Impact of pedigree depth and inclusion of historical data in the estimation of variance components and breeding values in Macrobrachium rosenbergii

Abstract. Title of the paper: The importance of variance components estimation in breeding of farm animals – a review The present paper showed the importance of variance components estimation in animal breeding. Beside the use of variance components for estimation of breeding values, the components have a high importance on further breeding aspects, such as indication of selection limits, optimisation of test period, change of Performance during growth, and determination of the best selection traits. Maternal and non-additive genetic variance components can be estimated and their high influence on choice of the optimal selection strategy are explained. Standard errors of crossbreeding parameters are influenced by genetic relationships and are only unbiased when using all genetic variances and covariances among animals. Genotype-environmental-interaction and heterogeneous variances, which result in high reduction in selection response, can be obtained in a variance components estimation. The high value of Bayesian methods in order to describe the sampling variance of variance components and to account for the Standard error of estimation of variance components in the estimation of breeding values is explained.

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Estimation of variance components and prediction of breeding values for scrotal circumference and weaning weight in Limousin cattle.

Journal of Animal Science ◽

10.2527/1996.74131x ◽

1996 ◽

Vol 74 (1) ◽

pp. 31 ◽

Cited By ~ 15

Author(s):

L L Keeton ◽

R D Green ◽

B L Golden ◽

K J Anderson

Keyword(s):

Variance Components ◽

Weaning Weight ◽

Breeding Values ◽

Scrotal Circumference ◽

Estimation Of Variance

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Estimation of variance components and prediction of breeding values based on group records from varying group sizes

Genetics Selection Evolution ◽

10.1186/s12711-018-0413-y ◽

2018 ◽

Vol 50 (1) ◽

Cited By ~ 6

Author(s):

Guosheng Su ◽

Per Madsen ◽

Bjarne Nielsen ◽

Tage Ostersen ◽

Mahmoud Shirali ◽

...

Keyword(s):

Variance Components ◽

Breeding Values ◽

Estimation Of Variance

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Additive genetic variance and covariance between relatives in synthetic wheat crosses with variable parental ploidy levels

Genetics ◽

10.1093/genetics/iyaa048 ◽

2021 ◽

Vol 217 (2) ◽

Author(s):

L E Puhl ◽

J Crossa ◽

S Munilla ◽

P Pérez-Rodríguez ◽

R J C Cantet

Keyword(s):

Hexaploid Wheat ◽

Variance Components ◽

Bayesian Model ◽

Synthetic Hexaploid Wheat ◽

Data Sets ◽

Hierarchical Bayesian Model ◽

Additive Variance ◽

Ploidy Levels ◽

Breeding Values ◽

Synthetic Hexaploid

Abstract Cultivated bread wheat (Triticum aestivum L.) is an allohexaploid species resulting from the natural hybridization and chromosome doubling of allotetraploid durum wheat (T. turgidum) and a diploid goatgrass Aegilops tauschii Coss (Ae. tauschii). Synthetic hexaploid wheat (SHW) was developed through the interspecific hybridization of Ae. tauschii and T. turgidum, and then crossed to T. aestivum to produce synthetic hexaploid wheat derivatives (SHWDs). Owing to this founding variability, one may infer that the genetic variances of native wild populations vs improved wheat may vary due to their differential origin and evolutionary history. In this study, we partitioned the additive variance of SHW and SHWD with respect to their breed origin by fitting a hierarchical Bayesian model with heterogeneous covariance structure for breeding values to estimate variance components for each breed category, and segregation variance. Two data sets were used to test the proposed hierarchical Bayesian model, one from a multi-year multi-location field trial of SHWD and the other comprising the two species of SHW. For the SHWD, the Bayesian estimates of additive variances of grain yield from each breed category were similar for T. turgidum and Ae. tauschii, but smaller for T. aestivum. Segregation variances between Ae. tauschii—T. aestivum and T. turgidum—T. aestivum populations explained a sizable proportion of the phenotypic variance. Bayesian additive variance components and the Best Linear Unbiased Predictors (BLUPs) estimated by two well-known software programs were similar for multi-breed origin and for the sum of the breeding values by origin for both data sets. Our results support the suitability of models with heterogeneous additive genetic variances to predict breeding values in wheat crosses with variable ploidy levels.

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Estimation of variance components in normal linear mixed models with additivity

10.1063/1.4912414 ◽

2015 ◽

Cited By ~ 1

Author(s):

Dário Ferreira ◽

Sandra S. Ferreira ◽

Célia Nunes ◽

João T. Mexia

Keyword(s):

Mixed Models ◽

Variance Components ◽

Linear Mixed Models ◽

Estimation Of Variance

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A note on genetic parameters and accuracy of estimated breeding values in honey bees

Genetics Selection Evolution ◽

10.1186/s12711-019-0510-6 ◽

2019 ◽

Vol 51 (1) ◽

Cited By ~ 6

Author(s):

Evert W. Brascamp ◽

Piter Bijma

Keyword(s):

Honey Bees ◽

Variance Components ◽

Genetic Parameters ◽

Additive Genetic Variance ◽

Breeding Value ◽

Phenotypic Variance ◽

Breeding Values ◽

Estimated Breeding Values ◽

Additive Genetic Relationship ◽

The Relationship

Abstract Background In honey bees, observations are usually made on colonies. The phenotype of a colony is affected by the average breeding value for the worker effect of the thousands of workers in the colony (the worker group) and by the breeding value for the queen effect of the queen of the colony. Because the worker group consists of multiple individuals, interpretation of the variance components and heritabilities of phenotypes observed on the colony and of the accuracy of selection is not straightforward. The additive genetic variance among worker groups depends on the additive genetic relationship between the drone-producing queens (DPQ) that produce the drones that mate with the queen. Results Here, we clarify how the relatedness between DPQ affects phenotypic variance, heritability and accuracy of the estimated breeding values of replacement queens. Second, we use simulation to investigate the effect of assumptions about the relatedness between DPQ in the base population on estimates of genetic parameters. Relatedness between DPQ in the base generation may differ considerably between populations because of their history. Conclusions Our results show that estimates of (co)variance components and derived genetic parameters were seriously biased (25% too high or too low) when assumptions on the relationship between DPQ in the statistical analysis did not agree with reality.

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