scholarly journals A Combined Analysis in Complementary Progeny Tests: Effects on breeding value accuracies

2016 ◽  
Vol 65 (1) ◽  
pp. 38-48 ◽  
Author(s):  
Eduardo P. Cappa ◽  
Michael U. Stoehr
Keyword(s):  
Mixed Model ◽  
Breeding Value ◽  
Forward Selection ◽  
Combined Analysis ◽  
Breeding Values ◽  
Mixed Model Approach ◽  
Additional Information ◽  
Genetic Entry ◽  
Sib Families ◽  
Progeny Tests

Abstract Complementary progeny tests allow for simultaneously ranking parents for their general combining ability (GCA) and within-family forward selection. To do this, progeny tests are established with different types of genetic entries (i.e., half-sib and full-sib seedlings, respectively), and different experimental designs. This study proposes a combined analysis of the GCA and full-sib (FS) tests using the mixed model approach to predict simultaneously the breeding values of grandparents, parents, full-sib families and offspring on the same scale. Moreover, a first order autoregressive spatial mixed model for the GCA tests was also implemented in the combined analysis. Our empirical study in coastal Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) shows that additional information provided from relatives and the overlap genetic entry among GCA and FS tests via the proposed combined analysis, improves the accuracies of breeding values compared to the non-combined analysis. The improvements in the accuracies of breeding values for backward and forward selections were generally modest. Spatial and combined analyses gave slightly better results than the non-spatial combined model.

Mathematical methods in the breeding evaluation of small  horned ruminants

2019 ◽  
Vol 14 (3) ◽  
pp. 101-110
Author(s):  
K. A. Katkov ◽  
L. N. Skorykh ◽  
V. S. Pashtetsky ◽  
P. S. Ostapchuk ◽  
T. A. Kuevda
Keyword(s):  
Statistical Methods ◽  
Mixed Model ◽  
Statistical Tests ◽  
Live Weight ◽  
Breeding Value ◽  
System Of Equations ◽  
Data Set ◽  
Breeding Values ◽  
Genetic Groups ◽  
Biometric Model

Aim. Traditionally, prediction of breeding values of male small horned ruminants   (rams) by referring to levels of economically useful traits of their progeny is carried  out by methods of statistical analysis. However, at the same time, there is a forecasting method based on the use of a mixed biometric model. The solution of the system  of equations constituting a mixed biometric model is associated with certain difficulties caused by the peculiarity of the system matrix. It is proposed to use integrated  mathematical packages in the forecast, by which the system of equations can be  solved in several ways, followed by analysis of the results. The prediction of progeny  values is carried out by statistical methods using three statistical tests, as well as with  the use of a mixed biometric model. It is of interest to compare estimates obtained  by using statistical methods with estimates using a mixed biometric model. Material and Methods. The initial data set was the live weight of Qigai rams, the  progeny of a group of sixteen rams belonging to eight genetic groups.   Results. It was found that the forecast of breeding values of each animal using a  mixed biometric model substantially clarifies the rank of each animal in the group  being evaluated.   Conclusion. The refinement of the estimation of breeding value is related to the  effects of the genetic groups to which the animals belong in the mixed model, as well  as the degree of relationship between them. Also the mixed model also allows one to  isolate environmental effects from the overall assessment. Solving the system of  equations in several ways will improve the reliability of the forecast.

Mixed model procedures for the Australian beef industry. 1. Multiple-trait model for estimation of breeding values for 200-day and final weights of cattle

1985 ◽  
Vol 36 (3) ◽  
pp. 527 ◽  
Author(s):  
H-U Graser ◽  
K Hammond
Keyword(s):  
Maternal Effects ◽  
Fixed Effects ◽  
Mixed Model ◽  
Growth Traits ◽  
Breeding Value ◽  
Relationship Matrix ◽  
Beef Industry ◽  
Multiple Trait ◽  
Breeding Values ◽  
Numerator Relationship Matrix

A multiple-trait mixed model is defined for regular use in the Australian beef industry for the estimation of breeding values for continuous traits of sires used non-randomly across a number of herds and/or years. Maternal grandsires, the numerator relationship matrix, appropriate fixed effects, and the capacity to partition direct and maternal effects are incorporated in this parent model. The model was fitted to the National Beef Recording Scheme's data bank for three growth traits of the Australian Simental breed, viz 200-, 365- and 550-day weights. Estimates are obtained for the effects of sex, dam age, grade of dam, age of calf and breed of base dam. The range in estimated breeding value is reported for each trait, with 200-day weight being partitioned into 'calves' and 'daughters' calves', for the Simmental sires commonly used in Australia. Estimates of the fixed effects were large, and dam age, grade of dam and breed of base dam had an important influence on growth to 365 days of age. The faster growth of higher percentage Simmental calves to 200 days continued to 550 days. Estimates of genetic variance for the traits were lower than reported for overseas populations of Simmental cattle, and the genetic covariance between direct and maternal effects for 200-day weight was slightly positive.

Breeding Value Estimation for Pigs in Closed Nucleus Herds

1988 ◽  
Vol 1988 ◽  
pp. 12-12 ◽  
Author(s):  
Naomi R. Wray
Keyword(s):  
Fixed Effects ◽  
Mixed Model ◽  
Phenotypic Selection ◽  
Breeding Value ◽  
Simultaneous Estimation ◽  
Genetic Trend ◽  
Traditional Methods ◽  
Breeding Values ◽  
Value Estimation ◽  
The Uk

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.

Comparison of rolling front and discrete generation breeding strategies for trees

1998 ◽  
Vol 28 (7) ◽  
pp. 987-993 ◽  
Author(s):  
Nuno MG Borralho ◽  
Gregory W Dutkowski
Keyword(s):  
Stochastic Simulation ◽  
Mixed Model ◽  
Accurate Prediction ◽  
Breeding Value ◽  
Breeding Strategies ◽  
Breeding Values ◽  
Generation Interval ◽  
Value Estimation ◽  
Discrete Generation

Discrete generation and rolling front breeding strategies are compared in terms of gain and inbreeding over a period of 40 years using stochastic simulation. In the rolling front strategy, crosses are made between the best available trees in each year, and new progeny trials are established using the crosses done in the previous year, rather than waiting for all crosses in that generation to be completed. For a given amount of resources, the rolling front strategy resulted in 25-35% greater gains per year, mainly due to a shorter generation interval. Inbreeding was also higher in the rolling front, although gains per unit of inbreeding were consistently greater than with the discrete generation strategy. Despite the smaller size of trials and greater imbalance between trials in rolling front, the results suggest that breeding value estimation using mixed-model BLUP is robust enough to ensure accurate prediction of breeding values and maintain the advantage of the rolling front strategy.

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.

Best linear unbiased prediction of clonal breeding values and genetic values from full-sib mating designs

2003 ◽  
Vol 33 (10) ◽  
pp. 2036-2043 ◽  
Author(s):  
Bin Xiang ◽  
Bailian Li
Keyword(s):  
Genetic Gain ◽  
Best Linear Unbiased Prediction ◽  
Breeding Value ◽  
Improvement Program ◽  
Linear Unbiased Prediction ◽  
Breeding Values ◽  
Sib Mating ◽  
Best Linear Unbiased ◽  
Progeny Tests ◽  
Unbiased Prediction

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.

scholarly journals Is it reasonable to use of a kinship matrix for best linear unbiased prediction?

2019 ◽  
Author(s):  
Bongsong Kim
Keyword(s):  
Mixed Model ◽  
Linear Mixed Model ◽  
Best Linear Unbiased Prediction ◽  
Breeding Value ◽  
Linear Unbiased Prediction ◽  
Breeding Values ◽  
Kinship Matrix ◽  
Combining Abilities ◽  
Best Linear Unbiased ◽  
Unbiased Prediction

AbstractThe linear mixed model (LMM) is characterized to account for the variance-covariance among entities in a population toward calculating the best linear unbiased prediction (BLUP). Animal and plant breeders widely use the LMM because it is perceived that the a BLUP estimate informs an estimated breeding value (EBV), so to speak a combining ability as a parent, obtained by relating each entity to his/her relatives using the variance-covariance. The LMM practice routinely substitutes an external kinship matrix for the variance-covariance. The challenge relevant to the LMM practice is the fact that it is unrealistic to validate the EBVs because the real breeding values are not measurable but conceptual. This unreality actually means that the EBVs are vague. Although some previous studies measured correlations between the EBVs and empirical combining abilities, they are not sufficient to remove the vagueness of EBVs because uncontrollable environmental factors might interfere with phenotypic observations for measuring the combining abilities. To overcome the challenge, this study scrutinized the soundness of the routine LMM practice from the mathematical perspective. As a result, it was demonstrated that the BLUP estimates resulting from the routine LMM practice mislead the breeding values. The genuine BLUP represents the arithmetic means of multiple phenotypic observations per each entity, given all phenotypic observations adjusted to the mean of zero.

scholarly journals A Fumonisin Prevention Tool for Targeting and Ranking Agroclimatic Conditions Favoring Exposure in French Maize-Growing Areas

Toxins ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 214
Author(s):  
Agathe Roucou ◽  
Christophe Bergez ◽  
Benoît Méléard ◽  
Béatrice Orlando
Keyword(s):  
Mixed Model ◽  
Linear Mixed Model ◽  
Fusarium Verticillioides ◽  
Agricultural Practices ◽  
Climatic Conditions ◽  
Agricultural Cooperatives ◽  
Mixed Model Approach ◽  
Food And Feed ◽  
Model Approach

The levels of fumonisins (FUMO)—mycotoxins produced by Fusarium verticillioides—in maize for food and feed are subject to European Union regulations. Compliance with the regulations requires the targeting of, among others, the agroclimatic factors influencing fungal contamination and FUMO production. Arvalis-Institut du végétal has created a national, multiyear database for maize, based on field survey data collected since 2003. This database contains information about agricultural practices, climatic conditions and FUMO concentrations at harvest for 738 maize fields distributed throughout French maize-growing regions. A linear mixed model approach highlights the presence of borers and the use of a late variety, high temperatures in July and October, and a water deficit during the maize cycle as creating conditions favoring maize contamination with Fusarium verticillioides. It is thus possible to target a combination of risk factors, consisting of this climatic sequence associated with agricultural practices of interest. The effects of the various possible agroclimatic combinations can be compared, grouped and classified as promoting very low to high FUMO concentrations, possibly exceeding the regulatory threshold. These findings should facilitate the creation of a national, informative and easy-to-use prevention tool for producers and agricultural cooperatives to manage the sanitary quality of their harvest.

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