A simulation study of the effect  of connectedness on genetic trend

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

doi:10.1051/gse:19960104

Cited by 10 publications

(17 citation statements)

References 15 publications

(16 reference statements)

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“…These schemes involve an agreement by breeders to mate a predetermined portion of their females to a common set of selected males. Simulation studies have shown that cooperative sire referencing schemes can improve genetic gain by 30 to 35% compared to within-unit selection programs while also improving accuracy of comparisons between units and slowing inbreeding [10,23,34]. Genetic differences among units are not required for increased gain if the number of breeding females is low in some of the member units [23] because the likelihood of producing extreme individuals is smaller in small (< 100 female) units.…”

Section: Comparing and Using Animals Across Subpopulationsmentioning

confidence: 99%

“…Miraei Ashtiani and James [27] and Hanocq et al [10] both showed that if management units differ in average genetic merit, the rate of genetic change increased most rapidly when the units first became connected as units with lower mean breeding values increased use of animals from units with higher mean breeding values. As a result, overall genetic gain in the system improved, with higher average gains in units with low initial genetic merit.…”

Section: Comparing and Using Animals Across Subpopulationsmentioning

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 management units (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.

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Section: Comparing and Using Animals Across Subpopulationsmentioning

confidence: 99%

Section: Comparing and Using Animals Across Subpopulationsmentioning

confidence: 99%

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

2007

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“…Information was requested on all stallions that had been in use over a time period corresponding to at least two full generations, but no fixed limitation on birth years of stallions was set. The ranges of birth-years for stallions in the data-set provided by each organization were: DWB (1956-1997), Han (1958-1995), Holst (1965-1996, KWPN (1960-1996) and SWB (1952-1997.…”

Section: Datamentioning

confidence: 99%

“…Also, the accuracy of the EBV of individuals increases with increasing connectedness among populations. Hanocq et al (1996) showed that weak genetic connectedness may introduce considerable bias in the estimation of the average genetic merit of populations across countries, because the differences among populations may not be estimable but still assumed to be zero. Hence, with limited connectedness, international genetic evaluation may still be feasible, but not recommended, because there will be too much fluctuation in the results (Kennedy and Trus, 1993;Klei and Lawlor, 2001;Árnason and Sigurdsson, 2004;Mark et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

Connectedness among five European sport horse populations

2008

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“…These ties can be used for across-country evaluations of bulls (MACE) (Schaeffer, 1994). It has been shown how ties between random factors affect prediction error variance (Kennedy & Trus, 1993) and bias (Hanocq et al, 1996).…”

Section: Introductionmentioning

confidence: 99%