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The number of eggs produced in a given period (egg production rate) is an important trait in layers that change over time and can be presented as a curve. This study aimed to fit the weekly egg production data of Khazak indigenous hens using non-linear regression models and to select an appropriate model for describing the egg production curve for this bird. Biweekly egg production of 144 laying hens over 52 weeks of egg production was used to evaluate the egg production curve. Seven non-linear models (Gamma, McNally, Compartmental II, Nelder, Yang, Lokhorst, and Narushin-Takma) were fitted to egg production data. The four goodness fit criteria (Akaike' s information criterion, Mean square error, Log Likelihood, and Bayesian information criterion) were used to compare the models. The results of the goodness of fit criteria showed that the Narushin-Takma and Yang models were the best and worst models, respectively, for describing the egg production curve of Khazak hens. The time and egg production at the peak with the Narushin-Takma model was similar to the actual values, and this model was significantly better than other studied models. The correlation between actual and predicted egg production indicated that the Narushin-Takma model could accurately predict the egg production of this breed. As a result, the Narushin-Takma model can be used to predict the egg production curve of Khazak hens in breeding programs and nutritional management. [ABSTRACT FROM AUTHOR]

Lysozyme activity is one of the nonspecific immunity parameters measured by changing the amount of adsorption at different times. The objective of the present study was to compare five non-linear models including Gompertz, Richards, Logistic, Lopez, and Weilbull to describe the cumulative plasma lysozyme activity in quails. In total 1364 plasma samples (1004 females and 360 males) were collected and the cumulative lysozyme activity was calculated by turbidimetric method assay in Micrococcus luteus. The goodness-of-fit of models was compared according to different criteria of Maximum loglikelihood, Akaike information criterion, Mean square error, and Bayesian information criterion. The results showed that the Gompertz model was the best model for describing of decreasing cumulative pattern of lysozyme activity in female and male quails and provided satisfactory predictions of lysozyme activity at different times (30, 60, 90, 120, 150, 180, 210, 240, 270, 300 seconds). The parameters of all models were higher in females than males except for the k parameter which was greater in the males. Male quails had higher values for time and lysozyme activity than females at inflection points, whereas the absolute growth rate in 30, 150, and 300 seconds was predicted higher in female quails. In conclusion, the Gompertz model can be used accurately to evaluate cumulative lysozyme activity patterns in both sexes of quails. [ABSTRACT FROM AUTHOR]

BACKGROUND: Animal breeds are often recognized through their specific characteristics, nevertheless, the breeds' anatomical and biochemical characteristics have not considered yet in many of the researches done on one-humped camels. There are some particular differences in camel breeds due to different climate conditions and rearing necessity OBJECTIVES: The aim of the current study was to compare differences in anatomical and plasma biochemical parameters of two Iranian one-humped camels, the Jammaz (racing) and Balouchi (dual-purpose) breeds. METHODS: Anatomical parameters were body length (BL), neck length (NL), chest girth (CG), barrel girth (BG), hump circumference (HC), fore limb length (FLL), hind limb length (HLL), height at hump (HH), fore limbs interval (FLI), hind limbs interval (HLI), fore hoof circumference (FHC), hind hoof circumference (HHC), abdominal circumference (AC), shank circumference (SC) and leg circumference (LC). Moreover, concentrations of Glucose, blood urea nitrogen (BUN), creatinine, cholesterol, total bilirubin (Bili-T), direct bilirubin (Bili- D), calcium (Ca), phosphorus (P), magnesium (Mg), aspartate aminotransferase (AST), creatine kinase (CK), lactate dehydrogenase (LDH), total protein (TP), albumin (Alb), triiodothyronine (T3) and thyroxine (T4) were measured as plasma biochemical parameters. Data analysis was conducted through t-test statistics. RESULTS: Results revealed significant differences between body type traits of breeds, especially in organs related to running ability, while the Jammaz breed has more height and longer limbs than the Balouchi breed. Some blood parameters (glucose, LDH, BUN, cortisol and T4) are considerably higher in Jammaz breed, too. CONCLUSIONS: Results of the current study suggest some anatomical (especially fore and hind limb lengths, neck length and body height at hump) and physiological characteristics (lactate dehydrogenase, blood urea nitrogen, cortisol, glucose and T4) of Iranian racing and dual purpose camels as breed markers. [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 present study was carried out to estimate the genetic and phenotypic parameters for growth traits in Ghezel sheep, using pedigree information and body weight records which collected from 2000 to 2009 at Ghezel Sheep Breeding Station. Traits of interest were birth weight (BW, n=2,073), weaning weight (WW, n=1,663) and six-month old weight (6MW, n=1,315). The environmental factors were studied using GLM procedure in SAS software. (Co) variance components estimated based on multivariate animal model by REML method, using DMU-package. All traits were significantly influenced by birth year, lamb's sex, type of birth and dam age (p<0.01). Heritability estimates for BW, WW and 6MW were 0.16±0.05, 0.24±0.06 and 0.35±0.07, respectively. All genetic correlations between traits were estimated positive and ranged from 0.57 (BW-6MW) to 0.97 (WW-6MW). The phenotypic correlations among all traits were also positive. The results of this study indicate that because of high genetic correlation between traits, selection for each of these traits may result in improvement in other traits as well. [ABSTRACT FROM AUTHOR]