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.

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.

Autoregressive repeatability model for genetic evaluation of longitudinal reproductive traits in dairy cattle

2020 ◽  
Vol 87 (1) ◽  
pp. 37-44 ◽  
Author(s):  
Hugo T. Silva ◽  
Paulo S. Lopes ◽  
Claudio N. Costa ◽  
Fabyano F. Silva ◽  
Delvan A. Silva ◽  
...  
Keyword(s):  
Dairy Cattle ◽  
Environmental Effects ◽  
Rank Correlation ◽  
Reproductive Traits ◽  
Genetic Evaluation ◽  
Information Criteria ◽  
Ar Model ◽  
Genetic Progress ◽  
Data Set ◽  
Genetic Evaluations

AbstractWe investigated the efficiency of the autoregressive repeatability model (AR) for genetic evaluation of longitudinal reproductive traits in Portuguese Holstein cattle and compared the results with those from the conventional repeatability model (REP). The data set comprised records taken during the first four calving orders, corresponding to a total of 416, 766, 872 and 766 thousand records for interval between calving to first service, days open, calving interval and daughter pregnancy rate, respectively. Both models included fixed (month and age classes associated to each calving order) and random (herd-year-season, animal and permanent environmental) effects. For AR model, a first-order autoregressive (co)variance structure was fitted for the herd-year-season and permanent environmental effects. The AR outperformed the REP model, with lower Akaike Information Criteria, lower Mean Square Error and Akaike Weights close to unity. Rank correlations between estimated breeding values (EBV) with AR and REP models ranged from 0.95 to 0.97 for all studied reproductive traits, when the total bulls were considered. When considering only the top-100 selected bulls, the rank correlation ranged from 0.72 to 0.88. These results indicate that the re-ranking observed at the top level will provide more opportunities for selecting the best bulls. The EBV reliabilities provided by AR model was larger for all traits, but the magnitudes of the annual genetic progress were similar between two models. Overall, the proposed AR model was suitable for genetic evaluations of longitudinal reproductive traits in dairy cattle, outperforming the REP model.

scholarly journals Recent developments in cattle, pig, sheep and horse breeding - a review

2014 ◽  
Vol 83 (4) ◽  
pp. 327-340 ◽  
Author(s):  
Alena Svitáková ◽  
Jitka Schmidová ◽  
Petr Pešek ◽  
Alexandra Novotná
Keyword(s):  
Dairy Cattle ◽  
Performance Test ◽  
Evaluation Method ◽  
Genetic Evaluation ◽  
Random Regression ◽  
Farm Animals ◽  
Breeding Objectives ◽  
Recent Developments ◽  
Genetic Evaluations ◽  
Method Of Evaluation

The aim of this review was to summarize new genetic approaches and techniques in the breeding of cattle, pigs, sheep and horses. Often production and reproductive traits are treated separately in genetic evaluations, but advantages may accrue to their joint evaluation. A good example is the system in pig breeding. Simplified breeding objectives are generally no longer appropriate and consequently becoming increasingly complex. The goal of selection for improved animal performance is to increase the profit of the production system; therefore, economic selection indices are now used in most livestock breeding programmes. Recent developments in dairy cattle breeding have focused on the incorporation of molecular information into genetic evaluations and on increasing the importance of longevity and health in breeding objectives to maximize the change in profit. For a genetic evaluation of meat yield (beef, pig, sheep), several types of information can be used, including data from performance test stations, records from progeny tests and measurements taken at slaughter. The standard genetic evaluation method of evaluation of growth or milk production has been the multi-trait animal model, but a test-day model with random regression is becoming the new standard, in sheep as well. Reviews of molecular genetics and pedigree analyses for performance traits in horses are described. Genome – wide selection is becoming a world standard for dairy cattle, and for other farm animals it is under development.

Dairy genetic improvement through artificial insemination, performance recording and genetic evaluation

2003 ◽  
Vol 83 (3) ◽  
pp. 385-392 ◽  
Author(s):  
B. J. Van Doormaal ◽  
G. J. Kistemaker
Keyword(s):  
Dairy Cattle ◽  
Artificial Insemination ◽  
Genetic Improvement ◽  
Genetic Evaluation ◽  
Production Traits ◽  
Genetic Progress ◽  
Genetic Gains ◽  
Holstein Breed ◽  
The Past ◽  
Genetic Evaluations

Artificial insemination (AI) of dairy cattle in Canada was started more than half a century ago and today it is estimated that at least 75% of all dairy cattle nationally are bred using this common reproductive technology. A Best Linear Unbiased Prediction sire model for estimating genetic evaluations for production traits was introduced in 1975. The combination of extensive use of AI with genetic evaluations for bulls and cows has resulted in significant phenotypic and genetic gains over the past 20 yr. In the Holstein breed, mature equivalent yields have increased by an average of 200 kg milk, 7.0 kg fat and 6.3 kg protein per year since 1980. Genetically, the relative emphasis realized for production traits versus overall type during the past 5 yr has followed the 60:40 breeding goal represented in the Lifetime Profit Index, which has increased at an average rate of 0.28 standard units per year. Examination of the generation interval in the Canadian Holstein breed, associated with each of the four pathways for genetic improvement, indicates a 46% increase in the rate of annual genetic gain today compared to 20 yr ago. The increased accuracy and intensity of selection associated with the use of AI and genetic evaluations have also contributed to the rates of phenotypic and genetic progress achieved over the years. In the future , AI will continue to be a critical component of the genetic gains possible in dairy cattle breeding but it will be complemented by other reproductive technologies aimed at further reducing generation intervals and increasing the accuracy and selection of intensity, especially on the female side. Key words: Dairy cattle, artificial insemination, genetic progress, genetic evaluation

Usefulness of pedigree indices in predicting future transmitting abilities of sons

1992 ◽  
Vol 72 (2) ◽  
pp. 409-412 ◽  
Author(s):  
T. E. Ali ◽  
L. R. Schaeffer ◽  
J. P. Gibson ◽  
E. B. Burnside
Keyword(s):  
Dairy Cattle ◽  
Key Words ◽  
Milk Fat ◽  
Quantitative Traits ◽  
Genetic Evaluation ◽  
Pedigree Information ◽  
Evaluation Bias ◽  
Genetic Evaluations

Two thousand three hundred and five bulls with complete pedigree information and proofs (ETA) in at least 1 of 10 yr for milk, fat and fat percent were used to study the reliability of genetic evaluations. No evidence was found that bulls with superior genetic evaluations failed to pass on this superiority to their sons and maternal grandsons. The assumption that all the genes controlling these quantitative traits are autosomes seems to be reasonable. Key words: Dairy cattle, genetic evaluation, bias

scholarly journals Genomic data enables genetic evaluation using data recorded on low-middle income country smallholder dairy farms

2019 ◽  
Author(s):  
Owen Powell ◽  
Raphael Mrode ◽  
R. Chris Gaynor ◽  
Martin Johnsson ◽  
Gregor Gorjanc ◽  
...  
Keyword(s):  
Genomic Prediction ◽  
Production Systems ◽  
Genomic Data ◽  
Genetic Evaluation ◽  
Pedigree Information ◽  
Dairy Production ◽  
Genomic Information ◽  
Smallholder Dairy ◽  
Genetic Evaluations ◽  
Smallholder Dairy Production

AbstractBackgroundGenetic evaluation is a central component of a breeding program. In advanced economies, most genetic evaluations depend on large quantities of data that are recorded on commercial farms. Large herd sizes and widespread use of artificial insemination create strong genetic connectedness that enables the genetic and environmental effects of an individual animal’s phenotype to be accurately separated. In contrast to this, herds are neither large nor have strong genetic connectedness in smallholder dairy production systems of many low to middle-income countries (LMIC). This limits genetic evaluation, and furthermore, the pedigree information needed for traditional genetic evaluation is typically unavailable. Genomic information keeps track of shared haplotypes rather than shared relatives. This information could capture and strengthen genetic connectedness between herds and through this may enable genetic evaluations for LMIC smallholder dairy farms. The objective of this study was to use simulation to quantify the power of genomic information to enable genetic evaluation under such conditions.ResultsThe results from this study show: (i) the genetic evaluation of phenotyped cows using genomic information had higher accuracy compared to pedigree information across all breeding designs; (ii) the genetic evaluation of phenotyped cows with genomic information and modelling herd as a random effect had higher or equal accuracy compared to modelling herd as a fixed effect; (iii) the genetic evaluation of phenotyped cows from breeding designs with strong genetic connectedness had higher accuracy compared to breeding designs with weaker genetic connectedness; (iv) genomic prediction of young bulls was possible using marker estimates from the genetic evaluations of their phenotyped dams. For example, the accuracy of genomic prediction of young bulls from an average herd size of 1 (μ=1.58) was 0.40 under a breeding design with 1,000 sires mated per generation and a training set of 8,000 phenotyped and genotyped cows.ConclusionsThis study demonstrates the potential of genomic information to be an enabling technology in LMIC smallholder dairy production systems by facilitating genetic evaluations with in-situ records collected from farms with herd sizes of four cows or less. Across a range of breeding designs, genomic data enabled accurate genetic evaluation of phenotyped cows and genomic prediction of young bulls using data sets that contained small herds with weak genetic connections. The use of smallholder dairy data in genetic evaluations would enable the establishment of breeding programs to improve in-situ germplasm and, if required, would enable the importation of the most suitable external germplasm. This could be individually tailored for each target environment. Together this would increase the productivity, profitability and sustainability of LMIC smallholder dairy production systems. However, data collection, including genomic data, is expensive and business models will need to be carefully constructed so that the costs are sustainably offset.

scholarly journals Cox Processes Associated with Spatial Copula Observed through Stratified Sampling

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 524
Author(s):  
Walguen Oscar ◽  
Jean Vaillant
Keyword(s):  
Environmental Effects ◽  
Gaussian Copula ◽  
Spatial Dependency ◽  
Spatial Effects ◽  
Doubly Stochastic ◽  
Cox Processes ◽  
Black Leaf Streak ◽  
Spatial Random Field ◽  
Black Leaf Streak Disease ◽  
Spatial Count Data

Cox processes, also called doubly stochastic Poisson processes, are used for describing phenomena for which overdispersion exists, as well as Poisson properties conditional on environmental effects. In this paper, we consider situations where spatial count data are not available for the whole study area but only for sampling units within identified strata. Moreover, we introduce a model of spatial dependency for environmental effects based on a Gaussian copula and gamma-distributed margins. The strength of dependency between spatial effects is related with the distance between stratum centers. Sampling properties are presented taking into account the spatial random field of covariates. Likelihood and Bayesian inference approaches are proposed to estimate the effect parameters and the covariate link function parameters. These techniques are illustrated using Black Leaf Streak Disease (BLSD) data collected in Martinique island.

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