The Experts below are selected from a list of 309 Experts worldwide ranked by ideXlab platform

Masahiro Satoh - One of the best experts on this subject based on the ideXlab platform.

  • genome wide association study and genomic evaluation of feed efficiency traits in japanese black cattle using single step genomic best Linear Unbiased Prediction method
    Animal Science Journal, 2020
    Co-Authors: Masayuki Takeda, Yoshinobu Uemoto, Keiichi Inoue, Atushi Ogino, Takayoshi Nozaki, Kazuhito Kurogi, Takanori Yasumori, Masahiro Satoh
    Abstract:

    The objectives of this study were to better understand the genetic architecture and the possibility of genomic evaluation for feed efficiency traits by (i) performing genome-wide association studies (GWAS), and (ii) assessing the accuracy of genomic evaluation for feed efficiency traits, using single-step genomic best Linear Unbiased Prediction (ssGBLUP)-based methods. The analyses were performed in residual feed intake (RFI), residual body weight gain (RG), and residual intake and body weight gain (RIG) during three different fattening periods. The phenotypes from 4,578 Japanese Black steers, which were progenies of 362 progeny-tested bulls and the genotypes from the bulls were used in this study. The results of GWAS showed that a total of 16, 8, and 12 gene ontology terms were related to RFI, RG, and RIG, respectively, and the candidate genes identified in RFI and RG were involved in olfactory transduction and the phosphatidylinositol signaling system, respectively. The realized reliabilities of genomic estimated breeding values were low to moderate in the feed efficiency traits. In conclusion, ssGBLUP-based method can lead to understand some biological functions related to feed efficiency traits, even with small population with genotypes, however, an alternative strategy will be needed to enhance the reliability of genomic evaluation.

  • an alternative derivation method of mixed model equations from best Linear Unbiased Prediction blup and restricted blup of breeding values not using maximum likelihood
    Animal Science Journal, 2018
    Co-Authors: Masahiro Satoh
    Abstract:

    Two mixed model equations (MME) for best Linear Unbiased Prediction (BLUP) of breeding values and for restricted BLUP of breeding values were derived by maximum likelihood from the joint normal probability distribution of the observations and breeding values. As a result, MME is actually more general than maximum likelihood because we can prove that each set of solutions of MME are identical to BLUP and restricted BLUP of breeding values and then it does not depend on normality. In the present study, the author shows deriving directly each MME from BLUP and restricted BLUP equations for breeding values without assuming the joint normal distribution of the data and random effects. However, if we cannot assume the multivariate normal density distribution of the estimated aggregate breeding value and each breeding value for selected traits, the response to selection by restricted BLUP may deviate from the expected values.

  • genomic best Linear Unbiased Prediction method reflecting the degree of linkage disequilibrium
    Journal of Animal Breeding and Genetics, 2015
    Co-Authors: Motohide Nishio, Masahiro Satoh
    Abstract:

    The degree of linkage disequilibrium (LD) between markers differs depending on the location of the genome; this difference biases genetic evaluation by genomic best Linear Unbiased Prediction (GBLUP). To correct this bias, we used three GBLUP methods reflecting the degree of LD (GBLUP-LD). In the three GBLUP-LD methods, genomic relationship matrices were conducted from single nucleotide polymorphism markers weighted according to local LD levels. The predictive abilities of GBLUP-LD were investigated by estimating variance components and assessing the accuracies of estimated breeding values using simulation data. When quantitative trait loci (QTL) were located at weak LD regions, the predictive abilities of the three GBLUP-LD methods were superior to those of GBLUP and Bayesian lasso except when the number of QTL was small. In particular, the superiority of GBLUP-LD increased with decreasing trait heritability. The rates of QTL at weak LD regions would increase when selection by GBLUP continues; this consequently decreases the predictive ability of GBLUP. Thus, the GBLUP-LD could be applicable for populations selected by GBLUP for a long time. However, if QTL were located at strong LD regions, the accuracies of three GBLUP-LD methods were lower than GBLUP and Bayesian lasso.

  • genomic best Linear Unbiased Prediction method including imprinting effects for genomic evaluation
    Genetics Selection Evolution, 2015
    Co-Authors: Motohide Nishio, Masahiro Satoh
    Abstract:

    Genomic best Linear Unbiased Prediction (GBLUP) is a statistical method used to predict breeding values using single nucleotide polymorphisms for selection in animal and plant breeding. Genetic effects are often modeled as additively acting marker allele effects. However, the actual mode of biological action can differ from this assumption. Many livestock traits exhibit genomic imprinting, which may substantially contribute to the total genetic variation of quantitative traits. Here, we present two statistical models of GBLUP including imprinting effects (GBLUP-I) on the basis of genotypic values (GBLUP-I1) and gametic values (GBLUP-I2). The performance of these models for the estimation of variance components and Prediction of genetic values across a range of genetic variations was evaluated in simulations. Estimates of total genetic variances and residual variances with GBLUP-I1 and GBLUP-I2 were close to the true values and the regression coefficients of total genetic values on their estimates were close to 1. Accuracies of estimated total genetic values in both GBLUP-I methods increased with increasing degree of imprinting and broad-sense heritability. When the imprinting variances were equal to 1.4% to 6.0% of the phenotypic variances, the accuracies of estimated total genetic values with GBLUP-I1 exceeded those with GBLUP by 1.4% to 7.8%. In comparison with GBLUP-I1, the superiority of GBLUP-I2 over GBLUP depended strongly on degree of imprinting and difference in genetic values between paternal and maternal alleles. When paternal and maternal alleles were predicted (phasing accuracy was equal to 0.979), accuracies of the estimated total genetic values in GBLUP-I1 and GBLUP-I2 were 1.7% and 1.2% lower than when paternal and maternal alleles were known. This simulation study shows that GBLUP-I1 and GBLUP-I2 can accurately estimate total genetic variance and perform well for the Prediction of total genetic values. GBLUP-I1 is preferred for genomic evaluation, while GBLUP-I2 is preferred when the imprinting effects are large, and the genetic effects differ substantially between sexes.

  • a simple method of computing restricted best Linear Unbiased Prediction of breeding values
    Genetics Selection Evolution, 1998
    Co-Authors: Masahiro Satoh
    Abstract:

    Methode simple de calcul d'un BLUP restreint. Le BLUP restreint est calcule directement en posant les contraintes en sus des equations correspondantes a un modele lineaire mixte multivariate. En consequence, la procedure du BLUP restreint demande la resolution d'un plus grand nombre d'equations que dans le cas habituel. Dans cet article, on presente un reparametrage qui permet d'aboutir a un systeme plus simple et de taille reduite. Cette technique est particulierement interessante quand un grand nombre de restrictions est impose dans un modele mixte multivariate, comme quand on cherche a obtenir des rapport predetermines de progres genetiques pour tous les caracteres.

Albrecht E Melchinger - One of the best experts on this subject based on the ideXlab platform.

  • Best Linear Unbiased Prediction and optimum allocation of test resources in maize breeding with doubled haploids
    Theoretical and Applied Genetics, 2011
    Co-Authors: Xuefei Mi, Thilo Wegenast, Baldev S. Dhillon, Albrecht E Melchinger
    Abstract:

    With best Linear Unbiased Prediction (BLUP), information from genetically related candidates is combined to obtain more precise estimates of genotypic values of test candidates and thereby increase progress from selection. We developed and applied theory and Monte Carlo simulations implementing BLUP in 2 two-stage maize breeding schemes and various selection strategies. Our objectives were to (1) derive analytical solutions of the mixed model equations under two breeding schemes, (2) determine the optimum allocation of test resources with BLUP under different assumptions regarding the variance component ratios for grain yield in maize, (3) compare the progress from selection using BLUP and conventional phenotypic selection based on mean performance solely of the candidates, and (4) analyze the potential of BLUP for further improving the progress from selection. The breeding schemes involved selection for testcross performance either of DH lines at both stages (DHTC) or of S_1 families at the first stage and DH lines at the second stage (S_1TC-DHTC). Our analytical solutions allowed much faster calculations of the optimum allocations and superseded matrix inversions to solve the mixed model equations. Compared to conventional phenotypic selection, the progress from selection was slightly higher with BLUP for both optimization criteria, namely the selection gain and the probability to select the best genotypes. The optimum allocation of test resources in S_1TC-DHTC involved ≥10 test locations at both stages, a low number of crosses (≤6) each with 100–300 S_1 families at the first stage, and 500–1,000 DH lines at the second stage. In breeding scheme DHTC, the optimum number of test candidates at the first stage was 5–10 times larger, whereas the number of test locations at the first stage and the number of test candidates at the second stage were strongly reduced compared to S_1TC-DHTC.

  • modified full sib selection and best Linear Unbiased Prediction of progeny performance in a european f2 maize population
    Plant Breeding, 2006
    Co-Authors: C Flachenecker, Matthias Frisch, J Muminovic, K C Falke, Albrecht E Melchinger
    Abstract:

    Four cycles of modified recurrent full-sib (FS) selection were conducted in an intermated F 2 population of European flint maize. The objectives of our study were to monitor trends across selection cycles in the estimates of population mean, inbreeding coefficients and variance components, and to investigate the usefulness of best Linear Unbiased Prediction (BLUP) of progeny performance under the recurrent FS selection scheme applied. We used a selection rate of 25% for a selection index, based on grain yield and dry matter content. A pseudo-factorial mating scheme was used for recombination. In this scheme, the selected FS families were divided into an upper-ranking group of parents mated to the lower-ranking group. Variance components were estimated with restricted maximum likelihood (REML). Average grain yield increased 1.2 t/ha per cycle, average grain moisture decreased 20.1 g/kg per cycle, and the selection index relative to the F 2 check entries decreased 0.3% per cycle. For a more precise calculation of selection response, the four cycles should be tested together in multi-environmental trials. We observed a significant decrease in additive variance in the selection index, suggesting smaller future selection response. Predictions of FS family performance in Cn + I based on mean performance of parental FS families in Cn were of equal precision as those based on the mean additive genetic BLUP of their parents, and corresponding correlations were of moderate size for grain moisture and selection index.

  • trends in population parameters and best Linear Unbiased Prediction of progeny performance in a european f2 maize population under modified recurrent full sib selection
    Theoretical and Applied Genetics, 2006
    Co-Authors: C Flachenecker, Matthias Frisch, K C Falke, Albrecht E Melchinger
    Abstract:

    Recurrent selection is a cyclic breeding procedure designed to improve the mean of a population for the trait(s) under selection. Starting from an F2 population of European flint maize (Zea mays L.) intermated for three generations, we conducted seven cycles of a modified recurrent full-sib (FS) selection scheme. The objectives of our study were to (1) monitor trends across selection cycles in the estimates of the population mean, additive and dominance variances, (2) compare predicted and realized selection responses, and (3) investigate the usefulness of best Linear Unbiased Prediction (BLUP) of progeny performance under the recurrent FS selection scheme applied. Recurrent FS selection was conducted at three locations using a selection rate of 25% for a selection index, based on grain yield and grain moisture. Recombination was performed according to a pseudo-factorial mating scheme, where the selected FS families were divided into an upper-ranking group of parents mated to the lower-ranking group. Variance components were estimated with restricted maximum likelihood. Average grain yield increased 9.1% per cycle, average grain moisture decreased 1.1% per cycle, and the selection index increased 11.2% per cycle. For the three traits we observed, no significant changes in additive and dominance variances occurred, suggesting future selection response at or near current rates of progress. Predictions of FS family performance in Cn+1 based on mean performance of parental FS families in Cn were of equal or higher precision as those based on the mean additive genetic BLUP of their parents, and corresponding correlations were of moderate size only for grain moisture. The significant increase in grain yield combined with the decrease in grain moisture suggest that the F2 source population with use of a pseudo-factorial mating scheme is an appealing alternative to other types of source materials and random mating schemes commonly used in recurrent selection.

Rex Bernardo - One of the best experts on this subject based on the ideXlab platform.

  • marker assisted best Linear Unbiased Prediction of single cross performance
    Crop Science, 1999
    Co-Authors: Rex Bernardo
    Abstract:

    Predicting the performance of untested single crosses is important in hybrid breeding programs. The objective of this study was to compare the effectiveness of best Linear Unbiased Prediction based on trait data alone (T-BLUP) and trait and marker data combined (TM-BLUP). The simulation procedure involved creating founder and recombinant inbreds in each of two heterotic groups, determining genetic and phenotypic values of 3025 single crosses, randomly partitioning the single crosses into 500 tested and 2525 untested hybrids, and calculating the correlation between the true and predicted performance of untested single crosses. The T-BLUP correlations ranged from 0.74 to 0.84, with n = 10, 50, or 100 quantitative trait loci (QTL) and trait heritability of 0.4 or 0.6. The advantage of TM-BLUP over T-BLUP decreased as n increased. With n = 50 or 100, the TM-BLUP correlations exceeded the T-BLUP correlations by 0.00 to 0.03, even when all QTL were tightly linked to flanking markers. The usefulness of TM-BLUP is doubtful, not only for predicting single-cross performance, but also for predicting breeding values of individuals within populations. The TM-BLUP procedure is useful when few QTL control a trait, or when genetic gain is sought only at a limited subset of QTL.

  • testcross additive and dominance effects in best Linear Unbiased Prediction of maize single cross performance
    Theoretical and Applied Genetics, 1996
    Co-Authors: Rex Bernardo
    Abstract:

    Best Linear Unbiased Prediction (BLUP) has been found to be useful in maize (Zea mays L.) breeding. The advantage of including both testcross additive and dominance effects (Intralocus Model) in BLUP, rather than only testcross additive effects (Additive Model), has not been clearly demonstrated. The objective of this study was to compare the usefulness of Intralocus and Additive Models for BLUP of maize single-cross performance. Multilocation data from 1990 to 1995 were obtained from the hybrid testing program of Limagrain Genetics. Grain yield, moisture, stalk lodging, and root lodging of untested single crosses were predicted from (1) the performance of tested single crosses and (2) known genetic relationships among the parental inbreds. Correlations between predicted and observed performance were obtained with a delete-one cross-validation procedure. For the Intralocus Model, the correlations ranged from 0.50 to 0.66 for yield, 0.88 to 0.94 for moisture, 0.47 to 0.69 for stalk lodging, and 0.31 to 0.45 for root lodging. The BLUP procedure was consistently more effective with the Intralocus Model than with the Additive Model. When the Additive Model was used instead of the Intralocus Model, the reductions in the correlation were largest for root lodging (0.06-0.35), smallest for moisture (0.00-0.02), and intermediate for yield (0.02-0.06) and stalk lodging (0.02-0.08). The ratio of dominance variance (v D) to total genetic variance (v G) was highest for root lodging (0.47) and lowest for moisture (0.10). The Additive Model may be used if prior information indicates that VD for a given trait has little contribution to VG. Otherwise, the continued use of the Intralocus Model for BLUP of single-cross performance is recommended.

  • best Linear Unbiased Prediction of maize single cross performance given erroneous inbred relationships
    Crop Science, 1996
    Co-Authors: Rex Bernardo
    Abstract:

    Best Linear Unbiased Prediction (BLUP) of maize (Zea mays L.) single-cross performance requires estimates of genetic relationship among parental inbreds. Error in estimates of genetic relationship may result from selection, genetic drift during inbreeding, or incomplete or unknown pedigrees. The objective of this study was to investigate the robustness of BLUP when estimates of genetic relationship among inbreds are erroneous. Grain yield, moisture, stalk lodging, and root lodging data were obtained for 2043 maize single crosses evaluated in the multilocation testing program of Limagrain Genetics, from 1990 to 1994. Three types of errors in inbred relationships were studied: (i) simulated random deviations from the expected contributions of parents to progeny; (ii) unknown parentage for 10% of the inbreds; and (iii) unknown parentage for 25% of the inbreds. Malecot's coefficients of coancestry (f) were calculated by tabular analysis of the correct and erroneous pedigrees and were subsequently used in BLUP. Average absolute deviations between erroneous f and f calculated from correct pedigrees ranged from near-zero to 0.30. But the correlations between predicted and observed single-cross performance were not affected by the relatively large errors in f. Most of the differences, caused by erroneous f, in the correlations between predicted and observed performance ranged from 0.001 to 0.030 and were too small to have any practical significance. These results indicated that BLUP is robust when inbred relationships are erroneously specified. Estimating f with molecular markers, prior to predicting the single-cross performance of inbreds with incomplete or unknown pedigrees, seems unnecessary.

  • best Linear Unbiased Prediction of the performance of crosses between untested maize inbreds
    Crop Science, 1996
    Co-Authors: Rex Bernardo
    Abstract:

    In some situations, maize (Zea mays L.) inbreds may not have testcross data available for best Linear Unbiased Prediction (BLUP) of single-cross performance. The objective of this study was to evaluate the effectiveness of BLUP when the parents of a single cross have not been tested in hybrid combination within a given heterotic pattern. Yield, moisture, stalk lodging, and root lodging data were obtained for 4099 single crosses evaluated by Limagrain Genetics in multilocation trials in 1990 to 1994. For each of 16 heterotic patterns, the performance of an i x j single cross was predicted as follows: (i) with all available testcross data for both i and j and their relatives; (ii) disregarding all testcross data for i; (iii) disregarding all testcross data for j; and (iv) disregarding all testcross data for both i and j. Correlations between predicted and observed performance, obtained with a cross-validation procedure, were highest when testcross data for both parents of a single cross were utilized. Except for moisture, these correlations were severely reduced to <0.50 when both i and j were assumed untested. Prediction of performance of the cross between two untested inbreds seems unwarranted, but this situation is rare because new inbreds are usually crossed to extensively tested inbreds. The performance of the cross between an untested inbred and a tested inbred was predicted effectively when the number of tested single crosses in the heterotic pattern was large. In this situation, the highest correlations were 90% of the corresponding correlations obtained when data were available for both parents of the single cross.

  • best Linear Unbiased Prediction of maize single cross performance
    Crop Science, 1996
    Co-Authors: Rex Bernardo
    Abstract:

    In preliminary studies, best Linear Unbiased Prediction (BLUP) has been found useful for identifying high-yielding maize (Zea mays L.) single crosses prior to field evaluation. In this study, the effectiveness of BLUP for large-scale Prediction of yield, moisture, stalk lodging, and root lodging was investigated. Multilocation data from 1990 to 1994 were obtained from the hybrid testing program of Limagrain Genetics. For each of 16 heterotic patterns, the performance of m untested single crosses was predicted from the performance of n tested single crosses as y M = C MP C PP -1 y P , where y M = m x 1 vector of predicted performance of the untested single crosses ; C MP = m x n matrix of genetic covariances between the untested single crosses and the tested single crosses ; C PP = n x n phenotypic covariance matrix among the tested single crosses ; and y P = n x 1 vector of average performance of the tested single crosses, corrected for yield trial effects. Correlations between predicted and observed performance were obtained with a delete-one cross-validation procedure. For heterotic patterns with large (>100) numbers of tested single crosses, the correlations ranged from 0.426 to 0.762 for yield, 0.754 to 0.933 for moisture, 0.300 to 0.739 for stalk lodging, and 0.164 to 0.532 for root lodging. The correlations, especially for lodging traits, increased as larger numbers of tested single crosses were available. The results in this study were obtained from large and diverse data sets (600 inbreds, 15 183 data points, and 4099 tested single crosses across 16 heterotic patterns) and provide strong evidence that BLUP is useful for routine identification of superior single crosses prior to field testing.

Daniel Gianola - One of the best experts on this subject based on the ideXlab platform.

  • a benchmarking between deep learning support vector machine and bayesian threshold best Linear Unbiased Prediction for predicting ordinal traits in plant breeding
    G3: Genes Genomes Genetics, 2019
    Co-Authors: Osval A Montesinoslopez, Daniel Gianola, Javier Martinvallejo, Jose Crossa, Carlos M Hernandezsuarez, Abelardo Montesinoslopez, Philomin Juliana, Ravi P Singh
    Abstract:

    Genomic selection is revolutionizing plant breeding. However, still lacking are better statistical models for ordinal phenotypes to improve the accuracy of the selection of candidate genotypes. For this reason, in this paper we explore the genomic based Prediction performance of two popular machine learning methods: the Multi Layer Perceptron (MLP) and support vector machine (SVM) methods vs. the Bayesian threshold genomic best Linear Unbiased Prediction (TGBLUP) model. We used the percentage of cases correctly classified (PCCC) as a metric to measure the Prediction performance, and seven real data sets to evaluate the Prediction accuracy, and found that the best Predictions (in four out of the seven data sets) in terms of PCCC occurred under the TGLBUP model, while the worst occurred under the SVM method. Also, in general we found no statistical differences between using 1, 2 and 3 layers under the MLP models, which means that many times the conventional neuronal network model with only one layer is enough. However, although even that the TGBLUP model was better, we found that the Predictions of MLP and SVM were very competitive with the advantage that the SVM was the most efficient in terms of the computational time required.

  • Prediction of complex traits robust alternatives to best Linear Unbiased Prediction
    Frontiers in Genetics, 2018
    Co-Authors: Daniel Gianola, Alessio Cecchinato, Hugo Naya, Chriscarolin Schon
    Abstract:

    A widely used method for Prediction of complex traits in animal and plant breeding is "genomic best Linear Unbiased Prediction" (GBLUP). BLUP is a Linear regression of phenotypes on a pedigree or on a genomic relationship matrix, depending on the type of input information. Normality of the distributions of random effects and of model residuals is not required for BLUP but a Gaussian assumption is made implicitly. A potential downside is that Gaussian Linear regressions are sensitive to outliers, genetic or environmental in origin. We present simple to implement robust alternatives to BLUP using a Linear model with residual t or Laplace distributions instead of a Gaussian one, and evaluate the methods with milk yield records on Italian Brown Swiss cattle, grain yield data in inbred wheat lines, and using three traits measured on accessions of Arabidopsis thaliana. The methods do not use Markov chain Monte Carlo and model hyper-parameters are tuned via some cross-validation. Uncertainty of Predictions can be evaluated employing bootstrapping or by random reconstruction of training and testing sets. It was found (e.g., test-day milk yield in cows, flowering time and FRIGIDA expression in Arabidopsis) that the best Predictions were often those obtained with the robust methods. The results obtained are encouraging and stimulate further investigation.

Masashi Yamamoto - One of the best experts on this subject based on the ideXlab platform.

  • erratum to evaluation of the best Linear Unbiased Prediction method for breeding values of fruit quality traits in citrus
    Tree Genetics & Genomes, 2016
    Co-Authors: Atsushi Imai, Takeshi Kuniga, Terutaka Yoshioka, Keisuke Nonaka, Nobuhito Mitani, H Fukamachi, N Hiehata, Masashi Yamamoto
    Abstract:

    Fruit-quality trait improvement is an important objective in citrus breeding; however, fruit breeding programs often accumulate highly unbalanced phenotypic records, which are a serious obstacle in comparing and selecting genotypes. The best Linear Unbiased Prediction (BLUP) method can be used to overcome these difficulties, but few fruit breeding programs have adopted the method, and to our knowledge, the method has not yet been used to predict breeding values of traits based on pedigree information and genetic correlations between traits in citrus. Accordingly, we used the BLUP method to predict the breeding values of nine fruit-quality traits (fruit weight, fruit skin color, fruit surface texture, peelability, flesh color, pulp firmness, segment firmness, sugar content, and acid content) utilizing phenotypic records collected over several years as part of the citrus breeding program conducted at the Kuchinotsu branch of the National Institute of Fruit Tree Science in Japan. Although the accumulated phenotypic records were highly unbalanced, the BLUP method was able to predict the breeding values of all 2122 genotypes (111 parental cultivars and 2011 F1 offspring from 126 pair-cross families), as well as estimates of several genetic parameters, including narrow-sense heritability and phenotypic and genotypic correlations. These findings demonstrate the utility of the BLUP method in citrus crossbreeding and provide predicted breeding values, which can be used to select superior genotypes in the Kuchinotsu Citrus Breeding Program and related genetic selection endeavors.