Genetic evaluation: use of genomic data in large-scale genetic evaluations in dairy cattle breeding

2019 ◽  
pp. 441-474
Author(s):  
Joel Ira Weller ◽  
Keyword(s):  
Dairy Cattle ◽  
Large Scale ◽  
Genomic Data ◽  
Genetic Evaluation ◽  
Cattle Breeding ◽  
Evaluation Use ◽  
Dairy Cattle Breeding ◽  
Genetic Evaluations

scholarly journals Relevance of genomic evaluation for egg quality traits in layers

2019 ◽  
Author(s):  
David Picard Druet ◽  
Amandine Varenne ◽  
Florian Herry ◽  
Frédéric Hérault ◽  
Sophie Allais ◽  
...  
Keyword(s):  
Dairy Cattle ◽  
Genomic Selection ◽  
Egg Quality ◽  
Pure Line ◽  
Genetic Evaluation ◽  
Evaluation Methodology ◽  
Quality Traits ◽  
Genomic Evaluation ◽  
Cattle Breeding ◽  
Dairy Cattle Breeding

AbstractBackgroundGenomic evaluation, based on thousands of genetic markers, has become the standard evaluation methodology in dairy cattle breeding programs over the past few years. Despite the many differences between dairy cattle breeding and poultry breeding, genomic selection seems very promising for the avian sector, and studies are currently being conducted to optimize avian selection schemes. In this optimization perspective, one of the key parameters is to properly predict the accuracy of genomic evaluation in pure line layers.MethodsBoth genetic evaluation and genomic evaluation were performed on three candidate populations (male and female), using different sizes of phenotypic records on five egg quality traits and at two different ages. The methodologies used were BLUP & ssGBLUP, and variance-covariance matrices were estimated through REML. To estimate evaluation accuracy, the LR method was implemented. Four statistics were used to assess the relative accuracy of the estimated breeding values of candidates, their bias and dispersion, as well as the differences between genetic evaluation and genomic evaluation.ResultsIt was observed that genomic evaluation, whether performed on males or females, always proved more accurate than genetic evaluation. The gain was higher when phenotypic information was narrowed and an augmentation of the size of the reference population led to an increase in accuracy prediction, for what regards genomic evaluation. By taking into account the increase of selection intensity and the decrease of the generation interval induced by genomic selection, the expected annual genetic gain would be higher with ancestry-based genomic evaluation of male candidates than with genetic evaluation based on collaterals. This advantage of genomic selection over genetic selection requires to be studied in more details for female candidates.ConclusionsIn conclusion, in the population studied, genomic evaluation for egg quality traits of breeding birds at birth seems a promising strategy, at least for what regards males selection.

scholarly journals Wechselwirkung zwischen Vater und Mutter in der Milchrinderzüchtung

1999 ◽  
Vol 42 (6) ◽  
pp. 527-534
Author(s):  
W. Brade ◽  
E. Groeneveld
Keyword(s):  
Animal Model ◽  
Dairy Cattle ◽  
Genetic Evaluation ◽  
Cattle Breeding ◽  
Additional Information ◽  
Dairy Cattle Breeding ◽  
Interaction Component

Abstract. Title of the paper: Interaction between sire and dam in dairy cattle breeding The traditional animal model in genetic evaluation has been extended by a sire * dam interaction component demonstrating the computational feasibility. The expected additional information response from including a sire * dam component is small, however, this effect come larger with an increase in the number of animals produced by biotechnical means which will likely get special treatments.

scholarly journals Spatial modelling improves genetic evaluation in smallholder breeding programs

2020 ◽  
Vol 52 (1) ◽  
Author(s):  
Maria L. Selle ◽  
Ingelin Steinsland ◽  
Owen Powell ◽  
John M. Hickey ◽  
Gregor Gorjanc
Keyword(s):  
Dairy Cattle ◽  
Environmental Effects ◽  
Genomic Data ◽  
Spatial Modelling ◽  
Genetic Evaluation ◽  
Spatial Relationships ◽  
Spatial Effects ◽  
Environmental Covariates ◽  
Genetic Evaluations ◽  
Smallholder Systems

Abstract Background Breeders and geneticists use statistical models to separate genetic and environmental effects on phenotype. A common way to separate these effects is to model a descriptor of an environment, a contemporary group or herd, and account for genetic relationship between animals across environments. However, separating the genetic and environmental effects in smallholder systems is challenging due to small herd sizes and weak genetic connectedness across herds. We hypothesised that accounting for spatial relationships between nearby herds can improve genetic evaluation in smallholder systems. Furthermore, geographically referenced environmental covariates are increasingly available and could model underlying sources of spatial relationships. The objective of this study was therefore, to evaluate the potential of spatial modelling to improve genetic evaluation in dairy cattle smallholder systems. Methods We performed simulations and real dairy cattle data analysis to test our hypothesis. We modelled environmental variation by estimating herd and spatial effects. Herd effects were considered independent, whereas spatial effects had distance-based covariance between herds. We compared these models using pedigree or genomic data. Results The results show that in smallholder systems (i) standard models do not separate genetic and environmental effects accurately, (ii) spatial modelling increases the accuracy of genetic evaluation for phenotyped and non-phenotyped animals, (iii) environmental covariates do not substantially improve the accuracy of genetic evaluation beyond simple distance-based relationships between herds, (iv) the benefit of spatial modelling was largest when separating the genetic and environmental effects was challenging, and (v) spatial modelling was beneficial when using either pedigree or genomic data. Conclusions We have demonstrated the potential of spatial modelling to improve genetic evaluation in smallholder systems. This improvement is driven by establishing environmental connectedness between herds, which enhances separation of genetic and environmental effects. We suggest routine spatial modelling in genetic evaluations, particularly for smallholder systems. Spatial modelling could also have a major impact in studies of human and wild populations.

scholarly journals Spatial modelling improves genetic evaluation in smallholder breeding programs

2020 ◽  
Author(s):  
Maria L. Selle ◽  
Ingelin Steinsland ◽  
Owen Powell ◽  
John M. Hickey ◽  
Gregor Gorjanc
Keyword(s):  
Dairy Cattle ◽  
Environmental Effects ◽  
Genomic Data ◽  
Spatial Modelling ◽  
Genetic Evaluation ◽  
Spatial Relationships ◽  
Spatial Effects ◽  
Environmental Covariates ◽  
Genetic Evaluations ◽  
Smallholder Systems

AbstractBreeders and geneticists use statistical models for genetic evaluation of animals to separate genetic and environmental effects on phenotype. A common way to separate these effects is to model a descriptor of an environment, a contemporary group or herd, and account for genetic relationship between animals across the environments. However, separating the genetic and environmental effects in smallholder systems is challenging due to small herd sizes and weak genetic connectedness across herds. Our hypothesis was that accounting for spatial relationships between nearby herds can improve genetic evaluation in smallholder systems. Further, geographically referenced environmental covariates are increasingly available and could be used to model underlying sources of the spatial relationships. The objective of this study was therefore to evaluate the potential of spatial modelling to improve genetic evaluation in smallholder systems. We focus solely on dairy cattle smallholder systems.We performed simulations and real dairy cattle data analysis to test our hypothesis. We used a range of models to account for environmental variation by estimating herd and spatial effects. We compared these models using pedigree or genomic data.The results show that in smallholder systems (i) standard models are not able to separate genetic and environmental effects, (ii) spatial modelling increases accuracy of genetic evaluation for phenotyped and non-phenotyped animals, (iii) environmental covariates do not substantially improve accuracy of genetic evaluation beyond simple distance-driven spatial relationships between herds, (iv) the benefit of spatial modelling was the largest when the genetic and environmental effects were hard to separate and (v) spatial modelling was beneficial when using either pedigree or genomic data.We have demonstrated the potential of spatial modelling to improve genetic evaluation in smallholder systems. This improvement is driven by establishing environmental connectedness between herds that enhances separation of the genetic and environmental effects. We suggest routine spatial modelling in genetic evaluations, particularly for smallholder systems. Spatial modelling could also have major impact in studies of human and wild populations.

Genetic evaluation of Iranian Holstein dairy cows with the use of a Covariance Function Model

2002 ◽  
Vol 2002 ◽  
pp. 189-189
Author(s):  
H. Farhangfar ◽  
P. Rowlinson ◽  
M.B. Willis
Keyword(s):  
Animal Model ◽  
Dairy Cattle ◽  
Covariance Function ◽  
Genetic Evaluation ◽  
Cattle Breeding ◽  
Factors Influencing ◽  
Lactation Curve ◽  
Function Test ◽  
Dairy Cattle Breeding ◽  
Breeding Programmes

The use of test day models has increasingly become of interest in genetic evaluation of dairy cattle. Traditionally, in most dairy cattle breeding programmes genetic evaluation of dairy sires and cows has been primarily based on using 305-day lactation yield. Since in this system of evaluation many incomplete lactation records have to be extended to complete lactations this could result in biased prediction of breeding values due to any over or underestimation of total lactation yields. In genetic evaluation of dairy cattle, test day models have many advantages over the traditional method (Lactation Animal Model) because of the fact that they can account for the lactation curve of individual cows and take more accurate account of the effects of environmental factors influencing test day milk yield over the course of lactation. The main aim of this study was to use a Covariance Function Test day Animal Model (CFTAM) in genetic evaluation of first parity Iranian Holsteins.

Sign in / Sign up

Export Citation Format

Share Document