ArticleAmerican journal of human genetics2010
Spoiling the whole bunch: quality control aimed at preserving the integrity of high-throughput genotyping.
Article in American journal of human genetics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 3 of them syntheses that pooled it.
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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
34 citing papers in PubMed, 3 syntheses or guidelines pooled it, 51 citations in OpenAlex.
- Association testing of previously reported variants in a large case-control meta-analysis of diabetic nephropathy.Diabetes · 2012Pooled it
- Pooled it
- Genome-wide meta-analysis for severe diabetic retinopathy.Human molecular genetics · 2011Pooled it
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- Germline determinants of aberrant signaling pathways in cancer.NPJ precision oncology · 2024Article
- Population-based genetic effects for developmental stuttering.HGG advances · 2022Article
- Phenome risk classification enables phenotypic imputation and gene discovery in developmental stuttering.American journal of human genetics · 2021Article
- Recommendations for Statistical Reporting in Cardiovascular Medicine: A Special Report From the American Heart Association.Circulation · 2021Review
- Genetic predictors of hippocampal subfield volume in PTSD cases and trauma-exposed controls.European journal of psychotraumatology · 2020Article
- Exome genotyping and linkage analysis identifies two novel linked regions and replicates two others for myopia in Ashkenazi Jewish families.BMC medical genetics · 2019Article
- Interaction Landscape of Inherited Polymorphisms with Somatic Events in Cancer.Cancer discovery · 2017Article
- Impact of genotyping errors on statistical power of association tests in genomic analyses: A case study.Genetic epidemiology · 2017Article
- Are Interactions between cis-Regulatory Variants Evidence for Biological Epistasis or Statistical Artifacts?American journal of human genetics · 2016Article
- Genome-wide gene-based analysis suggests an association between Neuroligin 1 (NLGN1) and post-traumatic stress disorder.Translational psychiatry · 2016Article
- Large-scale genomics unveil polygenic architecture of human cortical surface area.Nature communications · 2015Article
- A genome-wide sib-pair scan for quantitative language traits reveals linkage to chromosomes 10 and 13.Genes, brain, and behavior · 2015Article
- Ancestry of the Timorese: age-related macular degeneration associated genotype and allele sharing among human populations from throughout the world.Frontiers in genetics · 2015Article
- Association of Toll-like receptor polymorphisms with HIV status in North Americans.Genes and immunity · 2014Article
- Value of Mendelian laws of segregation in families: data quality control, imputation, and beyond.Genetic epidemiology · 2014Article
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
Abstract
False-positive or false-negative results attributable to undetected genotyping errors and confounding factors present a constant challenge for genome-wide association studies (GWAS) given the low signals associated with complex phenotypes and the noise associated with high-throughput genotyping. In the context of the genetics of kidneys in diabetes (GoKinD) study, we identify a source of error in genotype calling and demonstrate that a standard battery of quality-control (QC) measures is not sufficient to detect and/or correct it. We show that, if genotyping and calling are done by plate (batch), even a few DNA samples of marginally acceptable quality can profoundly alter the allele calls for other samples on the plate. In turn, this leads to significant differential bias in estimates of allele frequency between plates and, potentially, to false-positive associations, particularly when case and control samples are not sufficiently randomized to plates. This problem may become widespread as investigators tap into existing public databases for GWAS control samples. We describe how to detect and correct this bias by utilizing additional sources of information, including raw signal-intensity data.
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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.