SynthesisAmerican journal of epidemiology2009
Discovery properties of genome-wide association signals from cumulatively combined data sets.
Synthesis in American journal of epidemiology, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 17 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
41 citing papers in PubMed, 17 syntheses or guidelines pooled it.
- Meta-regression of genome-wide association studies to estimate age-varying genetic effects.European journal of epidemiology · 2024Pooled it
- Discerning asthma endotypes through comorbidity mapping.Nature communications · 2022Pooled it
- Data Integration, Imputation, and Meta-analysis for Genome-Wide Association Studies.Methods in molecular biology (Clifton, N.J.) · 2022Pooled it
- Reviewing and assessing existing meta-analysis models and tools.Briefings in bioinformatics · 2021Pooled it
- Genetic determinants of risk in pulmonary arterial hypertension: international genome-wide association studies and meta-analysis.The Lancet. Respiratory medicine · 2019Pooled it
- Power considerations for λ inflation factor in meta-analyses of genome-wide association studies.Genetics research · 2016Pooled it
- Replication of ZNF804A gene variant associations with risk of heroin addiction.Genes, brain, and behavior · 2015Pooled it
- An empirical comparison of meta-analysis and mega-analysis of individual participant data for identifying gene-environment interactions.Genetic epidemiology · 2014Pooled it
- Multivariate meta-analysis of the association of G-protein beta 3 gene (GNB3) haplotypes with cardiovascular phenotypes.Molecular biology reports · 2014Pooled it
- On individual genome-wide association studies and their meta-analysis.Human genetics · 2014Pooled it
- Meta-analysis of genome-wide studies identifies MEF2C SNPs associated with bone mineral density at forearm.Journal of medical genetics · 2013Pooled it
- Meta-analysis of genetic association studies under heterogeneity.European journal of human genetics : EJHG · 2012Pooled it
- WNT16 influences bone mineral density, cortical bone thickness, bone strength, and osteoporotic fracture risk.PLoS genetics · 2012Pooled it
- Genome-wide meta-analysis identifies 56 bone mineral density loci and reveals 14 loci associated with risk of fracture.Nature genetics · 2012Pooled it
- Genome-wide meta-analysis identifies novel multiple sclerosis susceptibility loci.Annals of neurology · 2011Pooled it
- Genome-wide meta-analysis identifies regions on 7p21 (AHR) and 15q24 (CYP1A2) as determinants of habitual caffeine consumption.PLoS genetics · 2011Pooled it
- Genome-wide meta-analyses identify multiple loci associated with smoking behavior.Nature genetics · 2010Pooled it
- Multiethnic genome-wide association study of cerebral white matter hyperintensities on MRI.Circulation. Cardiovascular genetics · 2015Trial
- Design and model choices shape inference of age-varying genetic effects on complex traits.Nature aging · 2026Article
- A review of post-GWAS studies in schizophrenia.Translational psychiatry · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Genetic effects for common variants affecting complex disease risk are subtle. Single genome-wide association (GWA) studies are typically underpowered to detect these effects, and combination of several GWA data sets is needed to enhance discovery. The authors investigated the properties of the discovery process in simulated cumulative meta-analyses of GWA study-derived signals allowing for potential genetic model misspecification and between-study heterogeneity. Variants with null effects on average (but also between-data set heterogeneity) could yield false-positive associations with seemingly homogeneous effects. Random effects had higher than appropriate false-positive rates when there were few data sets. The log-additive model had the lowest false-positive rate. Under heterogeneity, random-effects meta-analyses of 2-10 data sets averaging 1,000 cases/1,000 controls each did not increase power, or the meta-analysis was even less powerful than a single study (power desert). Upward bias in effect estimates and underestimation of between-study heterogeneity were common. Fixed-effects calculations avoided power deserts and maximized discovery of association signals at the expense of much higher false-positive rates. Therefore, random- and fixed-effects models are preferable for different purposes (fixed effects for initial screenings, random effects for generalizability applications). These results may have broader implications for the design and interpretation of large-scale multiteam collaborative studies discovering common gene variants.
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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.