Journal articles: 'Best Linear Unbiased Prediction (BLUP)'

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Bai, Chao, and Haiqi Li. "Simultaneous prediction in the generalized linear model." Open Mathematics 16, no. 1 (August 24, 2018): 1037–47. http://dx.doi.org/10.1515/math-2018-0087.

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AbstractThis paper studies the prediction based on a composite target function that allows to simultaneously predict the actual and the mean values of the unobserved regressand in the generalized linear model. The best linear unbiased prediction (BLUP) of the target function is derived. Studies show that our BLUP has better properties than some other predictions. Simulations confirm its better finite sample performance.

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VIANA, J. M. S., G. B. MUNDIM, R. O. DELIMA, F. F. E SILVA, and M. D. V. DE RESENDE. "Best linear unbiased prediction for genetic evaluation in reciprocal recurrent selection with popcorn populations." Journal of Agricultural Science 152, no. 3 (May 23, 2013): 428–38. http://dx.doi.org/10.1017/s0021859613000270.

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SUMMARYThe objective of the present study was to present the theory and application of best linear unbiased prediction (BLUP) in reciprocal recurrent selection (RRS). Seven progeny tests from two RRS programmes with popcorn (Zea mays L. ssp. mays [syn. Zea mays L. ssp. everta (Sturtev.) Zhuk.]) populations were conducted and analysed for expansion volume and grain yield. The interpopulation half- and full-sib family models were fitted using ASReml software. Half-sib selection is equivalent to selection for the general combining ability (GCA) of the common parents. With inbred full-sib progeny and BLUP analysis, it is possible to predict the general and specific combining ability effects. The standard error of prediction of the progeny effect was lower than the standard deviation of the best linear unbiased estimation (BLUE) estimate. For half- and full-sib RRS, the BLUE and BLUP provided highly correlated estimates of progeny genotypic values. The coincidence between selected parents ranged from 64 to 95%. With inbred full-sib progeny, the correlations between the BLUE of progeny genotypic values and the BLUP of GCA effects were lower. Consequently, the coincidence between selected parents was lower, ranging from 0 to 57%. The percentage of common selected inbred progeny based on the BLUE and BLUP of the progeny genotypic value ranged from 57 to 100%.

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Marcelo Soriano Viana, José, Vinícius Ribeiro Faria, Fabyano Fonseca e Silva, and Marcos Deon Vilela de Resende. "Combined selection of progeny in crop breeding using best linear unbiased prediction." Canadian Journal of Plant Science 92, no. 3 (May 2012): 553–62. http://dx.doi.org/10.4141/cjps2011-110.

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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.

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Grundy, B., and WG Hill. "A method for reducing inbreeding with Best Linear Unbiased Prediction." Proceedings of the British Society of Animal Production (1972) 1993 (March 1993): 33. http://dx.doi.org/10.1017/s030822960002362x.

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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.

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NOVIKOV, A. A., E. N. SUSLINA, G. S. POKHODNYA, D. G. SHIСHKIN, YA A. KHABIBRAKHMANOVA, and N. V. BASHMAKOVA. "SELECTION OF SOWS BY GENETIC MARKERS AND BLUP INDEX." Izvestiâ Timirâzevskoj selʹskohozâjstvennoj akademii, no. 4 (2021): 94–107. http://dx.doi.org/10.26897/0021-342x-2021-4-94-107.

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The authors conducted studies on the effect of the estrogen receptor (ESR), prolactin receptor (PRLR), and ryanodine receptor (RYR-1) genotypes on the breeding value of sows. Using the BLUP method, they evaluated the indicators of the large white, landrace, and Duroc breeds to develop a regional hybridization system in the pig industry of the Belgorod region. The research determined a significant influence of the “desirable” BB and AB genotypes of the ESR gene in large white sows and the “desirable” BB genotype of the RPLR gene in Landrace and Duroc sows on the maternal BLUP index.

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Klápště, J., M. Lstibůrek, and J. Kobliha. "Initial evaluation of half-sib progenies of Norway spruce using the best linear unbiased prediction." Journal of Forest Science 53, No. 2 (January 7, 2008): 41–46. http://dx.doi.org/10.17221/2136-jfs.

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The present paper deals with data obtained from fifteen years old Norway spruce (Picea abies [L.] Karst.) progeny test established at three sites in the Sázava River region. Parameter under the evaluation was a tree height in 15 years following the establishment of the trial. Genetic parameters were estimated using the REML (Restricted Maximum Likelihood) procedure followed by the BLUP (Best Linear Unbiased Prediction). Genetic parameters estimates were used to predict genetic gain in three alternative selection strategies. The value of gain depends on target value of gene diversity. 10−15% gain is due to selecting breeding population composed of 50 individuals. Based on these quantitative findings, current and future research orientation is discussed.

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FORKMAN, J., and H.-P. PIEPHO. "Performance of empirical BLUP and Bayesian prediction in small randomized complete block experiments." Journal of Agricultural Science 151, no. 3 (May 16, 2012): 381–95. http://dx.doi.org/10.1017/s0021859612000445.

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SUMMARYThe model for analysis of randomized complete block (RCB) experiments usually includes two factors: block and treatment. If treatment is modelled as fixed, best linear unbiased estimation (BLUE) is used, and treatment means estimate expected means. If treatment is modelled as random, best linear unbiased prediction (BLUP) shrinks the treatment means towards the overall mean, which results in smaller root-mean-square error (RMSE) in prediction of means. This theoretical result holds provided the variance components are known, but in practice the variance components are estimated. BLUP using estimated variance components is called empirical best linear unbiased prediction (EBLUP). In small experiments, estimates can be unreliable and the usefulness of EBLUP is uncertain. The present paper investigates, through simulation, the performance of EBLUP in small RCB experiments with normally as well as non-normally distributed random effects. The methods of Satterthwaite (1946) and of Kenward & Roger (1997, 2009), as implemented in the SAS System, were studied. Performance was measured by RMSE, in prediction of means, and coverage of prediction intervals. In addition, a Bayesian approach was used for prediction of treatment differences and computation of credible intervals. EBLUP performed better than BLUE with regard to RMSE, also when the number of treatments was small and when the treatment effects were non-normally distributed. The methods of Satterthwaite and of Kenward & Roger usually produced approximately correct coverage of prediction intervals. The Bayesian method gave the smallest RMSE and usually more accurate coverage of intervals than the other methods.

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Xiang, Bin, and Bailian Li. "A new mixed analytical method for genetic analysis of diallel data." Canadian Journal of Forest Research 31, no. 12 (December 1, 2001): 2252–59. http://dx.doi.org/10.1139/x01-154.

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Diallel is a popular mating design used for crop and tree breeding programs, but its unique feature of a single observation with two levels of the same main effect, general combining ability (GCA), makes it difficult to analyze with standard statistical programs. A new approach using the SAS PROC MIXED is developed in this study for analyzing genetic data from diallel mating. Dummy variables for GCA effects were first constructed with SAS PROC IML, then PROC MIXED procedure was used to estimate variance components and to obtain BLUE (best linear unbiased estimators) of fixed effects and BLUP (best linear unbiased predictors) of random genetic effects (GCA and specific combining ability (SCA) effects) simultaneously. The new method can also be used for predicting individual breeding values with BLUP methodology, applying SAS IML to the outputs provided by PROC MIXED to calculate breeding value for each individual in the progeny test, adjusted for the fixed effects such as test location. The accurate BLUP prediction, the ability to estimate individual breeding values, and the ease of use would make this new method especially attractive for analyzing tree breeding data.

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PRAJAPATI, B. M., J. P. GUPTA, J. D. CHAUDHARI, G. A. PARMAR, R. N. SATHWARA, H. H. PANCHASARA, P. A. PATEL, and M. N. PRAJAPATI. "Utility of first lactation fat energy corrected milk yield as a trait for genetic evaluation of Mehsana buffalo bulls using various sire evaluation methods." Indian Journal of Animal Sciences 90, no. 2 (March 6, 2020): 259–63. http://dx.doi.org/10.56093/ijans.v90i2.98821.

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India being a vegetarian country, milk is the major source of dietary bio-energy, but majority of animals are routinely being evaluated on the basis of their milk producing ability. The present study was aimed to come up with a sire evaluation methodology based on first lactation Fat Energy Corrected Milk Yield (FBE) in order to obtain an accurate and unbiased estimate of breeding value of Mehsana buffalo bulls and ranking them on the basis of their daughter's performance for future herd improvement. Data for the present study included 7825 she buffaloes in their first lactations, extended over a period of 25 years (1989 to 2013), from field progeny testing programme of Dudhsagar Research and Development Association (DURDA), Dudhsagar Dairy, Mehsana, Gujarat. The data were classified into different subclasses based on period, season, cluster and age at first calving group. The average breeding values of Mehsana buffalo bulls evaluated for FBE by least squares method (LSM), best linear unbiased prediction sire model (BLUP-SM) and best linear unbiased prediction animal model (BLUP-AM) methods were 1215.89, 1185.7 and 1185.7 kcal, respectively. BLUP-AM method had lowest error variance as compared to LSM and BLUP-SM methods of sire evaluation. This indicated that BLUP-AM was most efficient method.

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Bijma, Piter, and John A. Woolliams. "Prediction of Rates of Inbreeding in Populations Selected on Best Linear Unbiased Prediction of Breeding Value." Genetics 156, no. 1 (September 1, 2000): 361–73. http://dx.doi.org/10.1093/genetics/156.1.361.

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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.

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Quinton, V. M., and C. Smith. "An empirical check on best linear unbiased prediction genetic evaluation using pig field recording data." Canadian Journal of Animal Science 77, no. 2 (June 1, 1997): 211–16. http://dx.doi.org/10.4141/a96-102.

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The theory and use of best linear unbiased prediction in genetic evaluation are well developed. However, there has been little empirical checking of its efficacy in practice. The objective here was to use a large body of Canadian pig performance records to check on the predicted benefits of BLUP in genetic evaluation. Phenotype records were available on fat depth and on days to 100 kg on some 65 000 progeny born in 1994 and 1995 from parents evaluated before 1994. Rank correlations between parent and progeny in data were calculated within herd-year-season to avoid effects due to differences in these factors. Computer simulation studies were also run to check on the predicted results. The simulation results confirmed the expectations on the higher correlation of mid-parental EBV than of mid-parental phenotype with progeny genotype and a regression (of progeny phenotype on mid-parental EBV) of unity when all relevant pedigree and performance data were used. In the data analysis, the (rank) correlations with progeny phenotype were consistently higher (36 and 27%) for mid-parental BLUP genetic evaluation than for mid-parental phenotypes, confirming the superiority of the BLUP evaluations over phenotypes. However, the regression of progeny phenotype on mid-parent BLUP EBV was usually less than the predicted value of unity. Simulation results suggest that either the base population heritability was lower than that used in the evaluation or that the information used was incomplete. Key words: Best linear unbiased prediction, EBV, pigs, performance, selection

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Bueno Filho, Júlio Sílvio de Sousa, and Roland Vencovsky. "Selection in several environments by BLP as an alternative to pooled anova in crop breeding." Ciência e Agrotecnologia 33, no. 5 (October 2009): 1342–50. http://dx.doi.org/10.1590/s1413-70542009000500021.

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Plant breeders often carry out genetic trials in balanced designs. That is not always the case with animal genetic trials. In plant breeding is usual to select progenies tested in several environments by pooled analysis of variance (ANOVA). This procedure is based on the global averages for each family, although genetic values of progenies are better viewed as random effects. Thus, the appropriate form of analysis is more likely to follow the mixed models approach to progeny tests, which became a common practice in animal breeding. Best Linear Unbiased Prediction (BLUP) is not a "method" but a feature of mixed model estimators (predictors) of random effects and may be derived in so many ways that it has the potential of unifying the statistical theory of linear models (Robinson, 1991). When estimates of fixed effects are present is possible to combine information from several different tests by simplifying BLUP, in these situations BLP also has unbiased properties and this lead to BLUP from straightforward heuristics. In this paper some advantages of BLP applied to plant breeding are discussed. Our focus is on how to deal with estimates of progeny means and variances from many environments to work out predictions that have "best" properties (minimum variance linear combinations of progenies' averages). A practical rule for relative weighting is worked out.

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Soh, A. C. "Ranking parents by best linear unbiased prediction (BLUP) breeding values in oil palm." Euphytica 76, no. 1-2 (1994): 13–21. http://dx.doi.org/10.1007/bf00024016.

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Xiang, Bin, and Bailian Li. "Best linear unbiased prediction of clonal breeding values and genetic values from full-sib mating designs." Canadian Journal of Forest Research 33, no. 10 (October 1, 2003): 2036–43. http://dx.doi.org/10.1139/x03-118.

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Full-sib progeny tests with clonal replicates may provide better breeding value estimates and the greatest genetic gain in a tree improvement program. Clonal breeding values (CBV) that combine the family and within-family breeding values due to additive genetic effects can maximize the genetic gain for advanced generation breeding. Clonal genetic values (CGV) that further incorporate full-sib family specific combining ability due to nonadditive genetic effect can maximize gain for a deployment program with clonal propagation techniques. The best linear unbiased prediction (BLUP) is the best statistical method for estimating both CBV and CGV because of its desirable statistical properties compared with the heritability-based gain calculation. A BLUP method for determining both the CBV and CGV for full-sib clonal progeny tests was proposed in this paper. The formulas for CBV and CGV were derived using general BLUP methodology, and formulas were derived for the calculations of their standard errors. An analytical method by using a standard statistical package (SAS PROC MIXED) was presented for CBV and CGV calculations from any full-sib mating designs.

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ROBINSON, J. A. B., J. W. WILTON, and L. R. SCHAEFFER. "ACCURACY OF SELECTION INDEX AND BEST LINEAR UNBIASED PREDICTION FOR WITHIN-HERD SELECTION WITH ASSORTATIVE MATING OF BEEF CATTLE." Canadian Journal of Animal Science 69, no. 2 (June 1, 1989): 315–22. http://dx.doi.org/10.4141/cjas89-035.

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A simulation of a selection and mating scheme for beef herds was conducted to compare the genetic progress achieved over 20 generations through evaluation of the animals by best linear unbiased prediction and by a selection index. For comparison, the same selection and mating scheme was applied to the herd using the true genetic values of each animal. Traits considered in the simulation were direct maternal genetic calving ease, birth weight, weaning weight and yearling weight. The analysis was replicated 100 times for each method of evaluation. In general, the best linear unbiased prediction system achieved greater genetic response than the selection index system. The BLUP system gave 18.7% better genetic improvement in total net worth than the selection index system. However, the selection index system gave only 42.7% and the BLUP system gave 50.6% of the response from selection on true net worth values. Keywords: Beef cattle, selection index, assortative mating.

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Wang, T., R. L. Fernando, and M. Grossman. "Genetic Evaluation by Best Linear Unbiased Prediction Using Marker and Trait Information in a Multibreed Population." Genetics 148, no. 1 (January 1, 1998): 507–15. http://dx.doi.org/10.1093/genetics/148.1.507.

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Abstract Genetic evaluation by best linear unbiased prediction (BLUP) requires modeling genetic means, variances, and covariances. This paper presents theory to model means, variances, and covariances in a multibreed population, given marker and breed information, in the presence of gametic disequilibrium between the marker locus (ML) and linked quantitative trait locus (MQTL). Theory and algorithms are presented to construct the matrix of conditional covariances between relatives (Gv) for the MQTL effects in a multibreed population and to obtain the inverse of Gv efficiently. Theory presented here accounts for heterogeneity of variances among pure breeds and for segregation variances between pure breeds. A numerical example was used to illustrate how the theory and algorithms can be used for genetic evaluation by BLUP using marker and trait information in a multibreed population.

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Ferguson, P. W., and D. L. Harris. "Graphical representation of best linear unbiased prediction (BLUP) with different amounts of information available1." Zeitschrift für Tierzüchtung und Züchtungsbiologie 101, no. 1-5 (April 26, 2010): 321–29. http://dx.doi.org/10.1111/j.1439-0388.1984.tb00053.x.

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Gonzalez, Maria Y., Yusheng Zhao, Yong Jiang, Nils Stein, Antje Habekuss, Jochen C. Reif, and Albert W. Schulthess. "Genomic prediction models trained with historical records enable populating the German ex situ genebank bio-digital resource center of barley (Hordeum sp.) with information on resistances to soilborne barley mosaic viruses." Theoretical and Applied Genetics 134, no. 7 (March 25, 2021): 2181–96. http://dx.doi.org/10.1007/s00122-021-03815-0.

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Abstract Key message Genomic prediction with special weight of major genes is a valuable tool to populate bio-digital resource centers. Abstract Phenotypic information of crop genetic resources is a prerequisite for an informed selection that aims to broaden the genetic base of the elite breeding pools. We investigated the potential of genomic prediction based on historical screening data of plant responses against the Barley yellow mosaic viruses for populating the bio-digital resource center of barley. Our study includes dense marker data for 3838 accessions of winter barley, and historical screening data of 1751 accessions for Barley yellow mosaic virus (BaYMV) and of 1771 accessions for Barley mild mosaic virus (BaMMV). Linear mixed models were fitted by considering combinations for the effects of genotypes, years, and locations. The best linear unbiased estimations displayed a broad spectrum of plant responses against BaYMV and BaMMV. Prediction abilities, computed as correlations between predictions and observed phenotypes of accessions, were low for the marker-assisted selection approach amounting to 0.42. In contrast, prediction abilities of genomic best linear unbiased predictions were high, with values of 0.62 for BaYMV and 0.64 for BaMMV. Prediction abilities of genomic prediction were improved by up to ~ 5% using W-BLUP, in which more weight is given to markers with significant major effects found by association mapping. Our results outline the utility of historical screening data and W-BLUP model to predict the performance of the non-phenotyped individuals in genebank collections. The presented strategy can be considered as part of the different approaches used in genebank genomics to valorize genetic resources for their usage in disease resistance breeding and research.

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Jahuey-Martínez, Francisco J., Gaspar M. Parra-Bracamonte, Dorian J. Garrick, Nicolás López-Villalobos, Juan C. Martínez-González, Ana M. Sifuentes-Rincón, and Luis A. López-Bustamante. "Accuracies of direct genomic breeding values for birth and weaning weights of registered Charolais cattle in Mexico." Animal Production Science 60, no. 6 (2020): 772. http://dx.doi.org/10.1071/an18363.

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Context Genomic prediction is now routinely used in many livestock species to rank individuals based on genomic breeding values (GEBV). Aims This study reports the first assessment aimed to evaluate the accuracy of direct GEBV for birth (BW) and weaning (WW) weights of registered Charolais cattle in Mexico. Methods The population assessed included 823 animals genotyped with an array of 77000 single nucleotide polymorphisms. Genomic prediction used genomic best linear unbiased prediction (GBLUP), Bayes C (BC), and single-step Bayesian regression (SSBR) methods in comparison with a pedigree-based BLUP method. Key results Our results show that the genomic prediction methods provided low and similar accuracies to BLUP. The prediction accuracy of GBLUP and BC were identical at 0.31 for BW and 0.29 for WW, similar to BLUP. Prediction accuracies of SSBR for BW and WW were up to 4% higher than those by BLUP. Conclusions Genomic prediction is feasible under current conditions, and provides a slight improvement using SSBR. Implications Some limitations on reference population size and structure were identified and need to be addressed to obtain more accurate predictions in liveweight traits under the prevalent cattle breeding conditions of Mexico.

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Zhang, Jin, Jie Wang, Qinghe Li, Qiao Wang, Jie Wen, and Guiping Zhao. "Comparison of the Efficiency of BLUP and GBLUP in Genomic Prediction of Immune Traits in Chickens." Animals 10, no. 3 (March 3, 2020): 419. http://dx.doi.org/10.3390/ani10030419.

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Poultry diseases pose a large threat to poultry production. Selection to improve immune traits is a feasible way to prevent and control avian diseases. The objective of this study was to investigate the efficiency of estimation of genetic parameters for antibody response to avian influenza virus (Ab-AIV), antibody response to Newcastle disease virus (Ab-NDV), sheep red blood cell antibody titer (SRBC), the ratio of heterophils to lymphocytes (H/L), immunoglobulin G (IgG), the spleen immune index (SII), thymus immune index (TII), thymus weight at 100 d (TW) and the spleen weight at 100 d (SW) in Beijing oil chickens, by using the best linear unbiased prediction (BLUP) method and genomic best linear unbiased prediction (GBLUP) method. The phenotypic data used in the two methods were the same and were from 519 individuals. With the BLUP model, Ab-AIV, Ab-NDV, SRBC, H/L, IgG, TII, and TW had low heritability ranging from 0.000 to 0.281, whereas SII and SW had high heritability of 0.631 and 0.573. With the GBLUP model, all individuals were genotyped with Illumina 60K SNP chips, and Ab-AIV, Ab-NDV, SRBC, H/L and IgG had low heritability ranging from 0.000 to 0.266, whereas SII, TII, TW and SW had moderate heritability ranging from 0.300 to 0.472. We compared the prediction accuracy obtained from BLUP and GBLUP through 50 time 5-fold cross-validation (CV), and the results indicated that BLUP provided a slightly higher accuracy of prediction than GBLUP in this population.

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Oliveira, Gustavo H. F., Rodolfo Buzinaro, Lucas T. M. Revolti, Carlos H. B. Giorgenon, Kauê Charnai, Diego Resende, and Gustavo V. Moro. "An accurate prediction of maize crosses using diallel analysis and best linear unbiased predictor (BLUP)." Chilean journal of agricultural research 76, no. 3 (September 2016): 294–99. http://dx.doi.org/10.4067/s0718-58392016000300005.

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Naserkheil, Masoumeh, Hossein Mehrban, Deukmin Lee, and Mi Na Park. "Evaluation of Genome-Enabled Prediction for Carcass Primal Cut Yields Using Single-Step Genomic Best Linear Unbiased Prediction in Hanwoo Cattle." Genes 12, no. 12 (November 25, 2021): 1886. http://dx.doi.org/10.3390/genes12121886.

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There is a growing interest worldwide in genetically selecting high-value cut carcass weights, which allows for increased profitability in the beef cattle industry. Primal cut yields have been proposed as a potential indicator of cutability and overall carcass merit, and it is worthwhile to assess the prediction accuracies of genomic selection for these traits. This study was performed to compare the prediction accuracy obtained from a conventional pedigree-based BLUP (PBLUP) and a single-step genomic BLUP (ssGBLUP) method for 10 primal cut traits—bottom round, brisket, chuck, flank, rib, shank, sirloin, striploin, tenderloin, and top round—in Hanwoo cattle with the estimators of the linear regression method. The dataset comprised 3467 phenotypic observations for the studied traits and 3745 genotyped individuals with 43,987 single-nucleotide polymorphisms. In the partial dataset, the accuracies ranged from 0.22 to 0.30 and from 0.37 to 0.54 as evaluated using the PBLUP and ssGBLUP models, respectively. The accuracies of PBLUP and ssGBLUP with the whole dataset varied from 0.45 to 0.75 (average 0.62) and from 0.52 to 0.83 (average 0.71), respectively. The results demonstrate that ssGBLUP performed better than PBLUP averaged over the 10 traits, in terms of prediction accuracy, regardless of considering a partial or whole dataset. Moreover, ssGBLUP generally showed less biased prediction and a value of dispersion closer to 1 than PBLUP across the studied traits. Thus, the ssGBLUP seems to be more suitable for improving the accuracy of predictions for primal cut yields, which can be considered a starting point in future genomic evaluation for these traits in Hanwoo breeding practice.

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Oliveira, Claudine Gonçalves de, Elizângela Emídio Cunha, Paulo Luiz Souza Carneiro, Ricardo Frederico Euclydes, and Carlos Henrique Mendes Malhado. "Comparação de métodos de seleção em populações simuladas de frangos de corte." Pesquisa Agropecuária Brasileira 40, no. 10 (October 2005): 969–74. http://dx.doi.org/10.1590/s0100-204x2005001000004.

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Populações de frangos de corte foram simuladas utilizando-se o programa GENESYS, com o objetivo de avaliar a seleção com base no método da melhor predição linear não-viesada (BLUP - best linear unbiased prediction), a seleção individual para três tamanhos efetivos de população e três sistemas de acasalamento dos reprodutores selecionados. Simulou-se um genoma constituído de uma característica quantitativa, com valor de herdabilidade igual a 0,30, em seleção praticada durante 15 gerações consecutivas, com 30 repetições por geração. Para um mesmo tamanho efetivo e sistema de acasalamento, o BLUP foi sempre superior à seleção individual nas 15 gerações de seleção avaliadas.

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VITEZICA, Z. G., I. AGUILAR, I. MISZTAL, and A. LEGARRA. "Bias in genomic predictions for populations under selection." Genetics Research 93, no. 5 (July 18, 2011): 357–66. http://dx.doi.org/10.1017/s001667231100022x.

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SummaryPrediction of genetic merit or disease risk using genetic marker information is becoming a common practice for selection of livestock and plant species. For the successful application of genome-wide marker-assisted selection (GWMAS), genomic predictions should be accurate and unbiased. The effect of selection on bias and accuracy of genomic predictions was studied in two simulated animal populations under weak or strong selection and with several heritabilities. Prediction of genetic values was by best-linear unbiased prediction (BLUP) using data either from relatives summarized in pseudodata for genotyped individuals (multiple-step method) or using all available data jointly (single-step method). The single-step method combined genomic- and pedigree-based relationship matrices. Predictions by the multiple-step method were biased. Predictions by a single-step method were less biased and more accurate but under strong selection were less accurate. When genomic relationships were shifted by a constant, the single-step method was unbiased and the most accurate. The value of that constant, which adjusts for non-random selection of genotyped individuals, can be derived analytically.

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Maicon, Nardino, Baretta Diego, Ricardo Carvalho Ivan, Olivoto Tiago, Nicolau Follmann Diego, Jardel Szareski Vinícius, Ferrari Mauricio, Junior de Pelegrin Alan, Antonio Konflanz Valmor, and Queiróz de Souza Velci. "Restricted maximum likelihood/best linear unbiased prediction (REML/BLUP) for analyzing the agronomic performance of corn." African Journal of Agricultural Research 11, no. 48 (December 1, 2016): 4864–72. http://dx.doi.org/10.5897/ajar2016.11691.

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Purba, A. R., A. Flori, L. Baudouin, and S. Hamon. "Prediction of oil palm (Elaeis guineensis, Jacq.) agronomic performances using the best linear unbiased predictor (BLUP)." Theoretical and Applied Genetics 102, no. 5 (April 2001): 787–92. http://dx.doi.org/10.1007/s001220051711.

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Bauer, Andrea M., Tobias C. Reetz, and Jens Léon. "Estimation of Breeding Values of Inbred Lines using Best Linear Unbiased Prediction (BLUP) and Genetic Similarities." Crop Science 46, no. 6 (November 2006): 2685–91. http://dx.doi.org/10.2135/cropsci2006.01.0019.

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Huang, H., J. Harding, T. Byrne, and T. Famula. "Estimation of long-term genetic improvement for gerbera using the best linear unbiased prediction (BLUP) procedure." Theoretical and Applied Genetics 91, no. 5 (October 1995): 790–94. http://dx.doi.org/10.1007/bf00220961.

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Avendaño, S., B. Villanueva, and J. A. Woolliams. "Expected increases in genetic merit in the UK Aberdeen Angus beef cattle breed from applying optimised selection." Proceedings of the British Society of Animal Science 2002 (2002): 54. http://dx.doi.org/10.1017/s1752756200007109.

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Best Linear Unbiased Prediction (BLUP) estimates of breeding values (EBVs) for economically relevant traits have been used for selection decisions in the UK Aberdeen Angus (AA) population since the early nineteen nineties. Selection exclusively based on BLUP-EBVs is expected to give higher gains than less accurate selection but can also lead to increased rates of inbreeding (ΔF). Dynamic rules using BLUP-EBVs to maximise genetic merit while DF is constrained to a pre-defined level are currently available (e.g. Grundy et al 1998). They showed that the use of these rules gives higher gains than standard BLUP selection at the same level of ΔF. The objective of this study was to investigate the potential of these procedures for optimising selection decisions in the UK AA population.

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Wei, Xianming, and Nuno MG Borralho. "Genetic gains and levels of relatedness from best linear unbiased prediction selection of Eucalyptus urophylla for pulp production in southeastern China." Canadian Journal of Forest Research 30, no. 10 (October 1, 2000): 1601–7. http://dx.doi.org/10.1139/x00-092.

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Breeding values for diameter at breast height (DBH), tree height (HT), relative bark thickness (BKR), and pilodyn penetration (PP) in Eucalyptus urophylla St. Blake plantations were predicted with best linear unbiased prediction (BLUP) approach. These values along with their economic weights derived from a previous study were then used to estimate economic genetic gains for three breeding objectives (pulp, woodchips, and wood volume) in southeastern China. The results showed substantial gain can be expected from selecting top 5% trees, with a reduction of up to US$35 for producing a tonne of ovendry pulp. However, actual gains can be strongly influenced by how the breeding objectives have been defined and whether the key traits have been included in the selection criteria. This study also showed that problem in the increase of coancestry associated with selection on BLUP would not be serious, with average coancestry amongst the selected population was less than 1%. More importantly, an unrestricted multiple-trait BLUP selection did not result in the same increase in relatedness in the selected population than it does for the single trait situation.

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Nascimento, Ana Carolina, Moyses Nascimento, Camila Azevedo, Fabyano Silva, Leiri Barili, Naine Vale, José Eustáquio Carneiro, Cosme Cruz, Pedro Crescencio Carneiro, and Nick Serão. "Quantile Regression Applied to Genome-Enabled Prediction of Traits Related to Flowering Time in the Common Bean." Agronomy 9, no. 12 (November 23, 2019): 796. http://dx.doi.org/10.3390/agronomy9120796.

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Genomic selection (GS) aims to incorporate molecular information directly into the prediction of individual genetic merit. Regularized quantile regression (RQR) can be used to fit models for all portions of a probability distribution of the trait, enabling the conditional quantile that “best” represents the functional relationship between dependent and independent variables to be chosen. The objective of this study was to predict the individual genetic merits of the traits associated with flowering time (DFF—days to first flower; DTF—days to flower) in the common bean using RQR and to compare the predictive abilities obtained from Random Regression Best Linear Unbiased Predictor (RR-BLUP), Bayesian LASSO (BLASSO), BayesB, and RQR for predicting the genetic merit. GS was performed using 80 genotypes of common beans genotyped for 380 single nucleotide polymorphism (SNP) markers. Considering the “best” RQR fit models (RQR0.3 for DFF, and RQR0.2 for DTF), the gains in predictive ability in relation to BLASSO, BayesB, and RR-BLUP were 18.75%, 22.58%, and 15.15% for DFF, respectively, and 15.20%, 24.65%, and 12.55% for DTF, respectively. The potential cultivars selected, considering the RQR “best” models, were among the 5% of cultivars with the lowest genomic estimated breeding value (GEBV) for the DFF and DTF traits—the IAC Imperador, IPR Colibri, Capixaba Precoce, and IPR Andorinha were included in the list of early cycle cultivars.

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Guindón M.F., F. Cazzola, C. J. Bermejo, M. A. Espósito, I. Gatti, and E. L. Cointry. "COMPLEMENTARY TOOLS UTILIZED IN THE PEA (Pisum sativum L.) BREEDING PROGRAM AT UNIVERSIDAD NACIONAL DE ROSARIO." Journal of Basic and Applied Genetics 32, Issue 2 (December 2021): 24–30. http://dx.doi.org/10.35407/bag.2021.32.02.03.

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Conventional breeding can be complemented by different strategies that increase the efficiency of the methodologies and the current rate of increase in yields in order to meet demand. The use of molecular markers with the aim of developing linkage maps of the species, the use of Blup (Best Linear Unbiased Prediction) for an efficient selection of progenitors to hybridize, the use of in vitro culture to artificially increase the number of F1 plants or the use of digital phenotyping for efficient digital characterization that can be performed during the periodic and routine regeneration of accessions in germplasm collections. Key words: Molecular markers, Blup, in vitro culture, digital phenotyping.

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Grundy, B., A. Caballero, E. Santiago, and W. G. Hill. "A note on using biased parameter values and non-random mating to reduce rates of inbreeding in selection programmes." Animal Science 59, no. 3 (December 1994): 465–68. http://dx.doi.org/10.1017/s0003356100008011.

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The value of a parameter such as heritability (h2) or intra-dass correlation in best linear unbiased prediction (BLUP) with the animal model or a family selection index affects both the rate of response achieved and the rate of inbreeding. If in BLUP an estimate of h2 is used which is biased upwards above its actual value, the rate of inbreeding can be substantially reduced with little reduction in the rate of response. Further, by mating individuals from families in which many are selected to others from families with few selected (compensatory mating), rates of inbreeding can be further reduced without substantial effect on response.

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Meuwissen, T. H. E. "Developments in genetic evaluation: from test days to genomics." Proceedings of the British Society of Animal Science 2005 (2005): 237. http://dx.doi.org/10.1017/s1752756200011480.

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Genetic evaluations have come a long way during the past decades, where the development and implementation of Best Linear Unbiased Prediction (BLUP) was undoubtedly the most notable achievement. The most important advances during the past 10 years were probably the direct use of test-day data in the BLUP model, ie. test-day models, the correction for heterogeneous within herd variances, multiple across country genetic evaluations (MACE), and the inclusion of more and more functional, and often difficult, traits in the evaluations. This paper will review the developments in test-day models, and the future of the genetic evaluations field, namely the inclusion of genomic information in the evaluations.

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Avendaño, S., B. Villanueva, and J. A. Woolliams. "Optimisation of selection decisions in the UK Meatlinc breed of sheep." Proceedings of the British Society of Animal Science 2002 (2002): 194. http://dx.doi.org/10.1017/s1752756200008504.

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Best Linear Unbiased Prediction (BLUP) estimates of breeding values (EBVs) have been routinely used for selection decisions in the UK Meatlinc (ML) population since the early nineteen nineties. This has enabled accurate selection and has allowed higher genetic gains for traits of economic relevance than in other terminal sheep breeds (MLC, 1999). However, concerns regarding increased rates of inbreeding (ΔF) by selecting exclusively on BLUP-EBVs have arisen in this small population. Dynamic rules to maximise genetic merit while ΔF is constrained to a pre-defined level using BLUP EBVs are currently available (e.g. Grundy et al 1998). They found higher gains than standard BLUP selection at the same ΔF by using these rules. The objective of this study was to investigate the potential of these procedures for optimising selection decisions under constrained inbreeding in the UK ML sheep population.

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Chiorato, Alisson Fernando, Sérgio Augusto Morais Carbonell, Luiz Antônio dos Santos Dias, and Marcos Deon Vilela de Resende. "Prediction of genotypic values and estimation of genetic parameters in common bean." Brazilian Archives of Biology and Technology 51, no. 3 (June 2008): 465–72. http://dx.doi.org/10.1590/s1516-89132008000300005.

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Eighteen common bean (Phaseolus vulgaris L.) genotypes were evaluated in 25 environments of the state of São Paulo in 2001 and 2002. The estimation of genetic parameters by the Restricted Maximum Likelihood (REML) and the prediction of genotypic values via Best Linear Unbiased Prediction (BLUP) were obtained by software Selegen-REML/BLUP. The estimate of the broad-sense heritability was low for the grain yield (0.03), since it took individual plots into consideration and was free of the effects of interaction with years, cultivation periods and site. Nevertheless, the heritability at the level of line means across the various environments was high (0.75), allowing a high accuracy (0.87) in the selection of lines for planting in the environment mean. Among the 18 genotypes, the predicted genotypic values of nine were higher than the general mean. The genetic gain predicted with the selection of the best line, in this case line Gen 96A31 of the IAC, was 16.25%.

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Shihab, Osamah Hameed, Dhafir Shakir Abdullah, and Emad Ghaib Abdulrahman. "Estimation of Some Genetic Parameters of Some Productive Traits of local and Turkish Awassi Sheep." Tikrit journal for agricultural sciences 22, no. 3 (September 30, 2022): 60–69. http://dx.doi.org/10.25130/tjas.22.3.7.

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This study was carried out at the ruminant research station of the General Commission for Agricultural Research / Ministry of Agriculture (in Abu Ghraib 20km west of Baghdad) from December 2018 to December/2020. The research included 200 records belonging to the Turkish and local Awassi sheep herd to estimate some genetic parameters (heritability, genetic and phenotypic correlations, and evaluate the Best Linear Unbiased Prediction (BLUP) for some productive traits (total milk production, lactation period, birth weight Weaning weight, average weight gain). The estimate of heritability were for the productive traits 0.19, 0.12, 0.25, 0.29, 0.25, respectively. The genetic correlation for the effective traits ranged between positive and highly significant (P<0.01) and not significant, as its coefficient ranged between 0.81 and 0.15. The phenotypic correlation of the productive traits ranged between positive and highly effective (P<0.01) and was not significant, as the coefficient ranged between 0.87 and 0.12 for the above effective characteristics. The values of the Best Linear Prediction (BLUP) for traits ranged 12.376. The lowest of which is -0.641.

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McClure, Foster D., and Jung K. Lee. "Use of Prediction Methods to Assess Laboratory Bias and Mean Values Associated with an Interlaboratory Study for Method Validation and/or Proficiency Testing." Journal of AOAC INTERNATIONAL 97, no. 2 (March 1, 2014): 624–29. http://dx.doi.org/10.5740/jaoacint.12-457.

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Abstract Two methods of prediction of random variables, best predictor (BP) and best linear unbiased predictor (BLUP), are discussed as potential statistical methods to predict laboratory true mean and bias values using the sample laboratory mean (yi) from interlaboratory studies. The predictions developed here require that the interlaboratory and/or proficiency study be designed and conducted in a manner consistent with the assumptions of a one-way completely randomized model (CRM). Under the CRM the individual laboratory true mean and bias are not parameters but are defined to be random variables that are unobservable and considered as realized values that cannot be estimated but can be predicted using methods of “prediction.” The BP method is applicable when all salient parameters are known, e.g., the consensus true overall mean (μ) and repeatability and reproducibility components (σr2 and σR2), while the BLUP method is useful when σ2r and σR2 are known, but μ is estimated by the generalized least square estimator. Although the derivations of predictors are obtained by minimizing the mean-square error under the CRM assumptions, the predictors are the expected laboratory true mean and bias given the sample laboratory mean, i.e., conditional expectation.

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AMBHORE, G. S., AVTAR SINGH, D. K. DEOKAR, MANVENDRA SINGH, VED PRAKASH, and S. K. SAHOO. "Sire evaluation using REML and conventional methods for first lactation 300 day milk yield in Phule Triveni cattle." Indian Journal of Animal Sciences 88, no. 3 (March 26, 2018): 352–55. http://dx.doi.org/10.56093/ijans.v88i3.78376.

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The present study was undertaken to estimate the breeding values of 55 Phule Triveni sires for first lactation 300 day or less milk yield (FL300DMY) by using four different sire evaluation methods/models viz. Least Squares (LS), Simple Regressed Least Squares (SRLS), Best Linear Unbiased Prediction-Sire model (BLUP-SM) and Restricted Maximum Likelihood Method (REML). The REML method produced lowest error variance for FL300DMY and it was considered to be the most efficient method. The BLUP-SM method was second efficient followed by SRLS and LS methods. The estimated breeding values of sires for FL300DMY under study showed large variation between estimated breeding values of sires which revealed more genetic variation in the herd.

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Wray, Naomi R. "Breeding Value Estimation for Pigs in Closed Nucleus Herds." Proceedings of the British Society of Animal Production (1972) 1988 (March 1988): 12. http://dx.doi.org/10.1017/s0308229600016573.

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Best Linear Unbiased Prediction (BLUP) is now the method of choice for the estimation of breeding values in dairy and beef populations. The advantages of this mixed model methodology over traditional methods are well documented and include the simultaneous estimation of fixed effects and prediction of random effects and the utilization of records from all relatives to predict an individuals breeding value. In addition, account is taken of genetic trend and of reduction in genetic variance due to selection. In Canada, BLUP is now used for breeding value estimation of pigs but the structure of the Canadian pig industry is one of many herds practising selection with the herds linked by a widespread use of artificial insemination. The advantages of BLUP have not been investigated for the situation of the UK pig industry where most selection is performed within closed nucleus herds.The objectives of this study were to use computer simulation to determine rates of response, accuracy of prediction and accummulation of inbreeding for pigs in closed nucleus herds when selection decisions were based on estimated breeding values (EBVs) derived from BLUP compared to more traditional methods of phenotypic selection and index selection.

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Rosado, Renato Domiciano Silva, Cosme Damião Cruz, Leiri Daiane Barili, José Eustáquio de Souza Carneiro, Pedro Crescêncio Souza Carneiro, Vinicius Quintão Carneiro, Jackson Tavela da Silva, and Moyses Nascimento. "Artificial Neural Networks in the Prediction of Genetic Merit to Flowering Traits in Bean Cultivars." Agriculture 10, no. 12 (December 16, 2020): 638. http://dx.doi.org/10.3390/agriculture10120638.

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Flowering is an important agronomic trait that presents non-additive gene action. Genome-enabled prediction allow incorporating molecular information into the prediction of individual genetic merit. Artificial neural networks (ANN) recognize patterns of data and represent an alternative as a universal approximation of complex functions. In a Genomic Selection (GS) context, the ANN allows automatically to capture complicated factors such as epistasis and dominance. The objectives of this study were to predict the individual genetic merits of the traits associated with the flowering time in the common bean using the ANN approach, and to compare the predictive abilities obtained for ANN and Ridge Regression Best Linear Unbiased Predictor (RR-BLUP). We used a set of 80 bean cultivars and genotyping was performed with a set of 384 SNPs. The higher accuracy of the selective process of phenotypic values based on ANN output values resulted in a greater efficacy of the genomic estimated breeding value (GEBV). Through the root mean square error computational intelligence approaches via ANN, GEBV were shown to have greater efficacy than GS via RR-BLUP.

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Nunes, José Airton Rodrigues, Alexsander Luís Moreto, and Magno Antonio Patto Ramalho. "Using genealogy to improve selection efficiency of pedigree method." Scientia Agricola 65, no. 1 (February 2008): 25–30. http://dx.doi.org/10.1590/s0103-90162008000100004.

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In the pedigree method of conducting an autogamous population of segregating plants, the genealogy of the progenies is registered. Although labor-intensive, these data are rarely used. One possibility of exploiting this information is to improve selection efficiency using BLUP (Best Linear Unbiased Prediction). In this study BLUP with genealogy inclusion was compared to the mean in the progenies evaluation conducted by the pedigree method. Progenies of crosses of the common bean lines BRS MG Talismã and BRS Valente in F4:6 and F4:7 were used. The 256 F4:6 progenies were sown in February 2005, in southeast of Brazil, in a 16 ´ 16 simple lattice design. The grain yield data were subjected to BLUP analysis with inclusion of genealogy. Based on this analysis and the mean, the 30 progenies with best and worst performance were selected. These 60 F4:7 progenies were classified in relation to the origin, i.e., selected by BLUP, mean, or BLUP and mean and coincident results were obtained. In the selection for best performance, the efficiency of BLUP was 2.4% higher than the mean. In the selection for the opposite extreme, BLUP analysis was however not advantageous. The progenies ´ environments interaction indicates the need for an evaluation of the progenies in different environments before beginning selection.

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Mahboubeh, Rostami Angasi, Ardeshir, Nejati Javaremi, Hassan, Mehrabani Yeganeh, and Moradi Shahrebabak Mohammad. "Comparison of multiple-trait genetic evaluation accuracy using region marker relationships with traditional best linear unbiased prediction (BLUP)." African Journal of Biotechnology 9, no. 46 (November 15, 2010): 7781–87. http://dx.doi.org/10.5897/ajb10.549.

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Grundy, B., Z. W. Luo, B. Villanueva, and J. A. Woolliams. "The use of Mendelian indices for balancing genetic response and inbreeding." Proceedings of the British Society of Animal Science 1996 (March 1996): 114. http://dx.doi.org/10.1017/s1752756200593119.

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The difficulty in designing an optimal breeding programme arises from a conflict between improvement in genetic gain and increase in inbreeding since selection procedures which increase genetic progress are usually associated with increased rates of inbreeding. Best linear unbiased prediction (BLUP) has optimal properties regarding the expected genetic gain after one generation of selection. However, since full genetic relationships are accounted for, selected animals are likely to be more related, leading to a higher rate of inbreeding and a larger decrease in genetic variance than less accurate methods.

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Grundy, B., Z. W. Luo, B. Villanueva, and J. A. Woolliams. "The use of Mendelian indices for balancing genetic response and inbreeding." Proceedings of the British Society of Animal Science 1996 (March 1996): 114. http://dx.doi.org/10.1017/s0308229600030828.

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The difficulty in designing an optimal breeding programme arises from a conflict between improvement in genetic gain and increase in inbreeding since selection procedures which increase genetic progress are usually associated with increased rates of inbreeding. Best linear unbiased prediction (BLUP) has optimal properties regarding the expected genetic gain after one generation of selection. However, since full genetic relationships are accounted for, selected animals are likely to be more related, leading to a higher rate of inbreeding and a larger decrease in genetic variance than less accurate methods.

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COUTINHO, ALISSON ESDRAS, DIOGO GONÇALVES NEDER, MAIRYKON COÊLHO DA SILVA, ELIANE CRISTINA ARCELINO, SILVAN GOMES DE BRITO, and JOSÉ LUIZ SANDES DE CARVALHO FILHO. "PREDICTION OF PHENOTYPIC AND GENOTYPIC VALUES BY BLUP/GWS AND NEURAL NETWORKS." Revista Caatinga 31, no. 3 (July 2018): 532–40. http://dx.doi.org/10.1590/1983-21252018v31n301rc.

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ABSTRACT Genome-wide selection (GWS) uses simultaneously the effect of the thousands markers covering the entire genome to predict genomic breeding values for individuals under selection. The possible benefits of GWS are the reduction of the breeding cycle, increase in gains per unit of time, and decrease of costs. However, the success of the GWS is dependent on the choice of the method to predict the effects of markers. Thus, the objective of this work was to predict genomic breeding values (GEBV) through artificial neural networks (ANN), based on the estimation of the effect of the markers, compared to the Ridge Regression-Best Linear Unbiased Predictor/Genome Wide Selection (RR-BLUP/GWS). Simulations were performed by software R to provide correlations concerning ANN and RR-BLUP/GWS. The prediction methods were evaluated using correlations between phenotypic and genotypic values and predicted GEBV. The results showed the superiority of the ANN in predicting GEBV in simulations with higher and lower marker densities, with higher levels of linkage disequilibrium and heritability.

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Volpato, Leonardo, João Romero do Amaral Santos de Carvalho Rocha, Rodrigo Silva Alves, Willian Hytalo Ludke, Aluízio Borém, and Felipe Lopes Silva. "Inference of population effect and progeny selection via a multi-trait index in soybean breeding." Acta Scientiarum. Agronomy 43 (August 14, 2020): e44623. http://dx.doi.org/10.4025/actasciagron.v43i1.44623.

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The selection of superior genotypes of soybean entails a simultaneous evaluation of a number of favorable traits that provide a comparatively superior yield. Disregarding the population effect in the statistical model may compromise the estimate of variance components and the prediction of genetic values. The present study was undertaken to investigate the importance of including population effect in the statistical model and to determine the effectiveness of the index based on factor analysis and ideotype design via best linear unbiased prediction (FAI-BLUP) in the selection of erect, early, and high-yielding soybean progenies. To attain these objectives, 204 soybean progenies originating from three populations were examined for various traits of agronomic interest. The inclusion of the population effect in the statistical model was relevant in the genetic evaluation of soybean progenies. To quantify the effectiveness of the FAI-BLUP index, genetic gains were predicted and compared with those obtained by the Smith-Hazel and Additive Genetic indices. The FAI-BLUP index was effective in the selection of progenies with balanced, desirable genetic gains for all traits simultaneously. Therefore, the FAI-BLUP index is an adequate tool for the simultaneous selection of important traits in soybean breeding.

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Simm, G., and N. R. Wray. "Electronics in animal breeding." Proceedings of the British Society of Animal Production (1972) 1990 (March 1990): 107. http://dx.doi.org/10.1017/s0308229600018882.

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Two of the major steps in animal breeding programmes are (i) estimation of breeding values for a defined selection objective (such as milk production or carcass lean content), and (ii) design of optimum breeding programmes, including proportion of animals selected as parents, population size etc. Advances in electronics, and particularly in computer technology, have had a major Impact on these procedures in a number of ways. In this paper we aim to highlight four of these.The preferred method of estimating breeding values is universally recognised to be BLUP (Best Linear Unbiased Prediction). BLUP is superior to classical procedures, such as contemporary comparison, for several reasons. The most important is that it is more accurate in separating differences between animals which are attributable to genetic rather than environmental factors. BLUP was first proposed by Henderson in 1949 but the first BLUP evaluation was not implemented until 1970 (Henderson, 1987). This delay is almost entirely attributable to inadequate computing facilities and technology at that time, since a BLUP evaluation system requires a large number of equations to be stored and solved.

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El-Attrouny, M. M., E. A. Manaa, and S. I. Ramadan. "Genetic evaluation and selection correlated response of growth traits in Japanese quail." South African Journal of Animal Science 50, no. 2 (July 1, 2020): 325–33. http://dx.doi.org/10.4314/sajas.v50i2.16.

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Objectives of the current study were to i) investigate effects of selection for bodyweight at four weeks old on bodyweight (BW) and bodyweight gain (BWG) across four generations; ii) estimate correlated response to selection for BW and BWG at different ages; and iii) document best linear unbiased prediction (BLUP) of genetic trends for BW and BWG across four generations of selection. A total of 3540 chicks from 444 sires and 885 dams were used to estimate heritabilities, and genetic and phenotypic correlations for growth traits, including BW at 0, 2, 4, and 6 weeks, and BWG between 0 and 2, 2 and 4, 4 and 6, and 0 and 6 weeks. The selection effects, correlated responses and genetic trend for BW and BWG across generations were quantified by applying the animal model. Estimates of heritability for BW and BWG ranged from 0.22 to 0.42 and from 0.18 to 0.23, respectively. Ranges of genetic and phenotypic correlations for BW varied from 0.31 to 0.92 and 0.05 to 0.65, respectively. Moreover, estimates of genetic and phenotypic correlations for BWG at different ages were from 0.12 to 0.72 and 0.17 to 0.60, respectively. Bodyweight and BWG estimates after four generations of selection were significantly higher than those of the base generation. Moreover, contrasts of generation means were significant across the four generations. The genetic trends across the generations clarified that BLUP estimates for BW and BWG gradually increased with the advance of generations until the fourth generation. Keywords: best linear unbiased prediction, bodyweight, heritability, selection, genetic trend

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Park, Mi Na, Mahboob Alam, Sidong Kim, Byoungho Park, Seung Hwan Lee, and Sung Soo Lee. "Genomic selection through single-step genomic best linear unbiased prediction improves the accuracy of evaluation in Hanwoo cattle." Asian-Australasian Journal of Animal Sciences 33, no. 10 (October 1, 2020): 1544–57. http://dx.doi.org/10.5713/ajas.18.0936.

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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.

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Journal articles on the topic 'Best Linear Unbiased Prediction (BLUP)'

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