Genetic parameters for calving and conformation traits in Charolais × Montbéliard and Charolais × Holstein crossbred calves1

Vallée, Amélie; Arendonk, J.A.M. van; Bovenhuis, H.

doi:10.2527/jas.2013-6490

Cited by 10 publications

(12 citation statements)

References 28 publications

(29 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The correlation among the random regression coefficients used to adjust the sires BV did not differ from zero (Table 7), as found by Vallée et al (2013) and Ribeiro et al (2017). Those authors demonstrated that the sire BV is related to the breed composition of their progeny and the BV obtained by purebred progeny records differs from that of crossbred progeny.…”

Section: Field Datamentioning

confidence: 69%

“…Therefore, the development and implementation of genetic evaluations that account for genotypic means, genetic covariances among relatives (Lo et al, 1993), and the existence of specific genetic variances for each different breed group (Lukaszewicz et al, 2015) can contribute to the genetic improvement of multiple-breed populations. In addition, the identification of the interaction between progeny breed group and the breeding value (BV) of sires (Vallée et al, 2013;Lukaszewicz et al, 2015) can help in designing mating strategies and improve genetic gain over generations.…”

Section: Introductionmentioning

confidence: 99%

“…Multiple-trait models (MULT) consider the same phenotype as different traits when measured in different breed groups and can incorporate heterogeneity of variances and covariances in multiple breed evaluations (Vallée et al, 2013;Lukaszewicz et al, 2015;Ribeiro et al, 2017). However, it is important to validate alternatives that can handle the genetic evaluation of multiple breed populations with several breed groups and with groups with few records.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Multiple trait and random regression models using linear splines for genetic evaluation of multiple breed populations

Ribeiro

Raidan

Barbosa

et al. 2019

Journal of Dairy Science

View full text Add to dashboard Cite

This study aimed to verify if random regression models using linear splines (RRMLS) are suitable for identifying genetic parameters in multiple-breed populations and also to investigate whether an interaction exists between the breeding value (BV) of sires and their progeny breed group. Ten populations were simulated by crossing 2 breeds with distinct genetic variance and nonzero segregation variance. To obtain the genetic parameters, 2 models were used: a multiple-trait model (MULT), in which the trait was considered distinct when evaluated in each group (1/2P 1 + 1/2P 2 , 5/8P 1 + 3/8P 2 , and 3/4P 1 + 1/4P 2 ), and a RRMLS with the spline polynomial knots adjusted to these same groups. The genetic parameters estimated through MULT and RRMLS did not differ from the simulated values. The correlations between BV (simulated and estimated) of animals were high and varied from 0.74 to 0.76, which indicates the efficiency of using MULT and RRMLS for predicting BV. Using field data, the traits age at first calving (AFC), first lactation length (LL), and 305-d milk yield (MY-305) from a multiple-breed population of Holstein-Gyr cattle were analyzed. The BV of animals were modeled through RRMLS with 3, 5, and 7 knots, distributed in accordance with the fraction of Holstein breed in each progeny breed group. It was verified that RRMLS with 7 knots for adjusting mean trajectories and genetic effects, with homogeneous residual variance, best fit AFC and LL. For MY-305, the best fit for mean trajectory and genetic effects was the RRMLS with 5 knots and with homogeneous residual variance. The posterior means of heritability varied from 0.21 to 0.48, 0.21 to 0.38, and 0.10 to 0.33 for AFC, LL, and MY-305, respectively. Estimates from genetic parameters obtained by using RRMLS with field data showed that this model is a useful tool for genetic evaluations of populations formed by a great number of breed groups. An interaction occurred between the BV of sires and their progeny breed group, and the genetic parameters for AFC, LL, and MY-305 traits from a multiple-breed population depend on breed composition of the progeny from which the evaluations are based.

show abstract

Section: Field Datamentioning

confidence: 69%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multiple trait and random regression models using linear splines for genetic evaluation of multiple breed populations

Ribeiro

Raidan

Barbosa

et al. 2019

Journal of Dairy Science

View full text Add to dashboard Cite

show abstract

“…The other classifier scored 839 Montbéliard × Charolais calves and 206 Holstein × Charolais calves. More details on traits and data collection can be found in Vallée et al (2013).…”

Section: Datamentioning

confidence: 99%

“…Variance components used in BLUP and GBLUP were fixed at values estimated from a larger data set described in Vallée et al (2013) in which calves from both crossbred populations were included and traits were assumed genetically similar between populations. Genetic variance and residual variance were 0.121 and 0.407 for bone thinness, 0.198 and 0.391 for height, and 0.661 and 1.025 for muscular development, respectively.…”

Section: Estimation Of Breeding Valuesmentioning

confidence: 99%

Accuracy of genomic prediction when combining two related crossbred populations1

Vallée¹,

Arendonk

Bovenhuis

2014

Journal of Animal Science

Self Cite

View full text Add to dashboard Cite

Charolais bulls are selected for their crossbreed performance when mated to Montbéliard or Holstein dams. To implement genomic prediction, one could build a reference population for each crossbred population independently. An alternative could be to combine both crossbred populations into a single reference population to increase size and accuracy of prediction. The objective of this study was to investigate the accuracy of genomic prediction by combining different crossbred populations. Three scenarios were considered: 1) using 1 crossbred population as reference to predict phenotype of animals from the same crossbred population, 2) combining the 2 crossbred populations into 1 reference to predict phenotype of animals from 1 crossbred population, and 3) using 1 crossbred population as reference to predict phenotype of animals from the other crossbred population. Traits studied were bone thinness, height, and muscular development. Phenotypes and 45,117 SNP genotypes were available for 1,764 Montbéliard × Charolais calves and 447 Holstein × Charolais calves. The population was randomly spilt into 10 subgroups, which were assigned to the validation one by one. To allow fair comparison between scenarios, size of the reference population was kept constant for all scenarios. Breeding values were estimated with BLUP and genomic BLUP. Accuracy of prediction was calculated as the correlation between the EBV and the phenotypic values of the calves in the validation divided by the square root of the heritability. Genomic BLUP showed higher accuracies (between 0.281 and 0.473) than BLUP (between 0.197 and 0.452). Accuracies tended to be highest when prediction was within 1 crossbred population, intermediate when populations were combined into the reference population, and lowest when prediction was across populations. Decrease in accuracy from a prediction within 1 population to a prediction across populations was more pronounced for bone thinness (-27%) and height (-29%) than for muscular development (-14%). Genetic correlation between the 2 crossbred populations was estimated using pedigree relationships. It was 0.70 for bone thinness, 0.80 for height, and 0.99 for muscular development. Genetic correlation indicates the expected gain in accuracy of prediction when combining different populations into 1 reference population. The larger the genetic correlation is, the larger the benefit is to combine populations for genomic prediction.

show abstract

Genetic determinism of bone and mineral metabolism in meat-type chickens: A QTL mapping study

et al. 2016

View full text Add to dashboard Cite

Skeletal integrity in meat-type chickens is affected by many factors including rapid growth rate, nutrition and genetics. To investigate the genetic basis of bone and mineral metabolism, a QTL detection study was conducted in an intercross between two lines of meat-type chickens divergently selected for their high (D +) or low (D −) digestive efficiency. Tibia size (length, diameter, volume) and ash content were determined at 3 weeks of age as well as phosphorus (P) retention and plasma concentration. Heritability of these traits and their genetic correlations with digestive efficiency were estimated. A QTL mapping study was performed using 3379 SNP markers.Tibia size, weight, ash content and breaking strength were highly heritable (0.42 to 0.61). Relative tibia diameter and volume as well as P retention were strongly and positively genetically correlated with digestive efficiency (0.57 to 0.80).A total of 35 QTL were identified (9 for tibia weight, 13 for tibia size, 5 for bone strength, 5 for bone mineralization, 2 for plasma P concentration and 1 for P retention). Six QTL were genome-wide significant, and 3 QTL for tibia relative volume, weight and ash weight on chromosome 6 were fixed, the positive allele coming from the D-line. For two QTL for ash content on chromosome 18 and relative tibia length on chromosome 26, the confidence intervals were small enough to identify potential candidate genes.These findings support the evidence of multiple genetic loci controlling bone and mineral metabolism. The identification of candidate genes may provide new perspectives in the understanding of bone regulation, even beyond avian species.

show abstract

Genetic parameters for calving and conformation traits in Charolais × Montbéliard and Charolais × Holstein crossbred calves1

Cited by 10 publications

References 28 publications

Multiple trait and random regression models using linear splines for genetic evaluation of multiple breed populations

Multiple trait and random regression models using linear splines for genetic evaluation of multiple breed populations

Accuracy of genomic prediction when combining two related crossbred populations1

Genetic determinism of bone and mineral metabolism in meat-type chickens: A QTL mapping study

Contact Info

Product

Resources

About