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Haplotype misclassification from genotype error and statistical reconstruction and its impact on association estimates

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons50204

Hoehe,  Margret R.
Genetic Variation, Haplotypes, and Genetics of Complex Disease (Margret Hoehe), Dept. of Vertebrate Genomics (Head: Hans Lehrach), Max Planck Institute for Molecular Genetics, Max Planck Society;

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Lamina.pdf
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Zitation

Lamina, C., Küchenhoff, H., Chang-Claude, J., Paulweber, B., Wichmann, H.-E., Illig, T., et al. (2010). Haplotype misclassification from genotype error and statistical reconstruction and its impact on association estimates. Annals of Human Genetics, 74(5), 452-462. doi:10.1111/j.1469-1809.2010.00593.x.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0010-7A67-9
Zusammenfassung
Haplotypes are an important concept for genetic association studies, but involve uncertainty due to statistical reconstruction from single nucleotide polymorphism (SNP) genotypes and genotype error. We developed a re-sampling approach to quantify haplotype misclassification probabilities and implemented the MC-SIMEX approach to tackle this as a 3 × 3 misclassification problem. Using a previously published approach as a benchmark for comparison, we evaluated the performance of our approach by simulations and exemplified it on real data from 15 SNPs of the APM1 gene. Misclassification due to reconstruction error was small for most, but notable for some, especially rarer haplotypes. Genotype error added misclassification to all haplotypes resulting in a non-negligible drop in sensitivity. In our real data example, the bias of association estimates due to reconstruction error alone reached −48.2% for a 1% genotype error, indicating that haplotype misclassification should not be ignored if high genotype error can be expected. Our 3 × 3 misclassification view of haplotype error adds a novel perspective to currently used methods based on genotype intensities and expected number of haplotype copies. Our findings give a sense of the impact of haplotype error under realistic scenarios and underscore the importance of high-quality genotyping, in which case the bias in haplotype association estimates is negligible.