Using single-step genomic best linear unbiased prediction to improve the efficiency of genetic evaluation on body weight in Macrobrachium rosenbergii

Aquaculture ◽  
2020 ◽  
Vol 528 ◽  
pp. 735577
Author(s):  
Junyu Liu ◽  
Guoliang Yang ◽  
Jie Kong ◽  
Zhenglong Xia ◽  
Juan Sui ◽  
...  
Keyword(s):  
Body Weight ◽  
Macrobrachium Rosenbergii ◽  
Best Linear Unbiased Prediction ◽  
Genetic Evaluation ◽  
Single Step ◽  
Linear Unbiased Prediction ◽  
Best Linear Unbiased ◽  
Unbiased Prediction

scholarly journals Genomic selection in American mink (Neovison vison) using a single-step genomic best linear unbiased prediction model for size and quality traits graded on live mink

2021 ◽  
Vol 99 (1) ◽  
Author(s):  
Trine M Villumsen ◽  
Guosheng Su ◽  
Bernt Guldbrandtsen ◽  
Torben Asp ◽  
Mogens S Lund
Keyword(s):  
Body Weight ◽  
Genomic Selection ◽  
Genomic Prediction ◽  
Best Linear Unbiased Prediction ◽  
Single Step ◽  
Quality Traits ◽  
Linear Unbiased Prediction ◽  
Best Linear Unbiased ◽  
Regression Slopes ◽  
Unbiased Prediction

Abstract Genomic selection relies on single-nucleotide polymorphisms (SNPs), which are often collected using medium-density SNP arrays. In mink, no such array is available; instead, genotyping by sequencing (GBS) can be used to generate marker information. Here, we evaluated the effect of genomic selection for mink using GBS. We compared the estimated breeding values (EBVs) from single-step genomic best linear unbiased prediction (SSGBLUP) models to the EBV from ordinary pedigree-based BLUP models. We analyzed seven size and quality traits from the live grading of brown mink. The phenotype data consisted of ~20,600 records for the seven traits from the mink born between 2013 and 2016. Genotype data included 2,103 mink born between 2010 and 2014, mostly breeding animals. In total, 28,336 SNP markers from 391 scaffolds were available for genomic prediction. The pedigree file included 29,212 mink. The predictive ability was assessed by the correlation (r) between progeny trait deviation (PTD) and EBV, and the regression of PTD on EBV, using 5-fold cross-validation. For each fold, one-fifth of animals born in 2014 formed the validation set. For all traits, the SSGBLUP model resulted in higher accuracies than the BLUP model. The average increase in accuracy was 15% (between 3% for fur clarity and 28% for body weight). For three traits (body weight, silky appearance of the under wool, and guard hair thickness), the difference in r between the two models was significant (P < 0.05). For all traits, the regression slopes of PTD on EBV from SSGBLUP models were closer to 1 than regression slopes from BLUP models, indicating SSGBLUP models resulted in less bias of EBV for selection candidates than the BLUP models. However, the regression coefficients did not differ significantly. In conclusion, the SSGBLUP model is superior to conventional BLUP model in the accurate selection of superior animals, and, thus, it would increase genetic gain in a selective breeding program. In addition, this study shows that GBS data work well in genomic prediction in mink, demonstrating the potential of GBS for genomic selection in livestock species.

An empirical check on best linear unbiased prediction genetic evaluation using pig field recording data

1997 ◽  
Vol 77 (2) ◽  
pp. 211-216 ◽  
Author(s):  
V. M. Quinton ◽  
C. Smith
Keyword(s):  
Large Body ◽  
Best Linear Unbiased Prediction ◽  
Genetic Evaluation ◽  
Linear Unbiased Prediction ◽  
Field Recording ◽  
Best Linear Unbiased ◽  
Simulation Results ◽  
Pig Performance ◽  
Computer Simulation Studies ◽  
Unbiased Prediction

The theory and use of best linear unbiased prediction in genetic evaluation are well developed. However, there has been little empirical checking of its efficacy in practice. The objective here was to use a large body of Canadian pig performance records to check on the predicted benefits of BLUP in genetic evaluation. Phenotype records were available on fat depth and on days to 100 kg on some 65 000 progeny born in 1994 and 1995 from parents evaluated before 1994. Rank correlations between parent and progeny in data were calculated within herd-year-season to avoid effects due to differences in these factors. Computer simulation studies were also run to check on the predicted results. The simulation results confirmed the expectations on the higher correlation of mid-parental EBV than of mid-parental phenotype with progeny genotype and a regression (of progeny phenotype on mid-parental EBV) of unity when all relevant pedigree and performance data were used. In the data analysis, the (rank) correlations with progeny phenotype were consistently higher (36 and 27%) for mid-parental BLUP genetic evaluation than for mid-parental phenotypes, confirming the superiority of the BLUP evaluations over phenotypes. However, the regression of progeny phenotype on mid-parent BLUP EBV was usually less than the predicted value of unity. Simulation results suggest that either the base population heritability was lower than that used in the evaluation or that the information used was incomplete. Key words: Best linear unbiased prediction, EBV, pigs, performance, selection

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

2018 ◽  
Vol 135 (4) ◽  
pp. 251-262 ◽  
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

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

Animals ◽  
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.

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

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.

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

2020 ◽  
Vol 98 (12) ◽  
Author(s):  
Ignacy Misztal ◽  
Shogo Tsuruta ◽  
Ivan Pocrnic ◽  
Daniela Lourenco
Keyword(s):  
Prediction Accuracy ◽  
Best Linear Unbiased Prediction ◽  
Single Step ◽  
Relationship Matrix ◽  
Linear Unbiased Prediction ◽  
Breeding Values ◽  
Best Linear Unbiased ◽  
The Impact ◽  
Genomic Predictions ◽  
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.

Short Communication: Investigation of the feasibility of genomic selection in Icelandic Cattle

2021 ◽  
Author(s):  
Egill Gautason ◽  
Goutam Sahana ◽  
Guosheng Su ◽  
Baldur Helgi Benjamínsson ◽  
Guðmundur Jóhannesson ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Cattle Breed ◽  
Best Linear Unbiased Prediction ◽  
Single Step ◽  
Breeding Scheme ◽  
Linear Unbiased Prediction ◽  
Percentage Points ◽  
Best Linear Unbiased ◽  
Estimated Breeding Values ◽  
Unbiased Prediction

Abstract Icelandic Cattle is a local dairy cattle breed in Iceland. With about 26,000 breeding females, it is by far the largest among the indigenous Nordic cattle breeds. The objective of this study was to investigate the feasibility of genomic selection in Icelandic Cattle. Pedigree-based best linear unbiased prediction (PBLUP) and single-step genomic best linear unbiased prediction (ssGBLUP) were compared. Accuracy, bias, and dispersion of estimated breeding values (EBV) for milk yield (MY), fat yield (FY), protein yield (PY), and somatic cell score (SCS) were estimated in a cross validation-based design. Accuracy (r) was estimated by the correlation between EBV and corrected phenotype in a validation set. The accuracy (r) of predictions using ssGBLUP increased by 13, 23, 19 and 20 percentage points for MY, FY, PY, and SCS for genotyped animals, compared to PBLUP. The accuracy of non-genotyped animals was not improved for MY and PY, but increased by 0.9 and 3.5 percentage points for FY and SCS. We used the linear regression (LR) method to quantify relative improvements in accuracy, bias (∆), and dispersion (b) of EBV. Using the LR method, the relative improvements in accuracy of validation from PBLUP to ssGBLUP were 43%, 60%, 50%, and 48% for genotyped animals for MY, FY, PY, and SCS. Single-step GBLUP EBV were less underestimated (∆), and less over-dispersed (b) than PBLUP EBV for FY and PY. Pedigree-based BLUP EBV were close to unbiased for MY and SCS. Single-step GBLUP underestimated MY EBV but overestimated SCS EBV. Based on the average accuracy of 0.45 for ssGBLUP EBV obtained in this study, selection intensities according to the breeding scheme of Icelandic Cattle, and assuming a generation interval of 2.0 years for sires of bulls, sires of dams and dams of bulls, genetic gain in Icelandic Cattle could be increased by about 50% relative to the current breeding scheme.

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