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 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 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.

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.

scholarly journals ​Best Linear Unbiased Prediction with Additive Main and Multiplicative Interaction for Stability Analysis of Barley Genotypes Evaluated in Coordinated Program

2021 ◽  
Author(s):  
Ajay Verma ◽  
R.P.S. Verma ◽  
J. Singh ◽  
L. Kumar ◽  
G.P. Singh
Keyword(s):  
Weighted Average ◽  
Best Linear Unbiased Prediction ◽  
High Yield ◽  
Linear Unbiased Prediction ◽  
Yield Performance ◽  
Multiplicative Interaction ◽  
Absolute Value ◽  
Best Linear Unbiased ◽  
Barley Genotypes ◽  
Unbiased Prediction

Background: Additive main and multiplicative interaction (AMMI) analysis had been exploited for multi environment trials for most of the crops. Usage of the best linear unbiased prediction (BLUP), along with AMMI tools, of the genotypes would improve the estimation of interaction effects. Methods: AMMI based measures of adaptability have been enriched with the incorporation of BLUP of genotypes by new Superiority index that allowed variable weights for stability and yield of genotypes. Result: Stability measure weighted average of absolute scores (WAASB) based on all significant interaction principal components ranked suitability of KB1754, RD3000, NDB1445 genotypes. Superiority index while weighting 0.65 and 0.35 for mean yield and stability arranged DWRB201, NDB1445, RD2552 as of stable high yield performance of barley genotypes. Corrected measure Modified AMMI Stability Value (MASV1) found RD2552, DWRB201, KB1762 and Modified AMMI Stability Value (MASV) ranked DWRB201, RD2552, KB1762. ASTAB measure achieved the desirable lower values for DWRB201 DWRB207, HUB268 genotypes. Biplot graphical analysis based on 60.7% of variation of the stability measures observed MASV1, ASTAB (AMMI based stability parameter), EV(Averages of the squared eigenvector values), SIPC (Sums of the absolute value of the IPC scores), Za (Absolute value of the relative contribution of IPCs to the interaction), W3, WAASB and MASV had been clubbed together. For the second year lower value of WAASB measure had observed for RD3016, KB1815 HUB273. Ranking of genotypes as per Superiority index found RD3017, RD2907, HUB274 as of stable high yield performance. Genotypes RD3017, RD2907 and NDB1173 pointed out by MASV1 while RD3017, RD2907, NDB1173 identified by MASV as the genotypes of choice. RD3017 NDB1173, RD2907 genotypes were selected as per values of ASTAB measure. Total of 71.8% of variation of the considered measures in biplot analysis expressed larger cluster comprised of AMMI based measures and a separate cluster of Superiority indexes as per mean, Geometric Adaptability Index (GAI) and HMGV also observed.

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