SynthesisGenetic epidemiology2010
Imputation aware meta-analysis of genome-wide association studies.
Synthesis in Genetic epidemiology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 3 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
18 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Methods for the Analysis and Interpretation for Rare Variants Associated with Complex Traits.Current protocols in human genetics · 2019Pooled it
- Imputing Phenotypes for Genome-wide Association Studies.American journal of human genetics · 2016Pooled it
- Genome-wide interaction studies reveal sex-specific asthma risk alleles.Human molecular genetics · 2014Pooled it
- Genome-Wide Association Study to Identify Genetic Variants Associated With Diabetic Maculopathy.Investigative ophthalmology & visual science · 2025Article
- Accuracy of haplotype estimation and whole genome imputation affects complex trait analyses in complex biobanks.Communications biology · 2023Article
- Article
- Comparison of Two Meta-Analysis Methods: Inverse-Variance-Weighted Average and Weighted Sum of Z-Scores.Genomics & informatics · 2016Article
- Modeling disease risk through analysis of physical interactions between genetic variants within chromatin regulatory circuitry.Nature genetics · 2016Article
- Evaluation of a Two-Stage Approach in Trans-Ethnic Meta-Analysis in Genome-Wide Association Studies.Genetic epidemiology · 2016Article
- Meta-analysis methods for genome-wide association studies and beyond.Nature reviews. Genetics · 2013Review
- Re-ranking sequencing variants in the post-GWAS era for accurate causal variant identification.PLoS genetics · 2013Article
- The power of meta-analysis in genome-wide association studies.Annual review of genomics and human genetics · 2013Review
- Quick, "imputation-free" meta-analysis with proxy-SNPs.BMC bioinformatics · 2012Article
- Article
- Interpreting meta-analyses of genome-wide association studies.PLoS genetics · 2012Article
- The use of imputed values in the meta-analysis of genome-wide association studies.Genetic epidemiology · 2011Article
- Optimal methods for meta-analysis of genome-wide association studies.Genetic epidemiology · 2011Article
- Enhanced statistical tests for GWAS in admixed populations: assessment using African Americans from CARe and a Breast Cancer Consortium.PLoS genetics · 2011Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Genome-wide association studies have recently identified many new loci associated with human complex diseases. These newly discovered variants typically have weak effects requiring studies with large numbers of individuals to achieve the statistical power necessary to identify them. Likely, there exist even more associated variants, which remain to be found if even larger association studies can be assembled. Meta-analysis provides a straightforward means of increasing study sample sizes without collecting new samples by combining existing data sets. One obstacle to combining studies is that they are often performed on platforms with different marker sets. Current studies overcome this issue by imputing genotypes missing from each of the studies and then performing standard meta-analysis techniques. We show that this approach may result in a loss of power since errors in imputation are not accounted for. We present a new method for performing meta-analysis over imputed single nucleotide polymorphisms, show that it is optimal with respect to power, and discuss practical implementation issues. Through simulation experiments, we show that our imputation aware meta-analysis approach outperforms or matches standard meta-analysis approaches.
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.