Correlations between purebred and crossbred body weight traits in Limousin and Limousin–Angus populations

Łukaszewicz, M.; Davis, Ryan; Bertrand, J. K.; Misztal, I.; Tsuruta, S.

doi:10.2527/jas.2014-8285

Cited by 11 publications

(21 citation statements)

References 10 publications

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“…Other works on multiple trait evaluations, carried out with data on beef (Cardoso & Tempelman, 2004;Toral et al, 2012;Lukaszewicz et al, 2015) and dairy (Heins et al, 2011;VanRaden et al, 2011) cattle, showed that variances and heritabilities are specific to each genetic group. Similar results were obtained in the present study, since the additive genetic variances and heritabilities of AFC and 305MY were higher in the 1/2HG groups than in the 5/8HG and 3/4HG groups (Table 3).…”

Section: Resultsmentioning

confidence: 99%

“…This approach has already been adopted in other studies (Vallée et al, 2013;Lukaszewicz et al, 2015) and allows detecting differences related to variances and covariances. However, these models tend to become more parameterized as the number of genetic groups increases.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, the interactions between the breeding value of the bull and the genetic group its progenies belong to are important to establish breeding strategies aiming to obtain greater benefits for future generations (Vallée et al, 2013;Lukaszewicz et al, 2015). Therefore, the genetic evaluation of multiple-breed populations may be improved when the traits of each genetic group are specifically assessed.…”

Section: Introductionmentioning

confidence: 99%

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Genetic analysis of productive and reproductive traits in multiple-breed dairy cattle populations

Ribeiro

Merlo²,

Gouveia

et al. 2017

Pesq. agropec. bras.

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-The objective of this work was to determine whether the random regression model using linear splines (RRMLS) is suitable to estimate the genetic parameters for productive and reproductive traits of a multiple-breed dairy cattle population, as well as to investigate the effect of the genetic group of the progeny on the genetic merit of the sire. The multiple-trait model (MTM) and the RRMLS with one knot fitted for every genetic group were used to obtain the genetic parameters. Records of 1/2 Holstein + 1/2 Gyr (1/2HG), 5/8 Holstein + 3/8 Gyr (5/8HG), and 3/4 Holstein + 1/4 Gyr (3/4HG) crossbreed dams were considered. The RRMLS showed better fitting. The additive and residual variances estimated by the MTM and the RRMLS were similar. Heritability varied from 0.20 to 0.33 for age at first calving, from 0.09 to 0.22 for first lactation length, and from 0.15 to 0.35 for first lactation 305-day milk yield, according to the genetic composition of the dams. The RRMLS is suitable to estimate the genetic parameters for productive and reproductive traits of multiple-breed dairy cattle populations. The genetic merit of the sires is affected by the genetic group of the progeny by which they are evaluated.Index terms: Gyr, heritability, heterosis, Holstein, selection. Análise genética de características produtivas e reprodutivas em populações multirraciais de bovinos leiteirosResumo -O objetivo deste trabalho foi verificar se o modelo de regressão aleatória com polinômios lineares do tipo spline (MRAPLS) é adequado para estimar os parâmetros genéticos de características produtivas e reprodutivas em uma população multirracial de bovinos leiteiros, bem como investigar o efeito do grupo genético da progênie sobre o mérito genético do touro. Foram utilizados o modelo multicaracterístico (Mult) e o MRAPLS com nós ajustados aos grupos genéticos, para obtenção dos parâmetros genéticos. Foram considerados os registros de vacas 1/2 Holandesa + 1/2 Gir (1/2HG), 5/8 Holandesa + 3/8 Gir (5/8HG) e 3/4 Holandesa + 1/4 Gir (3/4HG). O MRAPLS apresentou os melhores ajustes. As variâncias aditivas e residuais estimadas por Mult e MRAPLS foram semelhantes. As herdabilidades variaram de 0,20 a 0,33 para idade ao primeiro parto, de 0,09 a 0,22 para duração da primeira lactação e de 0,15 a 0,35 para produção de leite em até 305 dias na primeira lactação, de acordo com a composição genética das vacas. O MRAPLS é adequado para estimar parâmetros genéticos de características produtivas e reprodutivas em populações multirraciais de bovinos leiteiros. O mérito genético do touro é influenciado pelo grupo genético da progênie por meio do qual ele é avaliado.Termos para indexação: Gir, herdabilidade, heterose, Holandesa, seleção.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Genetic analysis of productive and reproductive traits in multiple-breed dairy cattle populations

Ribeiro

Merlo²,

Gouveia

et al. 2017

Pesq. agropec. bras.

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“…the additive genetic correlation between PB and CB performance, ). Across species, estimates of are generally lower than one for many trait categories (Wei and van der Werf 1995;Lukaszewicz et al 2015;. When is lower than one, PB selection candidates that have the highest BV for PB performance may not have the highest BV for CB performance.…”

Section: Selecting For Cb Performancementioning

confidence: 99%

“…The aim of such breeding programs is usually to maximize crossbred (CB) performance, while selection decisions are made in the parental lines, usually based on measurements of purebred (PB) performance. As a result, the response to selection in CB performance depends partly on the genetic correlation between PB and CB performance ( ), which is generally lower than one across species and trait categories (Wei and van der Werf 1995;Besbes and Gibson 1999;Newman et al 2002;Lukaszewicz et al 2015;Mulder et al 2016;Duenk et al 2019b). Hence, is an important parameter in breeding programs of pig and poultry.…”

Section: Introductionmentioning

confidence: 99%

Genetics of crossbreeding

Duenk¹

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Duenk, P. (2020). Genetics of crossbreeding. PhD thesis, Wageningen University, the Netherlands In pig and poultry breeding programs, animals from genetically distinct purebred breeding lines are mated to produce crossbred animals, which provide food products to consumers. Although the aim of such breeding programs is to improve the performance of the crossbreds, selection takes place in the purebred lines, and is usually based on purebred performance. This strategy may be suboptimal because the genetic correlation between purebred and crossbred performance ( ) is usually lower than one. When is lower than one, it may be beneficial to make selection decisions based on information on crossbred performance instead of purebred performance. This is, however, a challenging task, because purebred animals cannot be tested directly for performance at the crossbred level. Now, with the recent developments in genomic prediction, it has become possible to estimate breeding values for crossbred performance of purebred animals. In this thesis, I studied the genetics of crossbreeding, with a focus on genomic prediction for crossbred performance in purebred lines. First, I illustrate how interactions between genes can lead to differences in genetic trait expression between lines, and how such interactions can lead to values that are lower than one. The results show that decreases as the genetic distance between parental lines increases. I derive expressions for based on genetic parameters in the parental lines, which allows breeders to estimate bounds of without having to collect crossbred data. Second, I show that genotype-based models lead to larger estimated with smaller standard errors than pedigree-based models. In contrast to my expectation, considering breed-of-origin of alleles in genotype-based models does not yield different estimates of . Third, I investigate the benefit of training the genomic prediction model with crossbred instead of purebred data. The results show that crossbred data improves the accuracy of breeding values for a trait with an of 0.8, but not for a trait with an of 0.96. Furthermore, taking the breedof-origin of alleles into account is beneficial for a trait with an of 0.8, but not for a trait with an of 0.96. Finally, I discuss the relationship between and heterosis in the presence of gene interactions, and strategies to estimate breeding values for crossbred performance of purebreds. The results in this thesis improve our understanding of the genetics of crossbreeding, and facilitate the optimization of breeding programs that aim to improve crossbred performance with selection in purebred breeding lines.

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Review: optimizing genomic selection for crossbred performance by model improvement and data collection

Duenk

Bijma

Wientjes

et al. 2021

Journal of Animal Science

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Breeding programs aiming to improve the performance of crossbreds may benefit from genomic prediction of crossbred (CB) performance for purebred (PB) selection candidates. In this review, we compared genomic prediction strategies that differed in (1) the genomic prediction model used, or (2) the data used in the reference population. We found 27 unique studies, two of which used deterministic simulation, 11 used stochastic simulation, and 14 real data. Differences in accuracy and response to selection between strategies depended on i) the value of the purebred crossbred genetic correlation (rpc), ii) the genetic distance between the parental lines, iii) the size of PB and CB reference populations, and iv) the relatedness of these reference populations to the selection candidates. In studies where a PB reference population was used, the use of a dominance model yielded accuracies that were equal to or higher than those of additive models. When rpc was lower than ~0.8, and was caused mainly by GxE, it was beneficial to create a reference population of PB animals that are tested in a CB environment. In general, the benefit of collecting CB information increased with decreasing rpc. For a given rpc, the benefit of collecting CB information increased with increasing size of the reference populations. Collecting CB information was not beneficial when rpc was higher than ~0.9, especially when the reference populations were small. Collecting only phenotypes of CB animals may slightly improve accuracy and response to selection, but requires that the pedigree is known. It is therefore advisable to genotype these CB animals as well. Finally, considering the breed-origin of alleles allows for modelling breed-specific effects in the CB, but this did not always lead to higher accuracies. Our review shows that the differences in accuracy and response to selection between strategies depend on several factors. One of the most important factors is rpc, and we therefore recommend to obtain accurate estimates of rpc of all breeding goal traits. Furthermore, knowledge about the importance of components of rpc (i.e., dominance, epistasis, and GxE) can help breeders to decide which model to use, and whether to collect data on animals in a CB environment. Future research should focus on the development of a tool that predicts accuracy and response to selection from scenario specific parameters.

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Correlations between purebred and crossbred body weight traits in Limousin and Limousin–Angus populations

Cited by 11 publications

References 10 publications

Genetic analysis of productive and reproductive traits in multiple-breed dairy cattle populations

Genetic analysis of productive and reproductive traits in multiple-breed dairy cattle populations

Genetics of crossbreeding

Review: optimizing genomic selection for crossbred performance by model improvement and data collection

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