ArticleAmerican journal of epidemiology2008
Estimating the single nucleotide polymorphism genotype misclassification from routine double measurements in a large epidemiologic sample.
Article in American journal of epidemiology, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- geck: trio-based comparative benchmarking of variant calls.Bioinformatics (Oxford, England) · 2018Article
- Single-variant and multi-variant trend tests for genetic association with next-generation sequencing that are robust to sequencing error.Human heredity · 2012Article
- Impact of genotyping errors on the type I error rate and the power of haplotype-based association methods.BMC genetics · 2009Article
- Incorporating duplicate genotype data into linear trend tests of genetic association: methods and cost-effectiveness.Statistical applications in genetics and molecular biology · 2009Article
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Authors and funding
18 authors.
Funding
Abstract
Previously, estimation of genotype misclassification of single nucleotide polymorphisms (SNPs) as encountered in epidemiologic practice and involving thousands of subjects was lacking. The authors collected representative data on approximately 14,000 subjects from 8 studies and 646,558 genotypes assessed in 2005 by means of matrix-assisted laser desorption ionization time-of-flight mass spectrometry. Overall discordance among 57,805 double genotypes from routine quality control was 0.36%. Fitting different misclassification models by maximum likelihood assuming identical misclassification for all SNPs, the estimated misclassification probabilities ranged from 0.0000 to 0.0035. When applying the misclassification simulation and extrapolation (MC-SIMEX) method for the first time to genetic data to account for the misclassification in a reanalysis of adiponectin-encoding (APM1) gene SNP associations with plasma adiponectin in 1,770 subjects, the authors found no impact of this small error on association estimates but increased estimates for a more substantial error. This study is the first to provide large-scale epidemiologic data on SNP genotype misclassification. The estimated misclassification in this example was small and negligible for association estimates, which is reassuring and essential for detecting SNP associations. In situations with more substantial error, the presented approach using duplicate genotyping and the MC-SIMEX method is practical and helpful for quantifying the genotyping error and its impact.
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