a. a. shadparvar

This study aimed to investigate the interaction effects between marker effect estimation methods, population structure, and genetic architecture of the trait on the accuracy of genomic evaluations. A reference population with two different effective population sizes (100 and 500) was simulated using the QMSim software. 500 markers and two different numbers of quantitative trait loci or QTLs (50 and 200) were distributed randomly through the genome including a 100 cM chromosome. In this study, three traits with different heritabilities (0.1, 0.3, and 0.5) were simulated. The genomic breeding values were predicted using Bayesian ridge regression, Bayes A, Bayes B, Bayes C, Bayesian LASSO, Reproducing kernel Hilbert space, and neural networks methods. Through the three heritabilities, as the effective population size increased, the accuracy of genomic evaluation decreased with different trends. As the number of QTLs increased, the accuracy of low heritability trait increased, but the accuracy of medium and high heritability traits decreased. Similarly, as the number of QTLs increased, the accuracy of the trait with normal distributed QTLs increased, but the accuracy of traits with gamma and univariate distributed QTLs decreased. For all types of QTL distributions, the increment of effective population size decreased the accuracy of genomic evaluations. The results of this study clearly showed the interaction effects between markers effect estimation methods, population structure, and genetic architecture of the trait on the accuracy of genomic evaluations.

Persian sturgeon (Acipenser persicus) is one of the most valuable species of fish native in the Caspian Sea and Iran's waters. In order to evaluate the heritability of reproductive traits in wild Persian sturgeon (A. persicus) three males and three  females’ fish were caught in the southern area of Caspian Sea and then were crossed by using a 3×3 factorial with the aim of selecting the best parent to produce good larvae. Prior to the crossing, sperm density, motility, pH and osmolality, spermatocrit percentages, and the number of micropyles on the eggs` surface were determined. One hundred grams of eggs from each females were fertilized by 1mL of sperm from each males for a total of and 9 treatments combinations, (F1M1, F1M2, F1M3, F2M1, F2M2, F2M3, F3M1, F3M2 and F3M3) with 3 replicates each. Hatching percent was calculated at 5 h after fertilization and 405 eggs were measured for weighting and determining their diameter. Sperm concentration and number of micropyles had no significant effects on fertilization rate (p>0.05). Sperm density also had negative correlations with fertilization (r=-0.603, p<0.01) and hatching rates (r =-0.175, p<0.01). However, fertilization was positively correlated with osmolality (r=0.511) and number of micropyles (r = 0.574). The correlation between osmolality and hatching rate was 0.288 and between eggs weight and its diameter was 0.698. Heritability estimates for weight and diameter of eggs were 0.043±0.035 and 0.207±0.103, respectively. The results show treatment 9 (F3M3) had higher genetic values compared to the rest of the treatments. Also due to the large number of micropyle on sturgeon ova, in order to increase the percentage of fertilization rate, sperm density should be low but motility duration should be high.

Genomic information offers new possibilities to control the level of progeny inbreeding. Traditionally, mating plans constrain the inbreeding of predicted progeny by using a matrix of relationships among individuals. In this study aims to improve genetic gain and restrict the inbreeding level of progeny, a founder population, with effective population size 1000 and 1000 generations, was simulated using the QMSim program to generate linkage disequilibrium. Thereafter, 500 males and 500 females were used to generate a secondary 10-generation population, with 1000 individuals per generation and its corresponding phenotype and genotype in SNP terms. Five hundred sires and 500 dams from generation 1010 were randomly selected to create generation G0 and a trait with heritability of 0.3 was simulated. The eight strategies consisting of two mating designs: minimum_inbreeding (minf) and random (rnd) and four relationship matrices as matrices of A, GRM, IBS and Weighted were used to estimate breeding value for ten generations. Comparison of strategies showed that type of mating designs and different relationship matrices had significant effect on amount of genetic gain, homozygosity and inbreeding (p < 0.05). The type of relationship matrix affected the accuracy of predictions. In strategies with weighted matrix (Gweighted), average of genetic gain during 10 generations was less than strategies using A, GRM and IBS matrices. In strategies with random mating, inbreeding rate had increasing trend. Accuracy of selection (0.63) in strategies with A matrix was lower than other strategies. In general,

The present study was aimed to assess the role of genotype× environment interaction (G×E) and generation interval on the genetic gain caused by importing the genetic materials of dairy cattle in a country without advanced breeding program. Changing allele substitution effects of quantitative trait loci (QTLs) underlying the trait was used to simulate G×E. To this end, three levels of 100 (control), 75 and 50% overlapping of QTL effects in imported sires at the heritability levels of 0.1, 0.3, and 0.5, as well as three scenarios of continuous replacing and discontinuous replacing with one and two generations of foreign genetic materials were considered. Moreover, the effect of decreasing the accuracy of genomic prediction in traits with the low level of heritability was investigated. Based on the performance of daughters of sires in the local population of control scenario, import-based strategies were better than on the strategies of local population. The genetic correlation created in 75% and 50% overlapping of QTL effects were as 0.7 and 0.5, respectively. Although the trend of genetic response increased by decreasing genetic correlation over the time, it was not equal to origin population. Maximum genetic gain (2.56) was observed with a heritability of 0.5 in continuous replacing and absence of G×E. A decrease in the accuracy of prediction resulted in exacerbation of G×E. In general, more genetic improvement is transmitted from origin population to local population only by importing the genetic materials related to populations with higher genetic mean and also by considering G×E in different traits.

One of the breeding programs for sheep is the nucleus breeding scheme (Kosgey and Okeyo 2007). The nucleus breeding system, including open or close nucleus, is a good start for genetic improvement in many populations. Unlike the close nucleus schemes, there is a reciprocal gene flow in open nucleus schemes, where the best animals from base layer could be transferred to the nucleus layer (Kosgey et al. 2006). In the original nucleus breeding scheme, a constant genetic variation was assumed for the trait of interest across different generations (Hopkins and James 1978). However, it is well realized that the additive genetic variance is decreased after several generations of intense selection. Thus, the actual genetic gain is likely less than the expected genetic gain. In other words, the outcomes of these breeding schemes are likely affected by the intensity of selection as well as the relative size of the nucleus population to the size of whole population. The aim of current study was to investigate the effect of relative nucleus size at three levels of 5, 10 and 15 percent and the effect of selection intensity in males and females at three levels of high (i.e. selection proportion in male and female candidates were 1 and 20 percent, respectively), moderate (i.e. selection proportion in male and female candidates were 5 and 50 percent, respectively) and weak (i.e. selection proportion in male and female candidates were 10 and 80 percent, respectively) on the amount of biasness of predicted genetic gain due to ignoring the effect of selection on genetic variance. An open nucleus breeding scheme was deterministically simulated using data from Lori Bakhtiari sheep breed, consisting a nucleus herd of 1005 ewes and a base herd of 4019 ewes (Vatankhah 2005). Birth weight was the breeding goal trait and selection was carried out using one individual performance record. The generation was assumed to be overlapped and three and six age groups were considered for males and females, respectively. The age of parents at first lambing was 2 years and 0.94 yearling lamb was produced per ewe. One ram was mated to 33 ewes. The additive genetic variance for breeding goal trait was 0.11 and its heritability was set to 0.30. For a given size of the nucleus and a constant rate of transferring ewes from base population to nucleus (x), there was an optimum rate of migration of nucleus rams into the base population (w), that maximized the expected genetic gain and minimized the genetic lag between nucleus and base populations, with or without accounting for the effect of selection on the genetic variance. By increasing the relative size of the nucleus, from 5 to 15 percent of whole population, the biasness of the predicted genetic gain was increased from 11.84 to 16.67 percent. At a given rate of x, the bias of genetic gain prediction which originated from ignoring the effect of selection, varied nonlinearly as the rate of w increased and had a minimum value at the optimal value of w. At a particular rate of w, increasing the rate of x, was associated with higher rate of bias. The rate of bias increased from 12.2 to 16.54 percent, as the intensity of selection increased. The reason could be the markedly reduction in the genetic variance of considered trait due to selection at high selection intensities. In open nucleus schemes, the amount of bias changed from 14.18 to 17.72, as a consequence of increasing the intensity of selection. Across all levels of selection intensity in open nucleus programs, the lowest bias was observed at the rate of 0.75 for w. Evaluation of joint effect of relative nucleus size and intensity of selection, showed that at high selection intensity and relative nucleus sizes higher than 10 percent, the amount of bias was not likely to be influenced by nucleus size. Ignoring the effect of selection on genetic variance could result in a biased prediction of genetic gain in the nucleus breeding schemes. However, the optimal ram transfer rate from nucleus to base population was independent from the effect of selection intensity. The lowest bias was observed for the scheme with optimum transfer rate. Results of the current study revealed that at weak selection intensity, the effect of selection could be ignored and the breeding program could be optimized for ram transfer rate from nucleus to the base populations. However, at high selection intensities, the ignorance of this effect might result in wrong conclusions.

Body weight traits prior to weaning are of special importance in sheep breeding programs.

In this study genetic parameters for pre-weaning growth traits in Shall sheep were estimated using data on birth weight (BW), weaning weight (WW) and average daily gain (ADG) from birth to weaning, collected during 1994 to 2009 in Qazvin province. The data belonged to four herds and included 1,992 records for BW, 1,584 records for WW and 1,568 records for ADG.

Six different models were used to estimate the genetic parameters. The most appropriate model was determined for each trait using likelihood ratio test.

The estimated direct heritability for BW, WW and ADG were 0.34, 0.48 and 0.21, respectively. Genetic correlations between traits were positive and medium to high. The phenotypic correlations were lower than the corresponding genetic correlations.

Due to high heritabilities estimated of birth and weaning weights and also high genetic correlation between these traits, direct selection for genetic improvement of these traits will be effective.

Although Wood function is of great importance to studying lactation curves because of its coefficients with biological interpretation, it has not been used under a random regression models. The objective of this study was to investigate the possibility of fitting Wood function on the test day records of dairy cows under a random regression model. Data was consisted of 743,205 test day records in first lactation of 81,192 Holstein cows from 46 herds in Iran which have been collected during 1991 to 2008. Linear form of Wood function which was a polynomial of 3 has been used. Three forms of Legendre polynomial functions with third order for additive genetic effects were also applied to evaluate the results of Wood function. Heritability estimates for daily milk yield obtained by linear form of Wood function were higher than those obtained by Legendre functions in most days indicating its advantage for genetic evaluations. Correlation between breeding values of animals predicted by linear form of Wood function and Legendre functions was 90%. In conclusion, Wood function can be employed in random regression models causing exploitation its desired characteristics and the advantages of random regression models to explain the genetic variations of dairy cows’ lactation curve.

In this research, economic value for production traits and reproduction traits of Afshari sheep were estimated using a deterministic bio-economic model. In production system of Afshari sheep, the variable costs comprised 99 % of total costs. Revenue sources consisted of meat, milk, wool and manure, where meat with 83.4 % of revenue had the highest effect. Economic value for a trait as the change in profit of production system by increasing one unit in the trait avrage, while avrage of other traits were constant was estimated.After that, milk, wool production and ewe survival were followed. Birth weight had negative economic value. The sensitivity of economic value of various traits to ±10 % changes in feed, non- feed costs and also to meat, wool and milk prices were examined, and the results showed the economic values of wool and milk productions were not sensitive to none of the costs. Also changes in the costs of rearing and marketing had no effect on the economic value for body weight in various ages of wool and milk production. By changing the price of meat, the economic values of all traits except birth weight, wool and milk production were changed, by increasing the price of meat results to increases in the economic values of those traits. Among various factors, meat price had the most effect on the economic value of traits.

Using a deterministic model for dairy herd according to milk pricing system in Iran, the economic weights of milk production, fat percentage, protein percentage and herd life with minimized cost were identified, while restricted total input and total costs were variable in animal unit. By data simulation and the data obtained from 3 different dairy herds in Iran, the sensitivity of economic values with the assumption of 20% change in forage and concentrate price, forage to concentrate ratio, feed-cost, non-feed cost, base price of milk, free-market price of milk, average milk production, fat percentage, protein percentage and herd life were estimated. In the base condition, the economic weights of milk production, fat and protein percentage and herd life in three herds were -0.55, -0.24, -0.54 -34, -20, -11 -34, -37, -33 -0.37, 0.67, -0.39, respectively. The effect of change in different factors of production system on economic weights of three farms was variable. The economic weights of milk production, fat percentage, protein percentage and herd life had the most sensitivity to change in the average milk productoin, base price of milk and herd life, respectively.