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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
Published on: June 21, 2018
Estimating the single nucleotide polymorphism genotype misclassification from routine double measurements in a large
Iris M Heid1, Claudia Lamina, Helmut Küchenhoff
1Helmholtz Zentrum München-German Research Center for Environmental Health, Institute of Epidemiology, 85764 Neuherberg, Germany. heid@helmholtz-muenchen.de
American Journal of Epidemiology
|September 16, 2008
Summary
Genotype misclassification in large-scale genetic studies is typically small, as shown by analyzing 646,558 genotypes. This study provides crucial data for epidemiologic research, ensuring reliable SNP association findings.
Area of Science:
- Genetics
- Epidemiology
- Bioinformatics
Background:
- Accurate genotype data is crucial for genetic epidemiology.
- Previous large-scale estimations of genotype misclassification were lacking.
- Single nucleotide polymorphisms (SNPs) are key genetic markers.
Purpose of the Study:
- To estimate genotype misclassification rates in a large cohort.
- To assess the impact of misclassification on SNP association studies.
- To validate methods for quantifying genotyping errors.
Main Methods:
- Analysis of approximately 14,000 subjects and 646,558 genotypes using mass spectrometry.
- Calculation of discordance rates from duplicate genotyping.
- Application of misclassification models and the MC-SIMEX method.
Main Results:
- Overall genotype discordance was 0.36% among 57,805 duplicate genotypes.
- Estimated SNP misclassification probabilities ranged from 0.0000 to 0.0035.
- MC-SIMEX analysis showed negligible impact on APM1 gene SNP associations with small error, but increased estimates with substantial error.
Conclusions:
- This study provides the first large-scale epidemiologic data on SNP genotype misclassification.
- The observed misclassification rates are generally small and reassuring for detecting SNP associations.
- The presented methods are practical for quantifying genotyping error and its impact in epidemiologic studies.
