Considerations on genetic connectedness between management units under an animal model

Kennedy, B. W.; Trus, D.

doi:10.2527/1993.7192341x

Cited by 107 publications

(151 citation statements)

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“…The vector of contrasts used in CD(x) could be the average of the breeding values in one unit minus the average of the breeding values in another unit; CD will be zero if the mean difference is not estimable. A third connectedness statistic based on the coefficient matrix was proposed by Kennedy and Trus [17]. They contended that the MSE of prediction of differences between candidates for selection was the most logical measure of connectedness.…”

Section: Measuring Connectednessmentioning

confidence: 99%

“…Kennedy and Trus [17] suggested using the variance of differences between estimates of the environmental effects of management units (e.g., herds), which was highly correlated to the average PEV of differences between animals in different herds in a small example data set. Bunter and Macbeth [2] developed this idea further by evaluating the variance of estimated differences in genetic group effects when fitting a model that included genetic groups.…”

Section: Measuring Connectednessmentioning

confidence: 99%

“…This measure was less dependent on herd size than the variance of the difference in herd effects of Kennedy and Trus [17]. Other connectedness measures involving counts of direct links between test station groups [35] or management units [41] have also been suggested, but the statistical properties of these measures are strongly dependent on data structure.…”

Section: Measuring Connectednessmentioning

confidence: 99%

“…Several statistics have been developed to assess the quality of across-unit connections [6,17,20]. An ideal statistic would provide insights into the potential risk of incorrect selection decisions associated with biased breeding value predictions.…”

Section: Introductionmentioning

confidence: 99%

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Managing the risk of comparing estimated breeding values across flocks or herds through connectedness: a review and application

2007

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-Comparing predicted breeding values (BV) among animals in different managementunits (e.g. flocks, herds) is challenging if units have different genetic means. Unbiased estimates of differences in BV may be obtained by assigning base animals to genetic groups according to their unit of origin, but units must be connected to estimate group effects. If many small groups exist, error of BV prediction may be increased. Alternatively, genetic groups can be excluded from the statistical model, which may bias BV predictions. If adequate genetic connections exist among units, bias is reduced. Several measures of connectedness have been proposed, but their relationships to potential bias in BV predictions are not well defined. This study compares alternative strategies to connect small units and assesses the ability of different connectedness statistics to quantify potential bias in BV prediction. Connections established using common sires across units were most effective in reducing bias. The coefficient of determination of the mean difference in predicted BV was a perfect indicator of potential bias remaining when comparing individuals in separate units. However, this measure is difficult to calculate; correlated measures such as prediction errors of differences in unit means and correlations among prediction errors are suggested as practical alternatives.connectedness / genetic evaluation / bias / genetic groups / breeding value

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Section: Measuring Connectednessmentioning

confidence: 99%

Section: Measuring Connectednessmentioning

confidence: 99%

Section: Measuring Connectednessmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Managing the risk of comparing estimated breeding values across flocks or herds through connectedness: a review and application

2007

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“…Therefore the particular herds are genetically isolated and their mutual comparison is difficult (Kennedy and Trus, 1993). A part of the optimum organisation of breeding is such a structure of the mating of sires that creates conditions for reliable estimation of breeding value and subsequent high genetic gain.…”

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confidence: 99%

Stochastic simulation of the influence of insemination on the estimation of breeding value and its reliability

Veselá¹,

Přibyl²,

Vostrý³

et al. 2007

CZECH J ANIM SCI

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ABSTRACT:Insemination is used less commonly in the population of beef cattle than in dairy cattle. As a consequence, herds are genetically isolated and their comparison is difficult. The objective of the paper was to evaluate the impact of the use of reference sires in insemination on the estimation of breeding value and its reliability by means of stochastic simulation based on a single addition of calves to the basic set. Simulation was divided into three variants according to the connectedness calves through sires (variant 1 -unrelated calves, variant 2 calves related between HYS but unrelated within HYS, variant 3 -calves related within and between HYS). The degree of connectedness was determined by computation of genetic drift variance (GDV). The average value of GDV in the basic set was 0.71. In our study the highest values of GDV were reached in simulated variant 3, in which the relationship increased not only between HYS but also within HYS. The lowest values of GDV were in variant 2, when the relationship increased only between HYS (simulated calves after reference sires did not have any halfsibs in HYS). The single use of reference sires in one generation to make herds connected slightly increased the average coefficient of reliability from 0.121 to 0.145 (by 2.4%) in the whole set. But this increase from 0.24 to 0.33 (by 8.9%) was observed only in animals that were directly connected through reference sires. The increase in reliability was mainly due to an increase in the number of contemporaries. A decrease in the effective number of sires' offspring was an indirect consequence of the increase in the connectedness of the set and higher use of reference sires in insemination. This is the reason why the increase in the connectedness of the set influenced the reliability of breeding value of the whole set less than we would expect. The use of reference sires influenced the fluctuation of breeding values. The use of a higher number of reference sires in insemination with only one offspring in each of the connected herds appeared as the most suitable approach from the aspect of the connectedness of the set and reliability of breeding value estimation. A marked influence of herd connectedness on the estimation of breeding value and its reliability, and on the result of breeding, is to be expected after reference sires have been used in several successive generations.

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Training set optimization under population structure in genomic selection

et al. 2014

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Key messagePopulation structure must be evaluated before optimization of the training set population. Maximizing the phenotypic variance captured by the training set is important for optimal performance.AbstractThe optimization of the training set (TRS) in genomic selection has received much interest in both animal and plant breeding, because it is critical to the accuracy of the prediction models. In this study, five different TRS sampling algorithms, stratified sampling, mean of the coefficient of determination (CDmean), mean of predictor error variance (PEVmean), stratified CDmean (StratCDmean) and random sampling, were evaluated for prediction accuracy in the presence of different levels of population structure. In the presence of population structure, the most phenotypic variation captured by a sampling method in the TRS is desirable. The wheat dataset showed mild population structure, and CDmean and stratified CDmean methods showed the highest accuracies for all the traits except for test weight and heading date. The rice dataset had strong population structure and the approach based on stratified sampling showed the highest accuracies for all traits. In general, CDmean minimized the relationship between genotypes in the TRS, maximizing the relationship between TRS and the test set. This makes it suitable as an optimization criterion for long-term selection. Our results indicated that the best selection criterion used to optimize the TRS seems to depend on the interaction of trait architecture and population structure.Electronic supplementary materialThe online version of this article (doi:10.1007/s00122-014-2418-4) contains supplementary material, which is available to authorized users.

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Considerations on genetic connectedness between management units under an animal model

Cited by 107 publications

References 0 publications

Managing the risk of comparing estimated breeding values across flocks or herds through connectedness: a review and application

Managing the risk of comparing estimated breeding values across flocks or herds through connectedness: a review and application

Stochastic simulation of the influence of insemination on the estimation of breeding value and its reliability

Training set optimization under population structure in genomic selection

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