scholarly journals Backsolving in combined-merit models for marker-assisted best linear unbiased prediction of total additive genetic merit

1997 ◽  
Vol 29 (5) ◽  
pp. 611 ◽  
Author(s):  
S Saito ◽  
H Iwaisaki
Keyword(s):  
Best Linear Unbiased Prediction ◽  
Linear Unbiased Prediction ◽  
Genetic Merit ◽  
Best Linear Unbiased ◽  
Additive Genetic Merit ◽  
Unbiased Prediction

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 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 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 ​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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