Invited review: Reliability computation from the animal model era to the single-step genomic model era

Zaabza, Hafedh Ben; Vandenplas, Jérémie; VanRaden, P.M.; Liu, Zengting; Eding, Herwin; McKay, Stephanie; Haugaard, Katrine; Lidauer, Martin; Mäntysaari, Esa; Strandén, Ismo

doi:10.3168/jds.2022-22629

Cited by 4 publications

(3 citation statements)

References 55 publications

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“…PL ijkl = BY i + ACD j + HYS k + Anim l + e ijkl (3) where PL ijkl is productive longevity at 78 months; BY i is the fixed effect of birth year (class effect); ACD j is the linear regression of the average calving difficulty during the cow s life; HYS k is the random effect of herd-year-season of the last calving (class effect); Anim l is the random animal additive genetic effect; and e ijk is random residual error.…”

Section: Genotypic Datasetmentioning

confidence: 99%

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Improving Breeding Value Reliability with Genomic Data in Breeding Groups of Charolais

Brzáková,

Veselá,

Vařeka

et al. 2023

Genes

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The aim of this study was to assess the impact of incorporating genomic data using the single-step genomic best linear unbiased prediction (ssGBLUP) method compared to the best linear unbiased prediction (BLUP) method on the reliability of breeding values for age at first calving, calving interval, and productive longevity at 78 months in Charolais cattle. The study included 48,590 purebred Charolais individuals classified into four subgroups based on genotyping and performance records. The results showed that considering genotypes significantly improved genomic estimated breeding values (GEBV) reliability across all categories except nongenotyped individuals. For young genotyped individuals, the increase in reliability was up to 27% for both sexes. The highest average reliability was achieved for genotyped proven bulls and cows with performance records, and the inclusion of genomic data further improved the reliability by up to 22% and 21% for cows and bulls, respectively. The gain in reliability was observed mainly during the first three calvings, and then the differences decreased. The imported individuals showed lower estimated breeding values (EBV) and GEBV reliabilities than the domestic population, probably due to the weak genetic connection with the domestic population. However, when the progeny of imported heifers were sired by domestic bulls, the reliability increased by up to 24%. For nongenotyped individuals, only a slight increase in reliability was observed; however, the number of genotyped individuals in the population was still relatively small.

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Section: Genotypic Datasetmentioning

confidence: 99%

“…Several approaches can be used for reliability calculation [2]. When the population is small, the direct matrix inversion of the best linear unbiased prediction (BLUP) mixed-model equations can be applied [3]. In the opposite case, it is necessary to use an approximation method for reliability calculation [2].…”

Section: Introductionmentioning

confidence: 99%

Improving Breeding Value Reliability with Genomic Data in Breeding Groups of Charolais

Brzáková,

Veselá,

Vařeka

et al. 2023

Genes

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“…Due to the large size of MME for most national genetic evaluations, inverting C is generally infeasible. As a consequence, a variety of methods for approximating PEVs have been proposed (Liu et al 2017;Edel et al 2019;Bermann et al 2021;Ben Zaabza et al 2022).…”

Section: Introductionmentioning

confidence: 99%

Approximating prediction error variances and accuracies of estimated breeding values from a SNP–BLUP model for genotyped individuals

Gurman

Swan

et al. 2023

Anim. Prod. Sci.

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Context The accuracy of estimated breeding values (EBVs) is an important metric in genetic evaluation systems in Australia. With reduced costs for DNA genotyping due to advances in molecular technology, more and more animals have been genotyped for EBVs. The rapid increase in genotyped animals has grown beyond the capacity of the current genomic best linear unbiased prediction (GBLUP) method. Aims This study aimed to implement and evaluate a new single-nucleotide polymorphism (SNP)–BLUP model for the computation of prediction error variances (PEVs) to accommodate the increasing number of genotyped animals in beef and sheep single-step genetic evaluations in Australia. Methods First, the equivalence of PEV estimates obtained from both GBLUP and SNP-BLUP models was demonstrated. Second, the computing resources required by each model were compared. Third, within the SNP-BLUP model, the PEVs obtained from subsets of SNP were evaluated against those from the complete dataset. Fourth, the new model was tested in the Australian Merino sheep and Angus beef cattle datasets. Key results The PEVs of genotyped animals calculated from the SNP–BLUP model were equivalent to the PEVs derived from the GBLUP model. The SNP–BLUP model used much less time than did the GBLUP model when the number of genotyped animals was larger than the number of SNPs. Within the SNP–BLUP model, the running time could be further reduced using a subset of SNPs makers, with high correlations (>0.97) observed between the PEVs obtained from the complete dataset and subsets. However, it is important to exercise caution when selecting the size of the subsets in the SNP–BLUP model, as reducing the subset size may result in an increase in the bias of the PEVs. Conclusions The new SNP-BLUP model for PEV calculation for genotyped animals outperforms the current GBLUP model. A new accuracy program has been developed for the Australian genetic evaluation system which uses much less memory and time to compute accuracies. Implications The new model has been implemented in routine sheep and beef genetic evaluation systems in Australia. This development ensures that the calculation of accuracies is sustainable, with increasing numbers of animals with genotypes.

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Marker effect p-values for single-step GWAS with the algorithm for proven and young in large genotyped populations

Leite,

Bermann,

Tsuruta

et al. 2024

Genet Sel Evol

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Background Single-nucleotide polymorphism (SNP) effects can be backsolved from ssGBLUP genomic estimated breeding values (GEBV) and used for genome-wide association studies (ssGWAS). However, obtaining p-values for those SNP effects relies on the inversion of dense matrices, which poses computational limitations in large genotyped populations. In this study, we present a method to approximate SNP p-values for ssGWAS with many genotyped animals. This method relies on the combination of a sparse approximation of the inverse of the genomic relationship matrix ($${\mathbf{G}}_{\mathbf{A}\mathbf{P}\mathbf{Y}}^\mathbf{-1}$$ G A P Y - 1 ) built with the algorithm for proven and young ($$\text{APY}$$ APY ) and an approximation of the prediction error variance of SNP effects which does not require the inversion of the left-hand side (LHS) of the mixed model equations. To test the proposed p-value computing method, we used a reduced genotyped population of 50K genotyped animals and compared the approximated SNP p-values with benchmark p-values obtained with the direct inverse of LHS built with an exact genomic relationship matrix ($${\mathbf{G}}^\mathbf{-1})$$ G - 1 ) . Then, we applied the proposed approximation method to obtain SNP p-values for a larger genotyped population composed of 450K genotyped animals. Results The same genomic regions on chromosomes 7 and 20 were identified across all p-value computing methods when using 50K genotyped animals. In terms of computational requirements, obtaining p-values with the proposed approximation reduced the wall-clock time by 38 times and the memory requirement by ten times compared to using the exact inversion of the LHS. When the approximation was applied to a population of 450K genotyped animals, two new significant regions on chromosomes 6 and 14 were uncovered, indicating an increase in GWAS detection power when including more genotypes in the analyses. The process of obtaining p-values with the approximation and 450K genotyped individuals took 24.5 wall-clock hours and 87.66GB of memory, which is expected to increase linearly with the addition of noncore genotyped individuals. Conclusions With the proposed method, obtaining p-values for SNP effects in ssGWAS is computationally feasible in large genotyped populations. The computational cost of obtaining p-values in ssGWAS may no longer be a limitation in extensive populations with many genotyped animals.

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Invited review: Reliability computation from the animal model era to the single-step genomic model era

Cited by 4 publications

References 55 publications

Improving Breeding Value Reliability with Genomic Data in Breeding Groups of Charolais

Improving Breeding Value Reliability with Genomic Data in Breeding Groups of Charolais

Approximating prediction error variances and accuracies of estimated breeding values from a SNP–BLUP model for genotyped individuals

Marker effect p-values for single-step GWAS with the algorithm for proven and young in large genotyped populations

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