Bayesian inference of genetic parameters for ultrasound scanning traits of Kivircik lambs

Abstract

Ultrasound scanning traits have been adapted in selection programs in many countries to improve carcass traits for lean meat production. As the genetic parameters of the traits interested are important for breeding programs, the estimation of these parameters was aimed at the present investigation. The estimated parameters were direct and maternal heritability as well as genetic correlations between the studied traits. The traits were backfat thickness (BFT), skin + backfat thickness (SBFT), eye muscle depth (MD) and live weights at the day of scanning (LW). The breed investigated was Kivircik, which has a high quality of meat. Six different multi-trait animal models were fitted to determine the most suitable model for the data using Bayesian approach. Based on deviance information criterion, a model that includes direct additive genetic effects, maternal additive genetic effects, direct maternal genetic covariance and maternal permanent environmental effects revealed to be the most appropriate for the data, and therefore, inferences were built on the results of that model. The direct heritability estimates for BFT, SBFT, MD and LW were 0.26, 0.26, 0.23 and 0.09, whereas the maternal heritability estimates were 0.27, 0.27, 0.24 and 0.20, respectively. Negative genetic correlations were obtained between direct and maternal effects for BFT, SBFT and MD. Both direct and maternal genetic correlations between traits were favorable, whereas BFT-MD and SBFT-MD had negligible direct genetic correlation. The highest direct and maternal genetic correlations were between BFT and SBFT (0.39) and between MD and LW (0.48), respectively. Our results, in general, indicated that maternal effects should be accounted for in estimation of genetic parameters of ultrasound scanning traits in Kivircik lambs, and SBFT can be used as a selection criterion to improve BFT.

Whole Genome Sequencing Analysis to Identify Candidate Genes Associated With the rib eye Muscle Area in Hu Sheep

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Genetic diversity and population structure of Anatolian Hair goats, an ancient breed

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Bayesian Analysis of Agricultural Experiments Using PROC MCM

“…Data collected from agricultural experiments have been analyzed using different statistical methods, such as analysis of variance, maximum likelihood, REML and Bayesian methods. Despite a great number of literatures on the design and analysis of agricultural field experiments, dating back to the early 1900s, and a large uptake of Bayesian methods in many other scientific fields in the last 30 years (Firat, et al, 1997a;Firat, et al, 1997b;Firat, 2001;Karaman, et al, 2014;Cemal, et al, 2016;Firat et al, 2016), Bayesian analysis for agricultural field experiments has not received very much attention. Statistical methods for the design and analysis of agricultural field experiments were essentially developed by R. A. Fisher, F. Yates and many others.…”