Data Harmonization for a Molecularly Driven Health System.

Data commons have emerged as the best current method for enabling data aggregation across multiple projects and multiple data sources. Good data harmonization techniques are critical to maintain quality of data within a data commons, as well as to allow future meta-analysis across different data commons. We present some of the current best practices for data harmonization.

FinnGen provides genetic insights from a well-phenotyped isolated population.

Population isolates such as those in Finland benefit genetic research because deleterious alleles are often concentrated on a small number of low-frequency variants (0.1% ≤ minor allele frequency < 5%). These variants survived the founding bottleneck rather than being distributed over a large number of ultrarare variants. Although this effect is well established in Mendelian genetics, its value in common disease genetics is less explored1,2. FinnGen aims to study the genome and national health register data of 500,000 Finnish individuals. Given the relatively high median age of participants (63 years) and the substantial fraction of hospital-based recruitment, FinnGen is enriched for disease end points. Here we analyse data from 224,737 participants from FinnGen and study 15 diseases that have previously been investigated in large genome-wide association studies (GWASs). We also include meta-analyses of biobank data from Estonia and the United Kingdom. We identified 30 new associations, primarily low-frequency variants, enriched in the Finnish population. A GWAS of 1,932 diseases also identified 2,733 genome-wide significant associations (893 phenome-wide significant (PWS), P < 2.6 × 10-11) at 2,496 (771 PWS) independent loci with 807 (247 PWS) end points. Among these, fine-mapping implicated 148 (73 PWS) coding variants associated with 83 (42 PWS) end points. Moreover, 91 (47 PWS) had an allele frequency of <5% in non-Finnish European individuals, of which 62 (32 PWS) were enriched by more than twofold in Finland. These findings demonstrate the power of bottlenecked populations to find entry points into the biology of common diseases through low-frequency, high impact variants.

GeneRaMeN enables integration, comparison, and meta-analysis of multiple ranked gene lists to identify consensus, unique, and correlated genes.

High-throughput experiments often produce ranked gene outputs, with forward genetic screening being a notable example. While there are various tools for analyzing individual datasets, those that perform comparative and meta-analytical examination of such ranked gene lists remain scarce. Here, we introduce Gene Rank Meta Analyzer (GeneRaMeN), an R Shiny tool utilizing rank statistics to facilitate the identification of consensus, unique, and correlated genes across multiple hit lists. We focused on two key topics to showcase GeneRaMeN: virus host factors and cancer dependencies. Using GeneRaMeN 'Rank Aggregation', we integrated 24 published and new flavivirus genetic screening datasets, including dengue, Japanese encephalitis, and Zika viruses. This meta-analysis yielded a consensus list of flavivirus host factors, elucidating the significant influence of cell line selection on screening outcomes. Similar analysis on 13 SARS-CoV-2 CRISPR screening datasets highlighted the pivotal role of meta-analysis in revealing redundant biological pathways exploited by the virus to enter human cells. Such redundancy was further underscored using GeneRaMeN's 'Rank Correlation', where a strong negative correlation was observed for host factors implicated in one entry pathway versus the alternate route. Utilizing GeneRaMeN's 'Rank Uniqueness', we analyzed human coronaviruses 229E, OC43, and SARS-CoV-2 datasets, identifying host factors uniquely associated with a defined subset of the screening datasets. Similar analyses were performed on over 1000 Cancer Dependency Map (DepMap) datasets spanning 19 human cancer types to reveal unique cancer vulnerabilities for each organ/tissue. GeneRaMeN, an efficient tool to integrate and maximize the usability of genetic screening datasets, is freely accessible via https://ysolab.shinyapps.io/GeneRaMeN.

Variability in the analysis of a single neuroimaging dataset by many teams.

Data analysis workflows in many scientific domains have become increasingly complex and flexible. Here we assess the effect of this flexibility on the results of functional magnetic resonance imaging by asking 70 independent teams to analyse the same dataset, testing the same 9 ex-ante hypotheses1. The flexibility of analytical approaches is exemplified by the fact that no two teams chose identical workflows to analyse the data. This flexibility resulted in sizeable variation in the results of hypothesis tests, even for teams whose statistical maps were highly correlated at intermediate stages of the analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Notably, a meta-analytical approach that aggregated information across teams yielded a significant consensus in activated regions. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset2-5. Our findings show that analytical flexibility can have substantial effects on scientific conclusions, and identify factors that may be related to variability in the analysis of functional magnetic resonance imaging. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for performing and reporting multiple analyses of the same data. Potential approaches that could be used to mitigate issues related to analytical variability are discussed.

A new paradigm for molecular dynamics databases: the COVID-19 database, the legacy of a titanic community effort.

Molecular dynamics (MD) simulations are keeping computers busy around the world, generating a huge amount of data that is typically not open to the scientific community. Pioneering efforts to ensure the safety and reusability of MD data have been based on the use of simple databases providing a limited set of standard analyses on single-short trajectories. Despite their value, these databases do not offer a true solution for the current community of MD users, who want a flexible analysis pipeline and the possibility to address huge non-Markovian ensembles of large systems. Here we present a new paradigm for MD databases, resilient to large systems and long trajectories, and designed to be compatible with modern MD simulations. The data are offered to the community through a web-based graphical user interface (GUI), implemented with state-of-the-art technology, which incorporates system-specific analysis designed by the trajectory providers. A REST API and associated Jupyter Notebooks are integrated into the platform, allowing fully customized meta-analysis by final users. The new technology is illustrated using a collection of trajectories obtained by the community in the context of the effort to fight the COVID-19 pandemic. The server is accessible at https://bioexcel-cv19.bsc.es/#/. It is free and open to all users and there are no login requirements. It is also integrated into the simulations section of the BioExcel-MolSSI COVID-19 Molecular Structure and Therapeutics Hub: https://covid.molssi.org/simulations/ and is part of the MDDB effort (https://mddbr.eu).

Neuroimaging meta regression for coordinate based meta analysis data with a spatial model.

Coordinate-based meta-analysis combines evidence from a collection of neuroimaging studies to estimate brain activation. In such analyses, a key practical challenge is to find a computationally efficient approach with good statistical interpretability to model the locations of activation foci. In this article, we propose a generative coordinate-based meta-regression (CBMR) framework to approximate a smooth activation intensity function and investigate the effect of study-level covariates (e.g. year of publication, sample size). We employ a spline parameterization to model the spatial structure of brain activation and consider four stochastic models for modeling the random variation in foci. To examine the validity of CBMR, we estimate brain activation on 20 meta-analytic datasets, conduct spatial homogeneity tests at the voxel level, and compare the results to those generated by existing kernel-based and model-based approaches. Keywords: generalized linear models; meta-analysis; spatial statistics; statistical modeling.

metaGWASmanager: a toolbox for an automated workflow from phenotypes to meta-analysis in GWAS consortia.

SUMMARY: This article introduces the metaGWASmanager, which streamlines genome-wide association studies within large-scale meta-analysis consortia. It is a toolbox for both the central consortium analysis group and participating studies to generate homogeneous phenotypes, minimize unwanted variability from inconsistent methodologies, ensure high-quality association results, and implement time-efficient quality control workflows. The toolbox features a plug-in-based approach for customization of association testing. RESULTS: The metaGWASmanager toolbox has been successfully deployed in both the CKDGen and MetalGWAS Initiative consortia across hundreds of participating studies, demonstrating its effectiveness in GWAS analysis optimization by automating routine tasks and ensuring the value and reliability of association results, thus, ultimately promoting scientific discovery. We provide a simulated data set with examples for script customization so that readers can reproduce the pipeline at their convenience. AVAILABILITY AND IMPLEMENTATION: GitHub: https://github.com/genepi-freiburg/metaGWASmanager.

Sugar-Sweetened Beverages and Adverse Human Health Outcomes: An Umbrella Review of Meta-Analyses of Observational Studies.

Our aim was to conduct an umbrella review of evidence from meta-analyses of observational studies investigating the link between sugar-sweetened beverage consumption and human health outcomes. Using predefined evidence classification criteria, we evaluated evidence from 47 meta-analyses encompassing 22,055,269 individuals. Overall, 79% of these analyses indicated direct associations between greater sugar-sweetened beverage consumption and higher risks of adverse health outcomes. Convincing evidence (class I) supported direct associations between sugar-sweetened beverage consumption and risks of depression, cardiovascular disease, nephrolithiasis, type 2 diabetes mellitus, and higher uric acid concentrations. Highly suggestive evidence (class II) supported associations with risks of nonalcoholic fatty liver disease and dental caries. Out of the remaining 40 meta-analyses, 29 were graded as suggestive or weak in the strength of evidence (classes III and IV), and 11 showed no evidence (class V). These findings inform and provide support for population-based and public health strategies aimed at reducing sugary drink consumption for improved health.

Nine quick tips for open meta-analyses.

Open science principles are revolutionizing the transparency, reproducibility, and accessibility of research. Meta-analysis has become a key technique for synthesizing data across studies in a principled way; however, its impact is contingent on adherence to open science practices. Here, we outline 9 quick tips for open meta-analyses, aimed at guiding researchers to maximize the reach and utility of their findings. We advocate for outlining preregistering clear protocols, opting for open tools and software, and the use of version control systems to ensure transparency and facilitate collaboration. We further emphasize the importance of reproducibility, for example, by sharing search syntax and analysis scripts, and discuss the benefits of planning for dynamic updating to enable living meta-analyses. We also recommend publication in open-access formats, as well as open data, open code, and open access publication. We close by encouraging active promotion of research findings to bridge the gap between complex syntheses and public discourse, and provide a detailed submission checklist to equip researchers, reviewers and journal editors with a structured approach to conducting and reporting open meta-analyses.

Empirically adjusted fixed-effects meta-analysis methods in genomic studies.

In recent years, meta-analyzing summary results from multiple studies has become a common practice in genomic research, leading to a significant improvement in the power of statistical detection compared to an individual genomic study. Meta analysis methods that combine statistical estimates across studies are known to be statistically more powerful than those combining statistical significance measures. An approach combining effect size estimates based on a fixed-effects model, called METAL, has gained extreme popularity to perform the former type of meta-analysis. In this article, we discuss the limitations of METAL due to its dependence on the theoretical null distribution, leading to incorrect significance testing results. Through various simulation studies and real genomic data application, we show how modifying the z-scores in METAL, using an empirical null distribution, can significantly improve the results, especially in presence of hidden confounders. For the estimation of the null distribution, we consider two different approaches, and we highlight the scenarios when one null estimation approach outperforms the other. This article will allow researchers to gain an insight into the importance of using an empirical null distribution in the fixed-effects meta-analysis as well as in choosing the appropriate empirical null distribution estimation approach.

Sensitivity and Specificity of Using GPT-3.5 Turbo Models for Title and Abstract Screening in Systematic Reviews and Meta-analyses.

BACKGROUND: Systematic reviews are performed manually despite the exponential growth of scientific literature. OBJECTIVE: To investigate the sensitivity and specificity of GPT-3.5 Turbo, from OpenAI, as a single reviewer, for title and abstract screening in systematic reviews. DESIGN: Diagnostic test accuracy study. SETTING: Unannotated bibliographic databases from 5 systematic reviews representing 22 665 citations. PARTICIPANTS: None. MEASUREMENTS: A generic prompt framework to instruct GPT to perform title and abstract screening was designed. The output of the model was compared with decisions from authors under 2 rules. The first rule balanced sensitivity and specificity, for example, to act as a second reviewer. The second rule optimized sensitivity, for example, to reduce the number of citations to be manually screened. RESULTS: Under the balanced rule, sensitivities ranged from 81.1% to 96.5% and specificities ranged from 25.8% to 80.4%. Across all reviews, GPT identified 7 of 708 citations (1%) missed by humans that should have been included after full-text screening at the cost of 10 279 of 22 665 false-positive recommendations (45.3%) that would require reconciliation during the screening process. Under the sensitive rule, sensitivities ranged from 94.6% to 99.8% and specificities ranged from 2.2% to 46.6%. Limiting manual screening to citations not ruled out by GPT could reduce the number of citations to screen from 127 of 6334 (2%) to 1851 of 4077 (45.4%), at the cost of missing from 0 to 1 of 26 citations (3.8%) at the full-text level. LIMITATIONS: Time needed to fine-tune prompt. Retrospective nature of the study, convenient sample of 5 systematic reviews, and GPT performance sensitive to prompt development and time. CONCLUSION: The GPT-3.5 Turbo model may be used as a second reviewer for title and abstract screening, at the cost of additional work to reconcile added false positives. It also showed potential to reduce the number of citations before screening by humans, at the cost of missing some citations at the full-text level. PRIMARY FUNDING SOURCE: None.

Leveraging pleiotropy for joint analysis of genome-wide association studies with per trait interpretations.

We introduce pleiotropic association test (PAT) for joint analysis of multiple traits using genome-wide association study (GWAS) summary statistics. The method utilizes the decomposition of phenotypic covariation into genetic and environmental components to create a likelihood ratio test statistic for each genetic variant. Though PAT does not directly interpret which trait(s) drive the association, a per trait interpretation of the omnibus p-value is provided through an extension to the meta-analysis framework, m-values. In simulations, we show PAT controls the false positive rate, increases statistical power, and is robust to model misspecifications of genetic effect. Additionally, simulations comparing PAT to three multi-trait methods, HIPO, MTAG, and ASSET, show PAT identified 15.3% more omnibus associations over the next best method. When these associations were interpreted on a per trait level using m-values, PAT had 37.5% more true per trait interpretations with a 0.92% false positive assignment rate. When analyzing four traits from the UK Biobank, PAT discovered 22,095 novel variants. Through the m-values interpretation framework, the number of per trait associations for two traits were almost tripled and were nearly doubled for another trait relative to the original single trait GWAS.

In grade C/D erosive esophagitis, vonoprazan ranks highest among PPIs and P-CABs for healing and maintaining remission.

SOURCE CITATION: Zhuang Q, Chen S, Zhou X, et al. Comparative efficacy of P-CAB vs proton pump inhibitors for grade C/D esophagitis: a systematic review and network meta-analysis. Am J Gastroenterol. 2024;119:803-813. 38345252.

In many mental health and some somatic disorders, efficacy of therapist-guided remote CBT and in-person CBT does not differ.

SOURCE CITATION: Zandieh S, Abdollahzadeh SM, Sadeghirad B, et al. Therapist-guided remote versus in-person cognitive behavioural therapy: a systematic review and meta-analysis of randomized controlled trials. CMAJ. 2024;196:E327-E340. 38499303.

In device-detected AF, DOACs vs. aspirin or placebo reduce ischemic stroke and increase major bleeding.

SOURCE CITATION: McIntyre WF, Benz AP, Becher N, et al. Direct oral anticoagulants for stroke prevention in patients with device-detected atrial fibrillation: a study-level meta-analysis of the NOAH-AFNET 6 and ARTESiA trials. Circulation. 2024;149:981-988. 37952187.

Invited Commentary: Combining Information to Answer Epidemiologic Questions About a Target Population.

Epidemiologists are attempting to address research questions of increasing complexity by developing novel methods for combining information from diverse sources. Cole et al. (Am J Epidemiol. 2023;192(3)467-474) provide 2 examples of the process of combining information to draw inferences about a population proportion. In this commentary, we consider combining information to learn about a target population as an epidemiologic activity and distinguish it from more conventional meta-analyses. We examine possible rationales for combining information and discuss broad methodological considerations, with an emphasis on study design, assumptions, and sources of uncertainty.

Interaction of gonadal hormones, dopaminergic system, and epigenetic regulation in the generation of sex differences in substance use disorders: A systematic review.

Substance use disorder (SUD) is a chronic condition characterized by pathological drug-taking and seeking behaviors. Remarkably different between males and females, suggesting that drug addiction is a sexually differentiated disorder. The neurobiological bases of sex differences in SUD include sex-specific reward system activation, influenced by interactions between gonadal hormone level changes, dopaminergic reward circuits, and epigenetic modifications of key reward system genes. This systematic review, adhering to PICOS and PRISMA-P 2015 guidelines, highlights the sex-dependent roles of estrogens, progesterone, and testosterone in SUD. In particular, estradiol elevates and progesterone reduces dopaminergic activity in SUD females, whilst testosterone and progesterone augment SUD behavior in males. Finally, SUD is associated with a sex-specific increase in the rate of opioid and monoaminergic gene methylation. The study reveals the need for detailed research on gonadal hormone levels, dopaminergic or reward system activity, and epigenetic landscapes in both sexes for efficient SUD therapy development.

ABO and Rhesus blood groups and multiple health outcomes: an umbrella review of systematic reviews with meta-analyses of observational studies.

BACKGROUND: Numerous studies have been conducted to investigate the relationship between ABO and Rhesus (Rh) blood groups and various health outcomes. However, a comprehensive evaluation of the robustness of these associations is still lacking. METHODS: We searched PubMed, Web of Science, Embase, Scopus, Cochrane, and several regional databases from their inception until Feb 16, 2024, with the aim of identifying systematic reviews with meta-analyses of observational studies exploring associations between ABO and Rh blood groups and diverse health outcomes. For each association, we calculated the summary effect sizes, corresponding 95% confidence intervals, 95% prediction interval, heterogeneity, small-study effect, and evaluation of excess significance bias. The evidence was evaluated on a grading scale that ranged from convincing (Class I) to weak (Class IV). We assessed the certainty of evidence according to the Grading of Recommendations Assessment, Development, and Evaluation criteria (GRADE). We also evaluated the methodological quality of included studies using the A Measurement Tool to Assess Systematic Reviews (AMSTAR). AMSTAR contains 11 items, which were scored as high (8-11), moderate (4-7), and low (0-3) quality. We have gotten the registration for protocol on the PROSPERO database (CRD42023409547). RESULTS: The current umbrella review included 51 systematic reviews with meta-analysis articles with 270 associations. We re-calculated each association and found only one convincing evidence (Class I) for an association between blood group B and type 2 diabetes mellitus risk compared with the non-B blood group. It had a summary odds ratio of 1.28 (95% confidence interval: 1.17, 1.40), was supported by 6870 cases with small heterogeneity (I2 = 13%) and 95% prediction intervals excluding the null value, and without hints of small-study effects (P for Egger's test > 0.10, but the largest study effect was not more conservative than the summary effect size) or excess of significance (P < 0.10, but the value of observed less than expected). And the article was demonstrated with high methodological quality using AMSTAR (score = 9). According to AMSTAR, 18, 32, and 11 studies were categorized as high, moderate, and low quality, respectively. Nine statistically significant associations reached moderate quality based on GRADE. CONCLUSIONS: Our findings suggest a potential relationship between ABO and Rh blood groups and adverse health outcomes. Particularly the association between blood group B and type 2 diabetes mellitus risk.