ArticleHuman genetics2012
Artifact due to differential error when cases and controls are imputed from different platforms.
Article in Human genetics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 3 of them syntheses that pooled it.
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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.
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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.
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Who cites it
22 citing papers in PubMed, 3 syntheses or guidelines pooled it, 36 citations in OpenAlex.
- Genome-wide association and Mendelian randomisation analysis provide insights into the pathogenesis of heart failure.Nature communications · 2020Pooled it
- Genome-wide association study of intracranial aneurysm identifies a new association on chromosome 7.Stroke · 2014Pooled it
- Genome-wide association analyses in East Asians identify new susceptibility loci for colorectal cancer.Nature genetics · 2013Pooled it
- Identification of novel reproducible combinatorial genetic risk factors for myalgic encephalomyelitis in the DecodeME patient cohort and commonalities with long COVID.Journal of translational medicine · 2026Article
- Accuracy of haplotype estimation and whole genome imputation affects complex trait analyses in complex biobanks.Communications biology · 2023Article
- Interaction Testing and Polygenic Risk Scoring to Estimate the Association of Common Genetic Variants With Treatment Resistance in Schizophrenia.JAMA psychiatry · 2022Article
- Polygenic risk score of shorter telomere length and risk of depression and anxiety in women.Journal of psychiatric research · 2018Article
- Genetic associations with childhood brain growth, defined in two longitudinal cohorts.Genetic epidemiology · 2018Article
- Imputation from SNP chip to sequence: a case study in a Chinese indigenous chicken population.Journal of animal science and biotechnology · 2018Article
- Bayesian and frequentist analysis of an Austrian genome-wide association study of colorectal cancer and advanced adenomas.Oncotarget · 2017Article
- Article
- Age at natural menopause genetic risk score in relation to age at natural menopause and primary open-angle glaucoma in a US-based sample.Menopause (New York, N.Y.) · 2017Article
- A comprehensive survey of genetic variation in 20,691 subjects from four large cohorts.PloS one · 2017Article
- A genome-wide association study identifies variants in KCNIP4 associated with ACE inhibitor-induced cough.The pharmacogenomics journal · 2016Article
- A genome-wide association study confirms PNPLA3 and identifies TM6SF2 and MBOAT7 as risk loci for alcohol-related cirrhosis.Nature genetics · 2015Article
- Gene-environment interactions in cancer epidemiology: a National Cancer Institute Think Tank report.Genetic epidemiology · 2013Article
- Imputation across genotyping arrays for genome-wide association studies: assessment of bias and a correction strategy.Human genetics · 2013Article
- Comparison of the performance of two commercial genome-wide association study genotyping platforms in Han Chinese samples.G3 (Bethesda, Md.) · 2013Article
- Re-ranking sequencing variants in the post-GWAS era for accurate causal variant identification.PLoS genetics · 2013Article
- Genome-wide association study of intracranial aneurysms confirms role of Anril and SOX17 in disease risk.Stroke · 2012Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Including previously genotyped controls in a genome-wide association study can provide cost-savings, but can also create design biases. When cases and controls are genotyped on different platforms, the imputation needed to provide genome-wide coverage will introduce differential measurement error and may lead to false positives. We compared genotype frequencies of two healthy control groups from the Nurses' Health Study genotyped on different platforms [Affymetrix 6.0 (n = 1,672) and Illumina HumanHap550 (n = 1,038)]. Using standard imputation quality filters, we observed 9,841 single-nucleotide polymorphisms (SNPs) out of 2,347,809 (0.4%) significant at the 5 × 10(-8) level. We explored three methods for controlling for this Type I error inflation. One method was to remove platform effects using principal components; another was to restrict to SNPs of highest quality imputation; and a third was to genotype some controls alongside cases to exclude SNPs that are statistical artifact. The first method could not reduce the Type I error rate; the other two could dramatically reduce the error rate, although both required that a portion of SNPs be excluded from analysis. Ideally, the biases we describe would be eliminated at the design stage, by genotyping sufficient numbers of cases and controls on each platform. Researchers using imputation to combine samples genotyped on different platforms with severely unbalanced case-control ratios should be aware of the potential for inflated Type I error rates and apply appropriate quality filters. Every SNP found with genome-wide significance should be validated on another platform to verify that its significance is not an artifact of study design.
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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.