymorphisms were correlated with altered gene expression or protein structure of eNOS (AlFadhli, eNOS polymorphisms and hemorrhagic cerebral vascular diseases. However, most investigators only focused on genetic associations in their works, so genetic–environmental interactions could not be explored in this meta‐analysis (Wang et al., Like all meta‐analyses, a few limitations of our pooled meta‐analyses have to be acknowledged. First, our pooled meta‐analyses results were derived from pooling unadjusted findings of eligible studies since the authors did not have raw data (Xie, Shi, & Liu, eNOS rs2070744 polymorphism might influence predisposition to hemorrhagic cerebral vascular diseases in East Asians. These results also suggested that eNOS might involve in the development of hemorrhagic cerebral vascular diseases, and it may serve as a potential therapeutic target for hemorrhagic cerebral vascular diseases.So to conclude, this meta‐analysis demonstrated that The authors declare that they have no conflict of interest.Qiuling Wang and Minfeng Zhou designed this meta‐analysis. Qiuling Wang and Hongri Sun searched literatures. Xiaoguang Qi analyzed the data. Qiuling Wang and Minfeng Zhou wrote the manuscript. All authors approved the final manuscript as submitted.This article does not contain any studies with human participants or animals performed by any of the authors; thus, ethical approval is not required. Click here for additional data file.


CYP2B6 variants on plasma concentrations of NVP by a systematic review and meta-analysis. A search for qualifying studies published until April 2020 was conducted using the EMBASE, PubMed, and Web of Science databases. The mean difference (MD) and 95% confidence intervals (CIs) were calculated. Data analysis was performed using R Studio (version 3.6) and Review Manager (version 5.3). In total, data from six studies involving 634 patients were analyzed in the systematic review and five studies in the meta-analysis. We found that carriers of the CYP2B6 516TT genotype had a 2.18 µg/mL higher NVP concentration than did the GG or GT (95% CI 1.28–3.08). In the respective comparisons of the three genotypes, it was found that the MD was 1.87 µg/mL between the TT and GT groups, 2.53 µg/mL between TT and GG, and 0.60 µg/mL between GT and GG. This meta-analysis confirmed that CYP2B6 polymorphisms was associated with plasma NVP concentrations. Therefore, CYP2B6 genotyping may be useful to predict the responses to NVP.Cytochrome P450 (CYP) is involved in the metabolism of nevirapine (NVP); especially, CYP2B6 has been known to be one of the main enzymes involved in NVP metabolism. The objective of this study was to investigate the effects of  Ever since highly active antiretroviral therapy (HAART) was introduced in 1996, AIDS prognosis has greatly improved2. Typically, HAART targets multiple viral replication cycles and includes two nucleoside reverse transcriptase inhibitors (NRTIs), a non-nucleoside reverse transcriptase inhibitor (NNRTI), and a protease inhibitor (PI) or an integrase inhibitor (INSTI)3. Especially, NNRTIs decrease HIV-1 reverse transcriptase activity through allosteric inhibition.Acquired Immunodeficiency Syndrome (AIDS) is one of the greatest public health challenges, with a World Health Organization (WHO) estimate of 37.9 million infected people around the world5. In NVP treatment, a significant relationship between trough concentrations and virologic response or toxicity has been reported6. Meanwhile, inter-individual variability in NVP plasma concentrations was observed to be approximately 50%, where such variability could partially be explained by differences in ethnicity, gender, existence of hepatic disease and concomitant medications9. Additionally, genetic disposition was also shown to contribute to inter-individual variability in NVP concentrations10. Among the various genetic factors that may be associated with individual variability in NVP concentrations, genetic polymorphisms of the enzyme cytochrome P450 (CYP) are prime candidates, as NVP is principally metabolized by CYP3A4 and CYP2B6 into its major metabolites12.Nevirapine (NVP), due to its low cost and high efficacy, is one of the most frequently utilized NNRTIs12. Among the metabolites, 2- and 3-hydroxynevirapine are known to be formed exclusively by CYP3A4 and CYP2B6, respectively. Since NVP has been suggested to be an inducer of the CYPs responsible for its own metabolism, and hepatic CYP2B genes are considered as the most-inducible CYP isoforms13, CYP2B6 could play an important role in NVP metabolism.NVP undergoes significant oxidative metabolism to 2-, 3-, 8- and 12-hydroxynevirapine and 4-carboxynevirapine, followed by glucuronidation and renal excretion. A study reported that more than 80% of the radioactivity in urine was consisted of glucuronidated conjugates of hydroxylated NVP metabolitesCYP2B6 gene polymorphisms and NVP concentration. However, since most such studies did not utilize a substantially large sample size, broad conclusions from separate studies are not readily attainable. Instead, assimilating data from such studies and deducing meaningful results from the pile of information is both prudent and necessary. In this context, we performed a meta-analysis of the published literature in search of significant conclusions on the effects of CYP2B6 genetic variants on plasma NVP concentrations.Several studies have investigated the association between CYP2B6 polymorphism-grouped concentration outcomes (n = 2), the researchers were not sure if the concentration outcomes were trough (n = 2), unable to extract the data (n = 2) and a study on children (n = 1). Although the study by Manhungu14 had one patient who received 600 mg once daily, we included the study because most patients (99%) underwent either 400 mg once daily or 200 mg twice daily. Ultimately, six articles were selected for this systematic review19.A detailed flow chart of the study selection process is presented in Fig. 20 ranging from 6 to 9.The characteristics of the included studies are presented in Table CYP2B6 polymorphisms and trough concentrations of NVP, which involved data from 634 patients. The most frequently studied polymorphism was 516 G > T. As one study only compared NVP concentrations between patients with GG genotypes and T allele carriers (GT or TT)16, data from 562 patients from the other five studies was included in meta-analysis. NVP concentrations in the CYP2B6 516TT genotype was 2.18 µg/mL : 1.28–3.08) higher compared with the concentration in the GG or GT genotypes . Neither Begg’s test nor Egger’s test showed significant publication bias .Six studies were evaluated to investigate the association between In respective comparisons of the three genotypes, every association was found to be significant. The mean difference (MD) was 2.53 µg/mL between TT and GG .Subgroup analysis by ethnicity was also performed Fig. . The MD CYP2B6 983 T > C, two studies that investigated 1459 C > T, and one study that investigated 785 A > G  carriers displayed higher NVP concentrations than variant allele carriers (CT or TT), although this was also not statistically significant. Regarding 785 A > G, there was only one study16 and there was no significant difference between wild allele homozygotes and variant allele carriers.There were two studies that investigated the polymorphism CYP2B6 gene polymorphisms on NVP trough concentrations. HIV–infected patients with the CYP2B6 516TT genotype showed higher NVP concentrations than did GG or GT genotypes. Notable statistical significance was also attained in each of the three respective comparisons of genotypes. There was no difference between subgroups by ethnicity.This is the first meta-analysis evaluating the influence of 21 with significant inter-variability22. The difference in CYP2B6 mRNA and protein expression between individuals has been observed to be up to 250-fold and 100-fold, respectively23. Such variability is possibly due to effects of genetic polymorphisms, with one of the most frequently studied polymorphisms being 516 G > T. CYP2B6 516 G > T, one of the two linked nonsynonymous polymorphisms in the *6 allele, is the causal mutation finally leading to reduced function. This results not only in an amino acid change but also in severely reduced levels of functional full-length mRNA transcript, protein, and activity24.CYP2B6 is a member of the cytochrome P450 family, constituting approximately 2–10% of the total hepatic CYP content27. In addition, as CYP2B6 is the major enzyme involved in the metabolism of efavirenz (EFV), another NNRTI, its polymorphism was shown to be related to EFV drug disposition. A recent meta-analysis noted that patients with the TT genotype exhibited significantly higher plasma concentrations of EFV compared with those that were homozygous for the G allele 28. Thus, the results of this study that suggest an association between 516 G > T and NVP concentrations are in line with previous research.Several studies have investigated the association between 516 G > T and drug concentrations. According to these studies, 516 G > T polymorphism was associated with concentrations of drugs that are metabolized by CYP2B6 28, which reported that the higher EFV concentration in patients with the TT genotype was found within all ethnicity groups; overall EFV concentration in the TT genotype carriers was comparable between different ethnic groups.There was no significant ethnicity-dependent difference in this study population. Patients with the TT genotype had higher NVP concentrations regardless of the ethnic background. This was consistent with the result from the aforementioned meta-analysis of EFVP = 0.015), along with hepatitis C virus co-infection (P = 0.039)6. Gonzalez de Requena et al. also showed that among patients with chronic hepatitis C coinfection, NVP concentrations > 6 µg/mL were associated with a 92% risk of liver toxicity29. A multivariate linear regression analysis also revealed that high trough concentrations was an independent predictor for elevated liver enzyme levels30. Moreover, several studies reported that the CYP2B6 516 G > T polymorphism was associated with adverse events of NVP. According to Yuan et al.31, cutaneous adverse events were associated with CYP2B6 516 T . Ciccacci et al.32 also suggested associations between CYP2B6 polymorphisms (516 G > T and 983 T > C) and SJS/TEN susceptibility. Furthermore, another meta-analysis confirmed that patients with the GT and TT genotypes of 516 G > T displayed a significantly increased risk of EFV-induced side effects compared to those with the GG genotype, possibly due to the slow EFV clearance33.Higher plasma concentrations of NVP is one of the important issues regarding safety. In a clinical test conducted in China, hepatotoxicity was significantly associated with median NVP trough concentrations in male patients  could not be adjusted due to lack of information from individual studies. Third, the NVP dosing regimen was not identical; we included studies with patients that received either 400 mg once daily or 200 mg twice daily. Despite these shortcomings, this is the first systematic review and meta-analysis to evaluate the association between CYP2B6 polymorphisms and NVP concentrations. By combining the results of several studies, this current study suggests that CYP2B6 polymorphisms, especially 516 G > T, may be associated with NVP trough concentrations in a significant way. Therefore, based on our findings, CYP2B6 genotyping may be useful in predicting response to NVP, although these results must be confirmed in studies on larger populations regarding pharmacodynamics.This meta-analysis has some limitations that should be considered when interpreting the results. First, the limited number of studies may lead to low statistical power in detecting differences or heterogeneity. However, according to Herbison et al35. Two reviewers independently searched published studies, which published until April 2020. An extensive search of electronic databases  was performed using the following search terms: (nevirapine OR NVP OR BI-RG-587 OR viramun*) AND (2B6 OR CYP2B6 OR Cytochrome P450 2B6 OR Cytochrome p‐4502B6 OR CYP2B6* OR P4502B6). There was no limitation on language.This meta-analysis was conducted according to the checklist outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)CYP2B6 genotypes with plasma trough concentrations of NVP (µg/mL) among HIV patients; (2) the patients received full-dose NVP (either 400 mg once daily or 200 mg twice daily) for at least 14 days or achieved steady-state conditions prior to pharmacokinetic sampling; and (3) the NVP concentration in plasma at 12 h after dosing (12 ± 2 h) were available and described separately according to different genotypes. Studies were excluded if they were (1) editorials, notes, abstracts, reviews, comments, letters, news, or editorials; (2) in vitro or in vivo studies; (3) unable to extract the data; (4) studies on children. In case of overlapping data, only the most recent and comprehensive data were included in the meta-analysis.Studies were included if (1) the studies evaluated the relationship of Two reviewers independently extracted data, and discrepancies were resolved by consensus. Extracted data included the following information: name of first author, publication year, number of patients, mean age, percentage of males, ethnicity, studied alleles, and plasma trough concentrations of NVP.Articles were assessed by two investigators based on the JBI Critical Appraisal Checklist for quality assessment. Quality was quantified by assigning scores ranging from 0 to 1 point/criteria. One point was assigned if the item was expressed in the study, and zero points were given if the item was not expressed or if it was unclear.CYP2B6 polymorphisms and plasma trough concentrations of NVP. For studies that only reported medians and interquartile ranges, we retrieved mean and variance values from authors of original reports or used appropriate formulas to calculate mean and variance, making no assumption on the distribution of the underlying data36. The heterogeneity across studies was estimated by way of a chi-square test and an I2 statistic. An I2 value of ≥ 50% was considered to indicate significant heterogeneity. The choice of the proper effect model was based on the analysis results: the fixed effect model was used if I2 < 50% and the random effect model was used if I2 ≥ 50%37. Subgroup analysis was performed according to ethnicity. Both Begg’s rank correlation test and Egger’s regression test of the funnel plot were also conducted using R Studio software  to detect publication bias39. A P-value < 0.05 was considered statistically significant.Meta-analysis was performed using Review Manager . The MD and 95% CIs were used to identify the relationship between the existence of