Sire‐by‐herd interaction effect when variances across herds are heterogeneous. I. Expected genetic progress

Canavesi, F.; Schaeffer, L.R.; Burnside, E. B.; Jansen, G.B.; Rozzi, P.

doi:10.1111/j.1439-0388.1995.tb00546.x

Cited by 7 publications

(10 citation statements)

References 30 publications

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“…However, for the weight traits, rank correlations between EBV obtained with homogenous and heterogeneous variances were near one. This suggested that any consequences of heteroscedasticity were not pronounced for weight traits, in agreement with previous results (Canavesi et al, 1995). Sire re-ranking was more evident for UMD and logUFD, suggesting heteroscedasticity would have a greater effect on the genetic evaluation of ultrasound traits.…”

Section: Discussionsupporting

confidence: 90%

“…Ultrasonic traits had greater indication of heteroscedasticity than weight traits, and also had a higher proportion of variation explained by the F × S interaction. Dickerson (1962) and Canavesi et al (1995) found that F × S interaction may be caused by, or at least inflated by, heterogeneous variances. When variances were standardized across farms, the variance component estimates, and the proportion of F × S interaction variance to total variance, did not change.…”

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Heterogeneous variances and genetics by environment interactions in genetic evaluation of crossbred lambs

Márquez

Haresign

Davies

et al. 2015

Animal

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Accounting for environmental heteroscedasticity and genetics by environment interaction (G × E) in genetic evaluation is important because animals may not perform predictably across environments. The objectives of this study were to evaluate the presence and consequences of heteroscedasticity and G × E on genetic evaluation. The population considered was crossbred lambs sired by terminal sires and reared under commercial conditions in the UK. Data on 6325 lambs sired by Charollais, Suffolk and Texel rams were obtained. The experiment was conducted between 1999 and 2002 on three farms located in England, Scotland and Wales. There were 2322, 2137 and 1866 lambs in England, Scotland and Wales, respectively. A total of 89 sires were mated to 1984 ewes of two types (Welsh and Scottish Mules). Most rams were used for two breeding seasons with some rotated among farms to create genetic links. Lambs were reared on pasture and had their parentage, birth, 5 week, 10 week, and slaughter weights recorded. Lambs were slaughtered at a constant fatness, at which they were ultrasonically scanned for fat and muscle depth. Heteroscedasticity was evaluated in two ways. First, data were separated into three subsets by farm. Within-farm variance component estimates were then compared with those derived from the complete data (Model 1). Second, the combined data were fitted, but with a heterogeneous (by farm) environmental variance structure (Model 2). To investigate G × E, a model with a random farm by sire (F × S) interaction was used (Model 3). The ratio of the F × S variance to total variance was a measure of the level of G × E in the population. Heterogeneity in environmental variability across farm was identified for all traits ( P < 0.01). Rank correlations of sire estimated breeding value between farms differed for Model 1 for all traits. However, sires ranked similarly (rank correlation of 0.99) for weight traits with Model 2, but less so for ultrasonic measures. Including the F × S interaction (Model 3) improved model fit for all traits. However, the F × S term explained a small proportion of variation in weights (<2%) although more in ultrasonic traits (at least 10%). In conclusion, heteroscedasticity and G × E were not large for these data, and can be ignored in genetic evaluation of weight but, perhaps, not ultrasonic traits. Still, before incorporating heteroscedasticity and G × E into routine evaluations of even ultrasonic traits, their consequences on selection response in the breeding goal should be evaluated.

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Section: Discussionsupporting

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Heterogeneous variances and genetics by environment interactions in genetic evaluation of crossbred lambs

Márquez

Haresign

Davies

et al. 2015

Animal

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“…Vários outros autores relataram que a composição genética dos rebanhos também é um dos fatores responsáveis pela heterogeneidade entre rebanhos. Variâncias heterogêneas para grupos genéticos têm sido Tabela 5 -Porcentagens de animais em comum entre os grupos de classificação com base no valor genético verdadeiro e no valor genético predito para 1 e 5% dos melhores touros jovens de 5% dos touros jovens, na qual a porcentagem de animais comuns aumentou de 24 para 38%, e, também, para a estrutura de heterogeneidade RMGS e seleção de 1% dos touros jovens, em que a porcentagem de animais comuns passou encontradas em gado de corte para várias características (Rodriguez-Almeida et al, 1995;Nuñez-Domingues et al, 1995;Crews & Franke, 1998;Oliveira et al, 2001). Estes mesmos autores também têm demonstrado a alteração na ordem de classificação dos animais quando considerada ou não a heterogeneidade de variância entre grupos gené-ticos.…”

Section: Resultsunclassified

“…A correlação média de ordem entre os valores genéticos estimados em diferentes ambientes tem sido, geralmente, igual ou superior a 0,90 (Boldman & Freeman, 1990;Canavesi et al, 1995). Meuwissen & Van Der Werf (1993) verificaram que, após corrigir os dados para efeito de rebanho-ano, o ganho genético não reduziu em razão da heterogeneidade de variância.…”

Section: Introductionunclassified

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Erros na classificação de touros, vacas e touros jovens geneticamente superiores avaliados na presença de heterogeneidade

Carneiro¹,

Torres²,

Lopes³

et al. 2006

R. Bras. Zootec.

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RESUMO -Foram simuladas quatro estruturas de dados com diferentes padrões de heterogeneidade entre rebanhos e com diferentes graus de conexidade genética. As estruturas simuladas diferiam quanto aos parâmetros heterogêneos entre rebanhos: 1) heterogeneidade para todos os parâmetros; 2) médias genéticas homogêneas e demais parâmetros heterogêneos; 3) heterogeneidade para parâmetros fenotípicos; e 4) rebanhos sem heterogeneidade. Após a predição dos valores genéticos, calculou-se a porcentagem de animais comuns entre dois grupos de classificação para os animais geneticamente superiores: grupo 1 -classificação com base nos valores genéticos verdadeiros e grupo 2 -classificação com base nos valores genéticos preditos. Para dados com heterogeneidade para todos os parâmetros e 0% de conexidade genética, a porcentagem de touros comuns aos dois grupos de classificação foi baixa e houve grandes erros na classificação dos touros geneticamente superiores. Quando os rebanhos possuíam 100% de conexidade genética, esta porcentagem foi superior a 73% e os erros na classificação dos touros foram menores. Para vacas e touros jovens, mesmo para dados com 100% de conexidade genética entre rebanhos, a predição dos valores genéticos foi muito afetada pela presença da heterogeneidade para todos os parâmetros. Para as estruturas sem heterogeneidade para média genética, as porcentagens de animais comuns entre os grupos de classificação foram altas e os erros na classificação dos animais geneticamente superiores foram pequenos, mesmo havendo heterogeneidade para outros parâmetros. Esses resultados sugerem que a heterogeneidade entre rebanhos para médias genéticas teve grande efeito sobre a acurácia da predição dos valores genéticos dos animais. Contudo, quando a heterogeneidade entre os rebanhos ocorreu para outros parâmetros como variância genética, média fenotípica e variância fenotípica, os resultados das avaliações genéticas dos animais foram próximos aos obtidos para dados sem heterogeneidade entre rebanhos.Palavras-chave: avaliação genética, conexidade, seleção, simulação Errors in ranking of bulls, cows and young bulls evaluated in the presence of heterogeneityABSTRACT -Four structures of data with different degree of genetic connect ness and heterogeneity of parameters among herds: 1) heterogeneity of all parameters; 2) genetic means homogeneous and other parameters heterogeneous; 3) heterogeneity of phenotypic parameters and 4) no heterogeneity for herds, were simulated. After prediction of breeding values, the percentage of top animals common to group 1 (animals ranked by the true breeding values) and group 2 (animals ranked by the predicted breeding values) was calculated. For data with heterogeneity of all parameters and 0% of genetic connectness, the percentage of bulls common to the two groups was low and large errors were observed in ranking of top bulls. For 100% of genetic connect ness that percentage was larger than 73% and errors in ranking of top bulls were low. For cows and young bulls, even for data with 100% of ge...

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Sire×contemporary group interactions for birth weight and preweaning growth traits in the Asturiana de los Valles beef cattle breed

et al. 2006

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Although a number of recent studies have focused on the existence of a non-negligible sire Â contemporary group interaction effect (s) affecting the estimation of genetic parameters for maternally influenced traits in beef cattle, the assessment and interpretation of this effect using field data remains poorly understood. In this study 27,639 records of both birth weight (BW) and weaning weight (WW) from the Asturiana de los Valles breed were used to assess the consequences of the inclusion of an s effect on the estimation of genetic parameters for BW, WW and average daily gain (ADG) fitting univariate and bivariate models. Estimations of s 2 for BW, WW and ADG were 0.040, 0.070 and 0.077 regardless of the fitted model. Inclusion of s in the estimation models induced a reduction of both the direct and the maternal heritability, varying between 8 and 28% with the trait and the estimation model employed. As expected, the correlations between both direct and maternal genetic effects for each trait were less negative when s was included in the estimation model. The estimated correlations between the s effect affecting BW, WW and ADG were 0.108, À 0.038 and 0.616 for the pairs BW-WW, BW-ADG and WW-ADG, respectively. These results suggest that misidentification of individuals cannot be the sole cause of the effect of s and that this effect is of a different nature and origin for different traits (i.e. selective matings for low BW's and unaccounted management practices for preweaning growth traits). Models including the s effect should be accepted as working models in beef improvement schemes. D

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Sire‐by‐herd interaction effect when variances across herds are heterogeneous. I. Expected genetic progress

Cited by 7 publications

References 30 publications

Heterogeneous variances and genetics by environment interactions in genetic evaluation of crossbred lambs

Heterogeneous variances and genetics by environment interactions in genetic evaluation of crossbred lambs

Erros na classificação de touros, vacas e touros jovens geneticamente superiores avaliados na presença de heterogeneidade

Sire×contemporary group interactions for birth weight and preweaning growth traits in the Asturiana de los Valles beef cattle breed

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