ABSTRACT:The aim of this study was to estimate allelic and genotypic frequencies of five DNA markers that are positional and functional candidates for milk production traits in Czech Fleckvieh cattle. In addition, we evaluated the association of these markers with milk production traits and breeding values for milk production traits and also estimated linkage disequilibrium (LD) between two markers within the prolactin (PRL) gene. As part of this study, 505 Czech Fleckvieh cows were genotyped. The markers in proliferator-activated receptor gamma coactivator 1-alpha (PPARGC1A), secreted phosphoprotein (SPP1), cytochrome P450 family 11 subfamily B hydroxylase (CYP11B1), and the two polymorphisms in the prolactin gene (PRL) showed evidence of segregation in our study. The PPARGC1A polymorphism was associated with milk yield, milk fat and protein traits. The polymorphism in SPP1 was significantly associated with milk protein percentage. The CYP11B1 polymorphism showed positive associations with milk composition traits and breeding values for milk yield, milk fat, and protein traits. Both polymorphisms within the PRL gene were associated with milk yield, milk fat and milk protein yield (individually and grouped). Linkage disequilibrium between the two polymorphisms in PRL was not observed. In conclusion, all markers examined in this study are important markers for milk production traits in Czech Fleckvieh cattle, and both markers within the PRL gene should be evaluated in future research.

AbStrAct:In association mapping, haplotype-based methods are generally regarded to provide higher power and increased precision than methods based on single markers. For haplotype-based association mapping most studies use a fixed haplotype effect in the model. However, an increase in haplotype length raises the number of parameters in the model, resulting in low accuracy of the estimates especially for the low-frequency haplotypes. Modeling of haplotype effects can be improved if they are assumed to be random effects, as only one parameter, i.e. haplotype variance, needs to be estimated compared to estimating the effects of all different haplotypes in a fixed haplotype model. Using simulated data, we investigated statistical models where haplotypes were fitted either as a fixed or random effect and we compared them for the power, precision, and type I error. We investigated five haplotype lengths of 2, 4, 6, 10 and 20. The simulated data resembled the Danish Holstein cattle pedigree representing a complex relationship structure and QTL effects of different sizes were simulated. We observed that the random haplotype models had high power and very low type I error rates (after the Bonferroni correction), while the fixed haplotype models had lower power and excessively high type I errors. Haplotype length of 4 to 6 gave the best results for random model in the present study. Though the present study was conducted on data structure more frequent in livestock, our findings on random vs. fixed haplotype effects in association mapping models are applicable to data from other species with a similar pedigree structure.