فهرست مطالب s. alijani

The genome-wide association study (GWAS) is a powerful approach to identify genomic regions associated with fertility traits that explain a significant portion of the genetic variance associated with these traits and identify the relevant causal mutations. Evaluating the correlation between each genotyped marker and trait is an essential strategy for GWAS studies that examine the effects of all markers by considering their possible interactions, environmental factors, and even mutual effects between markers. Recently, machine learning methods have been introduced to genomic topics, and the basis of these methods is different from the common methods of genomic evaluation. The machine learning method is used to estimate the genomic breeding values of the candidate animals by considering the training data (genotypic and phenotypic information of the reference population). One of the key advantages of this method is the ability to analyze large data. Machine learning is a branch of artificial intelligence whose goal is to achieve machines that can extract knowledge (learning) from the environment. A variety of machine learning methods (random forest, boosting, and deep learning) are used to model genetic variance and environmental factors, study gene networks, GWAS, study epistasis effects, and genomic evaluation. Random forest is one of the machine learning methods that has been successfully used in various fields of science. This research was conducted to identify markers and genes related to reproductive traits such as calving interval (CI), days open (DO), daughter pregnancy rate (DPR), and age at first calving (AFC) in Iranian Holstein dairy cattle. These traits have already been investigated with the ssGBLUP method and using a smaller sample size. However, in the present research, by using more genotyped animals, a random forest algorithm was used to identify markers and genes related to reproductive traits.
The records used in this research were provided by the National Animal Breeding Center and Promotion of Animal Products of Iran and included AFC, DO, CI, and DPR related to the genotyped bulls' daughters. In this research, the pedigree information of 2774183 animals was used. The genotypic information of the markers related to 2419 Holstein bulls was used. Genomic data quality control was performed using factors such as the number of genotyped SNPs per animal (ACR), the number of genotyped animals per SNP (CR), Hardy-Weinberg equilibrium (HWE), and minor allele Frequency (MAF). When filtering genomic data, the markers whose MAF was less than 5% were removed, and then the samples whose genotyped frequency was less than 90% were identified and removed. Then, the markers whose genotyping rate was less than 95% in the samples were identified and removed. Finally, the SNPs that deviated from the HWE test (P<10-6) were excluded from the analysis as a measure of genotyping error. To control the quality of genomic data, PLINK 1.9 software was used. Then Ranfog software was used in the Linux environment to perform analysis through random forest algorithm.
By using the random forest algorithm, a total of 21 important SNPs were observed, then important fertility trait candidate genes were identified by the gene ontology method, and 62 genes were within 250 Kb of these SNPs. The most significant SNP was observed for AFC. The main SNP for AFC is in ARS-BFGL-NGS-22647 BTA3, for CI is in ARS-BFGL-NGS-114194 (BTA11), for DO is in BTA-74076 -no-rs (BTA5), and for DPR is in ARS-BFGL-NGS-32553 (BTA26). The researchers, who studied fertility traits in Nellore cattle using machine learning methods, identified MPZL1 and CD247 genes on chromosome number 3 and this gene was associated with age at first calving. Many pathways of cell biology affect the performance of reproductive traits. Research has reported the relationship between the CD247 gene and pathways of biology, including cell development and function. Research has shown that the IFFO2 gene plays an important role in the molecular structure of cells, as well as in the mechanism of blastocyst formation, embryos, and the length of gestation in cattle. In a study conducted on the mouse population on the structure of the flagellum and the sperm maturation process, the role of the ALDH4A1 gene in the sperm maturation process was reported. The association of the RPS6KC1 gene with pregnancy rate and antral follicle number in Nellore heifers has been reported. The KAT2B gene is a transcriptional activator that plays an essential role in regulating the correction of histone acetylation and plays an important role in improving carcass quality, muscle and fat development, and metabolism in native Chinese cattle. In addition, they play a key role in regulating biological processes and are related to cell growth, metabolism and immune system function.
  According to the objectives of this research, new information on markers and candidate genes related to reproductive traits in Iranian Holstein dairy cattle was reported. The markers and candidate genes identified in the present research can be used in genomic selection to improve the reproductive traits of Holstein dairy cattle.

For the design of any successful breeding program, knowledge about semen quality traits is greatly important and per-required. Due to the critical role of bulls through artificial insemination in the genetic improvement of the dairy cattle herds, knowledge about semen characteristics and genetic factors which affect semen parameters is essential. In addition, semen with poor fertility requires more units of semen to establish a successful pregnancy and produce a live offspring. Nonetheless, both impacts are economically important factors, which are considered by the producers leading to rejection of bulls with poor semen quality, and investing mainly on highly fertile sires. Summary of previous literatures is highlighted semen traits and their key roles in upcoming fertility and pregnancy of cows within herds (Gacitua et al. 2005). Furthermore, some reports pointed DNA markers can identify the genetic background mechanism of resistance and susceptibility of sperm after freezing and thawing (Purdy et al., 2005). Additionally, previous reports supported  low hereditability scenario of semen characteristics in livestock species (England et al., 2010). Leptin is a globular protein with a tertiary structure similar to a haemopoietic cytokine synthesized by adipose tissue. It is involved in the regulation of feed intake, fetal growth, energy balance, fertility, and immune functions. The leptin molecule (16 kDa) is made up of 167 amino acids with an N-terminal secrotary signal sequence of 21 amino acids. In cattle, the leptin gene is located on the fourth chromosome. It consists of three exons and two introns. Only two exons are translated into the protein. The coding region of the leptin gene (501 nucleotides in length) is in exons 2 and 3, which are separated by an intron of approximately 2 kb. The leptin gene promoter region spans approximately 3 kb. Leptin is necessary for normal reproductive function, but when present in excess, it have detrimental effects on the male reproductive system. Human and animal studies pointed to leptin as a link between infertility and obesity, a suggestion that was corroborated by findings of low sperm count, increased sperm abnormalities, oxidative stress, and increased leptin levels in obese men. In addition, daily leptin administration to normal-weight rats has been shown to result in similar abnormalities in sperm parameters. The bovine growth hormone (bGH) is a 22 KDa single-chain polypeptide hormone, which is produced in the anterior pituitary gland. The encoding gene is approximately 1800 base pair (bp) and consists of five exons separated by four intervening sequences (Harvey et al. 2000). Recently, several studies have investigated the association between bGH locus and reproduction traits. A substitution of cytosine (C) for guanine (G) at the  position of 2141 causes an amino acid change from leucine (leu) to valine (val) at residue 127. This transversion enables the genotyping of this particular locus using the endonuclease AluI, since in the mutant bull, this enzyme does not recognize its target sequence. Several point mutations in the bovine growth hormone (GH) gene have been described, and as such, the Leu127Val polymorphism described by Lucy et al. (1993) has been extensively investigated based on  production and reproduction traits. In addition, Lechniak et al. (1999) have reported the relationship between individual semen quality traits and fertility. The purpose of the present study was to estimate the genetic parameters of some semen quality traits using animal models containing genotype of candidate gene data on Holstein bulls.

For this reason, 67 bulls were selected from two breeding stations in the Northwest of Iran (41 bulls) and the National Livestock Improvement Center (26 bulls), which were in the sperm production stage between 2003- 2013. In this regard, four related traits of semen quality were considered, including: volume of ejaculation, sperm concentration, live sperm percentage before freezing, and live sperm percentage after thawing. The DNA extraction was done from semen according to commercial DNA kit and quality and quantity tests were done using spectrophotometry and gel monitoring. The nucleotide polymorphisms were evaluated in two loci of the leptin gene (Exon 2 and intron 2) by the PCR-RFLP method and in the growth promoter receptor gene (promoter region) using PCR-SSR technique. Finally, using combined phenotypic and molecular information, genetic parameters were estimated through Bayesian statistics and Gibbs sampling. POPGENE software was used for molecular data and descriptive statistics (genotype frequencies, allele frequencies, and hetrozygosity index calculation). Univariate and multi variate analysis for semen characteristics was done using gibbs3f90 and renumf90 softwares.

According to the molecular outputs, in all investigate genes nucleotide variation and polymorphism was observed. The results of univariate analysis of animal model for estimating heritability for traits of semen volume, sperm count, live sperm percentage before freezing, and thawing point were 0.22, 0.14, 0.20, and 0.10, respectively. Reproductive performance is controlled by the genetic make-up of dam, sire, and offspring, but it is largely affectedby environment. Thus, the reproductive efficiency of the breeding herd depends on the fertility of the bulls(Gredler et al., 2005). Bull’s fertility is also essential, since bull's DNA is the primary mechanism through which genetic improvements can efficiently be accomplished. Implementation of artificial insemination (AI) in dairy cattle production allowed improving selection of bulls for production traits. Also, frequent freezing and thawing process of bull semens significantly affected quality index and consequent evaluation of fertilization potential of a semen sample for AI in Holstein breeding stations (Mathevon  et al., 1998).However, the preselection of the samples, the high number of sperm per doses, and the high quality of the semen used in the AI programs  can reduce the variability, thereby giving a low probability of detecting fertility differences associated with seminal parameters. Spermatogenesis is a complex process that involves stem-cell renewal, genome reorganization, and genome repackaging;  so, it culminates in the production of motile gametes (Kealey et al., 2006). The process of spermatogenesis is regulated by reproductive hormones in gonadotropin axis and is controlled by a large number of genes(Sun et al., 2012). Therefore, hormone and their receptors are presumed to be good candidate genes for reproductive traits.

The results of this study indicated that the range of heritability values for the different semen quality characteristics are low to moderate, which may indicate that improvement of environmental factors is more effective than genetics basis.

About 6 million heads of cows in Iran are native cattle, but their characteristics and production potential remains unknown. Inbreeding is one of the important factors which must be considered in the herd and can be of considerable economic loss to producers. In this research, we tried to investigate the occurrence of inbreeding and its effects on the production traits of Serabi native cattle in a breeding center related to agricultural ministry located in Sarab. The aim of this study was to evaluate inbreeding effects on milk yield, fat percentage, protein percentage, and birth weight of Sarabi dairy cattle. In this research, the effects of inbreeding and environmental factors on production traits and birth weight of Sarabi cows have been studied. The data were collected from 1250 Sarabi cows during 1992- 2012 years by Sarabi Breed Center in East Azerbaijan province. In the present study, a pedigree file containing 1250 head (764 females and 486 males) of dairy cows was used, including data about the registration number of the livestock, date of birth, gender, father's registration number, and registration number of the mother, date of birth, parity, and production records. After the removal of animals with unknown and unregistered parents in the flock, the pedigree was arranged using pedigree software. Pedigree software was used to calculate the inbreeding coefficient for each animal. The principles of this software in the calculation of inbreeding coefficient is based on the formation of a kinship matrix among the individuals. The inbreeding coefficients of animals were considered as a discrete trait in 4 groups. In this herd, inbreeding coefficient was estimated between 0- 0.28. Average inbreeding coefficients of all cows and inbred cows are 0.0109 and 0.0574, respectively. Totally, 19% of all animals were inbred. In this study, inbreeding coefficient has been included in the model either as a continuous variable or as a classification variable. When inbreeding factor considered as continuous variable in the model, its effect on milk yield, fat and protein percentage, and birth weight was significant (P<0.01). When inbreeding considered as a continuous trait, 1% increase in inbreeding coefficient had an effect equal to -2.69 kg, 1.59%, and +18.1% on milk yield, fat, and protein percentage, respectively. Miglior et al. (1995a) reported a 1% increase in inbreeding coefficient in Holstein cows reduced calf weaning by 0.44 kg and reduced milk production by 25 kg. Mohammadkarim (2006) using a univariate model reported for 1% increase in inbreeding coefficient, changes in milk yield, fat yield, open days and length of first lactation equal to -14.02, -0.349 kg, + 0.4839 and + 0.148 days, respectively. Inbreeding has a negative effect on production traits and birth weight. Results showed that the effects of year-season of calving and parity on production traits and birth weight were significant (P<0.01). The regression coefficient of milk, fat and protein percentages and birth weight on inbreeding, when inbreeding considered as a discrete variable, were -0.129kg, +0.093%, +0.072% and -0.564 kg, respectively. In this study, when inbreeding traits considered as a continuous variable, did not show any linear or nonlinear relation with birth weight trait of Sarabi cows. It can be concluded that in order to maintain the genetic reserves of Sarabi cattle breed, it is necessary to carefully monitor the reproduction breedings in this native cattle population. In the case of endangered breeds, such as Sarabi breed, these breeds have beneficial properties, particularly in relation to resistance to diseases, and tolerance to stressful environments. These desirable traits can be transferred to future genetic composition cattle breeds. Preserving the genetic resources of the country's livestock and pay attention to similar breeds of Sarabi should be taken into consideration by decision makers at livestock breeding centers. In Holstein cows it has been found that high milk production is associated with a genetic susceptibility to disease (Khaleghi & Rafat 2018), so the desirable characteristics of the Sarabi breed should be studied in this regard of susceptibility to diseases. The existence of a complete family tree in computer registrations is the first step in controlling inbreeding. The existence of this pedigree will help breeders to avoid breeding between close-up animals, like a brother-sister and a father -daughter, which will increase the incidence of inbreeding in the population. For this purpose, it is recommended to use optimal mixing softwares that controls inbreeding. There are programs for managing the breeding to identify animals as soon as possible and calculate their relativeness in the herd. By this method, it is possible to prevent the occurrence of high inbreeding in the herd and produce calves with high profitability. The prospect of this research is to carry out further research on the issue of inbreeding and effective population size on local breeds of dairy cattle to prevent the replacement of these breeds with Holstein. It is imperative to prevent the replacement of the foreigner breeds (Holstein generally) instead of Sarabi breed and its extinction as much as possible.

Methionine, as the first limiting amino acid in poultry, is necessary for protein synthesis and growth. Several studies evaluated the impact of in ovo injection of DL- methionine, but there is no report regarding the in ovo injection of L- methionine. Therefore, the purpose of this study was to investigate the effect of in ovo feeding of L- methionine on weight, carcass traits, small intestine morphology, and blood metabolites of day-old broiler chicks. To achieve the aim of the study, 240 broiler breeder eggs (Ross 308) were used based on a completely randomized design with eight experimental treatments and 30 eggs per each treatment. Experimental treatments included: six levels of L- methionine (0.19, 0.38, 0.57, 0.76, 0.95, and 1.14% L- methionine), a sham control (injection of distilled water), and a control (non- injected group), which were injected into the amniotic fluid at 14 days of incubation. After hatching, chicks were weighed, blood samples were collected, and they were humanly euthanized to evaluate carcass traits and small intestine morphology. After that, serum of blood samples was collected and used for measuring metabolite concentrations. The results of the study indicated that in ovo feeding of L- methionine increased carcass efficiency and thigh, breast, gizzard, and thymus weight (P<0.05), but did not influence hatchability (P>0.05). In addition, reducing effects of L- methionine treatments on serum glucose, triglyceride, and blood urea nitrogen were observed (P<0.05). Moreover, in ovo injection of L- methionine improved length and weight of small intestine, and duodenum and jejunum morphology parameters (villous height, villous height/crypt depth ratio, crypt diameter, and crypt depth), (P <0.05). According to the results of this study, in ovo feeding of 0.19% L- methionine had an improving effect on hatchability, carcass traits, blood metabolites, immune system organs, and morphology of small intestine; therefore, 0.19% L- methionine is an advisable level for in ovo feeding.

The unavoidable mating of related animals in closed populations leads to accumulation of inbreeding and decreased genetic diversity. The loss of diversity and an increase in homozygosity may result in decreased productions and fitness of inbred animals. It is apparent that different breeds as well as different traits vary in their response to inbreeding (Mackinnon 2003). It is important to account for the effects of inbreeding in populations undergoing selection to properly adjust the breeding program for the potential reduction in performance. The objectives of this study were to estimate the inbreeding trend and inbreeding depression on growth traits of Iranian Moghani sheep. Pedigree records on the research flock of the Iranian Moghani sheep kept at the Jafarabad Breeding Station from 1990 to 2016 were used for analysis. The pedigree completeness index (PCI) proposed by MacCluer et al. (1983) was used to describe the degree of completeness of pedigree. In addition, for each individual, the number of complete generations equivalent (CGE) was computed as the sum of (1/2)n, where n is the number of generations separating the individual to each known ancestor (Maignel et al. 1996). Inbreeding coefficient for all animals were estimated using the method of Meuwissen and Luo (1992). Average inbreeding coefficients per year were computed and annual increases in inbreeding were estimated by linear regression over time. Individual increase in inbreeding coefficients (ΔFi) was calculated according to the methodology described by González-Recio et al. (2007) and modified by Gutiérrez et al. (2009) as ∆Fi = 1− √1−Fit i −1 , where Fi is the inbreeding coefficient of individual i and ti is the number of known equivalent generations for this individual. The analyzed traits for estimating inbreeding depression were birth weight (BW), 3-month weight (3MW), 6-month weight (6MW), 9-month weight (9MW), and yearling weight (YW). For estimating the inbreeding depression, only animals with PCI > 0.6 were kept in all analyses. All animals were grouped into three classes: first class included non-inbred animals (F=0); second class included animals with 0<F≤0.10, and third class included animals with F>0.10. Then inbreeding included in the model as the categorical fixed effects. Also, in a separate analysis, inbreeding and individual increase in inbreeding were included in the model as a linear covariate. Proportion of animals with F=0, 0<F≤0.10 and F>0.10 in all population were 78%, 21%, and 1%, respectively. These proportions in animals with PCI>0.6 were 23%, 74% and 3%, respectively. Average inbreeding of all and inbred indivduals within the animals with PCI>0.6 were 1.00% and 1.32%, respectively. The rate of inbreeding is more important than estimated level of inbreeding in genetic management of the population (Falconer and Mackay 1996; Bijma 2000). The evolution of the mean inbreeding, pedigree completeness index (PCI), and complete generations equivalent (CGE) of animals across years of birth are given in Figure 1. Trend of inbreeding was more coincident with PCI than CGE. Such a discrepancies in the behavior of two index of pedigree completeness could be attributed to the differences in the computation methods. From the linear regressions estimated, the rate of inbreeding was 0.03% (P < 0.01) per year equal to 0.12% per generation in the studied period. This estimated rate of inbreeding was less than the critical levels (1% per generation) recommended by FAO (1998) and Bijma (2000). The estimated rate of inbreeding for Moghani sheep in this study was similar to those reported by Hossein-zadeh (2012) and Gholambabaeian et al. (2012) for this breed. Also it was close to values reported for Iranian LoriBakhtiari, Sangsari and Zandi sheep (Almasi et al. 2014; Rashedi Dehsahraei et al. 2013; Sheikhlou et al. 2017). Nevertheless, it was less than the estimates for the Iranian Karakul, Iran-Black and Baluchi sheep (Bahri et al. 2015; Mokhtari et al., 2014; Tahmoorespur and Sheikhlou 2011). This differences in inbreeding rate could be attributed to the differences in the pedigree completeness of animals and also to the low proportion of matings between relatives and better control of inbreeding in Moghani sheep. Table 3 shows the least square means for body weight traits in different inbreeding classes of animals. The mean BW and 3MW of animals in the second class was lower than non-inbred animals (P<0.05). However, means of the BW and 3MW of animals in third class were higher than the second class. In other words, some of the heavy animals were between the highly inbred animals. Similar to our results, Prod’Homme and Lauvergne (1993) reported increased prolificacy with increasing inbreeding. They concluded that the positive effects of selection and improved management on prolificacy were larger than the negative effect of inbreeding. However, it should be kept in mind that the low proportion of animals in the third class of inbreeding in this study may also have contributed to these results. According to the Table 3, means of the 6MW, 9MW and YW have decreased with increasing in inbreeding, but the differences between inbreeding classes were not significant. The regression coefficients of BW, 3MW, 6MW, 9MW and YW on inbreeding were 0.006, -0.023, -0.051, -0.017 and -0.119 and on individual increase in inbreeding were 0.022, -0.044, -0.185, -0.111 and -0.326, respectively and were not significant (P>0.05). Converting individual increase in inbreeding coefficients to the equivalent inbreeding for an animal with an average depth of pedigree resulted in inbreeding depression of 6, -13, -55, -33, and -96 gram in BW, 3MW, 6MW, 9MW and YW, respectively. Considering the results obtained in this study, large proportion of animals in this flock with good pedigree completeness were inbred. Thus, implementation of methods to control inbreeding in this flock are suggested to achieve desired genetic gain with minimum loos of genetic diversity in the future. It seems that preweaning traits (BW and 3MW) is more affected by inbreeding rather than other growth traits in this breed. In this study the pattern of the changes in inbreeding depression due to using individual increase in inbreeding in the model were not the same across the different body weight traits.

In the quantitative genetics area, random regression model is one of the most accurate models for estimating daily breeding value in dairy cattle. However, because of the higher number records per each cow, application of this model is labor and time consuming. In addition, breeding values of cows at different days of lactation are highly correlated. The main objectives of the current study were to determine the relative importance of each breeding value at different days of lactation and to estimate the genetic principal components for the breeding values of Iranian Holstein dairy cattle for milk production traits. Maternal and methods: records of milk production traits of first-parity dairy cows. Milk yield, fat percentage and protein percentage test-day records of 73839, 65165 and 46881 cows, respectively, from 230 herds with 176390 cows in their pedigree were used in the analyses. Only test-day records belonging to 5 to 305 days of lactation were used. The data belonged to cows were born between 1988 and 2015 with age at first calving ranged between 21 to 48 m. In addition, the existence of at least one monthly record in the first 90 days after calving was essential for the cow, otherwise it would be eliminated. These data were collected by National breeding center, Karaj, Iran. Genetic parameters were estimated by a random regression model and Bayesian approach using GIBSS3F90 software. The estimated breeding values at all days of lactation were calculated and standardized using the standard score (z). Then, Correlation matrices among breeding values at different days of lactation and genetic principal components of breeding values were estimated by PROC CORR and PROC PRINCOMP of SAS software, respectively. Finally, we could calculate principal component score as a selection criterion (selection index) for the selection of dairy cattle. For this purpose, the standardized score coefficient was obtained by dividing the daily eigenvector of each principal component by square root of its eigenvalue. The principal component score were calculated of the sum of the multiply between standardized score coefficient and daily standardized breeding values for each cow. However, the principal components could be used as an index to multiple traits evaluation of animals. The genetic correlations matrix between the estimated breeding values at different days of lactation demonstrated that the breeding values at the middle stage of lactation were highly correlated with the breeding values at the reaming stages of lactation. The genetic principal component analysis revealed that the first two principal components accounted for a high percent of total genetic variance of all studied traits. For milk yield, the first principal component explained 99.48% of genetic variance, while two first components explained almost 98.19% and 100% of genetic variance for fat percent and protein percent traits, respectively. The absolute value of correlations between the first principal component of milk yield and all breeding values (except for day 56 and day 231) were more than 0.056. The absolute values of correlations between the first principal component of fat percent and the daily breeding values were greater than 0.06 for days between 83 and 222; and for protein percent were greater than 0.07 for days 99 to 168 and days 289 to 305.
Considering the high correlation between breeding values seem to, were estimated breeding values for all days is not required. The first principal component milk yield trait with nearly all estimated breeding values, high correlation and first two principal component fat percent trait of estimated breeding values in the early and middle of lactation period had a high relationship. But first two principal component protein percent trait of estimated breeding values in the middle and later of lactation period had a high correlation. Considering the high cost of recording system in dairy cattle industry and the high correlation between the breeding values, it seems that there is no need to predict the breeding value for all days of lactation. In other words, reducing the number of records per each cow may be beneficial at both economic and genetics stand points. Furthermore, due to the high, direct correlation between the principal components and daily breeding values, the implementation of principal components in the genetic merit evaluation of selection candidates for production-related traits is suggested.

This study was conducted to survey a laying parent stock performance and egg quality traits. A laying parent stock unit was selected at the age of 52 wk and data including hen and cockerel number, mortality percent, hen to cockerel ratio, egg production percent (hen day and hen housed production), non- settable egg number and settable egg (%) were recorded daily up to 72 wk of age before beginning of molting. For calculating hatchability (%), the data were obtained from hatchery unit. The collected data were divided to 6 experimental periods (28 days/period). At beginning of the study, the number of hen and cockerel were 30013 and 4144, respectively. The hen to cockerel ratio was 14. The percent of hen and cockerel mortality at experimental periods were 0.033 and 0.022, respectively. Hen day egg production (%) was lowered as compared to the recommended data in parent stock management guide (5.55 %). Generally, the analysis of the data showed that with increasing experimental periods (or the age of birds), the non-settable and settable egg numbers were increased and decreased, respectively. The settable egg (%) was significantly different from the recommended data in parent stock management guide. With increasing experimental periods, the hatchability was deceased. The hatchability (%) of this farm was lowered as compared to recommended data in parent stock management guide. The analyzing of concerned production traits of this farm revealed that production and nutritional management need more attention in this laying parent stock unit.

(Co) variance components of milk, fat and protein yield of 11368 first lactation Iranian Holstein dairy cow were estimated by single trait random regression test-day models and restricted maximum likelihood (REML) method. In this study the homogeneous residual variance was assumed throughout lactation. Estimated heritability for 305-day production traits ranged from 0.25 to 0.31 and the highest genetic correlation between different days in milk was observed for protein yield. Persistency is one of the key factors determines the amount of total milk yield during a lactation period. Persistency is heritable economic trait and an important parameter of lactation curve in dairy cattle. In this research five different persistency measures were included: 1) different between estimated breeding values (EBVs) from d 290 and d 90; 2) different between EBVS from d 1 to 100 from d 101 to 200; 3) different average of EBVs from d 255 to d 305 from d 50 to d 70; 4) summation of different EBVS for d 61 to 280 from d 60; 5) summation of the EBVS from d 60 to 279 as a deviation from d 280, were used. Heritability’s as a function of time were calculated. Heritabilities estimated for persistency of protein yield (6-31 percentage) were slightly higher than this values for persistency of milk (7-28 percentage) and fat (3-25 percentage) yield, as well as genetic correlations between persistency and 305-day production were estimated slightly higher for protein and milk yield than thus for fat yield.