ArticleHuman genetics2013
Imputation across genotyping arrays for genome-wide association studies: assessment of bias and a correction strategy.
Article in Human genetics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 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
30 citing papers in PubMed, 6 syntheses or guidelines pooled it.
- False positive findings during genome-wide association studies with imputation: influence of allele frequency and imputation accuracy.Human molecular genetics · 2021Pooled it
- Genome-wide association and Mendelian randomisation analysis provide insights into the pathogenesis of heart failure.Nature communications · 2020Pooled it
- Use of polygenic risk scores of nicotine metabolism in predicting smoking behaviors.Pharmacogenomics · 2018Pooled it
- Genome-wide meta-analysis reveals common splice site acceptor variant in CHRNA4 associated with nicotine dependence.Translational psychiatry · 2015Pooled it
- Cis-Expression Quantitative Trait Loci Mapping Reveals Replicable Associations with Heroin Addiction in OPRM1.Biological psychiatry · 2015Pooled it
- Meta-analysis of 65,734 individuals identifies TSPAN15 and SLC44A2 as two susceptibility loci for venous thromboembolism.American journal of human genetics · 2015Pooled it
- Skim-sequencing for genomic selection in wheat: a comparison of marker platforms.The plant genome · 2026Article
- Contribution of common and rare variants to Asian neovascular age-related macular degeneration subtypes.Nature communications · 2023Article
- Natural variation of respiration-related traits in plants.Plant physiology · 2023Review
- Accuracy of haplotype estimation and whole genome imputation affects complex trait analyses in complex biobanks.Communications biology · 2023Article
- GAWMerge expands GWAS sample size and diversity by combining array-based genotyping and whole-genome sequencing.Communications biology · 2022Article
- Improved analyses of GWAS summary statistics by reducing data heterogeneity and errors.Nature communications · 2021Article
- Ultrafast homomorphic encryption models enable secure outsourcing of genotype imputation.Cell systems · 2021Article
- Genome-wide association studies: assessing trait characteristics in model and crop plants.Cellular and molecular life sciences : CMLS · 2021Review
- Inclusion of genetic variants in an ensemble of gradient boosting decision trees does not improve the prediction of citalopram treatment response.Scientific reports · 2021Article
- Fast and Scalable Private Genotype Imputation Using Machine Learning and Partially Homomorphic Encryption.IEEE access : practical innovations, open solutions · 2021Article
- Low coverage whole genome sequencing enables accurate assessment of common variants and calculation of genome-wide polygenic scores.Genome medicine · 2019Article
- Genotype imputation performance of three reference panels using African ancestry individuals.Human genetics · 2018Article
- Investigating the genetic architecture of dementia with Lewy bodies: a two-stage genome-wide association study.The Lancet. Neurology · 2018Article
- Association Between Substance Use Disorder and Polygenic Liability to Schizophrenia.Biological psychiatry · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
A great promise of publicly sharing genome-wide association data is the potential to create composite sets of controls. However, studies often use different genotyping arrays, and imputation to a common set of SNPs has shown substantial bias: a problem which has no broadly applicable solution. Based on the idea that using differing genotyped SNP sets as inputs creates differential imputation errors and thus bias in the composite set of controls, we examined the degree to which each of the following occurs: (1) imputation based on the union of genotyped SNPs (i.e., SNPs available on one or more arrays) results in bias, as evidenced by spurious associations (type 1 error) between imputed genotypes and arbitrarily assigned case/control status; (2) imputation based on the intersection of genotyped SNPs (i.e., SNPs available on all arrays) does not evidence such bias; and (3) imputation quality varies by the size of the intersection of genotyped SNP sets. Imputations were conducted in European Americans and African Americans with reference to HapMap phase II and III data. Imputation based on the union of genotyped SNPs across the Illumina 1M and 550v3 arrays showed spurious associations for 0.2 % of SNPs: ~2,000 false positives per million SNPs imputed. Biases remained problematic for very similar arrays (550v1 vs. 550v3) and were substantial for dissimilar arrays (Illumina 1M vs. Affymetrix 6.0). In all instances, imputing based on the intersection of genotyped SNPs (as few as 30 % of the total SNPs genotyped) eliminated such bias while still achieving good imputation quality.
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.