A criterion for measuring the degree  of connectedness in linear models  of genetic evaluation

Foulley, JL; Hanocq, E.; Boichard, D.

doi:10.1051/gse:19920403

Cited by 7 publications

(7 citation statements)

References 8 publications

(8 reference statements)

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“…An extremely interesting extension of this kind of method could concern connectedness studies [4, 17,18]. Whatever the method used to address these problems, off-diagonal coefficients of the PEV matrix (C uu ) [5,16] and offdiagonal coefficients of the relationship matrix (A) [18] are needed to estimate CD of a comparison. No analytical method can deliver these coefficients.…”

Section: Resultsmentioning

confidence: 99%

“…By extension, this method may be easily used to estimate off-diagonal elements of A and C uu which are of interest to study genetic connectedness between animals or populations (herds, years, countries...). The precision of a comparison between the genetic merits of animals or groups of animals can be estimated by looking at PEV [5,16] or at CD [17,18] of the corresponding contrast. This contrast may be seen as a linear combination of breeding values (x u) where x is a vector whose elements sum to 0 [17] e.g., the contrast between breeding values of two animals i and j is:…”

Section: Modelsmentioning

confidence: 99%

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A sampling method for estimating the accuracy of predicted breeding values in genetic evaluation

Fouilloux¹,

Laloë

2001

Genet. Sel. Evol.

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A sampling-based method for estimating the accuracy of estimated breeding values using an animal model is presented. Empirical variances of true and estimated breeding values were estimated from a simulated n-sample. The method was validated using a small data set from the Parthenaise breed with the estimated coefficient of determination converging to the true values. It was applied to the French Salers data file used for the 2000 on-farm evaluation (IBOVAL) of muscle development score. A drawback of the method is its computational demand. Consequently, convergence can not be achieved in a reasonable time for very large data files. Two advantages of the method are that a) it is applicable to any model (animal, sire, multivariate, maternal effects...) and b) it supplies off-diagonal coefficients of the inverse of the mixed model equations and can therefore be the basis of connectedness studies.

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Section: Resultsmentioning

confidence: 99%

Section: Modelsmentioning

confidence: 99%

A sampling method for estimating the accuracy of predicted breeding values in genetic evaluation

Fouilloux¹,

Laloë

2001

Genet. Sel. Evol.

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“…Different methods have been proposed to evaluate the connectedness of data, based on the prediction error variance (PEV) (Foulley et al 1992;Kennedy & Trus 1993) or functions of it, such as the coefficient of determination (CD) (Laloë 1993;Laloë et al 1996). The latter method considers the whole data design, as well as the balance between the decrease of PEV and the loss of genetic variability because of genetic relationships among animals.…”

Section: Discussionmentioning

confidence: 99%

Trends of the genetic connectedness measures among Nelore beef cattle herds

Pegolo¹,

Laloë²,

Oliveira³

et al. 2011

J Animal Breeding Genetics

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Validity of comparisons between expected breeding values obtained from best linear unbiased prediction procedures in genetic evaluations is dependent on genetic connectedness among herds. Different cattle breeding programmes have their own particular features that distinguish their database structure and can affect connectedness. Thus, the evolution of these programmes can also alter the connectedness measures. This study analysed the evolution of the genetic connectedness measures among Brazilian Nelore cattle herds from 1999 to 2008, using the French Criterion of Admission to the group of Connected Herds (CACO) method, based on coefficients of determination (CD) of contrasts. Genetic connectedness levels were analysed by using simple and multiple regression analyses on herd descriptors to understand their relationship and their temporal trends from the 1999-2003 to the 2004-2008 period. The results showed a high level of genetic connectedness, with CACO estimates higher than 0.4 for the majority of them. Evaluation of the last 5-year period showed only a small increase in average CACO measures compared with the first 5 years, from 0.77 to 0.80. The percentage of herds with CACO estimates lower than 0.7 decreased from 27.5% in the first period to 16.2% in the last one. The connectedness measures were correlated with percentage of progeny from connecting sires, and the artificial insemination spread among Brazilian herds in recent years. But changes in connectedness levels were shown to be more complex, and their complete explanation cannot consider only herd descriptors. They involve more comprehensive changes in the relationship matrix, which can be only fully expressed by the CD of contrasts.

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“…Since the spatial effects are estimated jointly for all herds and also jointly with other effects, such as management (herd) and genetic effects, it induces environmental connectedness and with this improves genetic connectedness. Animal breeders are very aware of the required data structures for accurate genetic evaluation (Foulley et al, 1990;Jorjani et al, 2001;Powell et al, 2019) and there are formal methods to asses genetic connectedness between contemporary groups (Foulley et al, 1992;Kennedy and Trus, 1993;Laloë, 1993;Laloë et al, 1996;Yu and Morota, 2019). Achieving sufficient genetic connectedness is particularly difficult when contemporary groups are small and there is limited genetic exchange between them.…”

Section: Why Spatial Modelling Improves Genetic Evaluationmentioning

confidence: 99%

Spatial modelling improves genetic evaluation in smallholder breeding programs

Selle

Steinsland

Powell

et al. 2020

Preprint

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Breeders 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.

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A criterion for measuring the degree of connectedness in linear models of genetic evaluation

Cited by 7 publications

References 8 publications

A sampling method for estimating the accuracy of predicted breeding values in genetic evaluation

A sampling method for estimating the accuracy of predicted breeding values in genetic evaluation

Trends of the genetic connectedness measures among Nelore beef cattle herds

Spatial modelling improves genetic evaluation in smallholder breeding programs

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