scholarly journals Soybean productivity, stability, and adaptability through mixed model methodology

2021 ◽  
Vol 51 (2) ◽  
Author(s):  
Jeniffer Santana Pinto Coelho Evangelista ◽  
Rodrigo Silva Alves ◽  
Marco Antônio Peixoto ◽  
Marcos Deon Vilela de Resende ◽  
Paulo Eduardo Teodoro ◽  
...  
Keyword(s):  
Mixed Model ◽  
High Reliability ◽  
Phenotypic Expression ◽  
Genetic Selection ◽  
Ratio Test ◽  
Linear Unbiased Prediction ◽  
Selection Strategies ◽  
Yield Trait ◽  
Soybean Genotypes ◽  
Best Linear Unbiased

ABSTRACT: The genotype × environment (G×E) interaction plays an essential role in phenotypic expression and can lead to difficulties in genetic selection. Thus, the present study aimed to estimate genetic parameters and to compare different selection strategies in the context of mixed models for soybean breeding. For this, data referring to the evaluation of 30 genotypes in 10 environments, regarding the grain yield trait, were used. The variance components were estimated through restricted maximum likelihood (REML) and genotypic values were predicted through best linear unbiased prediction (BLUP). Significant effects of genotypes and G×E interaction were detected by the likelihood ratio test (LRT). Low genotypic correlation was obtained across environments, indicating complex G×E interaction. The selective accuracy was very high, indicating high reliability. Our results showed that the most productive soybean genotypes have high adaptability and stability.

scholarly journals Random regression for modeling yield genetic trajectories in Jatropha curcas breeding

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0244021
Author(s):  
Marco Antônio Peixoto ◽  
Rodrigo Silva Alves ◽  
Igor Ferreira Coelho ◽  
Jeniffer Santana Pinto Coelho Evangelista ◽  
Marcos Deon Vilela de Resende ◽  
...  
Keyword(s):  
Jatropha Curcas ◽  
Repeated Measures ◽  
Variance Components ◽  
High Reliability ◽  
Genetic Selection ◽  
Information Criterion ◽  
Random Regression ◽  
Ratio Test ◽  
Sib Families ◽  
Over Time

Random regression models (RRM) are a powerful tool to evaluate genotypic plasticity over time. However, to date, RRM remains unexplored for the analysis of repeated measures in Jatropha curcas breeding. Thus, the present work aimed to apply the random regression technique and study its possibilities for the analysis of repeated measures in Jatropha curcas breeding. To this end, the grain yield (GY) trait of 730 individuals of 73 half-sib families was evaluated over six years. Variance components were estimated by restricted maximum likelihood, genetic values were predicted by best linear unbiased prediction and RRM were fitted through Legendre polynomials. The best RRM was selected by Bayesian information criterion. According to the likelihood ratio test, there was genetic variability among the Jatropha curcas progenies; also, the plot and permanent environmental effects were statistically significant. The variance components and heritability estimates increased over time. Non-uniform trajectories were estimated for each progeny throughout the measures, and the area under the trajectories distinguished the progenies with higher performance. High accuracies were found for GY in all harvests, which indicates the high reliability of the results. Moderate to strong genetic correlation was observed across pairs of harvests. The genetic trajectories indicated the existence of genotype × measurement interaction, once the trajectories crossed, which implies a different ranking in each year. Our results suggest that RRM can be efficiently applied for genetic selection in Jatropha curcas breeding programs.

scholarly journals Genome-wide Hierarchical Mixed Model Association Analysis

2021 ◽  
Author(s):  
Runqing Yang ◽  
Di Liu ◽  
Zhiyu Hao ◽  
Yuxin Song ◽  
Runqing Yang ◽  
...  
Keyword(s):  
Association Analysis ◽  
Statistical Power ◽  
Mixed Model ◽  
False Negative ◽  
Best Linear Unbiased Prediction ◽  
Least Square ◽  
Linear Unbiased Prediction ◽  
Genomic Breeding ◽  
Genome Wide ◽  
Best Linear Unbiased

Abstract We partitioned the genomic mixed model into two hierarchies to firstly estimate genomic breeding values (GBVs) using the genomic best linear unbiased prediction and then statistically infer the association of GBVs with each SNP using the generalized least square. The genome-wide hierarchical mixed model association study (named Hi-LMM) can correct effectively confounders with polygenic effects as residuals in association tests, preventing potential false negative errors produced with GRAMMAR or EMMAX. The Hi-LMM performs the same statistical power as the exact FaST-LMM with the same computing efficiency as EMMAX. When the GBVs have been estimated precisely, Hi-LMM outperforms existing methods in statistical power, especially through joint association analysis.

scholarly journals Estimates of Parental Best Linear Unbiased Predicted (BLUP) Additive Genetic Values for Oil Yield in an Indonesian Oil Palm Breeding Programme

Zuriat ◽  
2015 ◽  
Vol 15 (1) ◽  
Author(s):  
A. R. Purba ◽  
A. Flori ◽  
L. Baudouin ◽  
P. Amblard ◽  
S. Hamon
Keyword(s):  
Oil Palm ◽  
Oil Yield ◽  
Breeding Programme ◽  
Data Set ◽  
Linear Unbiased Prediction ◽  
Yield Trait ◽  
African Populations ◽  
Additive Gene ◽  
Best Linear Unbiased ◽  
Genetic Value

The additive genetic value for oil yield of 135 parents from several African populations tested in the first cycle of Indonesian Oil Palm Research Institute (IOPRI) breeding programme were estimated by best linear unbiased prediction (BLUP) using an unbalanced data set. These values were used to evaluate the possibility of reduction in generation selection time in an oil palm breeding programme. The ranks of parental additive genetic values obtained with early yield period and whole cycle was found to be consistent. These make that highly potential parents could be selected and recombined at a more precocious time in order to reduce the period between cycle. Since oil yield trait is mainly controlled under additive gene effect, the recombination should be done carefully for retaining as highly as possible this character in a parent to be improved.

MC Simulation of SEBLUP with Spatial Linear Mixed Model for SAE

2014 ◽  
Vol 685 ◽  
pp. 618-622
Author(s):  
Yan Yu Liu ◽  
Ming Zhong Jin ◽  
De You Xie ◽  
Min Qing Gong
Keyword(s):  
Spatial Correlation ◽  
Small Area ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Likelihood Estimation ◽  
Best Linear Unbiased Prediction ◽  
Linear Unbiased Prediction ◽  
Mutual Independence ◽  
Best Linear Unbiased ◽  
Unbiased Prediction

For small area estimation (SAE) Spatial Empirical Best Linear Unbiased Prediction, SEBLUP, is involved in linear mixed model with spatial correlation while Empirical Best Linear Unbiased Prediction, EBLUP, often with mutual independence. In this paper, we discussed maximum likelihood estimation (MLE) and compared the efficiency. Simulation shows that SEBLUP with spatial correlation data of spatial small area is more effective than EBLUP.

scholarly journals Small Area Estimation on Zero-Inflated Data Using Frequentist and Bayesian Approach

2020 ◽  
Vol 18 (1) ◽  
pp. 2-22
Author(s):  
Kusman Sadik ◽  
Rahma Anisa ◽  
Euis Aqmaliyah
Keyword(s):  
Bayesian Approach ◽  
Small Area ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Small Area Estimation ◽  
Prediction Method ◽  
Linear Unbiased Prediction ◽  
Area Estimation ◽  
Best Linear Unbiased ◽  
Sr Method

The most commonly used method of small area estimation (SAE) is the empirical best linear unbiased prediction method based on a linear mixed model. However, it is not appropriate in the case of the zero-inflated target variable with a mixture of zeros and continuously distributed positive values. Therefore, various model-based SAE methods for zero-inflated data are developed, such as the Frequentist approach and the Bayesian approach. Both approaches are compared with the survey regression (SR) method which ignores the presence of zero-inflation in the data. The results show that the two SAE approaches for zero-inflated data are capable to yield more accurate area mean estimates than the SR method.

scholarly journals Generalized Hierarchical Mixed Model Association Analysis

2021 ◽  
Author(s):  
Runqing Yang ◽  
Yuxin Song ◽  
Zhiyu Hao ◽  
Zhonghua Liu
Keyword(s):  
Association Analysis ◽  
Statistical Power ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Least Square ◽  
Linear Unbiased Prediction ◽  
Genomic Breeding ◽  
Genome Wide ◽  
Complex Population ◽  
Best Linear Unbiased

AbstractIn genome-wide association analysis for complex diseases, we partitioned the genomic generalized linear mixed model (GLMM) into two hierarchies—the GLMM regarding genomic breeding values (GBVs) and a generalized linear regression of the GBVs to the tested marker effects. In the first hierarchy, the GBVs were predicted by solving for the genomic best linear unbiased prediction for GLMM, and in the second hierarchy, association tests were performed using the generalized least square (GLS) method. The so-called Hi-GLMM method exhibited advantages over existing methods in terms of both genomic control for complex population structure and statistical power to detect quantitative trait nucleotides (QTNs), especially when the GBVs were estimated precisely, and using joint association analysis for QTN candidates obtained from a test at once.

scholarly journals Early selection and classification of yerba mate progênies

2018 ◽  
Vol 53 (3) ◽  
pp. 279-286
Author(s):  
Ivar Wendling ◽  
José Alfredo Sturion ◽  
Carlos André Stuepp ◽  
Cristiane Aparecida Fioravante Reis ◽  
Magno Antonio Patto Ramalho ◽  
...  
Keyword(s):  
Mass Production ◽  
Mixed Model ◽  
Early Selection ◽  
Ilex Paraguariensis ◽  
Yerba Mate ◽  
Linear Unbiased Prediction ◽  
Best Linear Unbiased ◽  
Selection For ◽  
Selection Of

Abstract: The objective of this work was to evaluate the feasibility of early selection of open-pollinated yerba mate (Ilex paraguariensis) progenies, and to classify the best parents and candidates for clones. The germplasm is composed of 140 progenies collected from areas within the natural distribution of the species in Southern Brazil and a commercial genotype (control). The experiment was established in Ivaí, in the state of Paraná, Brazil, in March 1997, in randomized complete blocks, with ten replicates, and linear plots of six plants. The commercial mass (kg per plant) of leaves and branches smaller than 7 mm was harvested at 2.5, 4.5, 6.5, and 18.7 years of age. The statistical evaluation was performed using the mixed model procedure with restricted maximum likelihood/best linear unbiased prediction, using the Selegen software. High selective accuracy, significant progeny effects, and genetic variability for commercial mass production were observed for the four harvesting ages. The application of early selection for leaf mass production is feasible for yerba mate progenies harvested at 6.5 years. The selection of the best trees, either for use as parents or as candidates for cloning, provides significant gains for the genetic breeding of yerba mate.

EVALUATION OF BEEF SIRES FOR MATERNAL GENETIC EFFECTS ON WEANING WEIGHT AND MEASUREMENT OF GENETIC TRENDS

1983 ◽  
Vol 63 (2) ◽  
pp. 279-283 ◽  
Author(s):  
G. H. CROW ◽  
W. E. HOWELL
Keyword(s):  
Variance Components ◽  
Mixed Model ◽  
Error Variance ◽  
Genetic Effects ◽  
Genetic Trend ◽  
Linear Unbiased Prediction ◽  
Weaning Weight ◽  
Best Linear Unbiased ◽  
Maternal Grandsire ◽  
Genetic Trends

Procedures for evaluating beef sires for maternal genetic effects for weaning weight were developed and used in the calculation of genetic trends. Using the maternal grandsire (MGS) and error variance components, predicted differences (PDs) of MGSs for parity one daughter performance were calculated using Record of Performance data from the Angus, Charolais and Hereford breeds. Best linear unbiased prediction (BLUP) methods were used with a mixed model which included herd-year and MGS effects. The average number of daughter records per MGS was 5.44, 5.26 and 5.12 for Angus, Hereford and Charolais, respectively. As a result of these small numbers of daughter records, the PDs of most MGSs had very large standard errors of prediction. Weighted averages of MGS PDs for each breed in each of the 5 years from 1975 to 1979 were calculated to determine if there was any genetic trend in MGS PDs. There were small trends evident but not sufficient to indicate that any selection pressure was being exerted on maternal ability. Key words: Beef sire evaluation, maternal effects, genetic trends

scholarly journals Identifying crisphead lettuce genotypes for a wider range of environments

2021 ◽  
Vol 23 (1) ◽  
Author(s):  
Dora Tobar Tosse ◽  
Willame Dos Santos Candido ◽  
Lucas Da Silva Santos ◽  
Edgard Henrique Costa Silva ◽  
Renata Castoldi ◽  
...  
Keyword(s):  
Mixed Model ◽  
Block Design ◽  
Total Production ◽  
Plant Analysis ◽  
Linear Unbiased Prediction ◽  
Breeding Lines ◽  
Lactuca Sativa L ◽  
Randomized Complete Block Design ◽  
Lettuce Breeding ◽  
Best Linear Unbiased

This work aims to select crisphead lettuce (Lactuca sativa L) genotypes superior in production, stability, and adaptability using a mixed model method: restricted maximum likelihood/best linear unbiased prediction. Ten genotypes were grown in different municipalities of the State of São Paulo, Brazil, and seasons of the year, resulting in twelve different environments. The experiment has a randomized complete block design with four repetitions. Genotypes comprise eight breeding lines and two commercial cultivars, Vanda and Vera. The evaluated traits include total production in g/plant, commercial production in g/plant, and numbers of leaves/plant. Analysis of joint deviance indicated that the genotypes responded differently to the environments evaluated. The crisphead lettuce breeding lines that were most productive, stable, and adapted to the twelve lettuce-growing environments, even outperforming the commercial Vanda and Vera cultivars, were lines L8, L2, and L6.

scholarly journals Biometrical analysis and selection of tetraploid progenies of Panicum maximum using mixed model methods

2004 ◽  
Vol 39 (4) ◽  
pp. 335-341 ◽  
Author(s):  
Rosangela Maria Simeão Resende ◽  
Liana Jank ◽  
Cacilda Borges do Valle ◽  
Ana Lídia Variani Bonato
Keyword(s):  
Mixed Model ◽  
Panicum Maximum ◽  
Hybrid Progeny ◽  
Linear Unbiased Prediction ◽  
Breeding Values ◽  
Green Matter ◽  
Sib Families ◽  
Best Linear Unbiased ◽  
Clonal Test ◽  
Selection Of

The objectives of this work were to estimate the genetic and phenotypic parameters and to predict the genetic and genotypic values of the selection candidates obtained from intraspecific crosses in Panicum maximum as well as the performance of the hybrid progeny of the existing and projected crosses. Seventy-nine intraspecific hybrids obtained from artificial crosses among five apomictic and three sexual autotetraploid individuals were evaluated in a clonal test with two replications and ten plants per plot. Green matter yield, total and leaf dry matter yields and leaf percentage were evaluated in five cuts per year during three years. Genetic parameters were estimated and breeding and genotypic values were predicted using the restricted maximum likelihood/best linear unbiased prediction procedure (REML/BLUP). The dominant genetic variance was estimated by adjusting the effect of full-sib families. Low magnitude individual narrow sense heritabilities (0.02-0.05), individual broad sense heritabilities (0.14-0.20) and repeatability measured on an individual basis (0.15-0.21) were obtained. Dominance effects for all evaluated characteristics indicated that breeding strategies that explore heterosis must be adopted. Less than 5% increase in the parameter repeatability was obtained for a three-year evaluation period and may be the criterion to determine the maximum number of years of evaluation to be adopted, without compromising gain per cycle of selection. The identification of hybrid candidates for future cultivars and of those that can be incorporated into the breeding program was based on the genotypic and breeding values, respectively. The prediction of the performance of the hybrid progeny, based on the breeding values of the progenitors, permitted the identification of the best crosses and indicated the best parents to use in crosses.

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