ArticleBMC bioinformatics2012
Quick, "imputation-free" meta-analysis with proxy-SNPs.
Article in BMC bioinformatics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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
10 citing papers in PubMed.
- PYRAMA: an open-source tool for advanced meta-analysis of genome wide association studies.Bioinformatics (Oxford, England) · 2026Article
- The goldmine of GWAS summary statistics: a systematic review of methods and tools.BioData mining · 2024Article
- The relationship between coffee-related factors and cortical and hippocampal structure: a triangulation of evidence approach and Mendelian randomization research.Frontiers in nutrition · 2024Article
- Investigating causal associations among gut microbiota, metabolites, and psoriatic arthritis: a Mendelian randomization study.Frontiers in microbiology · 2024Article
- Assessment of causal relationships between omega-3 and omega-6 polyunsaturated fatty acids in autoimmune rheumatic diseases: a brief research report from a Mendelian randomization study.Frontiers in nutrition · 2024Article
- Immunogenetic determinants of heterosexual HIV-1 transmission: key findings and lessons from two distinct African cohorts.Genes and immunity · 2021Review
- Accurate and adaptive imputation of summary statistics in mixed-ethnicity cohorts.Bioinformatics (Oxford, England) · 2018Article
- Genome-wide significant risk factors for Alzheimer's disease: role in progression to dementia due to Alzheimer's disease among subjects with mild cognitive impairment.Molecular psychiatry · 2017Article
- SUCLG2 identified as both a determinator of CSF Aβ1-42 levels and an attenuator of cognitive decline in Alzheimer's disease.Human molecular genetics · 2014Article
- Genome-wide association study and mouse expression data identify a highly conserved 32 kb intergenic region between WNT3 and WNT9b as possible susceptibility locus for isolated classic exstrophy of the bladder.Human molecular genetics · 2014Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMeta-analysis (MA) is widely used to pool genome-wide association studies (GWASes) in order to a) increase the power to detect strong or weak genotype effects or b) as a result verification method. As a consequence of differing SNP panels among genotyping chips, imputation is the method of choice within GWAS consortia to avoid losing too many SNPs in a MA. YAMAS (Yet Another Meta Analysis Software), however, enables cross-GWAS conclusions prior to finished and polished imputation runs, which eventually are time-consuming.
resultsHere we present a fast method to avoid forfeiting SNPs present in only a subset of studies, without relying on imputation. This is accomplished by using reference linkage disequilibrium data from 1,000 Genomes/HapMap projects to find proxy-SNPs together with in-phase alleles for SNPs missing in at least one study. MA is conducted by combining association effect estimates of a SNP and those of its proxy-SNPs. Our algorithm is implemented in the MA software YAMAS. Association results from GWAS analysis applications can be used as input files for MA, tremendously speeding up MA compared to the conventional imputation approach. We show that our proxy algorithm is well-powered and yields valuable ad hoc results, possibly providing an incentive for follow-up studies. We propose our method as a quick screening step prior to imputation-based MA, as well as an additional main approach for studies without available reference data matching the ethnicities of study participants. As a proof of principle, we analyzed six dbGaP Type II Diabetes GWAS and found that the proxy algorithm clearly outperforms naïve MA on the p-value level: for 17 out of 23 we observe an improvement on the p-value level by a factor of more than two, and a maximum improvement by a factor of 2127.
conclusionsYAMAS is an efficient and fast meta-analysis program which offers various methods, including conventional MA as well as inserting proxy-SNPs for missing markers to avoid unnecessary power loss. MA with YAMAS can be readily conducted as YAMAS provides a generic parser for heterogeneous tabulated file formats within the GWAS field and avoids cumbersome setups. In this way, it supplements the meta-analysis process.
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.