Measuring connectedness among herds in mixed linear models: From theory to practice in large-sized genetic evaluations

Fouilloux, Marie-Noëlle; Clément, Virginie; Laloë, Dénis

doi:10.1051/gse:2007041

Cited by 8 publications

(23 citation statements)

References 15 publications

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“…In addition to PEVD, CD, and r, other connectedness measures have been applied to pedigree data ( e.g. , Foulley et al 1992; Fouilloux et al 2008), which have their own characteristics. Advancement of molecular biotechnology now enables us to assess connectedness at the genomic level.…”

Section: Discussionmentioning

confidence: 99%

Genomic Relatedness Strengthens Genetic Connectedness Across Management Units

Hyeong-keun

Spangler

Lewis

et al. 2017

G3 Genes|Genomes|Genetics

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Genetic connectedness refers to a measure of genetic relatedness across management units (e.g., herds and flocks). With the presence of high genetic connectedness in management units, best linear unbiased prediction (BLUP) is known to provide reliable comparisons between estimated genetic values. Genetic connectedness has been studied for pedigree-based BLUP; however, relatively little attention has been paid to using genomic information to measure connectedness. In this study, we assessed genome-based connectedness across management units by applying prediction error variance of difference (PEVD), coefficient of determination (CD), and prediction error correlation r to a combination of computer simulation and real data (mice and cattle). We found that genomic information (boldnormalG) increased the estimate of connectedness among individuals from different management units compared to that based on pedigree (boldnormalA). A disconnected design benefited the most. In both datasets, PEVD and CD statistics inferred increased connectedness across units when using boldnormalG- rather than boldnormalA-based relatedness, suggesting stronger connectedness. With r once using allele frequencies equal to one-half or scaling boldnormalG to values between 0 and 2, which is intrinsic to A, connectedness also increased with genomic information. However, PEVD occasionally increased, and r decreased when obtained using the alternative form of G, instead suggesting less connectedness. Such inconsistencies were not found with CD. We contend that genomic relatedness strengthens measures of genetic connectedness across units and has the potential to aid genomic evaluation of livestock species.

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Section: Discussionmentioning

confidence: 99%

Genomic Relatedness Strengthens Genetic Connectedness Across Management Units

Hyeong-keun

Spangler

Lewis

et al. 2017

G3 Genes|Genomes|Genetics

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“…Garcia-Cortes et al [16] and Fouilloux and Laloë [17] have proposed sampling methods that, theoretically, allow the estimation of entire variance-covariance matrices, and, as a result, the estimation of the CD of contrasts among genetic levels of herds. Based on these methods, Fouilloux et al [18] have described a new two-step process to analyze connectedness among herds: the first step involves computing the CD of comparisons between groups of animals using a sampling method, while in the second step, clusters of well-connected groups are formed based on a "criterion of admission to the group of connected herds" (CACO) that reflects the level of connectedness of each herd. The procedure accounts for known pedigree and data structure efficiently when measuring connectedness among herds.…”

Section: Introductionmentioning

confidence: 99%

“…This clustering method was appropriate in condensing the relevant information of large matrices of similarities (here, the CD of contrasts between genetic levels of herds). It meets the requirement to construct sets of well-connected herds, and may handle large problems very quickly [18]. …”

Section: Introductionmentioning

confidence: 99%

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Connectedness among herds of beef cattle bred under natural service

2010

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BackgroundA procedure to measure connectedness among herds was applied to a beef cattle population bred by natural service. It consists of two steps: (a) computing coefficients of determination (CDs) of comparisons among herds; and (b) building sets of connected herds.MethodsThe CDs of comparisons among herds were calculated using a sampling-based method that estimates empirical variances of true and predicted breeding values from a simulated n-sample. Once the CD matrix was estimated, a clustering method that can handle a large number of comparisons was applied to build compact clusters of connected herds of the Bruna dels Pirineus beef cattle. Since in this breed, natural service is predominant and there are almost no links with reference sires, to estimate CDs, an animal model was used taking into consideration all pedigree information and, especially, the connections with dams. A sensitivity analysis was performed to contrast single-trait sire and animal model evaluations with different heritabilities, multiple-trait animal model evaluations with different degrees of genetic correlations and models with maternal effects.ResultsUsing a sire model, the percentage of connected herds was very low even for highly heritable traits whereas with an animal model, most of the herds of the breed were well connected and high CD values were obtained among them, especially for highly heritable traits (the mean of average CD per herd was 0.535 for a simulated heritability of 0.40). For the lowly heritable traits, the average CD increased from 0.310 in the single-trait evaluation to 0.319 and 0.354 in the multi-trait evaluation with moderate and high genetic correlations, respectively. In models with maternal effects, the average CD per herd for the direct effects was similar to that from single-trait evaluations. For the maternal effects, the average CD per herd increased if the maternal effects had a high genetic correlation with the direct effects, but the percentage of connected herds for maternal effects was very low, less than 12%.ConclusionsThe degree of connectedness in a bovine population bred by natural service mating, such as Bruna del Pirineus beef cattle, measured as the CD of comparisons among herds, is high. It is possible to define a pool of animals for which estimated breeding values can be compared after an across-herds genetic evaluation, especially for highly heritable traits.

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“…However, the method entails absorbing all random effects into the dense part of the coefficient matrix containing the test series means, which is still a computationally intensive procedure. Fouilloux et al (2008) have proposed a method that entails repeatedly sampling genetic values and observations for individuals in different herds, solving the mixed model equations and deriving empirical estimates of the variances required for computing the CD, namely Var(x′(û−u)) and Var(x′u). Again, it is conjecture if a sampling method for estimating an entire prediction error variance-covariance matrix is any faster than inversion of the coefficient matrix.…”

Section: Discussionmentioning

confidence: 99%

Connectedness among test series in mixed linear models of genetic evaluation for forest trees

Kerr

Dutkowski

Jansson

et al. 2015

Tree Genetics & Genomes

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In forest tree species with large natural ranges, there are usually several to many separate breeding populations, each designed to capture elite material suited to a particular geographic region. Separate test series are often dedicated to each population. Because the aim is to optimise gain in the meta-population, it is important to ensure that test series are linked so that individuals can be compared across test series as well as within. Computer simulation was used to determine the most efficient strategy for obtaining linkage. The average accuracy of a genetic value contrast between individuals in the same and in different test series was used as the criterion for assessing the optimal level of linkage. Accuracy is a function of the elements of the inverse coefficient matrix for a mixed linear model within a best linear unbiased prediction framework (BLUP). Material used to link test series was either common test families, common check-lots such as seed orchard bulks, or families generated by inter-crossing parents from different test series. Use of common test families was the most efficient strategy for the scenarios tested, which included having 50 parents crossed to produce 50 test families in each of three populations. For a low-heritability scenario, the amount of linkage material, relative to test material, needed to be 8 and 12 %, for progeny and parents, respectively, in order for a contrast between individuals in different test series to have equivalent accuracy as a contrast between individuals in the same test series. Other strategies were less efficient in terms of the amount of linkage material needed to obtain this equivalency.

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Measuring connectedness among herds in mixed linear models: From theory to practice in large-sized genetic evaluations

Cited by 8 publications

References 15 publications

Genomic Relatedness Strengthens Genetic Connectedness Across Management Units

Genomic Relatedness Strengthens Genetic Connectedness Across Management Units

Connectedness among herds of beef cattle bred under natural service

Connectedness among test series in mixed linear models of genetic evaluation for forest trees

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