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

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.

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 Creators:
Lamina, Claudia, Author
Küchenhoff, Helmut, Author
Chang-Claude, Jenny, Author
Paulweber, Bernhard, Author
Wichmann, H.-Erich, Author
Illig, Thomas, Author
Hoehe, Margret R.1, Author           
Kronenberg, Florian, Author
Heid, Iris M., Author
Affiliations:
1Genetic 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, ou_1479651              

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Free keywords: Haplotypes; measurement error; genotyping error; association studies; misclassification
 Abstract: 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.

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Language(s): eng - English
 Dates: 2010-09
 Publication Status: Issued
 Pages: -
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 Rev. Type: -
 Identifiers: eDoc: 556533
DOI: 10.1111/j.1469-1809.2010.00593.x
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Title: Annals of Human Genetics
Source Genre: Journal
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Pages: - Volume / Issue: 74 (5) Sequence Number: - Start / End Page: 452 - 462 Identifier: ISSN: 1469-1809