نشریه پژوهش های علوم دامی
سال سی و یکم شماره 4 (زمستان 1400)

Stresses such as climatic, environmental, nutritional, social and psychological stresses have negative effect on animal welfare, performance and immunological parameters. Following the increase in consumption of poultry products and the widespread production of these products, stressors can have a negative impact on the production and health of chickens, thereby affecting human nutrition. Heat stress is one of the most challenging environmental conditions affecting commercial poultry. Compared with other species of domestic animals, broiler chickens are more sensitive to high ambient temperatures. High temperature imposes severe stress on birds and leads to important economic losses in the poultry industry. Heat stress is one of the most serious problems for poultry industry, and is results in poor welfare, low performance, inferior meat quality, and high mortality. High ambient temperature is a major problem in many parts of the world such as Iran, especially during summer. Exposure to high temperature has been reported to cause undesirable changes in growth rate, meat yield and breast protein content of broilers. In ovo administration of L-Leucine on embryonic day (ED) 7 has caused the hypothermia at hatching, and thermotolerance in young broiler chicks under heat stress. Branched-chain amino acids, especially leucine, potentially activate the mTOR signaling pathway and activate protein synthesis by initiating translation. As an essential amino acid, leucine has certain biological properties, such as providing energy, regulating protein, carbohydrate and lipid metabolism, adjusting immune function and mRNA translational origination (by activating the mTOR signaling pathway). Leucine inhibits the expression of lipogenic enzymes (fatty acid synthetase and acetyl-CoA carboxylase) in adipose cells and enhances muscle fatty acid oxidation (Sun and Zemel 2007). Therefore, considering the role of leucine in activating the mTOR signaling pathway and the metabolism of protein and fat, it is expected that leucine will improve the performance and quality of meat in broiler chickens under heat stress.

Two hundred, one-day-old male broiler chicks (Ross 308) were obtained from a commercial hatchery. The birds were initially weighed, so that the pens had similar initial weight distribution and were randomly assigned to five experimental treatments, with five replicates of 8 chicks each. Experimental diets were the control diet (corn-soybeanbased diet), control diet + 0.1% leucine, control diet + 0.2% leucine, control diet + 0.3% leucine and control diet + 0.4% leucine. lighting program was used according to the guidelines of the Ross 308. Environmental temperature was maintained at 32 ± 1°C the whole period experiment. The diet was based on corn and soybean meal and formulated according Ross (308) requirements for three period of starter (1-10 d), growth (11-24 d) and finisher (25-42 d) days. Body weight gain and feed intake was measured periodically and calculated during the whole experiment on a pen basis, and the feed conversion ratio was calculated subsequently. At day 42 of age, two birds per pen were randomly selected, weighed, and slaughtered. After slaughter, percentage of carcass, breast, thigh, abdominal fat, pancreas, liver, heart, bursa of fabricius and spleen were calculated as a ratio of the live weight. To determine the meat quality, 50g of left breast muscle was collected in plastic bags and stored at a negative temperature of 20°C. Meat quality parameters including water holding capacity, moisture, ash, pH, fat, protein, malondialdehyde and total antioxidant activity were measured. Data were analyzed using the general Linear Model procedures of SAS 9.1. When the analysis of variance was significant, Duncan's multiple-range test was used to separate the means. Statements of statistical significance were based on P<0.05.

There was no significant difference between feed intake, body weight gain and feed conversion ratio of experimental diets (P>0.05). According to the present results, Erwan et al. (2009) reported that adding 0.5% and 0.67% leucine to the diet containing 20% crude protein had a significant effect on feed intake, weight gain and feed conversion ratio of broilers. Moreover, relative weights of the different organs and the carcass components (except the abdominal fat) were not affected by the dietary treatments. Abdominal fat of 0.1 and 0.2% leucine fed birds was significantly lower than that of the control birds (P<0.05).Addition of dietary 0.1, 0.2 and 0.3% leucine levels resulted in a lower breast fat as compared to 0.4% leucine and control diet fed birds (P<0.05).Possibly leucine increases fat metabolism of adipocytes by increasing fat flow to skeletal muscle and therefore increases the energy substrate to support leucine-stimulated protein synthesis which results in decreased abdominal fat weight. Breast meat protein of 0.4% leucine fed birds was significantly higher than that of control chicks (P<0.05). The enzymatic activities responsible for the metabolism of branched amino acids are found mainly in the skeletal muscle, heart, kidney, but less so in the liver. Thus, branched amino acids do not break down directly in the liver and most are available for metabolism in skeletal muscle and other tissues. No significant differences were observed between the treatments for blood levels of glucose, triglycerides, total protein, uric acid and cholesterol (P>0.05).The use of dietary leucine had no effect on breast meat malondialdehyde and total antioxidant activity of broiler chickens (P>0.05).

According to the results of current experiment, dietary leucine supplementation in the diet of broiler chickens under heat stress conditions had no effect on performance of broiler chickens. Leucine supplementation at low levels (0.1 and 0.2%) reduces the meat fat but higher levels of leucine (0.4 %) is needed to stimulate the protein synthesis.

Milk and other dairy products are important sources of human nutrition for providing protein, vitamins and minerals. Milk fat contains substances known to have anti-cancer properties. Fat milk ruminants due to extensive bio hydrogenation of unsaturated fatty acids in the rumen and the synthesis of Short and medium saturated fatty acids in the chest glands have a high concentration of saturated fatty acids. Goat is grown as one of the domestic ruminants in different parts of the world. This livestock produces a variety of products. One of the main goals of breeders is milk production. The small, high adaptability, low tolerance and high resistance of goats to many diseases and unfavorable conditions in the environment have caused the maintenance and cultivation of this animal, especially in rural and small conditions. One of the strategies for livestock breeding is livestock nutrition, which greatly affects the economic aspects of production. The lactating animals during and after the transition period undergo significant changes in the metabolism of glucose, fatty acids and minerals. In order to meet the metabolic needs of livestock, different nutritional strategies have been used in this period. Therefore, feeding non forage carbohydrates and fat in the diet or reducing the percentage of milk fat at the start of lactation through some specific fatty acids to reduce energy demand in early lactation it has been used.This experiment was conducted to investigate the effect of using Cannula seed as a source of fatty acid in corn and barley diets on the functional and metabolic responses of the Mahabadian goat breeder in early lactation. Therefore, the use of fat in the diet has become a management tool in controlling metabolic events around childbirth.

The treatments consisted of diet based on barley, corn-based diet, barley-based diet with 5 per centage Cannula seed and corn with 5 per centage Cannula seed diet. Treatments were given to TMR as livestock. This experiment was carried out on 16 female Mahabadian goat breeders in the early stage of lactation in a 2 × 2 factorial arrangement in a completely randomized design ( CRD) with two source factors of cereals and Cannula seed. The mean of treatments was compared with Duncan test at 5 per centage level. This study was conducted in a one-month trial period after birth and 14 days of adolescence. During the experimental period, the amount of feed intake and weight gain was recorded. Sampling of feeds to determine dry matter intake and nutrient feedings weekly to measure the amount of organic matter, dry matter, crude protein, insoluble fiber in neutral detergent ( NDF) and Insoluble fiber in acidic detergent ( ADF). In order to determine the blood parameters of the blood sample from the veins and veins of the goats on the final day of the test, 4 hours after the morning meal was taken. The blood samples taken were immediately transferred to the laboratory and after centrifugation at 6000 rpm for 7 minutes, the serum was isolated and kept in the freezer at -20 ° C. Separated serum was used to measure the triglycerides, cholesterol, urea nitrogen, total protein and glucose, using Pars-test and ELISA reader made by Garni Company in Germany. In order to determine the rumen parameters, a sample of rumen fluid in the final day of the test, 4 hours after feeding. The pH of the rumen fluid was measured immediately using pH meter (Schott Titrator Titroline easy model). Rumen fluid samples were smoothed using 4 layer sackcloth and 2 samples of 50 ml of rumen fluid with 1 ml sulfuric acid 50 per centage with a ratio of 1 to 50 sulfuric acid with rumen fluid was mixed to determine the amount of ammonia nitrogen in the rumen fluid and the profile of volatile fatty and immediately and kept in the freezer at -20 ° C until further tests were performed.

addition of canola to feed was not significant on dry mater intake. The apparent digestibility of DM, OM, CP and EE was not significant, but the amount of NDF, ADF decreased. The effect of fatty acid on total cholesterol and total protein was significant and increase this metabolite but no significant changes were observed in albumin, urea, triglyceride and glucose concentrations. Milk composition was not significantly affected by treatments but milk production in corn with canola, barley with canola increased significantly. The pH of the rumen fluid did not change, but the amount of protozoa decreased. The amount of iso-voleric and voleric acid increased from ruminal fluid escaping VFA, but there was no significant change in the amount of propionic acid and butyric acid.

According to the results, it can be concluded that the use of Cannula seed as a source of fatty acid in a grain-based diet improves the performance of lactating goats. Considering that in the majority of studies done in the feeding of oily seeds, a significant decrease in feed intake was reported. Therefore, it seems that using 5 per centage of Cannula seed in corn and barley not only causes Reducing dry matter consumption does not even increase milk production. Therefore, the use of 5 per centage is recommended in grain-based rations at the lactation stage.The results of this study showed addition of Cannula seed as a source of fatty acid in the diet improved the performance of the Mahabadian goats in the early stages of lactation.

Dairy milk is one of the most important economic products for any country. Also, milk fat has high impact on taste of milk and dairy products. The liver in ruminant animals including dairy cows plays an important role in carbohydrates metabolism, fats, vitamins, hormones, and etc. The absorbed nutrients pass through the liver from the gastrointestinal tract and enter the blood circulation system, and eventually enter the mammary glands of dairy cows. Therefore, the liver plays an essential role in cow lactation and fat production. All components that determine milk quality can be considered as quantitative traits controlled by many genes and influenced by environmental factors. If genetic markers can explain a significant part of the variation, they can be considered as ideal candidates for genomic selection. Previously, microsatellite markers were used to identify quantitative trait locus (QTL). But now, with the progress of science and the advent of the single-nucleotide polymorphisms (SNP) is used in genome-wide association studies (GWAS) to identify QTL. In dairy cows, some of the major genes with significant effects on milk fat have been identified in previous GWAS studies. Therefore, despite a large number of GWAS studies in dairy cows, the studies can be combined using meta-analysis to achieve higher power results. Meta-analysis is a statistical analysis that combines the results of scientific multiple studies. These studies contribute to our current understanding of the genetic regulation of milk fat yield traits. This approach provides a better understanding of the genetic architecture of complex traits. The network clustering algorithm and cluster identification is an important tool in the structural analysis of networks. The many clustering algorithms in different types are used for protein-protein interactions (PPIs) networks analysis. In this study, we used an algorithm known as MCODE to identify dense regions in the PPIs diagram. The overall purpose of PPIs network clustering is to a grouping of genes or proteins that are related according to some scales. The network of PPIs contains different proteins that play a role in different pathways. Because these genes or proteins are clustered based on the similarity of metric and are known as matrix distances. It is also important to predict molecular assemblies of protein interaction data because it provides another level of functional annotation. A total, the purpose of this study is to conduct a meta-analysis of GWAS in cluster analysis to identify genes that are effective in milk fat yield in dairy cows.

In this study, the data used were GWAS summary data. All data were collected from 19 published studies from 2010 to 2019. This research included main papers and dissertation (valid dissertations with published papers). All available genes were combined, synthesized, and evaluated using a meta-analysis method. The Cytoscape v3.7.2 software was used to analyze and visualize the genes examined by the STRING v1.5.0 plugin and to extract clusters from the MCODE v1.5.1 algorithm. Therefore, the results of the GWAS summary data were combined in molecular networks with PPIs, which have a significant role in increasing the association studies power to identify genes affecting milk fat trait. Also, the DAVID server was used to identify the gene ontology (GO) term enrichment in order todetect enriched biological terms associated with genomic regions and to identify gene networks using functional annotation clustering tools based on enriched pathways analysis.

A total, 223 genes were analyzed using the STRING plugin in Cytoscape software. Also, these genes were associated with at least one other gene and had a direct and partial correlation. In the gene network, the correlation created for milk fat yield trait included 213 genes or nodes and 219 edges (gene connection). The P-value calculated in the STRING network was statistically significant for enriched pathways in PPIs ( ).The collection of important genes and popular were evaluated using the MCODE plugin. Seven clusters were identified and grouped in this network. For instance, proteins in cluster 1 included: ARHGAP39, CPSF1, CYHR1, PPP1R16A, GRINA, MROH1, and SMPD5 genes. As shown in Table 2, cluster 1 (score=7) was connected with 7 node density to 21 nodes. This cluster showed proteins that play important roles in the internal space of the endoplasmic reticulum (cellular components), metal ion binding (molecular function), and integral to the membrane (cellular components). CPSF1, CYHR1, and GRINA are major genes involved in the internal space of the endoplasmic reticulum, metal ion binding, and membrane integral, respectively. It was found that clusters 1 and 2 have the highest score between all reported clusters.

The chart-based protein clustering was extracted from the PPIs network using the MCODE algorithm and the enriched pathways were extracted from the DAVID tool. This method determines the quality of the proteins involved in fat yield and helps to understand the molecular structure of proteins. These clusters based on existing biological knowledge can help data mining and system models understand network interactions and pathways. These protein clusters provide a deep insight into how genes interaction with each other in network analysis for fat yield. However, it was observed that meta-analysis of GWAS summary data can play an important role in the wide understanding of network visualization and cluster analysis of identified genes in enriched pathways. Therefore, cluster analysis can improve the identified genes power for economically important traits such as milk fat yield in a population of dairy cows and can be used in future genomic evaluations and breeding programs.

Due to the widespread distribution of SNPs throughout the genome, these markers are widely used in livestock breeding research. These markers were used to predict the disease risk in human, to localize genetic variations responsible for complex traits through genome wide association study (GWAS), and to predict the genetic values of economically important traits in plant and animal breeding (Zhang et al 2015). Mostly whole genome scanning methods are based on two SSGWAS (Single SNP Genome-Wide Association Studies) and multiple markers methods. The SSGWAS method is able to identify a large number of common variables affecting quantitative traits. However, a large proportion of the genetic variance remains to be explained (Shirali et al 2018). In quantitative traits the proportion of phenotypic variance explained by SNPs is related to the number of adjacent SNPs in the genomic region. The heritability created by these genomic regions is defined as the regional heritability. The RHM (Regional Heritability Mapping) method is used to identify small genomic regions. This method can capture more of the missing genetic variation (Nagamine et al 2012). In RHM, a mixed model framework based on Restricted Maximum Likelihood (REML) is used, and two variance components, one contributed by the whole genome and a second one by a specific genomic region, are fitted in the model to estimate genomic and regional heritabilities, respectively (Uemoto et al 2013). Also fastBAT (fast and flexible set-Based Association Test) is a method that performs a fast set-based association analysis (Bakshi et al 2016). The purpose of this study is compare SNPs and regions identified by the Genome-Wide Association methods, compare these results with the simulated QTLs and also investigate and determine the false positive results in each method.

In this study, markers and populations were simulated as a Forward-in-time process using QMSim software (Sargolzaei and Schenkel 2009). For this population, 27586 single nucleotide polymorphisms (SNPs) were counted on 3 pairs of autosomal chromosomes. Simulation was performed in 3 scenarios with 75, 150 and 300 quantitative trait loci (QTL). The minimum and maximum number of SNPs in the analysis after quality control were 19662 and 23817 SNPs, respectively. For each scenario, 10 replicates were simulated, in all scenarios, heritability was 0.2 which corresponded equally to the polygenic and QTLs effects. Whole genomic relationship and pedigree base genetic relationship matrices were used in all 3 methods to estimate genetic parameters. To create the whole genomic relationships matrix, whole genomic additive effects was estimated using all SNPs. Also the additive effect of genomic regions was estimated using the regional genomic relationship matrix. Whole genomic relationships matrix and regional genomic relationship matrix were estimated based on genetic relationships between individuals using SNPs by GCTA software (Yang et al 2011). Pedigree based genetic relationship matrix was created by the kinship relationship between individuals using pedigree package (Coster 2013) of RStudio software (RStudio Inc 2013). To perform RHM and to estimate variance components, windows containing 50 genotyped SNPs were considered. Also windows containing 25 genotyped SNPs to overlap between two consecutive windows throughout the genome were used. SSGWAS analysis were performed by MLMA (Yu et al 2006) method using GCTA software. MLMA results were adjusted based on P-value at 5% significant threshold using Bonferroni correction. To evaluate the results of SSGWAS using fastBAT method, GCTA software was used.

For each replication after identifying significant SNPs, the genetic variance explained by these SNPs was estimated by equation (Faulkner & McKay 1996). In Table 1, the number of QTLs detected by the SSGWAS method, the MAF of QTLs, the range and mean of genetic variance explained by significant SNPs and QTLs are reported. For 30 replicates of simulation in SSGWAS, 16 QTLs were detected containing 2 QTLs with MAF≤0.1 and other detected QTLs with MAF≥0.1. 107 Significant regions were identified in fastBAT method. In this method, 120 QTLs were detected in 3 scenarios containing 52 QTLs with MAF≤0.1. All QTLs detected in the fastBAT and SSGWAS methods were also detected in the RHM method. In RHM method, 612 regions containing simulated QTLs and number of 316 QTLs with MAF≤0.1 were detected. In all replications, the variance explained by SNPs was equal to the variance explained by QTLs. In SSGWAS, less number of QTLs were detected than the other two methods and the maximum variance explained by QTLs was 14.9%. The criterion used to determine false positive QTLs was the absence of significant QTL in the before and after significant windows containing QTLs. In SSGWAS method the percentage of false positive QTLs was higher than the other two methods. In fastBAT, unlike the other two methods, detected QTLs were not false positive. In table 5 Number of detected QTLs, MAF range of QTLs, range and mean of genetic variance explained by detected QTLs and SNPs in fastBAT are shown. Many QTLs and regions detected by RHM method were not detected by SSGWAS and fastBAT methods. The genetic variance explained by detected QTLs in the RHM was at the range of 7.26 to 46.86% that was higher than other two methods. In table 6 the three methods compared by the number of detected QTLs, number of false positive QTLs, number of stable QTLs and the number of detected QTLs with MAF≤0.1. We found that QTLs with MAF≤0.1 were more frequently detected in RHM than the other two methods. These results confirmed that the RHM method was able to identifying more of QTLs affecting the trait variance.

The time between the first and the last chick hatching is called the hatch window and the time required for all eggs to hatch is between 24 and 48 hours (Decuypere et al. 2001). In addition, processes such as sex determination, vaccination, and on-farm transfers require more time, resulting in hatched chicks not being able to access water and feed for 72 hours. (Willemsen et al. 2010). Post-hatch fasting leads to high chicken mortality, low growth in the first week and a reduction in disease resistance, due to insufficient storage of yolk sac and incomplete bird's immune system in this period. One suggested option for overcoming these challenges is in ovo feeding (IOF), (Zhai et al. 2008). The in ovo injection of nutrients around days of 17 to18 called in ovo feeding (Foye et al 2006), supplies the embryo with additional nutrients via amniotic fluid prior hatching and those nutrients continue to be utilized by the chick post-hatch during this fasting period. Yolk protein is a source of maternal antibodies. The results suggest that IOF increases immunoglobulins, weight of bursa of fabricius, and lymphocyte proliferation on 28 days of age (Dibnere et al 1998). Results from another study indicate that, drinking water contains glucose leads to decrease in gluconeogenic enzymes, which means reduction in proteins breakdown and beneficial antibodies (Donaldson 1995). In most studies, egg is inoculated with a single nutrient, while other factors assumed to be constant. In such studies, the interrelationships between factors are ignored. Therefore, designing experiments using modern statistical mathematical methods in order to optimize test conditions and obtain more accurate results will be useful (Ahmadi and Golian 2010). Response surface methodology (RSM) is a proper model, because it is a useful set of statistical and mathematical procedures to survey responses from the combinations of the factors for searching optimum results, optimizing the interactions between factors and their levels, and to reduce investigation costs. In this method, in addition to examine the effect of each factor, all coefficients of the quadratic regression model and the interaction of factors can be estimated. Box-Behnken design (BBD) is a multivariate experimental design, where multiple factors are studied simultaneously. The BBD is considered to be more efficient than full factorial designs, because it requires a significantly smaller number of treatments and allows researchers to estimate and optimize processes (Box and Hunter 1957). This study was performed to determine the immune response of broilers to in ovo feeding of beta-hydroxy beta-methylbutyrate (HMB) and dextrin and the first time of access to feed, using Box-Behnken design.

A total of 1800 fertile eggs of 65 wk-old broiler breeders was obtained from a commercial broiler breeder flock (Ross 308) and stored under commercial conditions for two days before incubation begins. Eggs were weighed, and those within a similar weight distribution (66.4 ± 1.3 g) were randomly distributed into 60 groups of 30 eggs. Eggs were then incubated in a commercial hatchery at 37.5°C and 56%relative humidity. On the 15th day of incubation all eggs were candled and cracked and unfertilized or contained dead embryos eggs were discard (Kornasio et al. 2011). On the 18th day, 1500 eggs were randomly assigned into 15 treatments based on a Box-Behnken design (Box et al. 1987) with four replicates and three levels of in ovo administration of beta-hydroxy beta-methylbutyrate (0, 0.5 and 1.0 %) and dextrin (0, 20 and 40 %) in a 0.9 % saline as well as timing of the first feeding (6, 21 and 48 h). The injection was performed manually through a hole made in the wide part of the egg, using needle number 23 and injection of 0.5 ml of sterile solution, which was inserted to a depth of 17 mm of amniotic fluid (Uni et al. 2005). From the hatched chicks of each replication, 14 chicks were randomly selected, weighed and transferred to the farm, and according to the box-Benken design in 15 experimental treatments (each treatment has four replications and totally 60 experimental units were reared). Access to water and food was free during the 42-day trial period. Chickens were fed three diets of starter, grower, and finisher based on the Ross 308 strain nutrients requirement (Ross Broiler Guide 2014). The feed form and composition, were in crumble form with 2850 kcal/kg metabolizable energy and 21.92% crude protein) during starter period (1 to 10 days), in pellet form (3 mm) with 2880 kcal/kg ME and 19.97% CP during grower period (11 to 24 days) and in pellet form (6 mm) with 2950 kcal/kg metabolic energy and 17.98%crude protein during finisher (25 to 42 days) period (Ghanaatparast et al. 2018 a,b). Experimental data (60 data lines) gained by BBD were fitted to the second-order polynomial equation by SAS through the RSREG procedure. A ridge analysis (Draper, 1963, SAS Institute 2011) was used to compute the optimal response for TPB and PPB maximization (RIDGE MAX procedure in SAS). Using ANOVA and corresponding absolute t-value of the model parameters, a process of the sensitivity analysis was performed on developed RSM models to find which model term is considered more important throughout the modeling process (Box et al. 1987).

Increasing the time of the first access to feed and post hatch fasting reduced the humoral and cellular immunity (P < 0.05). In ovo feeding of HMB and dextrin improved primary humoral immune response (P < 0.05). In Ovo feeding of HMB and dextrin resulted in improving primary humoral immune response (P <0.05), and in Ovo injection of HMB enhanced primary and secondary humoral immune response simultaneously with fasting. By increasing fasting time, in ovo feeding of HMB and dextrin increased the cutaneous basophilic hypersensitivity response in both times of PHA-P injection (24 and 48 hours) and the second injection of PHA-P (48 hours), respectively. Limited access to the post-hatch feed causes stress and corticosteroid secretion and prevents the proper growth of immune cells. Therefore, increased immunity in chickens depends on the first access to feed, during which the acquired immunity is strengthened and the bird is protected against pathogens through the respiratory tract, which is known to be a source of infection (Yi et al. 2005). The interaction between inoculated nutrients and their concentrations in egg, as well as post hatch starvation affected broiler’s performance and such interactions can affect the optimal levels determined in an experiment (Tako et al.2004; Kornasio et al. 2011). In general, injections of carbohydrates such as fructose or ribose improve the humoral and cellular immune response in broilers, and of course the extent of the effect on the poultry immune system depends on the type of carbohydrate (Bhanja et al. 2014).

The first access of day- old chick to feed is considered as effective factor to prevent declined bird's immune system function. If access to water and feed is delayed during post-hatch (for reasons such as vaccination, sex determination, and transport to farm), in-ovo feeding of HMB and dextrin is effective to improv the humoral and cellular immune response. The Box-Benken design for minimally invasive experiments is a good method to explain the relationship between in-ovo feeding of nutrients and the first access to feed on the immune response of broilers.

Performance of native fowl can be improved by making changes in feeding, rearing and health issues. On the other hand, genetic improvement of these breeds can be achieved through the breeding programs such as selection, crossbreeding, or both. Selection programs may be time consuming, but implementing them will lead to a continued improvement (Padhi 2016). Considering the genetic diversity among the native fowl breeds of Iran, several breeding stations were established in different provinces of the country for the purpose of reproduction and genetic improvement of these breeds. Mating related animals in closed populations leading to accumulated inbreeding and reduced genetic diversity has destructive effects on additive genetic variance and phenotypic values (Falconer and Mackay 1996). Inbreeding is associated with an increase in homozygosity and usually decrease the fitness of individuals in the population, which is referred to as inbreeding depression (Ayroles et al. 2009). Inbreeding depression in domestic animals can cause reduced selection response and potential genetic gain in economic traits (Selvaggi et al. 2010). Since the improved Fars native fowls have been raised in a closed population and selected for some important economic traits during the successive generations, their inbreeding coefficients may increase and reduce the effectiveness of breeding programs. Therefore, it is of significant importance to monitor the inbreeding rate and its consequences on different traits. The purpose of this study was to monitor the inbreeding rate and evaluate its possible effects on some important economic traits in improved Fars native fowl population using the pedigree information of 25 generations via different models.

Data of 63250 birds during the period 1369-1397 (25 generations) recorded in the breeding station of Fars native fowl were included in the study. Studied traits include body weight at hatch (BW1), body weight at 8 weeks of age (BW8), body weight at 12 weeks of age (BW12), age at sexual maturity (ASM), weight at sexual maturity (WSM), egg weight at 1st day of laying (EW1), egg number (EN) and average egg weight (AEW). Individual and maternal inbreeding coefficients of all birds estimated using the CFC program. Estimated inbreeding coefficients grouped into seven different categories of inbreeding: 0, 0 to 5%, 5 to 10%, 10 to 15%, 15 to 20%, 20 to 25% and 25 to 30%. Regression coefficients of studied traits on individual and maternal inbreeding percentage were estimated by Wombat software (May 2007) and restricted maximum likelihood (REML) method using six different models. Individual and maternal inbreeding coefficients were also included as a covariate in the model. In this study, among the six statistical models considered for each trait, finally, the appropriate model for each of them was selected through three methods of likelihood ratio test (LRT), Akaike’s information criterion (AIC) and Bayesian information criterion (BIC).

Pedigree analysis showed that 40184 birds were inbred and the mean of individual and maternal inbreeding was relatively low over 25 generations. The average individual and maternal inbreeding did not differ much over the generations. According to the results, the mean inbreeding for all birds was approximately equal to 2 % and in inbred birds was 4 %. From the fifth generation onwards, the average inbreeding (individual and maternal) of birds in the whole population was increasing. In the first four generations, inbreeding rate of population was estimated to be zero, which may be due to the unknown pedigree information in the first generations. Various studies have shown that accurate estimation of inbreeding is highly dependent on pedigree information. The results of a previous study on laying hen strains indicated that pedigree information influenced the inbreeding estimation in the first generations (Szwaczkowski et al. 2003). In another study, Cassell et al. (2003) reported that the use of incomplete pedigree in estimating the mean inbreeding reduces the mean inbreeding estimate and the variance of these estimates in cows. From the fifth to eighth generation, the rate of individual and maternal inbreeding was very small. Distribution of birds in different categories of inbreeding showed that 36.57 % (23066 birds) were non-inbred. Classifying birds into different inbreeding groups indicated that the highest number of inbred birds was in the inbreeding group 0 to 5 % (47.72 %) and 5 to 10 % (15.48 %). Although the number of inbred birds is high, but the amount of inbreeding coefficient is significantly low, reflecting careful planning of mating in the station. The study of Kamali et al. (2007) on the pedigree information of Fars native fowl (21245 birds) during eight generations demonstrated that the inbreeding rate is low. In addition, in the previous study on the improved Mazandaran native fowl during 26 generations, it was shown that the rate of inbreeding is relatively low, which is in accordance with the results observed in the present study (Ghorbani and Omrani 1399). According to the fitted models, model 5 for BW1, BW8 and BW12, model 6 for ASM and AEW, model 2 for WSM, model 4 for EN and EW1 considered as the most suitable model. Estimating the inbreeding depression in the studied traits revealed the most effect of inbreeding on the BW12, so that for every 1 % increase in individual inbreeding, BW12 is reduced by 2.14 grams. Also, for every one % increase in individual inbreeding, BW8 decreases by 1.07 grams. The highest effect of inbreeding was observed on BW8 and BW12, so that for every 1 % increase in individual inbreeding, BW12 decreases by 2.14 grams and BW8 decreases by 1.07 grams. ASM was significantly affected by inbreeding depression. ASM increased by 0.38 day per 1 % increase in inbreeding. The finding of earlier studies regarding the inbreeding effect on the ASM have shown that increased inbreeding does not have the same effect on different strains. For example, increase in inbreeding level results in increased ASM in the Leghorn (Sewalem et al. 1999) and decreased ASM in the New Hampshire (Szwaczkowski et al. 2003). Taken together, pedigree analysis showed that the depression effect of inbreeding on egg traits including EN, EW1 and AEW was negligible.

According to the results of pedigree analysis, inbreeding rate of improved Fars native fowl population is increasing at an acceptable level with a relatively gentle slope. In addition, depression caused by inbreeding in the population was fairly low. Since maintaining genetic diversity and keeping down the inbreeding rate in the station are considered as main factors in developing the breeding programs, it can be concluded that the implementation of breeding programs and selection of superior birds during the generations has gone in the right direction.

Understanding feed efficiency (FE) in dairy cows and its improvement is essential. Dry matter intake (DMI) is fundamentally important in nutrition because it establishes the amount of nutrients available to an animal for health and production. Residual feed intake (RFI) is calculated as the residual in the linear model to predict feed intake of individual animals and thus is essentially the difference between an individual’s observed feed consumption and its predicted feed consumption. An animal with a negative RFI consumes less than expected for its level of production and thus is more efficient when RFI is used to define feed efficiency. Because RFI is independent of production level, recent attention has been given to using RFI as a tool to assess feed efficiency in dairy cattle for purposes of genetic selection. Also, feed efficiency based fat-corrected milk (FCM4) dived DMI (FCM4/DMI) is another factor to depict feed efficiency. Therefore, this research aimed to investigate the dynamics of RFI and FCM4/DMI in Iranian Holstein dairy cows.

Lactating Iranian Holstein cows (n = 30; 10 primiparous and 20 multiparous), averaging (mean ± standard deviation, SD) 594 ± 62.6 kg of body weight, 38.81 ± 6.22 kg of milk/d, and 94.5 ± 21.5 day postpartum, were fed a diet balanced with CPM Dairy V3 ration software. Diet consisted of 40 % forage and 60 % concentrate and fed as total mixed ration (TMR). Cows were fed once per day a fresh diet and orts were removed and weighed daily before feeding. Milk yield was recorded electronically at each milking, and milk samples were obtained from 3 consecutive milkings per week. Milk samples were analyzed for fat, true protein, and lactose with infrared spectroscopy. Body weight (BW) for each cow was recorded 2 consecutive days per month immediately after the morning milking. Daily weight gain was calculated based on body weight for each cow at the beginning and end of each period (30 and 60-days period). Body condition score (BCS) was determined on a 5-point scale in 0.25 increments by a trained investigator and recorded for each cow at the beginning and end of each period (30 and 60-days period). Also, milk energy output (MilkE; Mcal/d), metabolic body weight for a cow (MBW), and energy expended for body tissue gain (ΔBodyE; Mcal/d) were estimated based on NRC 2001 equations. Dry matter intake for an individual cow during each 30 and 60-days period was regressed as a function of major energy sinks through the four different models (Model 1, Model 2, Model, and Model 4) using Mintab software (version 19). To define RFI, DMI was modeled as follows:〖Model 1: DMI〗_i= β_0+ β_1 ×MILKE_i+ β_2 × MBW_i+ β_3 × ∆BodyE_i+〖β_4 × 〖Weight G〗_i +β〗_5 × Parity_i+ β_6 × 〖Lactation W〗_i+ ε_i〖Model 2: DMI〗_i= β_0+ β_1 ×〖FCM4〗_i+ β_2 × MBW_i+ β_3 × 〖Weight G〗_i+ ε_i〖Model 3: DMI〗_i= β_0+ β_1 ×MILKE_i+ β_2 × MBW_i+ β_3 × ∆BodyE_i+β_4 × Parity_i+ ε_i〖Model 4: DMI〗_i= β_0+ β_1 ×MILKE_i+ ε_iWhere DMIi was the observed DMI, MilkEi was the observed milk energy output, MBWi was the average BW0.75, ΔBodyEi was the estimated change in body energy, based on measured BW and BCS, Weight Gi was daily weight gain, Parityi was the parity, Lactation Wi was week of lactation, and FCM4i was fat-corrected milk for ith cow. RFI was defined as the error term (ε_i) in the model. Also, we reported Pearson correlation coefficient between DMI, FCM4/DMI, and RFI with measured and estimated traits for a 30-days and 60-days period.

The results showed that RFI was observed and measured in our study population and it is possible to classify animals based on RFI. The model adjusted R2 for models 1, 2, 3 and 4 were 88.51, 78.82, 80.05, and 64.41 respectively, in a 60-days period. The mean ± SD for models 1, 2, 3 and 4 were 0 ± 0.86, 0 ± 1.25, 0 ± 1.18, and 0 ± 1.68 (kg DM per a day), respectively. For model 1 (in 60-days period, full model), milk energy output (MilkE), metabolic body weight for a cow (MBW), energy expended for body tissue gain (ΔBodyE), daily weight gain, and week of lactation were significant (P<0.05) except for parity which showed a significant trend (P<0.1). Partial regression coefficients for MilkE, MBW, ΔBodyE, daily weight gain, parity, and week of lactation for the model 1 used to predict DMI were 0.566, 0.1, 3.46, -9.51, 4.13, and 0.1771, respectively. For model 3 (in 60-days period), milk energy output (MilkE), metabolic body weight for a cow (MBW), and energy expended for body tissue gain (ΔBodyE) were significant (P<0.05) except for parity. Partial regression coefficients for MilkE, MBW, ΔBodyE, and parity in the model 3, were 0.429, 0.1381, 0.791, and 0.229, respectively. Pearson correlation coefficient for RFI between model 1 with 2, 3 and 4 was 0.817, 0.728, and 0.515, respectively (P<0.01). The same trend was observed for a 30-days period. Pearson correlation coefficient for DMI between 60-days and 30-days period was 0.994 and for RFI between 60-days and 30-days period was 0.882. Also, based on Pearson correlation coefficient for DMI, FCM4/DMI, and RFI with other biological parameters, we observed that there were the reasonable correlations, significant at the P=0.01. Surprisingly, there was a negative correlation between FCM4/DMI and milk protein percentage (P<0.0001). Also, negatively significant trend between RFI with milk fat and protein percentage (P<0.1) was observed.

Measuring feed efficiency through RFI in a 30-days period is predictable. Although the model 1 used in this study and its parameters explained DMI in accurate manner, scientific exploration for finding other parameters for improvement of model R2 is suggested. Therefore, finding the effective models would result in an accurate estimation of RFI for individual dairy cows, classifying efficient and inefficient dairy cows correctly and clarifying the reasons for these differences through a holistic approach. Also, the study of the reasons for the significant negative correlation between FE and RFI with milk protein and fat percentage as a novel observation in our study is recommended in the future.

Growth traits of domestic animals and their economic significance have been always considered important by breeders and breeding specialist. Growth models, calculated non-linearly, are used to express growth rate and statistical ratio between age and body weight (Kum et al. 2010). One of the ways to draw and describe the growth curve is to use growth models. By selection, you can change the shape of the growth curve and increase body weight (Anthony et al. 1996), although selecting to increase body weight has adverse effects such as obesity, leg problems and sudden death syndrome in poultry. Growth curve parameters are suitable and efficient criterion allowing to change the relationship between age and body weight through selection (Narince et al. 2010). Many researchers have used linear and nonlinear regression models to model the growth phenomenon. These models are more reliable (Vuori et al. 2006) than linear models due to the limited number of parameters and have a better fit of the data (Lambe et al. 2006). Depending on the breed of livestock and population studied, a growth function presents different results, so it is necessary to model growth for each herd separately (Bahreini Behzadi et al. 2015). Though many have investigated the growth traits of quails, studies estimating the heritability of growth curve parameters, genetic correlations between these parameters and genetic correlation between growth curve parameters and other traits (e.g., growth traits, production, conversion ratio and carcass traits) are very limited. Therefore, the aim of this study was to estimate heritability, phenotypic and genetic correlations of growth curve parameters and different body weight traits in Japanese quail.

Data were obtained from 242 male and 242 female quail hatched between 2017-2019 that belonged to the Khorasan Razavi Agricultural and Natural Resources Research and Education Research Center. Birds were weighed individually for four generations from hatching to 42 days of age over four generations. Pedigree information included bird number, sire number and cage number, year, month, and birthday of the bird and hatching times over 4 generations. The data were first edited with CFC software and prepared for analysis. The traits studied in this study included body weight records at different ages (1, 7, 14, 21, 28, 35 and 45 days of age). SAS software's linear model and GLM procedure were used to identify constant factors affecting traits. Nonlinear regression model was used to estimate the growth curve parameters. Covariance components and genetic parameters of growth and body weight trait at different ages in quail were estimated using animal model and restricted maximum likelihood method using multivariate analysis by DMU software.

In the present study, moderate to high heritability (0.21-0.72) was obtained for different traits of body weight. Estimation of heritability at 1 week (0.45 ± 0.06), 2 weeks (0.42 ± 0.05), 3 weeks (0.44 ± 0.06), 4 weeks (0.45 ± 0.07), 5 weeks (0.52 ± 0.07) and 6 weeks (0.72 ± 0.06) were higher than the values reported by Barbieri et al. (2015) yet less than the estimate of Narinc et al. (2010). Estimation of moderate heritability obtained for body weight at 1 and 4 weeks of age and high heritability at 5 and 6 weeks of age show that the response to selection is low at early ages and increases with age at 5 and 6 weeks. The strongest correlations were between 3-4 weeks weight (0.93) and 1-2 weeks (0.92). Except for genetic correlations between hatching weight and final ages, most correlations were positive. The highest phenotypic correlation was between 3-4 weeks (0.83) and the phenotypic correlation between hatch weight and 6 weeks (0.006) was the lowest estimate. The results showed that the Gompertz model with the highest coefficient of determination (0.998) and the lowest error variance (1.8262) was the best growth predictor model in Japanese quails. Estimation of growth curve parameters (a, b and k) were 298.58, 3.5 and 0.053, respectively. The small difference between the observed and predicted body weight values indicates that the Gampertz model was able to describe the growth of quails well. Heritability of growth curve parameters (a, b and k) were estimated to be 0.21, 0.48 and 0.22, respectively. Phenotypic and genetic correlations between parameters a and b with different traits of body weight were generally negative. The highest genetic correlation (0.86) and phenotypic (0.66) were obtained between k parameter and 3-week weight. Genetic correlation between mean growth rate (b) and various traits of body weight was 0.9-0.62. The highest genetic and phenotypic correlations were estimated between maturity rate (k) and 2, 3 and 4weeks weights. Genetic and phenotypic correlations between growth curve parameters were generally negative. The only correlation between asymptotic weight (a) and mean growth rate (b) was positive and high. These negative correlations indicate that if selection is used to increase puberty weight, we will have negative effects on growth rate and the growth rate will tend to decrease.

Estimation of heritability of growth curve parameters and their correlation with body weights at different ages showed that increasing production efficiency by selection based on growth curve parameters rather than selection for body weight could be useful. Curve deformations are more important than other parameters for productive purposes, especially the parameters a and k that are related to growth rates. Choosing a higher growth rate at 0- to 14-day-old does not change maturity weight, but choosing a higher body weight, that is, the near-inflection point of the curve, changes the shape of the curve without any significant change in parameter a. Nonetheless, k would change without any change in parameter a when limiting selection index is applied. The heritability of the Gompertz growth curve parameters and their correlation with weekly weights suggest that the growth rate parameter may be useful in selecting animals that are of early growth.