Browsing by Person "Plieschke, Laura Isabel"

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Investigations on methodological and strategic aspects of genomic selection in dairy cattle using real and simulated data
(2018) Plieschke, Laura Isabel; Bennewitz, Jörn
In Chapter one a method was developed to separate the genomic relationship matrix into two independent covariance matrices. Here, the base group component describes the covariance that results from systematic differences in allele frequencies between groups at the pedigree base. The remaining segregation component describes the genomic relationship that is corrected for the differences between base populations. To investigate the proposed decomposition three different models were tested on six traits, where the covariance between animals was described either only by the segregation component or by a combination of the two components. An additional variant examining the effect of a fixed modeling of the group effects was included. In total, 7965 genotyped Fleckvieh and 4257 genotyped Brown Swiss and 143 genotyped Original Braunvieh bulls were available for this study. The proposed decomposition of the genomic relationship matrix helped to examine the relative importance of the effects of base groups and segregation component in a given population. It was possible to estimate significant differences between the means of base groups in most cases for both breeds and for the traits analyzed. Analysis of the matrix of base group contributions to the populations investigated revealed several general breed-specific aspects. Comparing the three models, it was concluded that the segregation component is not sufficient to describe the covariance completely. However, it also was found that the model applied has no strong impact on predictive power if the animals used for validation show no differences in their genetic composition with respect to the base groups and if the majority of them have complete pedigrees of sufficient depth. The subject of the chapter two was investigation to systematically increase the reliability of genomic breeding values by integrating cows into the reference population of genomic breeding value estimation. For this purpose a dataset was generated by simulation resembling the German-Austrian dual-purpose Fleckvieh population.. The concept investigated is based on genotyping a fixed number of daughters of each AI bull of the last or last two generation of the reference population and, together with their phenotypic performance, to integrate them into the reference population of the genomic evaluation. Different scenarios with different numbers of daughters per bull were compared. In the base scenario the reference population was made up of 4200 bulls. In the extended scenarios, more and more daughters were gradually integrated in the reference population. The reference population of the most extended scenario contained 4200 bulls and 420,000 cows. It was found that the inclusion of genotypes and phenotypes of female animals can increase the reliabilities genomic breeding values considerably. Changes in validation reliability of 6-54% for a trait with a heritability of 0.4 depending on scenario were found. As the number of daughters increased, the validation reliability increased as well. It should be noted that the composition of the daughter samples had a very great influence on whether the additional genotyped and phenotyped animals in the reference population can have a positive effect on the reliability of genomic breeding values. If pre-selected daughter samples were genotyped, the mean validation reliability decreased significantly compared to a correspondingly large unselected daughter sample. In addition, a higher bias was observable in these cases. Chapter three expands the investigations of chapter two by a low-heritability trait, as well as the aspect of so called new traits. The results found in chapter two were confirmed in chapter three for a low-heritability trait. Changes in validation reliability of 5-21% for a heritability of 0.05 depending on scenario were found. The negative effects of pre-selected daughter samples were even more pronounced in chapter three. In the case of an ‘old’ trait, the number of phenotypes is expected to be (nearly) unlimited, since a recording system is well established. In the case of a new trait recording of phenotypes just started, therefore the number of phenotypes is limited. Two different genotyping strategies were compared for new traits. On the one hand, the sires of the phenotyped cows were genotyped and on the other hand the phenotyped cows were genotyped themselves. It was found in all compared scenarios that it is more sensible to genotype cows themselves instead of the genotyping their sires. However, if usual strategy of phenotyping female animals and genotyping of males is applied, it is at least important to ensure that many daughters are phenotyped in a balanced system. If different numbers of daughters per bull are phenotyped and unbalancedness becomes severe, the average validation reliability decreased significantly.