Core-dependent changes in genomic predictions using the Algorithm for Proven and Young in single-step genomic best linear unbiased prediction

Abstract Single-step genomic best linear unbiased prediction with the Algorithm for Proven and Young (APY) is a popular method for large-scale genomic evaluations. With the APY algorithm, animals are designated as core or noncore, and the computing resources to create the inverse of the genomic relationship matrix (GRM) are reduced by inverting only a portion of that matrix for core animals. However, using different core sets of the same size causes fluctuations in genomic estimated breeding values (GEBVs) up to one additive standard deviation without affecting prediction accuracy. About 2% of the variation in the GRM is noise. In the recursion formula for APY, the error term modeling the noise is different for every set of core animals, creating changes in breeding values. While average changes are small, and correlations between breeding values estimated with different core animals are close to 1.0, based on the normal distribution theory, outliers can be several times bigger than the average. Tests included commercial datasets from beef and dairy cattle and from pigs. Beyond a certain number of core animals, the prediction accuracy did not improve, but fluctuations decreased with more animals. Fluctuations were much smaller than the possible changes based on prediction error variance. GEBVs change over time even for animals with no new data as genomic relationships ties all the genotyped animals, causing reranking of top animals. In contrast, changes in nongenomic models without new data are small. Also, GEBV can change due to details in the model, such as redefinition of contemporary groups or unknown parent groups. In particular, increasing the fraction of blending of the GRM with a pedigree relationship matrix from 5% to 20% caused changes in GEBV up to 0.45 SD, with a correlation of GEBV > 0.99. Fluctuations in genomic predictions are part of genomic evaluation models and are also present without the APY algorithm when genomic evaluations are computed with updated data. The best approach to reduce the impact of fluctuations in genomic evaluations is to make selection decisions not on individual animals with limited individual accuracy but on groups of animals with high average accuracy.

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Best Linear Unbiased Prediction of Genomic Breeding Values Using a Trait-Specific Marker-Derived Relationship Matrix

PLoS ONE ◽

10.1371/journal.pone.0012648 ◽

2010 ◽

Vol 5 (9) ◽

pp. e12648 ◽

Cited By ~ 100

Author(s):

Zhe Zhang ◽

Jianfeng Liu ◽

Xiangdong Ding ◽

Piter Bijma ◽

Dirk-Jan de Koning ◽

...

Keyword(s):

Best Linear Unbiased Prediction ◽

Relationship Matrix ◽

Specific Marker ◽

Linear Unbiased Prediction ◽

Genomic Breeding ◽

Breeding Values ◽

Best Linear Unbiased ◽

Trait Specific Marker ◽

Unbiased Prediction

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The impact of truncating data on the predictive ability for single-step genomic best linear unbiased prediction

Journal of Animal Breeding and Genetics ◽

10.1111/jbg.12334 ◽

2018 ◽

Vol 135 (4) ◽

pp. 251-262 ◽

Cited By ~ 1

Author(s):

Jeremy T. Howard ◽

Tom A. Rathje ◽

Caitlyn E. Bruns ◽

Danielle F. Wilson-Wells ◽

Stephen D. Kachman ◽

...

Keyword(s):

Predictive Ability ◽

Best Linear Unbiased Prediction ◽

Single Step ◽

Linear Unbiased Prediction ◽

Best Linear Unbiased ◽

The Impact ◽

Unbiased Prediction

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The Effect of Integrating Genomic Information into Genetic Evaluations of Chinese Merino Sheep

Animals ◽

10.3390/ani10040569 ◽

2020 ◽

Vol 10 (4) ◽

pp. 569

Author(s):

Chen Wei ◽

Hanpeng Luo ◽

Bingru Zhao ◽

Kechuan Tian ◽

Xixia Huang ◽

...

Keyword(s):

Rank Correlation ◽

Best Linear Unbiased Prediction ◽

Single Step ◽

Breeding Value ◽

Genomic Information ◽

Linear Unbiased Prediction ◽

Breeding Values ◽

Merino Sheep ◽

Best Linear Unbiased ◽

Unbiased Prediction

Genomic evaluations are a method for improving the accuracy of breeding value estimation. This study aimed to compare estimates of genetic parameters and the accuracy of breeding values for wool traits in Merino sheep between pedigree-based best linear unbiased prediction (PBLUP) and single-step genomic best linear unbiased prediction (ssGBLUP) using Bayesian inference. Data were collected from 28,391 yearlings of Chinese Merino sheep (classified in 1992–2018) at the Xinjiang Gonaisi Fine Wool Sheep-Breeding Farm, China. Subjectively-assessed wool traits, namely, spinning count (SC), crimp definition (CRIM), oil (OIL), and body size (BS), and objectively-measured traits, namely, fleece length (FL), greasy fleece weight (GFW), mean fiber diameter (MFD), crimp number (CN), and body weight pre-shearing (BWPS), were analyzed. The estimates of heritability for wool traits were low to moderate. The largest h2 values were observed for FL (0.277) and MFD (0.290) with ssGBLUP. The heritabilities estimated for wool traits with ssGBLUP were slightly higher than those obtained with PBLUP. The accuracies of breeding values were low to moderate, ranging from 0.362 to 0.573 for the whole population and from 0.318 to 0.676 for the genotyped subpopulation. The correlation between the estimated breeding values (EBVs) and genomic EBVs (GEBVs) ranged from 0.717 to 0.862 for the whole population, and the relative increase in accuracy when comparing EBVs with GEBVs ranged from 0.372% to 7.486% for these traits. However, in the genotyped population, the rank correlation between the estimates obtained with PBLUP and ssGBLUP was reduced to 0.525 to 0.769, with increases in average accuracy of 3.016% to 11.736% for the GEBVs in relation to the EBVs. Thus, genomic information could allow us to more accurately estimate the relationships between animals and improve estimates of heritability and the accuracy of breeding values by ssGBLUP.

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Genomic prediction using pooled data in a single-step genomic best linear unbiased prediction framework

Journal of Animal Science ◽

10.1093/jas/skaa184 ◽

2020 ◽

Vol 98 (6) ◽

Author(s):

Johnna L Baller ◽

Stephen D Kachman ◽

Larry A Kuehn ◽

Matthew L Spangler

Keyword(s):

Phenotypic Variation ◽

Best Linear Unbiased Prediction ◽

Single Step ◽

Pool Size ◽

Individual Data ◽

Linear Unbiased Prediction ◽

Breeding Values ◽

Best Linear Unbiased ◽

Genetic Evaluations ◽

Unbiased Prediction

Abstract Economically relevant traits are routinely collected within the commercial segments of the beef industry but are rarely included in genetic evaluations because of unknown pedigrees. Individual relationships could be resurrected with genomics, but this would be costly; therefore, pooling DNA and phenotypic data provide a cost-effective solution. Pedigree, phenotypic, and genomic data were simulated for a beef cattle population consisting of 15 generations. Genotypes mimicked a 50k marker panel (841 quantitative trait loci were located across the genome, approximately once per 3 Mb) and the phenotype was moderately heritable. Individuals from generation 15 were included in pools (observed genotype and phenotype were mean values of a group). Estimated breeding values (EBV) were generated from a single-step genomic best linear unbiased prediction model. The effects of pooling strategy (random and minimizing or uniformly maximizing phenotypic variation within pools), pool size (1, 2, 10, 20, 50, 100, or no data from generation 15), and generational gaps of genotyping on EBV accuracy (correlation of EBV with true breeding values) were quantified. Greatest EBV accuracies of sires and dams were observed when there was no gap between genotyped parents and pooled offspring. The EBV accuracies resulting from pools were usually greater than no data from generation 15 regardless of sire or dam genotyping. Minimizing phenotypic variation increased EBV accuracy by 8% and 9% over random pooling and uniformly maximizing phenotypic variation, respectively. A pool size of 2 was the only scenario that did not significantly decrease EBV accuracy compared with individual data when pools were formed randomly or by uniformly maximizing phenotypic variation (P > 0.05). Pool sizes of 2, 10, 20, or 50 did not generally lead to statistical differences in EBV accuracy than individual data when pools were constructed to minimize phenotypic variation (P > 0.05). Largest numerical increases in EBV accuracy resulting from pooling compared with no data from generation 15 were seen with sires with prior low EBV accuracy (those born in generation 14). Pooling of any size led to larger EBV accuracies of the pools than individual data when minimizing phenotypic variation. Resulting EBV for the pools could be used to inform management decisions of those pools. Pooled genotyping to garner commercial-level phenotypes for genetic evaluations seems plausible although differences exist depending on pool size and pool formation strategy.

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Improving the accuracy of genomic evaluation for linear body measurement traits using single-step genomic best linear unbiased prediction in Hanwoo beef cattle

BMC Genetics ◽

10.1186/s12863-020-00928-1 ◽

2020 ◽

Vol 21 (1) ◽

Author(s):

Masoumeh Naserkheil ◽

Deuk Hwan Lee ◽

Hossein Mehrban

Keyword(s):

Prediction Accuracy ◽

Best Linear Unbiased Prediction ◽

Single Step ◽

Farm Animals ◽

Body Measurement ◽

Data Set ◽

Linear Unbiased Prediction ◽

Body Measurement Traits ◽

Best Linear Unbiased ◽

Unbiased Prediction

Abstract Background Recently, there has been a growing interest in the genetic improvement of body measurement traits in farm animals. They are widely used as predictors of performance, longevity, and production traits, and it is worthwhile to investigate the prediction accuracies of genomic selection for these traits. In genomic prediction, the single-step genomic best linear unbiased prediction (ssGBLUP) method allows the inclusion of information from genotyped and non-genotyped relatives in the analysis. Hence, we aimed to compare the prediction accuracy obtained from a pedigree-based BLUP only on genotyped animals (PBLUP-G), a traditional pedigree-based BLUP (PBLUP), a genomic BLUP (GBLUP), and a single-step genomic BLUP (ssGBLUP) method for the following 10 body measurement traits at yearling age of Hanwoo cattle: body height (BH), body length (BL), chest depth (CD), chest girth (CG), chest width (CW), hip height (HH), hip width (HW), rump length (RL), rump width (RW), and thurl width (TW). The data set comprised 13,067 phenotypic records for body measurement traits and 1523 genotyped animals with 34,460 single-nucleotide polymorphisms. The accuracy for each trait and model was estimated only for genotyped animals using five-fold cross-validations. Results The accuracies ranged from 0.02 to 0.19, 0.22 to 0.42, 0.21 to 0.44, and from 0.36 to 0.55 as assessed using the PBLUP-G, PBLUP, GBLUP, and ssGBLUP methods, respectively. The average predictive accuracies across traits were 0.13 for PBLUP-G, 0.34 for PBLUP, 0.33 for GBLUP, and 0.45 for ssGBLUP methods. Our results demonstrated that averaged across all traits, ssGBLUP outperformed PBLUP and GBLUP by 33 and 43%, respectively, in terms of prediction accuracy. Moreover, the least root of mean square error was obtained by ssGBLUP method. Conclusions Our findings suggest that considering the ssGBLUP model may be a promising way to ensure acceptable accuracy of predictions for body measurement traits, especially for improving the prediction accuracy of selection candidates in ongoing Hanwoo breeding programs.

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Domestic and Interbull information in the single step genomic evaluation of Holstein milk production

Czech Journal of Animal Science ◽

10.17221/7652-cjas ◽

2014 ◽

Vol 59 (No. 9) ◽

pp. 409-415 ◽

Cited By ~ 6

Author(s):

J. Přibyl ◽

J. Bauer ◽

P. Pešek ◽

J. Přibylová ◽

L. Vostrý ◽

...

Keyword(s):

Animal Model ◽

Milk Production ◽

Best Linear Unbiased Prediction ◽

Single Step ◽

Linear Unbiased Prediction ◽

Breeding Values ◽

One Step ◽

Best Linear Unbiased ◽

Estimated Breeding Values ◽

Unbiased Prediction

Estimated breeding values and genomic enhanced breeding values for milk production of young genotyped Holstein bulls were predicted using a conventional animal model, ridge regression genomic prediction procedure, genomic best linear unbiased prediction, single-step genomic best linear unbiased prediction, and one-step blending procedures. For prediction, the nation-wide database of domestic Czech production records was combined with deregressed proofs from Interbull files through 2008, which had been transformed by multiple across country evaluation to reflect domestic production conditions. 1259 genotyped bulls had already been proven in 2008. Analyses were run that used Interbull values only for these genotyped bulls and used Interbull values for all available sires. Predictions were validated by comparing correlations of breeding value predictions with estimated breeding values and daughter-yield-deviations after progeny test in 2012 of 140 young genotyped bulls and their associated reliabilities. Combining domestic data with Interbull estimated breeding values improved prediction of both estimated breeding values and genomic enhanced breeding values. Prediction by animal model (traditional estimated breeding values) using only the domestic database had 0.29 validated reliability of prediction; whereas combining the nation-wide domestic database with all available deregressed proofs for genotyped and non-genotyped sires from Interbull resulted in reliability of 0.34, compared to 0.36 when using Interbull data only. The highest reliabilities were for predictions from the single-step genomic best linear unbiased prediction procedure using combined data, or with all available deregressed proofs from Interbull only (one-step blending approach), which reached validated reliabilities for genomic enhanced breeding values predictions 0.53 and 0.54, respectively.  

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