SynthesisGenetic epidemiology2014
An empirical comparison of meta-analysis and mega-analysis of individual participant data for identifying gene-environment interactions.
Synthesis in Genetic epidemiology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 6 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
31 citing papers in PubMed, 6 syntheses or guidelines pooled it.
- Genome-wide association analysis using multiple Atlantic salmon populations.Genetics, selection, evolution : GSE · 2025Pooled it
- Better together against genetic heterogeneity: A sex-combined joint main and interaction analysis of 290 quantitative traits in the UK Biobank.PLoS genetics · 2024Pooled it
- Developing a synthetic psychosocial stress measure and harmonizing CVD-risk data: a way forward to GxE meta- and mega-analyses.BMC research notes · 2018Pooled it
- Traumatic stress and accelerated DNA methylation age: A meta-analysis.Psychoneuroendocrinology · 2018Pooled it
- Pooled it
- Genetic variation and dopamine D2 receptor availability: a systematic review and meta-analysis of human in vivo molecular imaging studies.Translational psychiatry · 2016Pooled it
- Continental Patterns of Phenotypic Variation Along Replicated Urban Gradients: A Mega-Analysis.Ecology letters · 2025Review
- Genome-wide study for signatures of selection identifies genomic regions and candidate genes associated with milk traits in sheep.Mammalian genome : official journal of the International Mammalian Genome Society · 2025Article
- Are women really (not) more talkative than men? A registered report of binary gender similarities/differences in daily word use.Journal of personality and social psychology · 2025Article
- Mutual information for detecting multi-class biomarkers when integrating multiple bulk or single-cell transcriptomic studies.Bioinformatics (Oxford, England) · 2024Article
- Memory for abstract control states does not decay with increasing retrieval delays.Psychological research · 2024Article
- Federated generalized linear mixed models for collaborative genome-wide association studies.iScience · 2023Article
- Bayesian Dynamic Borrowing of Historical Information with Applications to the Analysis of Large-Scale Assessments.Psychometrika · 2023Article
- Integrating variant functional annotation scores have varied abilities to improve power of genome-wide association studies.Scientific reports · 2022Article
- Youth Well-being During the COVID-19 Pandemic.Pediatrics · 2022Article
- Improving the resolution of canine genome-wide association studies using genotype imputation: A study of two breeds.Animal genetics · 2021Article
- A cross-institutional analysis of the effects of broadening trainee professional development on research productivity.PLoS biology · 2021Article
- A threat in the network: STEM women in less powerful network positions avoid integrating stereotypically feminine peers.Group processes & intergroup relations : GPIR · 2021Article
- Article
- Toward Cumulative Cognitive Science: A Comparison of Meta-Analysis, Mega-Analysis, and Hybrid Approaches.Open mind : discoveries in cognitive science · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
For analysis of the main effects of SNPs, meta-analysis of summary results from individual studies has been shown to provide comparable results as "mega-analysis" that jointly analyzes the pooled participant data from the available studies. This fact revolutionized the genetic analysis of complex traits through large GWAS consortia. Investigations of gene-environment (G×E) interactions are on the rise since they can potentially explain a part of the missing heritability and identify individuals at high risk for disease. However, for analysis of gene-environment interactions, it is not known whether these methods yield comparable results. In this empirical study, we report that the results from both methods were largely consistent for all four tests; the standard 1 degree of freedom (df) test of main effect only, the 1 df test of the main effect (in the presence of interaction effect), the 1 df test of the interaction effect, and the joint 2 df test of main and interaction effects. They provided similar effect size and standard error estimates, leading to comparable P-values. The genomic inflation factors and the number of SNPs with various thresholds were also comparable between the two approaches. Mega-analysis is not always feasible especially in very large and diverse consortia since pooling of raw data may be limited by the terms of the informed consent. Our study illustrates that meta-analysis can be an effective approach also for identifying interactions. To our knowledge, this is the first report investigating meta-versus mega-analyses for interactions.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.