1,721,179 research outputs found
Estimation of genotype × environment interaction for yield, health and fertility in dairy cattle
Full text linkIn dairy cattle breeding,health and fertility traits have recently been included in a large number of national breeding goals.The effectiveness of breeding decisions and management changes to improve health and fertility possibly interact through genotype × environment interaction (G×E). G×E is a phenomenon in which different genotypes respond differently to changes in an environment. It can consist of the following effects: heterogeneous genetic variances across environments, genetic correlation of a trait expressed in different environments being less than 1.0 (reranking), and heterogeneous genetic correlations between traits across environments. In this thesis, G×E for health andfertiltity, as well as for yield, has been investigated using reaction norm models. In the reaction norm models, breeding values and genetic parameters were modeled as a function of an environmental descriptor using random regression. The dimensions of the model were expanded from linear random regressions to higher order random regressions, to include two parameters to define the environment, and to multivariate reaction norm models.Many environmental descriptors were investigated in this thesis, such as production level, farm size, average somatic cell score and calving interval, however, it appears that the herd parameters linked to nutrition and energy balance are most important for G×E.Significant G×E was detected in 86% of the situations for yield traits, but only in 14% of the situations for health and fertility traits, although significantrerankingwas found for SCS, mastitis and survival. Estimated G×E effects mainly consisted of heterogeneous genetic variances with limitedreranking. As a result of heterogeneous variances in different traits, the relative importance of fertility compared to yield doubled across environments. Estimated G×E effects for SCS indicated morererankingof animals based on analysis of test day records, than on lactation averages. It was shown that selection for increased yield is expected to lead to increased environmental sensitivity for yield, while selection for better fertility is expected to lead to decreased environmental sensitivity for fertility. The models presented in this thesis can be used to account for the effect of herd environment on a trait and the relations between traits, and therefore enable to make accurate predictions of breeding values across environments
Genomics revolution
No full textGenomic selection is the talk of the day in the breeding world. The Netherlands is in the lead. The use of tens of thousands of markers is creating a revolution in breeding. When they are just over 12 months old, young bulls can already be used as proven bull
Composition of genetic covariance between traits using high density SNP information, a quantative perspective
No full textGenetic association between traits, and correlated responses resulting from those associations, are generally predicted using genetic correlations. However, depending on the composition and magnitude of the genetic covariance, asymmetry of correlated responses might occur and predictions may last for one generation only (Bohren et al. 1966). Genome-wide association studies with denser marker genotypes might be useful to investigate the makeup of the genetic covariance between traits. Therefore, the objective was to investigate the makeup of the genetic covariances between quantitative traits in more detail. Phenotypic records of 1737 heifers of farms in four different countries were used after homogenizing and adjusting for management effects. All cows had a genotype for 37,590 SNPs. Initially, only the milk yield traits were considered, with a univariate Bayesian Stochastic Search Variable Selection (BSSVS) model including a separate polygenic effect. SNP estimated heritability and covariances differed from pedigree based estimates for some of the traits and the SNPs without a significant association explained most of the genetic variances and covariances of the traits. Ten regions were found with an association with multiple traits, in one of these regions the DGAT1 gene was previously reported with an association with multiple traits. DGAT1 explained up to 41% of the variances of four traits and explained a major part of the correlation between fat yield and fat% and contributes to asymmetry in correlated response between fat yield and fat%. Some of the prior assumptions of the model (few QTL assumed and fitting a polygenic effect), and using a univariate model might have favoured the infinitesimal model like description of the covariances. Therefore, subsequently, a multi-trait BSSVS model was used and prior model assumptions were varied to investigate the effect on the estimated composition of the underlying the covariances
Genomic breeding value prediction:methods and procedures
No full textAnimal breeding faces one of the most significant changes of the past decades – the implementation of genomic selection. Genomic selection uses dense marker maps to predict the breeding value of animals with reported accuracies that are up to 0.31 higher than those of pedigree indexes, without the need to phenotype the animals themselves, or close relatives thereof. The basic principle is that because of the high marker density, each quantitative trait loci (QTL) is in linkage disequilibrium (LD) with at least one nearby marker. The process involves putting a reference population together of animals with known phenotypes and genotypes to estimate the marker effects. Marker effects have been estimated with several different methods that generally aim at reducing the dimensions of the marker data. Nearly all reported models only included additive effects. Once the marker effects are estimated, breeding values of young selection candidates can be predicted with reported accuracies up to 0.85. Although results from simulation studies suggest that different models may yield more accurate genomic estimated breeding values (GEBVs) for different traits, depending on the underlying QTL distribution of the trait, there is so far only little evidence from studies based on real data to support this. The accuracy of genomic predictions strongly depends on characteristics of the reference populations, such as number of animals, number of markers, and the heritability of the recorded phenotype. Another important factor is the relationship between animals in the reference population and the evaluated animals. The breakup of LD between markers and QTL across generations advocates frequent re-estimation of marker effects to maintain the accuracy of GEBVs at an acceptable level. Therefore, at low frequencies of re-estimating marker effects, it becomes more important that the model that estimates the marker effects capitalizes on LD information that is persistent across generations
Updating the reference population to achieve constant genomic prediction reliability across generations
No full textThe reliability of genomic breeding values (DGV) decays over generations. To keep the DGV reliability at a constant level, the reference population (RP) has to be continuously updated with animals from new generations. Updating RP may be challenging due to economic reasons, especially for novel traits involving expensive phenotyping. Therefore, the goal of this study was to investigate a minimal RP update size to keep the reliability at a constant level across generations. We used a simulated dataset resembling a dairy cattle population. The trait of interest was not included itself in the selection index, but it was affected by selection pressure by being correlated with an index trait that represented the overall breeding goal. The heritability of the index trait was assumed to be 0.25 and for the novel trait the heritability equalled 0.2. The genetic correlation between the two traits was 0.25. The initial RP (n=2000) was composed of cows only with a single observation per animal. Reliability of DGV using the initial RP was computed by evaluating contemporary animals. Thereafter, the RP was used to evaluate animals which were one generation younger from the reference individuals. The drop in the reliability when evaluating younger animals was then assessed and the RP was updated to re-gain the initial reliability. The update animals were contemporaries of evaluated animals (EVA). The RP was updated in batches of 100 animals/update. First, the animals most closely related to the EVA were chosen to update RP. The results showed that, approximately, 600 animals were needed every generation to maintain the DGV reliability at a constant level across generations. The sum of squared relationships between RP and EVA and the sum of off-diagonal coefficients of the inverse of the genomic relationship matrix for RP, separately explained 31% and 34%, respectively, of the variation in the reliability across generations. Combined, these parameters explained 53% of the variation in the reliability across generations. Thus, for an optimal RP update an algorithm considering both relationships between reference and evaluated animals, as well as relationships among reference animals, is required.</p
Elk bedrijf eigen fokwaarde? : ID Lelystad onderzoekt of fokwaarden gevoelig zijn voor omgevingsfactoren
No full textID-Lelystad doet onderzoek naar bedrijfsspecifieke fokwaarden op melkveebedrijven. Als start van dit onderzoek is gekeken naar de omgevingsgevoeligheid van fokwaarden voor melk, vet en eiwitproducti
Genomic selection: een revolutie in fokkerij : selecteren op basis van DNA-merkers
No full textGenomic selection is in de fokkerijwereld het gesprek van de dag. Maar wat houdt het nu precies in? En wat voor gevolgen heeft het voor fokprogramma’s
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