Pszczola M, Strabel T, Mulder HA, Calus MPL.
#Asreml mean square error r manual#
GenSel – user manual for a portfolio of genomic selection related analyses, create 9.1. Accuracy of genomic breeding values in multibreed beef cattle populations derived from deregressed breeding values and phenotypes. Weber KL, Thallman RM, Keele JK, Snelling WM, Bennet GL, Smith TPL, et al. ASREML User Guide Release 3.0 VSN International Ltd, Hemel Hempstead, HP1 1ES, UK. Gilmour AR, Gogel, BJ, Cullis, BR, Thompson R. Correcting for bias in estimation of quantitative trait loci effects.
#Asreml mean square error r simulator#
QMSim: a large-scale genome simulator for livestock. Comparación de la exactitud de valores genómicos de animales predichos a través del análisis con dos modelos alternativos. J Anim Sci 2013 91:1076-1085.Īlarcón-Zúñiga B, Ramírez-Flores F, Ruíz-Flores A, Ramírez-Valverde R, Saavedra-Jiménez LA, Zepeda-Batista JL. Computation of deregressed proofs for genomic selection when own phenotypes exist with an application in French show-jumping horses. Deregressed EBV as the response variable yield more reliable genomic predictions than traditional EBV in pure-bred pigs. Ostersen T, Christensen OF, Henryon M, Nielsen B, Su G, Madsen P. Guo G, Lund MS, Zhang Y, Su G: Comparison between genomic predictions using daughter yield deviation and conventional estimated breeding value as response variables. Deregressing estimated breeding values and weighting information for genomic regression analyses. Genomic selection: A paradigm shift in animal breeding. Genomic selection in commercial pig breeding. Genomic selection: Prediction of accuracy and maximization of long term response. There were only slight advantages of using DEBVs as response variables over using EBVs. They decreased when PEn and PEv were farther apart.
![asreml mean square error r asreml mean square error r](https://www.researchgate.net/profile/Abebe-Hassen/publication/12125899/figure/tbl2/AS:669290583838733@1536582718714/Multiple-coefficient-of-determination-and-root-mean-square-error-of-Aloka-500V-and.png)
Genetic correlation estimates between true genetic values and GVs varied from 0.41 to 0.53 in the two scenarios studied. The trends for R 2 and PEV held true for both EBV and DEBV used as response variables. The closer PEn was to PEv, the higher the R 2, and correspondingly, the lower the predicted error variance.
![asreml mean square error r asreml mean square error r](https://d3i71xaburhd42.cloudfront.net/3e431f395681cc17d8b2366d571fd4a4d2c0d8f6/55-Table4.1-1.png)
When PEn was the largest, the mean R 2 of GV was the highest, 0.77 ± 0.01. GVs, their accuracies, and genetic correlations were obtained using the GenSel and ASREML programs. Generations 7 to 14 of the second population were used in several combinations as training (PEn) and evaluation (PEv) subpopulations. Thereafter, 20 males and 200 females were used to generate a second 14-generation population, with 6,400 individuals per generation and its corresponding phenotype and genotype in SNP terms. A first population, effective population size 800 and 100 generations, was simulated using the QMSim program to generate linkage disequilibrium. The objectives of this study were to compare accuracies (R 2) of genomic values (GVs) and to estimate genetic correlation between true genetic values and genomic values obtained using predicted breeding values (EBV) and deregressed EBV (DEBV) as response variables.
![asreml mean square error r asreml mean square error r](https://i.ytimg.com/vi/zyT3YMUQHts/maxresdefault.jpg)
Highly accurate predicted genetic values must be obtained at an early age to promote rapid genetic progress.