Effects of data structure on variance of prediction error and accuracy of genetic evaluation1

Tosh, J. J.; Wilton, J. W.

doi:10.2527/1994.72102568x

Cited by 21 publications

(21 citation statements)

References 12 publications

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“…As shown in Table VI, and in contrast to what has been observed by several authors for the estimation of genetic parameters [10,11] or for the prediction of genetic value [1,21,45], no clear pattern of reduction of precision related to a lack of connectedness was observed in this study, but it is possible that the number of replicates was insufficient to see an effect of connectedness on the precision of the estimates. However, standard deviation between replicates of covariance increased when genetic difference between flocks became more important.…”

Section: Precision Of the Estimatescontrasting

confidence: 99%

Simulation analysis to test the influence of model adequacy and data structure on the estimation of genetic parameters for traits with direct and maternal effects

Clément¹,

Bibé²,

Verrier

et al. 2001

Genet Sel Evol

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-Simulations were used to study the influence of model adequacy and data structure on the estimation of genetic parameters for traits governed by direct and maternal effects. To test model adequacy, several data sets were simulated according to different underlying genetic assumptions and analysed by comparing the correct and incorrect models. Results showed that omission of one of the random effects leads to an incorrect decomposition of the other components. If maternal genetic effects exist but are neglected, direct heritability is overestimated, and sometimes more than double. The bias depends on the value of the genetic correlation between direct and maternal effects. To study the influence of data structure on the estimation of genetic parameters, several populations were simulated, with different degrees of known paternity and different levels of genetic connectedness between flocks. Results showed that the lack of connectedness affects estimates when flocks have different genetic means because no distinction can be made between genetic and environmental differences between flocks. In this case, direct and maternal heritabilities are under-estimated, whereas maternal environmental effects are overestimated. The insufficiency of pedigree leads to biased estimates of genetic parameters.genetic parameters / animal model / maternal effects / simulations / connectedness

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Section: Precision Of the Estimatescontrasting

confidence: 99%

Simulation analysis to test the influence of model adequacy and data structure on the estimation of genetic parameters for traits with direct and maternal effects

Clément¹,

Bibé²,

Verrier

et al. 2001

Genet Sel Evol

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show abstract

“…The values of PEV depended mainly on number of progeny tested per boar but depended also on direct connections [i.e., number of other sires with progeny in the same batch (Tosh and Wilton, 1994)] or on the number of batch where the sire had progeny tested and the availability of on-farm correlated data. Rehability for ADG between 100 and 210 d was computed using reliabilities of EBV at 100 and 210 d and genetic correlation between BW at 100 and 210 d. The computations used the same selection index based on the approach applied by Gole and VanRaden (2010).…”

Section: Estimation Of Breeding Values and Reliabilitiesmentioning

confidence: 99%

Using test station and on-farm data for the genetic evaluation of Piétrain boars used on Landrace sows for growth performance1

Dufrasne

Rustin

Jaspart

et al. 2011

Journal of Animal Science

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The aim of this study was to develop a new genetic evaluation model to estimate the genetic merit of boars for growth based on 1) performance of their crossbred progeny fattened in the test station and 2) their own performance or those of relatives from the on-farm testing system. The model was a bivariate random regression animal model with linear splines and was applied to Piétrain boars from the Walloon Region of Belgium mated with Landrace sows. Data contained 1) 12,610 BW records from the test station collected on 1,435 crossbred pigs from Piétrain boars and Landrace sows, and 2) 52,993 BW records from the on-farm testing system collected on 50,670 pigs with a breed composition of at least 40% Piétrain or Landrace. Since 2007, 56 Piétrain boars have been tested in the station. Data used to estimate variance components and breeding values were standardized for the age to take into account heterogeneity of variances and then pre-adjusted at 210 d of age to put all records on the same scale. Body weight records from the test station and from the on-farm testing system were considered as 2 different traits. The heterosis effect was modeled as fixed regression on the heterozygosity coefficient. As all test station animals were similarly crossbred, smaller variation in heterozygosity caused the sampling error of the regression estimate at 210 d to be larger in the test station than in on-farm data with estimates of 28.35 ± 14.55 kg and 9.02 ± 0.67 kg, respectively. Therefore, the most likely reason for the large differences in estimates was sampling. Heritability estimates ranged from 0.37 to 0.60 at 210 and 75 d, respectively, for test station BW and from 0.42 to 0.60 at 210 d and 175 d, respectively, for on-farm BW. Genetic correlation decreased when the age interval between records increased, and were greater between ages for test station than for on-farm data. Genetic correlations between test station and on-farm BW at the same age were high: 0.90 at 175 d and 0.85 at 210 d. For the 56 boars tested in the station, the average reliability of their EBV for ADG between 100 and 210 d was improved from 0.60 using only test station data to 0.69 using jointly test station and on-farm data. Based on these results, the new model developed was considered as a good method of detection of differences in growth potential of Piétrain boars based on test station and on-farm data.

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“…Program was modified to permit direct extraction of the elements of the inverse coefficient matrix. Additional details about PEV estimation were mentioned by Tosh and Wilton (1994).…”

Section: Genetic Parametersmentioning

confidence: 99%

Genetic analysis of lactoferrin content in bovine milk

Arnould

Soyeurt

Gengler

et al. 2009

Journal of Dairy Science

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Bovine lactoferrin (LF) is mainly present in milk and shows important physiological and biological functions. The aim of this study was to estimate the heritability and correlation values of LF content in bovine milk with different economic traits as milk yield (MY), fat and protein percentages, and somatic cell score (SCS). Variance components of the studied traits were estimated by REML using a multiple-trait mixed model. The obtained heritability (0.22) for LF content predicted using mid-infrared spectrometry (pLF) suggested the possibility of animal selection based on the increase of LF content in milk. The phenotypic and genetic correlation values calculated between pLF and SCS were moderate (0.31 and 0.24, respectively). Furthermore, a preliminary study of bovine LF gene polymorphism effects was performed on the same production traits. By PCR, all exons of the LF gene were amplified and then sequenced. Three new polymorphisms were detected in exon 2, exon 11, and intron 8. We examined the effects of LF gene polymorphisms of exons 2, 4, 9, 11, and 15, and intron 8 on pLF, MY, fat and protein percentages, and SCS. The different observed effects did not reach a significant level probably because of the characteristics of the studied population. However, the results were promising, and LF may be a potential indicator of mastitis. Further studies are necessary to evaluate the effect of genetic selection based on LF content on the improvement of mastitis resistance.

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Effects of data structure on variance of prediction error and accuracy of genetic evaluation1

Cited by 21 publications

References 12 publications

Simulation analysis to test the influence of model adequacy and data structure on the estimation of genetic parameters for traits with direct and maternal effects

Simulation analysis to test the influence of model adequacy and data structure on the estimation of genetic parameters for traits with direct and maternal effects

Using test station and on-farm data for the genetic evaluation of Piétrain boars used on Landrace sows for growth performance1

Genetic analysis of lactoferrin content in bovine milk

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