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Haplotype Misclassification Resulting from Statistical Reconstruction and Genotype Error, and Its Impact on Association Estimates
Author(s) -
Lamina Claudia,
Küchenhoff Helmut,
ChangClaude Jenny,
Paulweber Bernhard,
Wichmann H.Erich,
Illig Thomas,
Hoehe Margret R.,
Kronenberg Florian,
Heid Iris M.
Publication year - 2010
Publication title -
annals of human genetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.537
H-Index - 77
eISSN - 1469-1809
pISSN - 0003-4800
DOI - 10.1111/j.1469-1809.2010.00593.x
Subject(s) - haplotype , haplotype estimation , genotype , single nucleotide polymorphism , genotyping , statistics , snp , genetics , genetic association , biology , mathematics , gene
Summary 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.