This work focused on misclassification of genetic variants and its impact on genetic association studies. The initial question was, if and in which amount non-replication and inconsistency in these studies can be explained by errors in genotypes and reconstruction of haplotypes. The amount and structure of genotyping errors were estimated via maximum-likelihood method based on double genotype measurements. These measurements were derived within routine quality control from genetic epidemiological association studies. Thereby it was possible to yield realistic error size estimates, as they can be expected in association analyses. Genotyping error per SNP in a high quality laboratory using an established genotyping method has been found to be small (<0.1%). The data suggested the allelic drop out model to be appropriate and to some extent also the symmetric model. The bias due to genotype misclassification, as shown in a re-analysis of the association of SNPs of the APM1 gene on plasma adiponectin concentrations, was found to be negligible. In other settings, e.g. relaxed quality control, genotype error might be higher. Then, a higher bias is expected, which was shown to be efficiently corrected with the MC-SIMEX method, a statistical method to correct for misclassification within a generalized linear model. Regarding the uncertainties in haplotypes induced by statistical reconstruction from genotypes, a classification of the various haplotype error measures was provided, introducing sensitivity and specificity into the context of haplotypes. Results from simulations and analytical derivations emphasized the dependence of the haplotype reconstruction error on the specific situation, particularly on minor allele frequency, correlation between SNPs, number of loci and ambiguity fraction. Generally, the sensitivity was greatly reduced for some rare haplotypes, posing a potential problem of rare haplotypes in association studies. Extension to a full 3x3 misclassification matrix, which has not been performed before in other methodological studies, allowed the inclusion of genotype errors. It could be shown that errors in genotypes add substantially to the pure reconstruction error. The impact of haplotype misclassification, induced by a combination of genotype error and haplotype reconstruction error, on haplotype association analyses was evaluated in simulations as well as in a re-analysis of haplotypes on the APM1 gene. In the case of a high genotype error per allele (1% or more), a rather high bias on haplotype association estimates was observed, which could be corrected using the MC-SIMEX method. The MC-SIMEX was presented as an efficient method to calculate error-corrected association estimates in haplotype association studies. Altogether, assuming good quality standards in the laboratory and thus a small genotype error rate (<0.5%) as it was estimated in this investigation, the impact on haplotype association estimates was rather moderate to negligable. Moreover, calculation of misclassification matrices for specific haplotypes additionally assures the correctness of haplotype assignments and simplifies the interpretation of association estimates. These findings argue that non-replication of genetic association studies are only to a minor extent due to errors in genetic variants, if the genotyping process is performed in experienced laboratories using established methods with sufficient quality control. The multiple testing problem is likely to play the biggest role in the non-replication problem of genetic epidemiological studies
