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

Prediction of carcass traits in fattening Chios and Serres lambs using real-time ultrasonography and live body weight measurements pre-slaughter

The objective of this study was to assess the capability of predicting carcass traits and meat cuts weights, in fattening lambs of indigenous Greek dairy sheep breeds, using ultrasound measurements and live body weight measurements pre-slaughter. A total of 187 lambs of Chios and Serres breeds were involved in the study. Body condition score, live body weight (LBW), and ultrasound measurements of Longissimus lumborum muscle depth (LMD) and subcutaneous fat thickness (SFT) at the lumbar region were recorded pre-slaughter. After slaughter, the carcasses were classified using five-degree grading systems for muscle development and fat deposition, while hot (HCW) and cold carcass (CCW) and meat cuts weights were measured. The statistical analyses included descriptive statistics and linear regression models to estimate the fixed effects of sex and the covariances of LBW, BCS, and ultrasound measurements on the studied traits. High R2 values (0.60 ≤ R2 ≤ 0.92) were observed in the models predicting HCW, CCW, forequarter, leg chump on shank off, the short loin, the eye of the short loin, and foreshank weights. Among the models estimated LMD, SFT, and LBW as significant predictors, the ones predicting hot and cold carcass weights, the short loin, the eye of the short loin, and the eye of the rack weights were successfully validated. Other models including BCS, LBW, sex, and either one or none of the ultrasonography measurements as predictors were also validated and presented.

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

In sheep meat production, the rib eye area is an important index to evaluate carcass traits. However, conventional breeding programs have led to slow genetic progression in rib eye muscle area. Operationalizing molecular marker assisted breeding is an optimized breeding method that might improve this situation. Therefore, the present study used whole genome sequencing data to excavate candidate genes associated with the rib eye muscle. Male Hu lambs (n = 776) with pedigrees and 274 lambs with no pedigree were included. The genetic parameters of the rib eye area were estimated using a mixed linear mixed model. The rib eye area showed medium heritability (0.32 ± 0.13). Whole-genome sequencing of 40 large rib eye sheep [17.97 ± 1.14, (cm²)] and 40 small rib eye sheep [7.89 ± 0.79, (cm²)] was performed. Case-control genome-wide association studies and the fixation index identified candidate rib eye-associated genes. Seven single nucleotide polymorphisms (SNPs) in six genes (ALS2, ST6GAL2, LOC105611989, PLXNA4, DPP6, and COL12A1) were identified as candidates. The study population was expanded to 1050 lambs to perform KASPar genotyping on five SNPs, which demonstrated that SNPs in LOC105611989, DPP6, and COL12A1 correlated significantly with the rib eye area, which could be used as genetic markers for molecular breeding of the rib eye area. The results provided genetic parameters estimated on the rib eye area and information for breeding based on carcass traits in Hu sheep.

Estimation of additive and non-additive genetic variance component for growth traits in Adani goats

Non-additive genetic effects are important to increase the accuracy of estimating genetic parameters for growth traits. The aim of this study was to estimate genetic parameters and variance components, specially dominance and epistasis genetic effects, for growth traits (birth weight (BW), weaning weight (WW), 3 (W3), 6 (W6), 9 (W9), and 12 (W12) month weight) in Adani goats. Analyses were carried out using Bayesian method via Gibbs sampler animal model by fitting of 18 different models. All fixed effects (sex, type of birth, age of dam, and year) showed significant effects on BW, WW, W3, and W6, whereas the type of birth and age of dam were not significant on W9 and W12. With the best model, direct heritability estimates were 0.347, 0.178, 0.158, 0.359, 0.278, and 0.281 for BW, WW, W3, W6, W9, and W12 traits, respectively. Maternal permanent environmental effect was significant for BW and WW, but maternal genetic effect was significant only for W3. Dominance and epitasis effects were significant almost for all traits and as a proportion of phenotypic variance were ranged from 0.115 to 0.258 and 0.107 to 0.218, respectively. The range of accuracy of breeding values estimated for growth traits with appropriate evaluation models was from 0.521 to 0.652, 0.616 to 0.694, and 0.548 to 0.684 for the all animals, 10% of the best males and 50% of the best females, respectively. When dominance and epistasis effects added to models, the error variance was reduced and the accuracy of estimated breeding values increased. The accuracy of the best model showed a significant difference with the accuracy of other models (p < 0.01). The result of the present study suggests that non-additive genetic effects should be in genetic evaluation models for goat growth traits because of its effect on accuracy of estimated breeding values.