Generalized linear mixed models for the genetic evaluation of binary reproductive traits: a simulation study

Garcia, Diogo Anastácio; Pereira, Idalmo Garcia; Silva, Fabyano Fonseca e; Rosa, Guilherme Jordão de Magalhães; Pires, Aldrin Vieira; Leandro, Roseli Aparecida

doi:10.1590/s1516-35982012000100008

Cited by 2 publications

(2 citation statements)

References 20 publications

(23 reference statements)

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“…This implies a correction of the residual variance by factor  2 /3 (Southey et al 2003). For the logit function, the residual variance is an approximation based on the variance of the logistic regression and is described as: W here, b = Standard deviation of the logistic distribution which is generally assumed to be 1 (Southey et al, 2003;Garcia et al, 2012) as in the present study. Thus following expression can be used to calculate the heritability (h 2 ): W here, = Sire variance.…”

Section: Estimation Of Variance Componentand Prediction Of Breeding V...mentioning

confidence: 97%

Genetic Evaluation of Gir Cattle for Success Rate to Conception using GLIMMIX Model

Kumar¹,

Sharma²,

Saini³

et al. 2023

AJDFR

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Background: The objective of this study was to assess the genetic and phenotypic parameters associated with the success rate to conception using 90-days non-return rate (NRR90F), conception Rate (CR) and number of insemination to conception (INS) traits in service bull. Methods: A total of 2590 insemination including 1776 first time insemination was carried out during the period of 2018-2020 in the Gir cattle distributed in the semi-arid region of RajasthanNRR90F and CR were analysed as binary variable and INS as multi-nominal trait. Generalized linear models were used to for the evaluation of non-genetic factors and generalized linear mixed models for genetic effects. Result: The average success rate of artificial insemination was estimated as 0.35 for NRR90F and 0.41 for CR. Further, cumulative logit link function was used to analyse the number of successful insemination to conception (INS) and cumulative estimates was observed as 0.95 for two number of insemination to conception. Period and season were the non-genetic factors that influenced the traits studied. Decreasing trend was observed over the period for success of insemination for all studied traits. Summer season showed highest odd ratio and estimated as 1.213 for NRR90F, 1.388 for CR and 1.440 for INS with reference to autumn season. Heritability was estimated as 0.015 for NRR90F, 0.028 for CR and 0.086 for INS.

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Section: Estimation Of Variance Componentand Prediction Of Breeding V...mentioning

confidence: 97%

Genetic Evaluation of Gir Cattle for Success Rate to Conception using GLIMMIX Model

Kumar¹,

Sharma²,

Saini³

et al. 2023

AJDFR

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“…Using such variables in breeding programs has required the development of adequate statistical methods for the estimation of parameters and the prediction of breeding values because these variables do not follow a normal distribution (Garcia et al 2012). A first possibility consists in transforming the nonGaussian data and using the LMM.…”

Section: Impact Of Variable Transformation On Variance Component Hermentioning

confidence: 99%

Assessing the additive and dominance genetic effects of vegetative propagation ability in Eucalyptus—influence of modeling on genetic gain

Makouanzi¹,

Bouvet

Denis

et al. 2014

Tree Genetics & Genomes

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To study the genetic determinism of propagation by cutting, 2,115 individuals of 83 full-sib families of the Eucalyptus urophylla×Eucalyptus grandis hybrid were used as stock plants and propagated by cuttings. Shoot production (PROD) and cutting success (CUT) were measured in two periods corresponding to the dry and rainy seasons. The experiments showed a significant effect of propagation period, suggesting the combined influence of environmental conditions and physiological state of stock plants. Using the linear mixed model (LMM) and the generalized one (GLMM) to take into account the non-normal distribution, the additive and dominance variances were estimated. They were significantly different from zero for PROD and CUT, as was the interaction between genetic effects and periods. The dominance variance was equal or higher than additive variance for both traits (1 < σ 2 D /σ 2 A < 1.5). Broad-and narrow-sense heritabilities change with the model type. For PROD, with LMM, they were moderate (h 2 ss =0.182 and H 2 sl =0.443) but high with GLMM (h 2 ss =0.431 and H 2 sl =0.891). For CUT, the same trend was observed for variances but the genetic control was weaker with heritabilities smaller than 0.3. The selection accuracy (r) was affected by the statistical model, r=0.94 and r=0.42 for PROD using LMM and GLMM, respectively. Genetic correlations between PROD, CUT, and the field growth of clones at 25 months were relatively low. These results are important elements to consider for breeding strategies that target genetic gain for both field growth and cutting success.

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Generalized linear mixed models for the genetic evaluation of binary reproductive traits: a simulation study

Cited by 2 publications

References 20 publications

Genetic Evaluation of Gir Cattle for Success Rate to Conception using GLIMMIX Model

Genetic Evaluation of Gir Cattle for Success Rate to Conception using GLIMMIX Model

Assessing the additive and dominance genetic effects of vegetative propagation ability in Eucalyptus—influence of modeling on genetic gain

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