ReviewNucleic acids research2012
Comprehensive literature review and statistical considerations for GWAS meta-analysis.
Review in Nucleic acids research, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 107 papers, 20 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
107 citing papers in PubMed, 20 syntheses or guidelines pooled it.
- Genome-wide association analysis using multiple Atlantic salmon populations.Genetics, selection, evolution : GSE · 2025Pooled it
- A terminal metabolite of niacin promotes vascular inflammation and contributes to cardiovascular disease risk.Nature medicine · 2024Pooled it
- Genome-wide meta-analysis, functional genomics and integrative analyses implicate new risk genes and therapeutic targets for anxiety disorders.Nature human behaviour · 2024Pooled it
- Sequence-based GWAS meta-analyses for beef production traits.Genetics, selection, evolution : GSE · 2023Pooled it
- Genome-Wide Association Study of NAFLD Using Electronic Health Records.Hepatology communications · 2022Pooled it
- Potential Pleiotropic Genes and Shared Biological Pathways in Epilepsy and Depression Based on GWAS Summary Statistics.Computational intelligence and neuroscience · 2022Pooled it
- Pooled it
- Reviewing and assessing existing meta-analysis models and tools.Briefings in bioinformatics · 2021Pooled it
- Meta-analysis for milk fat and protein percentage using imputed sequence variant genotypes in 94,321 cattle from eight cattle breeds.Genetics, selection, evolution : GSE · 2020Pooled it
- P-value evaluation, variability index and biomarker categorization for adaptively weighted Fisher's meta-analysis method in omics applications.Bioinformatics (Oxford, England) · 2020Pooled it
- Genome-Wide Association Analyses of Equine Metabolic Syndrome Phenotypes in Welsh Ponies and Morgan Horses.Genes · 2019Pooled it
- Common variants on 6q16.2, 12q24.31 and 16p13.3 are associated with major depressive disorder.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2018Pooled it
- Strong impact of natural-selection-free heterogeneity in genetics of age-related phenotypes.Aging · 2018Pooled it
- MetaGenyo: a web tool for meta-analysis of genetic association studies.BMC bioinformatics · 2017Pooled it
- Functional variants in DCAF4 associated with lung cancer risk in European populations.Carcinogenesis · 2017Pooled it
- A meta-analytic framework for detection of genetic interactions.Genetic epidemiology · 2016Pooled it
- Power considerations for λ inflation factor in meta-analyses of genome-wide association studies.Genetics research · 2016Pooled it
- Exploring the Major Sources and Extent of Heterogeneity in a Genome-Wide Association Meta-Analysis.Annals of human genetics · 2016Pooled it
- Meta-analysis of genome-wide association from genomic prediction models.Animal genetics · 2016Pooled it
- GWA meta-analysis of personality in Korean cohorts.Journal of human genetics · 2015Pooled it
47 more citing papers are in PubMed but not listed here.
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
Over the last decade, genome-wide association studies (GWAS) have become the standard tool for gene discovery in human disease research. While debate continues about how to get the most out of these studies and on occasion about how much value these studies really provide, it is clear that many of the strongest results have come from large-scale mega-consortia and/or meta-analyses that combine data from up to dozens of studies and tens of thousands of subjects. While such analyses are becoming more and more common, statistical methods have lagged somewhat behind. There are good meta-analysis methods available, but even when they are carefully and optimally applied there remain some unresolved statistical issues. This article systematically reviews the GWAS meta-analysis literature, highlighting methodology and software options and reviewing methods that have been used in real studies. We illustrate differences among methods using a case study. We also discuss some of the unresolved issues and potential future directions.
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.