scholarly journals Weighted Genomic Best Linear Unbiased Prediction for Carcass Traits in Hanwoo Cattle

Genes ◽  
2019 ◽  
Vol 10 (12) ◽  
pp. 1019 ◽  
Author(s):  
Bryan Irvine Lopez ◽  
Seung-Hwan Lee ◽  
Jong-Eun Park ◽  
Dong-Hyun Shin ◽  
Jae-Don Oh ◽  
...  
Keyword(s):  
Genomic Prediction ◽  
Carcass Traits ◽  
Best Linear Unbiased Prediction ◽  
Birth Date ◽  
Muscle Area ◽  
Eye Muscle ◽  
Linear Unbiased Prediction ◽  
Best Linear Unbiased ◽  
Hanwoo Cattle ◽  
Unbiased Prediction

The genomic best linear unbiased prediction (GBLUP) method has been widely used in routine genomic evaluation as it assumes a common variance for all single nucleotide polymorphism (SNP). However, this is unlikely in the case of traits influenced by major SNP. Hence, the present study aimed to improve the accuracy of GBLUP by using the weighted GBLUP (WGBLUP), which gives more weight to important markers for various carcass traits of Hanwoo cattle, such as backfat thickness (BFT), carcass weight (CWT), eye muscle area (EMA), and marbling score (MS). Linear and different nonlinearA SNP weighting procedures under WGBLUP were evaluated and compared with unweighted GBLUP and traditional pedigree-based methods (PBLUP). WGBLUP methods were assessed over ten iterations. Phenotypic data from 10,215 animals from different commercial herds that were slaughtered at approximately 30-month-old of age were used. All these animals were genotyped using Illumina Bovine 50k SNP chip and were divided into a training and a validation population by birth date on 1 November 2015. Genomic prediction accuracies obtained in the nonlinearA weighting methods were higher than those of the linear weighting for all traits. Moreover, unlike with linear methods, no sudden drops in the accuracy were noted after the peak was reached in nonlinearA methods. The average accuracies using PBLUP were 0.37, 0.49, 0.40, and 0.37, and 0.62, 0.74, 0.67, and 0.65 using GBLUP for BFT, CWT, EMA, and MS, respectively. Moreover, these accuracies of genomic prediction were further increased to 4.84% and 2.70% for BFT and CWT, respectively by using the nonlinearA method under the WGBLUP model. For EMA and MS, WGBLUP was as accurate as GBLUP. Our results indicate that the WGBLUP using a nonlinearA weighting method provides improved predictions for CWT and BFT, suggesting that the ability of WGBLUP over the other models by weighting selected SNPs appears to be trait-dependent.

scholarly journals Correction: Weighted Genomic Best Linear Unbiased Prediction for Carcass Traits in Hanwoo Cattle. Genes 2019, 10, 1019

Genes ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 1013
Author(s):  
Bryan Irvine Lopez ◽  
Seung-Hwan Lee ◽  
Jong-Eun Park ◽  
Dong-Hyun Shin ◽  
Jae-Don Oh ◽  
...  
Keyword(s):  
Carcass Traits ◽  
Best Linear Unbiased Prediction ◽  
Linear Unbiased Prediction ◽  
Best Linear Unbiased ◽  
Hanwoo Cattle ◽  
Unbiased Prediction

The authors wish to make the following corrections to this paper [...]

scholarly journals Genomic Prediction Using Alternative Strategies of Weighted Single-Step Genomic BLUP for Yearling Weight and Carcass Traits in Hanwoo Beef Cattle

Genes ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 266
Author(s):  
Hossein Mehrban ◽  
Masoumeh Naserkheil ◽  
Deuk Hwan Lee ◽  
Chungil Cho ◽  
Taejeong Choi ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Beef Cattle ◽  
Genomic Prediction ◽  
Quantitative Trait ◽  
Carcass Traits ◽  
Best Linear Unbiased Prediction ◽  
Single Step ◽  
Linear Unbiased Prediction ◽  
Single Nucleotide ◽  
Best Linear Unbiased

The weighted single-step genomic best linear unbiased prediction (GBLUP) method has been proposed to exploit information from genotyped and non-genotyped relatives, allowing the use of weights for single-nucleotide polymorphism in the construction of the genomic relationship matrix. The purpose of this study was to investigate the accuracy of genetic prediction using the following single-trait best linear unbiased prediction methods in Hanwoo beef cattle: pedigree-based (PBLUP), un-weighted (ssGBLUP), and weighted (WssGBLUP) single-step genomic methods. We also assessed the impact of alternative single and window weighting methods according to their effects on the traits of interest. The data was comprised of 15,796 phenotypic records for yearling weight (YW) and 5622 records for carcass traits (backfat thickness: BFT, carcass weight: CW, eye muscle area: EMA, and marbling score: MS). Also, the genotypic data included 6616 animals for YW and 5134 for carcass traits on the 43,950 single-nucleotide polymorphisms. The ssGBLUP showed significant improvement in genomic prediction accuracy for carcass traits (71%) and yearling weight (99%) compared to the pedigree-based method. The window weighting procedures performed better than single SNP weighting for CW (11%), EMA (11%), MS (3%), and YW (6%), whereas no gain in accuracy was observed for BFT. Besides, the improvement in accuracy between window WssGBLUP and the un-weighted method was low for BFT and MS, while for CW, EMA, and YW resulted in a gain of 22%, 15%, and 20%, respectively, which indicates the presence of relevant quantitative trait loci for these traits. These findings indicate that WssGBLUP is an appropriate method for traits with a large quantitative trait loci effect.

scholarly journals Accounting for Group-Specific Allele Effects and Admixture in Genomic Predictions: Theory and Experimental Evaluation in Maize

Genetics ◽  
2020 ◽  
Vol 216 (1) ◽  
pp. 27-41
Author(s):  
Simon Rio ◽  
Laurence Moreau ◽  
Alain Charcosset ◽  
Tristan Mary-Huard
Keyword(s):  
Genomic Prediction ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Best Linear Unbiased Prediction ◽  
Linear Unbiased Prediction ◽  
Modeling Group ◽  
A Genome ◽  
Specific Allele ◽  
Best Linear Unbiased ◽  
Unbiased Prediction

Populations structured into genetic groups may display group-specific linkage disequilibrium, mutations, and/or interactions between quantitative trait loci and the genetic background. These factors lead to heterogeneous marker effects affecting the efficiency of genomic prediction, especially for admixed individuals. Such individuals have a genome that is a mosaic of chromosome blocks from different origins, and may be of interest to combine favorable group-specific characteristics. We developed two genomic prediction models adapted to the prediction of admixed individuals in presence of heterogeneous marker effects: multigroup admixed genomic best linear unbiased prediction random individual (MAGBLUP-RI), modeling the ancestry of alleles; and multigroup admixed genomic best linear unbiased prediction random allele effect (MAGBLUP-RAE), modeling group-specific distributions of allele effects. MAGBLUP-RI can estimate the segregation variance generated by admixture while MAGBLUP-RAE can disentangle the variability that is due to main allele effects from the variability that is due to group-specific deviation allele effects. Both models were evaluated for their genomic prediction accuracy using a maize panel including lines from the Dent and Flint groups, along with admixed individuals. Based on simulated traits, both models proved their efficiency to improve genomic prediction accuracy compared to standard GBLUP models. For real traits, a clear gain was observed at low marker densities whereas it became limited at high marker densities. The interest of including admixed individuals in multigroup training sets was confirmed using simulated traits, but was variable using real traits. Both MAGBLUP models and admixed individuals are of interest whenever group-specific SNP allele effects exist.

scholarly journals Improvement of Genomic Prediction in Advanced Wheat Breeding Lines by Including Additive × additive Epistasis

2021 ◽  
Author(s):  
Miguel Angel Raffo ◽  
Pernille Sarup ◽  
Xiangyu Guo ◽  
Huiming Liu ◽  
Jeppe Reitan Andersen ◽  
...  
Keyword(s):  
Genomic Prediction ◽  
Cross Validation ◽  
Best Linear Unbiased Prediction ◽  
Wheat Breeding ◽  
Breeding Cycle ◽  
Linear Unbiased Prediction ◽  
Breeding Lines ◽  
Genetic Merit ◽  
Best Linear Unbiased ◽  
Unbiased Prediction

Abstract Epistasis is the principal non-additive genetic effect in inbred wheat lines and can be used to develop cultivars based on total genetic merit. Correct models for variance components (VCs) estimation are needed to disentangle the genetic architecture of complex traits in wheat. We aimed to i) evaluate the performance of extended genomic best linear unbiased prediction (EG-BLUP) and the natural and orthogonal interactions approach (NOIA) for VCs estimation in a commercial wheat-breeding population, and ii) investigate whether including epistasis in genomic prediction enhance predictive ability (PA) for wheat breeding lines. In total, 2,060 sixth-generation (F6) lines from Nordic Seed A/S breeding company were phenotyped for grain yield over 21-year-x-location combinations in Denmark, and genotyped using 15K Illumina-BeadChip. Four models were used to estimate VCs and heritability at plot level: i) Baseline, ii) Genomic best linear unbiased prediction (G-BLUP), iii) EG-BLUP, and iv) NOIA. Narrow- and broad-sense heritabilities estimated with G-BLUP were 0.15 and 0.31, respectively. EG-BLUP and NOIA failed to achieve orthogonal partition of genetic variances. Even though NOIA removed Hardy-Weinberg equilibrium assumption, both models yielded very similar estimates, indicating that linkage disequilibrium causes the lack of orthogonality. The PA was studied using leave-one-line-out and leave-one-breeding-cycle-out cross-validations. Both EG-BLUP and NOIA increased PA significantly (16.5%) compared to G-BLUP in leave-one-line-out cross-validation. However, the improvement for including epistasis was not observed in the leave-one-breeding-cycle-out cross-validation. We conclude that although the variance partition into orthogonal genetic effects was not possible, epistatic models can be useful to enhance predictions of total genetic merit.

scholarly journals Genomic selection through single-step genomic best linear unbiased prediction improves the accuracy of evaluation in Hanwoo cattle

2020 ◽  
Vol 33 (10) ◽  
pp. 1544-1557
Author(s):  
Mi Na Park ◽  
Mahboob Alam ◽  
Sidong Kim ◽  
Byoungho Park ◽  
Seung Hwan Lee ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Population Level ◽  
Best Linear Unbiased Prediction ◽  
Single Step ◽  
Breeding Value ◽  
Eye Muscle ◽  
Linear Unbiased Prediction ◽  
Best Linear Unbiased ◽  
Increase In Accuracy ◽  
Unbiased Prediction

Objective: Genomic selection (GS) is becoming popular in animals’ genetic development. We, therefore, investigated the single-step genomic best linear unbiased prediction (ssGBLUP) as tool for GS, and compared its efficacy with the traditional pedigree BLUP (pedBLUP) method.Methods: A total of 9,952 males born between 1997 and 2018 under Hanwoo proven-bull selection program was studied. We analyzed body weight at 12 months and carcass weight (kg), backfat thickness, eye muscle area, and marbling score traits. About 7,387 bulls were genotyped using Illumina 50K BeadChip Arrays. Multiple-trait animal model analyses were performed using BLUPF90 software programs. Breeding value accuracy was calculated using two methods: i) Pearson’s correlation of genomic estimated breeding value (GEBV) with EBV of all animals (rM1) and ii) correlation using inverse of coefficient matrix from the mixed-model equations (rM2). Then, we compared these accuracies by overall population, info-type (PHEN, phenotyped-only; GEN, genotyped-only; and PH+GEN, phenotyped and genotyped), and bull-types (YBULL, young male calves; CBULL, young candidate bulls; and PBULL, proven bulls).Results: The rM1 estimates in the study were between 0.90 and 0.96 among five traits. The rM1 estimates varied slightly by population and info-type, but noticeably by bull-type for traits. Generally average rM2 estimates were much smaller than rM1 (pedBLUP, 0.40 to0.44; ssGBLUP, 0.41 to 0.45) at population level. However, rM2 from both BLUP models varied noticeably across info-types and bull-types. The ssGBLUP estimates of rM2 in PHEN, GEN, and PH+ GEN ranged between 0.51 and 0.63, 0.66 and 0.70, and 0.68 and 0.73, respectively. In YBULL, CBULL, and PBULL, the rM2 estimates ranged between 0.54 and 0.57, 0.55 and 0.62, and 0.70 and 0.74, respectively. The pedBLUP based rM2 estimates were also relatively lower than ssGBLUP estimates. At the population level, we found an increase in accuracy by 2.0% to 4.5% among traits. Traits in PHEN were least influenced by ssGBLUP (0% to 2.0%), whereas the highest positive changes were in GEN (8.1% to 10.7%). PH+GEN also showed 6.5% to 8.5% increase in accuracy by ssGBLUP. However, the highest improvements were found in bull-types (YBULL, 21% to 35.7%; CBULL, 3.3% to 9.3%; PBULL, 2.8% to 6.1%).Conclusion: A noticeable improvement by ssGBLUP was observed in this study. Findings of differential responses to ssGBLUP by various bulls could assist in better selection decision making as well. We, therefore, suggest that ssGBLUP could be used for GS in Hanwoo provenbull evaluation program.

scholarly journals Benchmarking between item based collaborative filtering algorithm and genomic best linear unbiased prediction (GBLUP) model in terms of prediction accuracy for wheat and maize//Estudio comparativo en términos de capacidad predictiva para datos de trigo y maíz entre el algoritmo de filtrado colaborativo y el modelo genómico mejor predictor lineal insesgado (GBLUP)

Biotecnia ◽  
2020 ◽  
Vol 22 (2) ◽  
pp. 136-146
Author(s):  
Osval A. Montesinos-López ◽  
Emeterio Franco-Pérez ◽  
Francisco J. Luna-Vázquez ◽  
Josafat Salinas-Ruiz ◽  
Sara Sandoval-Carrillo ◽  
...  
Keyword(s):  
Collaborative Filtering ◽  
Genomic Prediction ◽  
Prediction Models ◽  
Best Linear Unbiased Prediction ◽  
Linear Unbiased Prediction ◽  
Collaborative Filtering Algorithm ◽  
New Methodologies ◽  
Best Linear Unbiased ◽  
Better Than ◽  
Unbiased Prediction

Aim/background: in view of the growing demand for food, new methodologies are needed to improve the genomic selection (GS) methodology to obtain more productive plant varieties and there is empirical evidence that GS it is revolutionizing plant breeding for food production around the world. Methods: since the prediction models play a key role in GS, for this reason Montesinos-López et al. (2018) proposed the item based collaborative filtering (IBCF) algorithm for Genomic prediction. For this reason, in this paper we compare the IBCF algorithm with the most popular genomic prediction model called the Genomic Best Linear Unbiased Prediction (GBLUP). Results: We found that the GBLUP is superior than the IBCF model, but the IBCF is competitive to the GBLUP model since produced very similar predictions, but with the large advantage that it is extremely efficient in terms of time for implementation. Conclusions: we found that the GBLUP is better than the IBCF algorithm but the IBCF is more than 400 times more efficient than the GBLUP model in terms of time for implementation. Limitations: The main limitation of the study is that it was performed in univariate terms and it is possible that the IBCF will perform better with multivariate data.RESUMENObjetivo / antecedentes: en vista de la creciente demanda de alimentos, se necesitan nuevas metodologías para mejorar la selección genómica (GS) para obtener variedades de plantas más productivas y en menor tiempo y existe evidencia que la SG está revolucionando el mejoramiento de plantas que ayudará a incrementar la producción de alimentos a nivel mundial. Métodos: dado que los modelos de predicción juegan un papel clave en GS, Montesinos-López et al. (2018) propusieron el algoritmo de filtrado colaborativo (IBCF) para la predicción genómica. Por esta razón, en este artículo comparamos el algoritmo IBCF con el modelo de predicción genómica más popular denominado mejor predictor lineal insesgado Bayesiano (GBLUP). Resultados: Encontramos que el GBLUP es superior en capacidad predictiva al modelo IBCF, pero el IBCF es competitivo con el modelo GBLUP ya que produjo predicciones muy similares, pero con la ventaja de que es eficiente en términos de tiempo de implementación. Conclusiones: encontramos que el GBLUP es mejor que el algoritmo IBCF, pero el IBCF es 400 veces más eficiente que el modelo GBLUP en términos de tiempo de implementación. Limitaciones: la principal limitación del estudio es que se realizó en términos univariados y es posible que el IBCF se desempeñe mejor con datos multivariados.

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.

A method for reducing inbreeding with Best Linear Unbiased Prediction

1993 ◽  
Vol 1993 ◽  
pp. 33-33
Author(s):  
B Grundy ◽  
WG Hill
Keyword(s):  
Best Linear Unbiased Prediction ◽  
Breeding Value ◽  
Best Linear Unbiased Predictor ◽  
Linear Unbiased Prediction ◽  
Genetic Merit ◽  
Selection Decisions ◽  
Best Linear Unbiased ◽  
Estimated Breeding Value ◽  
Commercial Environment ◽  
Unbiased Prediction

An optimum way of selecting animals is through a prediction of their genetic merit (estimated breeding value, EBV), which can be achieved using a best linear unbiased predictor (BLUP) (Henderson, 1975). Selection decisions in a commercial environment, however, are rarely made solely on genetic merit but also on additional factors, an important example of which is to limit the accumulation of inbreeding. Comparison of rates of inbreeding under BLUP for a range of hentabilities highlights a trend of increasing inbreeding with decreasing heritability. It is therefore proposed that selection using a heritability which is artificially raised would yield lower rates of inbreeding than would otherwise be the case.

Sign in / Sign up

Export Citation Format

Share Document