Detecting effective starting point of genomic selection by divergent trends from best linear unbiased prediction and single-step genomic best linear unbiased prediction in pigs, beef cattle, and broilers

Abdollahi-Arpanahi, Rostam; Lourenço, Daniela; Misztal, I.

doi:10.1093/jas/skab243

Cited by 5 publications

(2 citation statements)

References 30 publications

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“…The selected animals with many genotyped progenies are also more likely to have MS greater than zero [34]. The larger MS has an impact on genetic trends when animals are selected based on genomic information, especially if the selection happens before phenotypes are recorded [35].…”

Section: Modelmentioning

confidence: 99%

Accounting for Missing Pedigree Information with Single-Step Random Regression Test-Day Models

Koivula

Strandén

Aamand³

et al. 2022

Agriculture

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Genomic selection is widely used in dairy cattle breeding, but still, single-step models are rarely used in national dairy cattle evaluations. New computing methods have allowed the utilization of very large genomic data sets. However, an unsolved model problem is how to build genomic- (G) and pedigree- (A22) relationship matrices that satisfy the theoretical assumptions about the same scale and equal base populations. Incompatibility issues have also been observed in the manner in which the genetic groups are included in the model. In this study, we compared three approaches for accounting for missing pedigree information: (1) GT_H used the full Quaas and Pollak (QP) transformation for the genetic groups, including both the pedigree-based and the genomic-relationship matrices, (2) GT_A22 used the partial QP transformation that omitted QP transformation in G−1, and (3) GT_MF used the metafounder approach. In addition to the genomic models, (4) an official animal model with a unknown parent groups (UPG) from the QP transformation and (5) an animal model with the metafounder approach were used for comparison. These models were tested with Nordic Holstein test-day production data and models. The test-day data included 8.5 million cows with a total of 173.7 million records and 10.9 million animals in the pedigree, and there were 274,145 genotyped animals. All models used VanRaden method 1 in G and had a 30% residual polygenic proportion (RPG). The G matrices in GT_H and GT_A22 were scaled to have an average diagonal equal to that of A22. Comparisons between the models were based on Mendelian sampling terms and forward prediction validation using linear regression with solutions from the full- and reduced-data evaluations. Models GT_H and GT_A22 gave very similar results in terms of overprediction. The MF approach showed the lowest bias.

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Section: Modelmentioning

confidence: 99%

Accounting for Missing Pedigree Information with Single-Step Random Regression Test-Day Models

Koivula

Strandén

Aamand³

et al. 2022

Agriculture

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“…Advancements in animal breeding and the role of national breeding organizations have considerably contributed to the improvements of many traits such as growth rate, beef quality, and milk production and quality, among others. For instance, in Angus, Abdollahi-Arpanahi et al (2021a) reported annual genetic gains for weaning weight between 4.06 kg and 4.96 kg from 2006 to 2018. In US Holstein, fat yield increased by about 300 kg between 1957 and 2015, 50% of which was due to genetic progress (Cole and VanRaden 2018).…”

Section: Genetic Evaluations Of Quantitative Traitsmentioning

confidence: 99%

International genetic and genomic evaluations of beef cattle

Bonifazi¹

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. International genetic and genomic evaluations of beef cattle.PhD thesis, Wageningen University, the Netherlands.In cattle, advancements in reproductive technologies such as artificial insemination allowed breeders to access the genetic material of superior bulls from foreign countries. However, animals' estimated breeding values (EBV) computed from different national evaluations are not directly comparable across countries due to differences in scales and genetic bases, trait and model definitions, and the possible presence of genotype-by-environment interactions. International evaluations account for such differences by jointly analysing all national data and modelling the same trait recorded in different countries as different correlated traits. The resulting international EBV (EBV INT ) facilitate the comparison of domestic and foreign sires and worldwide trading of their genetic material. This thesis aimed to improve and further develop methodologies for international beef cattle evaluations (Interbeef) by addressing various challenges, mainly related to: i) the estimation of across-country genetic correlations (r g ) and their impact on the EBV INT ; ii) the inclusion of national genomic information; and iii) the development of an official procedure for participating countries to integrate the distributed EBV INT back into their national evaluations. First, I showed that both large and small participating countries benefit from current Interbeef pedigree-based evaluations. Second, I showed the feasibility of estimating across-country r g using a multi-trait approach that simultaneously fits data from all countries and proposed data sub-setting strategies to improve computational time. Third, I showed that the current practice of assuming across-country direct-maternal r g to be 0 has limited impact on the EBV INT of animals of interest such as publishable sires, i.e., sires that meet Interbeef publications rules. On the other hand, assuming zero within-country direct-maternal r g impacts the EBV INT of such animals. Fourth, I developed international genomic evaluations for beef cattle using a single-step SNPBLUP approach (ssSNPBLUP). International ssSNPBLUP evaluations lead to higher accuracies compared to both current international pedigree evaluations and either national pedigree or genomic evaluations, while keeping similar or slightly reduced level and dispersion bias. Finally, I developed a generalized procedure to integrate pedigree-based or singlestep EBV INT of publishable sires into national evaluations. The procedure can be easily implemented with current software. Compared to national evaluations without integration, the integration reduces level bias, gives similar dispersion, and increases accuracies of publishable sires' EBV INT . Overall, this thesis contributes to the development of international evaluations of beef cattle.

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Breeding value predictive accuracy for scarcely recorded traits in a Eucalyptus grandis breeding population using genomic selection and data on predictor traits

Jurcic

Villalba

Dutour³

et al. 2023

Tree Genetics & Genomes

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Detecting effective starting point of genomic selection by divergent trends from best linear unbiased prediction and single-step genomic best linear unbiased prediction in pigs, beef cattle, and broilers

Cited by 5 publications

References 30 publications

Accounting for Missing Pedigree Information with Single-Step Random Regression Test-Day Models

Accounting for Missing Pedigree Information with Single-Step Random Regression Test-Day Models

International genetic and genomic evaluations of beef cattle

Breeding value predictive accuracy for scarcely recorded traits in a Eucalyptus grandis breeding population using genomic selection and data on predictor traits

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