Snp_blup_rel: software for calculating individual animal  SNP-BLUP model reliabilities

Zaabza, Hafedh Ben; Mäntysaari, Esa; Strandén, Ismo

doi:10.23986/afsci.95617

Cited by 4 publications

(1 citation statement)

References 9 publications

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“…Step 3, which includes making and inverting the coefficient matrix in the SNPBLUP model, was the most time-consuming part, while all the other steps were processed in seconds for the datasets in our example. Although this process can be further improved with the saved inverse matrix [ 28 ], further investigations should be carried out to assess the impact of using subsets of SNPs or genotyped animals on the quality of the approximated reliabilities of GEBV. Moreover, a large dataset and a complex model can have a large computational load in Step 1, particularly when the number of data records exceeds the number of pedigree records such as in test day models.…”

Section: Discussionmentioning

confidence: 99%

A computationally efficient method for approximating reliabilities in large-scale single-step genomic prediction

et al. 2023

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Background In this study, computationally efficient methods to approximate the reliabilities of genomic estimated breeding values (GEBV) in a single-step genomic prediction model including a residual polygenic (RPG) effect are described. In order to calculate the reliabilities of the genotyped animals, a single nucleotide polymorphism best linear unbiased prediction (SNPBLUP) or a genomic BLUP (GBLUP), was used, where two alternatives to account for the RPG effect were tested. In the direct approach, the genomic model included the RPG effect, while in the blended method, it did not but an index was used to weight the genomic and pedigree-based BLUP (PBLUP) reliabilities. In order to calculate the single-step GBLUP reliabilities for the breeding values for the non-genotyped animals, a simplified weighted-PBLUP model that included a general mean and additive genetic effects with weights accounting for the non-genomic and genomic information was used. We compared five schemes for the weights. Two datasets, i.e., a small (Data 1) one and a large (Data 2) one were used. Results For the genotyped animals in Data 1, correlations between approximate reliabilities using the blended method and exact reliabilities ranged from 0.993 to 0.996 across three lactations. The slopes observed by regressing the reliabilities of GEBV from the exact method on those from the blended method were 1.0 for all three lactations. For Data 2, the correlations and slopes ranged, respectively, from 0.980 to 0.986 and from 0.91 to 0.96, and for the non-genotyped animals in Data 1, they ranged, respectively, from 0.987 to 0.994 and from 0.987 to 1, which indicate that the approximations were in line with the exact results. The best approach achieved correlations of 0.992 to 0.994 across lactations. Conclusions Our results demonstrate that the approximated reliabilities calculated using our proposed approach are in good agreement with the exact reliabilities. The blended method for the genotyped animals is computationally more feasible than the direct method when RPG effects are included, particularly for large-scale datasets. The approach can serve as an effective strategy to estimate the reliabilities of GEBV in large-scale single-step genomic predictions.

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Section: Discussionmentioning

confidence: 99%

A computationally efficient method for approximating reliabilities in large-scale single-step genomic prediction

et al. 2023

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

Zaabza

Vandenplas

VanRaden

et al. 2023

Journal of Dairy Science

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Estimation of individual animal SNP-BLUP reliability using full Monte Carlo sampling

2021

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Reliabilities of estimated breeding values from a SNP-BLUP model can be calculated using elements of the inverse coefficient matrix of the mixed model equations. Computation of the inverse is not feasible for large data sets when the model has a residual polygenic (RPG) effect. We developed a full Monte Carlo (MC) samplingbased method for approximating reliabilities in the SNP-BLUP model with an RPG effect. Reliabilities obtained by the full MC approach were compared with the corresponding exact reliabilities obtained by the GBLUP model. The full MC approach provided good approximations to the exact values with only a small upward bias. The full MC approach is computationally efficient even for large data sets. Highlights• Computing time for the full Monte Carlo (MC)-SNP-BLUP was less than for the exact genomic BLUP.• The full MC-SNP-BLUP better approximated estimated breeding value reliability than an incomplete MCbased SNP-BLUP approach when the residual polygenic effect was high. • The higher the exact reliability, the smaller the inflation.

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Snp_blup_rel: software for calculating individual animal SNP-BLUP model reliabilities

Cited by 4 publications

References 9 publications

A computationally efficient method for approximating reliabilities in large-scale single-step genomic prediction

A computationally efficient method for approximating reliabilities in large-scale single-step genomic prediction

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

Estimation of individual animal SNP-BLUP reliability using full Monte Carlo sampling

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