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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. [ABSTRACT FROM AUTHOR]

Exploration of genetic variance has mostly been limited to additive effects estimated using pedigree data and non-additive effects have been ignored. This study aimed to evaluate the performance of single nucleotide polymorphisms (SNPs) marker models in the mixed and orthogonal framework including both additive and nonadditive effects for estimating variances and genomic prediction in four diabetes-related traits in heterogeneous stock mice. Models have performed differently in detecting SNPs affecting traits. Dominance variances explained over 14.7 and 3.8% of genetic and phenotype variance in a Genomic prediction and variance component estimation method (GVCBLUP) framework. Reliabilities of additive Genomic best linear unbiased prediction model (GBLUP) in different traits ranged from 44.8 to 66.6%, for GVCBLUPs framework including both additive and dominance effects (MAD), and 46.1 to 69% for the model including additive effect (MA). Dominance GBLUP reliabilities ranged from 6 to 26.4% for MAD and from 22.5 to 50.5% in the model including dominance (MD). MA and MD had higher reliability for additive and dominance GBLUPs compared to MAD. Reliabilities of GBLUPs in MAD and MA for all traits were not significant except for growth slope (P < 0.01). In orthogonal framework models, epistasis variances accounted for a greater proportion (87.3, 89.1, 95.5, and 77.2%) of genetic variation for end weight, growth slope, body mass index, and body length, respectively. Heritability in a broad sense was estimated at 1.12, 1.67, 3.64, and 2.0%, in which non-additive heritability had a significant contribution. Genetic variances explained by dominance using GVCBLUPs were 16.8, 29.4, 14.6, and 14.9% for the traits. Generally, the non-additive models had a lower value of deviance information criterion (DIC) and performed better in estimating the variance component. Comparing the estimated variance by orthogonal framework models confirmed the results previously estimated by GVCBLUPs, with the difference that the estimates were shrinking. Following significant SNPs affecting diabetes-related traits by post-genome-wide studies could reveal unknown aspects and contribute to genetic control of the disease. [ABSTRACT FROM AUTHOR]

The developmental potential of in vitro matured porcine oocytes is still lower than that of oocytes matured and fertilized in vivo. Major problems that account for the lower efficiency of in vitro production include the improper nuclear and cytoplasmic maturation of oocytes. With the aim of improving this issue, the single and combined effects of epidermal growth factor (EGF) and glial cell line-derived neurotrophic factor (GDNF) on oocyte developmental competence were investigated. Porcine cumulus-oocyte cell complexes (COCs) were matured in serum-free medium supplemented with EGF (0, 10 or 50 ng/ml) and/or GDNF (0, 10 or 50 ng/ml) for 44 h, and subsequently subjected to fertilization and cultured for 7 days in vitro. The in vitro-formed blastocysts derived from selected growth factor groups (i.e. EGF = 50 ng/ml; GDNF = 50 ng/ml; EGF = 50 ng/ml + GDNF = 50 ng/ml) were also used for mRNA expression analysis, or were subjected to Hoechst staining. The results showed that the addition of EGF and/or GDNF during oocyte maturation dose dependently enhanced oocyte developmental competence. Compared with the embryos obtained from control or single growth factor-treated oocytes, treatment with the combination of EGF and GDNF was shown to significantly improve oocyte competence in terms of blastocyst formation, blastocyst cell number and blastocyst hatching rate (P < 0.05), and also simultaneously induced the expression of BCL-xL and TERT and suppressed the expression of caspase-3 in resulting blastocysts (P < 0.05). These results suggest that both GDNF and EGF may play an important role in the regulation of porcine in vitro oocyte maturation and the combination of these growth factors could promote oocyte competency and blastocyst quality.