Analysis of components of genetic variance and breeding value of kiwifruit (Actinidia chinensisPlanch.) using best linear unbiased prediction (BLUP) and pedigree information

Breeding Value ◽

Best Linear Unbiased Predictor ◽

Genetic Merit ◽

Selection Decisions ◽

Best Linear Unbiased ◽

Estimated Breeding Value ◽

Commercial Environment ◽

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.

Best linear unbiased prediction of breeding value: models with random environmental effects.

Linear models for the prediction of animal breeding values ◽

10.1079/9781780643915.0061 ◽

2015 ◽

pp. 61-69

Author(s):

R. Mrode

Keyword(s):

Environmental Effects ◽

Breeding Value ◽

Best Linear Unbiased ◽

Best linear unbiased prediction of breeding value: univariate models with one random effect.

Linear models for the prediction of animal breeding values ◽

10.1079/9781780643915.0034 ◽

2015 ◽

pp. 34-60

Author(s):

R. Mrode

Keyword(s):

Random Effect ◽

Breeding Value ◽

Best Linear Unbiased ◽

Combined selection of progeny in crop breeding using best linear unbiased prediction

Canadian Journal of Plant Science ◽

10.4141/cjps2011-110 ◽

2012 ◽

Vol 92 (3) ◽

pp. 553-562 ◽

Cited By ~ 4

Author(s):

José Marcelo Soriano Viana ◽

Vinícius Ribeiro Faria ◽

Fabyano Fonseca e Silva ◽

Marcos Deon Vilela de Resende

Keyword(s):

Least Squares ◽

Pedigree Information ◽

Crop Breeding ◽

Additive Variance ◽

Best Linear Unbiased ◽

Combined Selection ◽

Selection Of ◽

Viana, J. M. S., Faria, V. R., Fonseca e Silva, F. and Vilela de Resende, M. D. 2012. Combined selection of progeny in crop breeding using best linear unbiased prediction. Can. J. Plant Sci. 92: 553–562. Combined selection is an important strategy in crop breeding. As the classical index does not consider pedigree information, the objective of this study was to evaluate the efficiency of the best linear unbiased prediction (BLUP) methodology for combined selection of progeny. We analyzed expansion volume (EV) and grain yield of parents and inbred and non-inbred progeny from the popcorn population Viçosa. The BLUP analyses, single-trait and of the same character measured in parents and progeny (combined parent-family) were performed using the ASReml software. Because the experiments were balanced, the estimates of the additive variance from the BLUP and least squares analyses were generally equivalent. The accuracies of the BLUP analyses do not clearly establish the superior technique. The accuracy of the classical index tended to be higher than that obtained from BLUP analyses. There was equivalence between BLUP and least squares analyses relative to half-sib and inbred progeny selection, and superiority of the combined parent-family BLUP index for full-sib selection. The BLUP analyses also differed from the least squares analysis on the coincidence of selected parents. The populations obtained by selection based on BLUP of breeding values presented a lower effective size.

Prediction of Rates of Inbreeding in Populations Selected on Best Linear Unbiased Prediction of Breeding Value

Genetics ◽

10.1093/genetics/156.1.361 ◽

2000 ◽

Vol 156 (1) ◽

pp. 361-373

Author(s):

Piter Bijma ◽

John A Woolliams

Keyword(s):

Linear Model ◽

Goodness Of Fit ◽

Breeding Value ◽

Quadratic Model ◽

Breeding Values ◽

Wide Range ◽

Best Linear Unbiased ◽

Abstract Predictions for the rate of inbreeding (ΔF) in populations with discrete generations undergoing selection on best linear unbiased prediction (BLUP) of breeding value were developed. Predictions were based on the concept of long-term genetic contributions using a recently established relationship between expected contributions and rates of inbreeding and a known procedure for predicting expected contributions. Expected contributions of individuals were predicted using a linear model, μi(x) = α βsi, where si denotes the selective advantage as a deviation from the contemporaries, which was the sum of the breeding values of the individual and the breeding values of its mates. The accuracy of predictions was evaluated for a wide range of population and genetic parameters. Accurate predictions were obtained for populations of 5–20 sires. For 20–80 sires, systematic underprediction of on average 11% was found, which was shown to be related to the goodness of fit of the linear model. Using simulation, it was shown that a quadratic model would give accurate predictions for those schemes. Furthermore, it was shown that, contrary to random selection, ΔF less than halved when the number of parents was doubled and that in specific cases ΔF may increase with the number of dams.

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 ◽

Single Step ◽

Breeding Value ◽

Genomic Information ◽

Breeding Values ◽

Merino Sheep ◽

Best Linear Unbiased ◽

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.

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

Asian-Australasian Journal of Animal Sciences ◽

10.5713/ajas.18.0936 ◽

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 ◽

Single Step ◽

Breeding Value ◽

Eye Muscle ◽

Best Linear Unbiased ◽

Increase In Accuracy ◽

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.