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

Duenk, Pascal; Bijma, P.; Wientjes, Y.C.J.; Calus, M.P.L.

doi:10.1093/jas/skab205

Cited by 14 publications

(5 citation statements)

References 63 publications

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“…These crossbred animals are pedigreed to allow the estimation of genetic parameters and breeding values of purebreds. The purebred–crossbred genetic correlation for a trait is often lower than 1 [ 21 , 22 ], which implies that crossbred information is valuable to estimate breeding values of purebred birds for crossbred performance [ 23 , 24 ].…”

Section: Introductionmentioning

confidence: 99%

Using egg production longitudinal recording to study the genetic background of resilience in purebred and crossbred laying hens

et al. 2022

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Background There is growing interest in using genetic selection to obtain more resilient farm animals (i.e. that are minimally affected by disturbances or rapidly recover from them). The aims of this study were to: (i) estimate the genetic parameters of resilience indicator traits based on egg production data, (ii) assess whether these traits are genetically correlated in purebreds and crossbreds, and (iii) assess the genetic correlations of these traits with egg production (EP) as total number of eggs between 25 and 83 weeks. Purebred hens (33,825 from a White Leghorn (WA) line and 34,397 from a Rhode Island (BD) line were housed in individual cages, while crossbred hens were housed in collective cages of 6 to 8 paternal half-sibs (12,852 WA and 3898 BD crossbred groups, where the name of the group refers to the line used as the sire). Deviations of a hen’s weekly egg production from the average of the corresponding batch were calculated. Resilience indicator traits investigated were the natural logarithm of the variance (LNVAR), the skewness (SKEW), and the lag-one autocorrelation (AUTO-R) of these deviations. Results In both purebred lines, EP was estimated to be lowly heritable (WA: 0.11 and BD: 0.12). Resilience indicators were also estimated to be lowly heritable in both lines (LNVAR: 0.10 and 0.12, SKEW: 0.04 and 0.02, AUTO-R: 0.06 and 0.08 in WA and BD, respectively). In both crossbred groups, EP, AUTO-R, and SKEW were estimated to be less heritable than in purebreds (EP: $$h^{2}$$ h 2 ≤ 0.07; and resilience indicator traits: $$h^{2}$$ h 2 ≤ 0.03), while LNVAR had an $$h^{2}$$ h 2 estimate that was similar to or higher in crossbreds ($$h^{2}$$ h 2 ranged from 0.13 to 0.21) than in purebreds. In both purebreds and crossbreds, resilience indicator traits were estimated to have favorable genetic correlations with EP and between each other. For all traits and in both lines, estimates of genetic correlations between purebreds and crossbreds ($$r_{pc}$$ r pc ) differed from 1 and ranged from 0.16 to 0.63. Conclusions These results show that selection for resilience based on EP data can be considered in breeding programs for layers. Genetic improvement of resilience in crossbreds can be achieved by using information on purebreds, but would be greatly enhanced by the integration of information on crossbreds in breeding programs.

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Section: Introductionmentioning

confidence: 99%

Using egg production longitudinal recording to study the genetic background of resilience in purebred and crossbred laying hens

et al. 2022

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“…However, considering purebred and crossbred performance as separate traits is less relevant for crossbred dairy cattle for at least 2 reasons. First, part of the difference in crossbred and purebred performance in terminal crossbreeding systems, such as pigs and chickens, is due to the different environments that the crossbred and purebred are often raised in (Wientjes and Calus, 2017;Duenk et al, 2021), which is generally not the case for dairy cows. Second, the crossbred group in dairy cattle crossbreeding setting is typically not uniform, and thus genetic background and the level of heterosis are expected to vary.…”

Section: Crossbred Performancementioning

confidence: 99%

“…Genomic prediction in crossbred and admixed populations is challenging because effects of marker genotypes depend on linkage between marker and causative loci, genetic background, and allele frequency, all of which tend to differ between breeds and populations (Dekkers and Hospital, 2002;de Roos et al, 2008;Lund et al, 2014). Although various models have been investigated for genetic and genomic evaluations of crossbred animals (Stock et al, 2020;Duenk et al, 2021) it is still unclear which models perform best in different scenarios, especially for more complicated crossbreeding schemes (Stock et al, 2020), as are practiced for dairy cattle.…”

Section: Introductionmentioning

confidence: 99%

Genomic predictions for crossbred dairy cows by combining solutions from purebred evaluation based on breed origin of alleles

Eiríksson

Byskov

et al. 2022

Journal of Dairy Science

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Genomic predictions have been applied for dairy cattle for more than a decade with great success, but genomic estimated breeding values (GEBV) are not widely available for crossbred dairy cows. The large reference populations already in place for genomic evaluations of many pure breeds makes it interesting to use the accurate solutions, in particular the estimated marker effects, from these evaluations for calculation of GEBV for crossbred heifers and cows. Effects of marker alleles in crossbred animals can depend on breed origin of the alleles (BOA). Therefore, our aim was to investigate if reliable GEBV for crossbred dairy cows can be obtained by combining estimated marker effects from purebred evaluations based on BOA. We used data on 5,467 Danish crossbred dairy cows with contributions from Holstein, Jersey, and Red Dairy Cattle breeds. We assessed BOA assignment on their genotypes and found that we could assign 99.3% of the alleles to a definite breed of origin. We compared GEBV for 2 traits, protein yield and interval between first and last insemination of cows, with 2 models that both combine estimated marker effects from the genomic evaluations of the pure breeds: a breed of origin model that accounts for BOA and a breed proportion model that only accounts for genomic breed proportions in the crossbred animals. We accounted for the difference in level between the purebred evaluations by including intercepts in the models based on phenotypic averages. The predictive ability for protein yield was significantly higher from the breed of origin model, 0.45 compared with 0.43 from the breed proportion model. Furthermore, for the breed proportion model, the GEBVs had level bias, which made comparison across groups with different breed composition skewed. We therefore concluded that reliable genomic predictions for crossbred dairy cows can be obtained by combining estimated marker effects from the genomic evaluations of purebreds using a model that accounts for BOA.

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“…The implementation of genomic selection in pig and poultry breeding programs has renewed the interest to use crossbred information to estimate breeding values of purebred selection candidates for crossbred performance [ 1 ]. Depending on the type of crossbreeding, there may be variation in the line composition of crossbred animals.…”

Section: Introductionmentioning

confidence: 99%

Estimation of dam line composition of 3-way crossbred animals using genomic information

Calus

Henshall²,

Hawken³

et al. 2022

Genet Sel Evol

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Background In genomic prediction including data of 3- or 4-way crossbred animals, line composition is usually fitted as a regression on expected line proportions, which are 0.5, 0.25 and 0.25, respectively, for 3-way crossbred animals. However, actual line proportions for the dam lines can vary between ~ 0.1 and 0.4, and ignoring this variation may affect the genomic estimated breeding values of purebred selection candidates. Our aim was to validate a proposed gold standard to evaluate different approaches for estimating line proportions using simulated data, and to subsequently use this in actual 3-way crossbred broiler data to evaluate several other methods. Results Analysis of simulated data confirmed that line proportions computed from assigned breed-origin-of-alleles (BOA) provide a very accurate gold standard, even if the parental lines are closely related. Alternative investigated methods were linear regression of genotypes on line-specific allele frequencies, maximum likelihood estimation using the program ADMIXTURE, and the genomic relationship of crossbred animals with their maternal grandparents. The results from the simulated data showed that the genomic relationship with the maternal grandparent was most accurate, and least affected by closer relationships between the dam lines. Linear regression and ADMIXTURE performed similarly for unrelated lines, but their accuracy dropped considerably when the dam lines were more closely related. In almost all cases, estimates improved after adjusting them to ensure that the sum of dam line contributions within animals was equal to 0.5, and within dam line and across animals the average was equal to 0.25. Results from the broiler data were much more similar between methods. In both cases, stringent linkage disequilibrium pruning of genotype data led to a relatively low accuracy of predicted line proportions, due to the loss of too many single nucleotide polymorphisms. Conclusions With relatively unrelated parental lines as typical in crosses in pigs and poultry, linear regression of crossbred genotypes on line-specific allele frequencies and ADMIXTURE are very competitive methods. Thus, linear regression may be the method of choice, as it does not require genotypes of grandparents, is computationally very efficient, and easily implemented and adapted for considering the specific nature of the crossbred animals analysed.

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

Cited by 14 publications

References 63 publications

Using egg production longitudinal recording to study the genetic background of resilience in purebred and crossbred laying hens

Using egg production longitudinal recording to study the genetic background of resilience in purebred and crossbred laying hens

Genomic predictions for crossbred dairy cows by combining solutions from purebred evaluation based on breed origin of alleles

Estimation of dam line composition of 3-way crossbred animals using genomic information

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