ArticlePloS one2013
Assessing the impact of differential genotyping errors on rare variant tests of association.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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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
13 citing papers in PubMed.
- Association Study ofGenes · 2025Article
- Establishing analytical validity of BeadChip array genotype data by comparison to whole-genome sequence and standard benchmark datasets.BMC medical genomics · 2022Article
- Impact of genotyping errors on statistical power of association tests in genomic analyses: A case study.Genetic epidemiology · 2017Article
- A POWERFUL METHOD FOR INCLUDING GENOTYPE UNCERTAINTY IN TESTS OF HARDY-WEINBERG EQUILIBRIUM.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2017Article
- The impact of genotype calling errors on family-based studies.Scientific reports · 2016Article
- Testing Rare-Variant Association without Calling Genotypes Allows for Systematic Differences in Sequencing between Cases and Controls.PLoS genetics · 2016Article
- Whole-genome sequence data uncover loss of genetic diversity due to selection.Genetics, selection, evolution : GSE · 2016Article
- Value of Mendelian laws of segregation in families: data quality control, imputation, and beyond.Genetic epidemiology · 2014Article
- Effective filtering strategies to improve data quality from population-based whole exome sequencing studies.BMC bioinformatics · 2014Article
- Comparison of genotype clustering tools with rare variants.BMC bioinformatics · 2014Article
- Evaluating the concordance between sequencing, imputation and microarray genotype calls in the GAW18 data.BMC proceedings · 2014Article
- Evaluating the impact of genotype errors on rare variant tests of association.Frontiers in genetics · 2014Article
- A geometric framework for evaluating rare variant tests of association.Genetic epidemiology · 2013Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Genotyping errors are well-known to impact the power and type I error rate in single marker tests of association. Genotyping errors that happen according to the same process in cases and controls are known as non-differential genotyping errors, whereas genotyping errors that occur with different processes in the cases and controls are known as differential genotype errors. For single marker tests, non-differential genotyping errors reduce power, while differential genotyping errors increase the type I error rate. However, little is known about the behavior of the new generation of rare variant tests of association in the presence of genotyping errors. In this manuscript we use a comprehensive simulation study to explore the effects of numerous factors on the type I error rate of rare variant tests of association in the presence of differential genotyping error. We find that increased sample size, decreased minor allele frequency, and an increased number of single nucleotide variants (SNVs) included in the test all increase the type I error rate in the presence of differential genotyping errors. We also find that the greater the relative difference in case-control genotyping error rates the larger the type I error rate. Lastly, as is the case for single marker tests, genotyping errors classifying the common homozygote as the heterozygote inflate the type I error rate significantly more than errors classifying the heterozygote as the common homozygote. In general, our findings are in line with results from single marker tests. To ensure that type I error inflation does not occur when analyzing next-generation sequencing data careful consideration of study design (e.g. use of randomization), caution in meta-analysis and using publicly available controls, and the use of standard quality control metrics is critical.
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