Bayesian estimates of genetic parameters and genetic trends for morphometric traits and their relationship with yearling weight in Moghani sheep

Hossein-Zadeh, Navid Ghavi; Ghahremani, Darioush

doi:10.1080/1828051x.2017.1403296

Cited by 10 publications

(8 citation statements)

References 25 publications

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“…Estimates of genetic correlations between BW and CG, BL and HW are high, suggesting these indicator traits could be used to indirectly select for better growth. As it has been reported elsewhere [22,43], the most important correlation was found between BW and CG (0.79). This may be because GC and BW are both tissue-related measurements while BL and HW are skeletal-related measurements [44].…”

Section: Discussionsupporting

confidence: 80%

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Genetic Parameters for Growth and Kid Survival of Indigenous Goat under Smallholding System of Burundi

Manirakiza

Hatungumukama

Detilleux

2020

Animals

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The goal of this study was to estimate genetic parameters for the growth, conformation, and survival of goat kids raised in smallholder farming systems in Burundi. To do this, measurements were taken on live weight, thoracic perimeter, length, and height at birth (n = 1538 animals), at 3 months (n = 1270 animals), at 6 months (n = 992 animals), at 9 months (n = 787 animals), and at 12 months (n = 705 animals). Kids were born between 2016 and 2019, from 645 dams and 106 bucks. Three bivariate animal models were used to estimate genetic parameters of body weight and conformation measurements as potential indicators of this weight. According to the measure, heritability was estimated between 15 and 17% and genetic correlations between 65 and 79%. An accelerated failure time animal model was used to estimate the heritability of survival for kids under one year, adjusted for birth weight. Goat survival was significantly prolonged by 0.64 days per kilogram of birth weight. The estimated heritability for this trait was 2%. Overall, these results suggest that a selection program could be implemented to improve animal growth, either directly on weight or indirectly on conformational traits. At the same time, efforts need to be made to improve rearing conditions to increase the survival of kids.

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Section: Discussionsupporting

confidence: 80%

“…Output of the iterations were used to calculate the mean, median, and highest posterior density (HPD) interval for each variance/covariance components [18,21,22]. Phenotypic variances (σ 2 ph ) of body weights and linear measurements were computed as: σ 2 ph = σ 2 a + σ 2 m + σ 2 p + σ 2 c + σ 2 e .…”

Section: Bayesian Estimationmentioning

confidence: 99%

Genetic Parameters for Growth and Kid Survival of Indigenous Goat under Smallholding System of Burundi

Manirakiza

Hatungumukama

Detilleux

2020

Animals

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“…In this same study, the authors found genetic correlations between body height and chest circumference ranging from 0.39 to 0.68. Estimates of genetic correlation between RH and chest circumference of 0.46 and 0.38±0.22 were obtained, respectively, by Jafari and Hashemi [ 26 ], in Makuie sheep, and by Hossein-Zadeh and Ghahremani [ 27 ], in Moghani sheep.…”

Section: Resultsmentioning

confidence: 99%

Genetic evaluation of sheep for resistance to gastrointestinal nematodes and body size including genomic information

et al. 2021

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Objective: The genetic evaluation of Santa Inês sheep was performed for resistance to gastrointestinal nematode infection (RGNI) and body size using different relationship matrices to assess the efficiency of including genomic information in the analyses.Methods: There were 1,637 animals in the pedigree and 500, 980, and 980 records of RGNI, thoracic depth (TD), and rump height (RH), respectively. The genomic data consisted of 42,748 SNPs and 388 samples genotyped with the OvineSNP50 BeadChip. The (co)variance components were estimated in single- and multi-trait analyses using the numerator relationship matrix (A) and the hybrid matrix H, which blends A with the genomic relationship matrix (G). The BLUP and single-step genomic BLUP methods were used. The accuracies of estimated breeding values and Spearman rank correlation were also used to assess the feasibility of incorporating genomic information in the analyses.Results: The heritability estimates ranged from 0.11±0.07, for TD (in single-trait analysis using the A matrix), to 0.38±0.08, for RH (using the H matrix in multi-trait analysis). The estimates of genetic correlation ranged from –0.65±0.31 to 0.59±0.19, using A, and from –0.42±0.30 to 0.57±0.16 using H. The gains in accuracy of estimated breeding values ranged from 2.22% to 75.00% with the inclusion of genomic information in the analyses.Conclusion: The inclusion of genomic information will benefit the direct selection for the traits in this study, especially RGNI and TD. More information is necessary to improve the understanding on the genetic relationship between resistance to nematode infection and body size in Santa Inês sheep. The genetic evaluation for the evaluated traits was more efficient when genomic information was included in the analyses.

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“…This is the first research report to evaluate different non-linear models for representing WH and BW:WH growth curves in different breeds of dairy heifer. Measurements of morphometric parameters are highly beneficial to breeders for identifying appropriate animals at an earlier stage of growth for selection purposes (Ghavi Hossein-Zadeh and Ghahremani, 2018). Regarding variation in WH during the first 24 months of age, Kertz et al .…”

Section: Discussionmentioning

confidence: 99%

Modelling growth in dairy heifers based on linear body measurements (withers height) using non-linear functions

Kuhi

Hossein-Zadeh

France

et al. 2022

Journal of Dairy Research

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This research communication reports a study to model the growth curves for withers height (WH) and body weight (BW) to withers height ratio (BW:WH) using monthly records (from 1 to 24 months of age) for three breeds of dairy heifer (Holstein, Jersey and Brown Swiss). The data sets used were those reported by the Dairy Heifer Evaluation Project of Penn State Extension (USA) between 1991 and 1992. Four growth functions (monomolecular and Michaelis-Menten, both with diminishing returns behaviour, and Schumacher and López, both with asymptotic sigmoidal behaviour) were fitted using the non-linear regression procedure of the SigmaPlot software and the parameters estimated. The models were judged for goodness of fit using adjusted coefficient of determination ( $R_{{\rm adj}}^2 $ ), root mean square error (RMSE), Akaike's information criterion (AIC) and Bayesian information criterion (BIC). Assessing the goodness of fit by $R_{{\rm adj}}^2 $ (>0.99 in all cases) reveals the generally appropriate fit of the models to the data. The non-sigmoidal functions (i.e. Michaelis–Menten and monomolecular) provided the best fits giving the lowest values of RMSE, AIC and BIC. Based on the chosen statistical criteria, the Schumacher and López equations provided acceptable fits to the WH and BW:WH growth curves, but showed points of inflexion at times before birth, indicating that these growth curves are not sigmoidal. In conclusion, evaluation of the different non-linear growth functions used in this study indicated their potential for modelling growth patterns in dairy heifers.

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Bayesian estimates of genetic parameters and genetic trends for morphometric traits and their relationship with yearling weight in Moghani sheep

Cited by 10 publications

References 25 publications

Genetic Parameters for Growth and Kid Survival of Indigenous Goat under Smallholding System of Burundi

Genetic Parameters for Growth and Kid Survival of Indigenous Goat under Smallholding System of Burundi

Genetic evaluation of sheep for resistance to gastrointestinal nematodes and body size including genomic information

Modelling growth in dairy heifers based on linear body measurements (withers height) using non-linear functions

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