Comparison of models for missing pedigree in single-step genomic prediction

Masuda, Yutaka; Tsuruta, S.; Bermann, Matias; Bradford, H. L.; Misztal, I.

doi:10.1093/jas/skab019

Cited by 18 publications

(30 citation statements)

References 36 publications

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“…Our results indicate that when the QP transformation is used, GT_H is as practical an alternative as GT_A 22 . This result is different from the results from other studies [16,18], where GT_A 22 , which was called altered H-inverse, had better predictive abilities than the full QP model GT_H.…”

Section: Resultscontrasting

confidence: 99%

“…Thus, the results indicate that, both in terms of bias and reliability, the MF model was slightly better than the other models. This conclusion is similar to that of other studies where the use of the MF model improved the single-step evaluations, e.g., [17,18]. Similarly, the MF approach seems to give better b 1 and R 2 values than the UPG model for the animal model without genomic information.…”

Section: Resultssupporting

confidence: 89%

“…The full QP transformation as described in [14,15] includes UPGs in A −1 , A 22 −1 , and G −1 in the MMEs of the single-step models. However, there is an alternative approach where the contributions of G −1 are ignored in the QP transformation, or the altered QP H inverse is used [16][17][18]. Ignoring G −1 in the QP transformation can be justified by considering that the G matrix contains all the information, whereas the pedigree information to build the A matrix is incomplete and requires a UPG.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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: Resultscontrasting

confidence: 99%

Section: Resultssupporting

confidence: 89%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Koivula

Strandén

Aamand³

et al. 2022

Agriculture

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“…In this method, breeding values of animals with missing pedigrees are adjusted according to the fraction of genes originating from each unknown parent group [34]. The accurate estimates of unknown parent groups rely on having many observations [35,36]. In this research study, the small size of the dataset limited our ability to investigate the differences in the accuracy of predictions with and without unknown parent groups in the model.…”

Section: Resultsmentioning

confidence: 99%

Validation of single-step genomic predictions using the linear regression method for milk yield and heat tolerance in a Thai-Holstein population

et al. 2021

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Background and Aim: Genomic selection improves accuracy and decreases the generation interval, increasing the selection response. This study was conducted to assess the benefits of using single-step genomic best linear unbiased prediction (ssGBLUP) for genomic evaluations of milk yield and heat tolerance in Thai-Holstein cows and to test the value of old phenotypic data to maintain the accuracy of predictions. Materials and Methods: The dataset included 104,150 milk yield records collected from 1999 to 2018 from 15,380 cows. The pedigree contained 33,799 animals born between 1944 and 2016, of which 882 were genotyped. Analyses were performed with and without genomic information using ssGBLUP and BLUP, respectively. Statistics for bias, dispersion, the ratio of accuracies, and the accuracy of estimated breeding values were calculated using the linear regression (LR) method. A partial dataset excluded the phenotypes of the last generation, and 66 bulls were identified as validation individuals. Results: Bias was considerable for BLUP (0.44) but negligible (–0.04) for ssGBLUP; dispersion was similar for both techniques (0.84 vs. 1.06 for BLUP and ssGBLUP, respectively). The ratio of accuracies was 0.33 for BLUP and 0.97 for ssGBLUP, indicating more stable predictions for ssGBLUP. The accuracy of predictions was 0.18 for BLUP and 0.36 for ssGBLUP. Excluding the first 10 years of phenotypic data (i.e., 1999-2008) decreased the accuracy to 0.09 for BLUP and 0.32 for ssGBLUP. Genomic information doubled the accuracy and increased the persistence of genomic estimated breeding values when old phenotypes were removed. Conclusion: The LR method is useful for estimating accuracies and bias in complex models. When the population size is small, old data are useful, and even a small amount of genomic information can substantially improve the accuracy. The effect of heat stress on first parity milk yield is small.

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“…Those authors used about 860k genotyped animals and demonstrated that old phenotypic records (i.e., before 2000) can be removed without reducing prediction accuracy of young selection candidates. Moreover, they concluded that the best way to consider UPG in ssGBLUP is to include them in both A and the relationship matrix among genotyped animals (A 22 ), as also described by Tsuruta et al (2019) and Masuda et al (2021). Another advantage of ssGBLUP is the consideration of genomic preselection (Masuda et al, 2018), that is, animals selected based on their GEBV before they have phenotypes available (Patry and Ducrocq, 2011;Jibrila et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Multibreed genomic evaluation for production traits of dairy cattle in the United States using single-step genomic best linear unbiased predictor

Cesarani

Lourenço

Tsuruta

et al. 2022

Journal of Dairy Science

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Official multibreed genomic evaluations for dairy cattle in the United States are based on multibreed BLUP evaluation followed by single-breed estimation of SNP effects. Single-step genomic BLUP (ssGBLUP) allows the straight computation of genomic (G)EBV in a multibreed context. This work aimed to develop ssGBLUP multibreed genomic predictions for US dairy cattle using the algorithm for proven and young (APY) to compute the inverse of the genomic relationship matrix. Only purebred Ayrshire (AY), Brown Swiss (BS), Guernsey (GU), Holstein (HO), and Jersey (JE) animals were considered. A 3-trait model with milk (MY), fat (FY), and protein (PY) yields was applied using about 45 million phenotypes recorded from January 2000 to June 2020. The whole data set included about 29.5 million animals, of which almost 4 million were genotyped. All the effects in the model were breed specific, and breed was also considered as fixed unknown parent groups. Evaluations were done for (1) each single breed separately (single); (2) HO and JE together (HO_JE); (3) AY, BS, and GU together (AY_BS_GU); (4) all the 5 breeds together (5_BREEDS). Initially, 15k core animals were used in APY for AY_BS_GU and 5_BREEDS, but larger core sets with more animals from the least represented breeds were also tested. The HO_JE evaluation had a fixed set of 30k core animals, with an equal representation of the 2 breeds, whereas HO and JE single-breed analysis involved 15k core animals. Validation for cows was based on correlations between adjusted phenotypes and (G)EBV, whereas for bulls on the regression of daughter yield deviations on (G)EBV. Because breed was correctly considered in the model, BLUP results for single and multibreed analyses were the same. Under ssGBLUP, predictability and reliability for AY, BS, and GU were on average 7% and 2% lower in 5_BREEDS compared with single-breed evaluations, respectively. However, validation parameters for these 3 breeds became better than in the single-breed evaluations when 45k animals were included in the core set for 5_BREEDS. Evaluations for Holsteins were more stable across scenarios because of the greatest number of genotyped animals and amount of data. Combining AY, BS, and GU into one evaluation resulted in predictions similar to the ones from single breed, especially when using about 30k core animals in APY. The results showed that single-step large-scale multibreed evaluations are computationally feasible, but fine tuning is needed to avoid a reduction in reliability when numerically dominant breeds are combined. Having evaluations for AY, BS, and GU separated from HO and JE may reduce inflation of GEBV for the first 3 breeds.

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Comparison of models for missing pedigree in single-step genomic prediction

Cited by 18 publications

References 36 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

Validation of single-step genomic predictions using the linear regression method for milk yield and heat tolerance in a Thai-Holstein population

Multibreed genomic evaluation for production traits of dairy cattle in the United States using single-step genomic best linear unbiased predictor

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