Year: 2022 | Month: October | Volume 12 | Issue 5

Comparison of Reliability of Animal Models and Bayesian Approach for Estimation of Genetic Parameters of Growth Traits in Chokla Sheep

Garima Choudhary Urmila Pannu H.K. Narula Ashish Chopra G. Gowane Manju Nehara N.K. Poonia
DOI:10.30954/2277-940X.05.2022.18

Abstract:

Data on 6785 Chokla sheep maintained at Central Sheep and Wool Research Institute, Bikaner, Rajasthan, India and recorded between 1974 and 2020, were collected for present study. Six different animal models used by wombat and BLUPF90 software (Bayesian method) were used to determine (co)variance components and genetic parameters of growth traits with various combinations of direct and maternal effects. Body weight at different age as birth (BW), 3(WW), 6 (6W), 9 (9W), 12 months of age (YW) and Average daily gain and kleiber ratio at different age interval as 0-3 (ADG1/KR1), 3-6 (ADG2/KR2) and 6-12 months (ADG3/KR3) were used to study. The direct heritability estimates increased from birth to twelve months of age. Direct heritability estimates (from the best model as per AIC) for various growth traits ranged from 0.2 to 0.5 except for birth weight (0.17) by animal models. Highly inflated values of additive heritability were obtained in both methods due to negative and high estimate of correlation between additive and maternal effect. In this condition, it is more useful to use the total heritability (h2t) for evaluation of the response for selection based on phenotypic values to prevent the use of biased estimates of additive heritability. The maternal genetic effect (m2) was found to be highest at birth weight by BLUPF90 and at weaning stage by animal model. Bayesian approach was found best as compared to WOMBAT for all traits to study (co)variance components and genetic parameters.

Highlights

  • Compared reliability of animal models and bayesian approach for estimation of genetic parameters.
  • Bayesian approach was found best as compared to WOMBAT for all traits to study (co)variance components and genetic parameters.


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