using two diverse approaches, GBLUP and BayesC, using simulated data under different quantitative trait locus (QTL) effect distributions. Data were simulated with three different distributions for the QTL effect which were uniform, normal and gamma (1.66, 0.4). The number of QTL was assumed to be either 5, 10 or 20. In total, 9 different scenarios were generated to compare the markers estimated breeding values obtained from these scenarios using t-tests. In comparisons between GBLUP and BayesC within different scenarios for a trait of interest, the genomic estimated breeding values produced and the true breeding values in a training set were highly correlated (r>0.80), despite diverse assumptions and distributions. BayesC produced more accurate estimations than GBLUP in most simulated traits. In all scenarios, GBLUP had a consistently high accuracy independent of different distributions of QTL effects and at all numbers of QTL. BayesC produced estimates with higher accuracies in traits influenced by a low number of QTL and with gamma QTL effects distribution. In conclusion, GBLUP and BayesC had persistent high accuracies in all scenarios, al-though BayesC performed better in traits with low numbers of QTL and a Gamma effect distribution.

Introduction: Genetic evaluation and estimation of breeding value are one of the most fundamental elements of breeding programmes for genetic improvement. Recently, genomic selection has become an efficient method to approach this aim. The accuracy of estimated Genomic breeding value is the most important factor in genomic selection. Different studies have been performed addressing the factors affecting the accuracy of estimated Genomic breeding value. The aim of this study was to evaluate the effect of beta and gamma distributions on the accuracy of genetic evaluation.Materials and Methods: A genome consisted of 10 chromosomes with 200 cm length was simulated. Markers were spaced on 0.2 cm intervals and different numbers of QTL with random distribution were simulated. Only additive gene effects were considered. The base population was simulated with an effective size of 100 animals and this structure continued up to generation 50 to creating linkage disequilibrium between the markers and QTL. The population size was increased to 1000 animals in generation 51 (reference generation). Marker effects were calculated from the genomic and phenotypic information. Genomic breeding value was computed in generations 52 to 57 (training generation). Effects of gamma 1 distribution (shape=0.4, scale=1.66), gamma 2 distribution (shape=0.4, scale=1) and beta distribution (shape1=3.11, shape2=1.16) were studied in the reference and training groups. The heritability values were 0.2 and 0.05.Results and Discussion: The results showed that accuracy of genomic breeding value reduced with passing generation (from 51 to 57) for two gamma distributions and beta distribution; this decrease may be due to two factors: recombination has negative impact on accuracy of genomic breeding value and selection reduces genetic variance as the number of generations increases. Accuracy of genomic estimated breeding value increased as the heritability increased so that the high heritability had more accuracy than low heritability in same QTL number. Number and distribution of genes is an important factor for accuracy of estimated breeding value. Duncan test was conducted by SPSS software. Results illustrated that there was no significant difference between the different distributions. Comparing accuracy of estimated breeding value showed that in the low heritability scenario with 10 and 20 QTL, gamma distribution 2 and gamma distribution 1 performed well, respectively, whilst in 50 and 100 QTL scenario, beta distribution was superior in both Lasso and Ridge methods. In the high heritability scenario with 50, 100 QTL gamma distributions 2 were superior in both Lasso and Ridge methods. With four QTL (10, 20, 50 & 100), in high heritability scenario, estimated genomic breeding value was often increased by increasing the number of QTL. This may be due to increasing linkage disequilibrium between markers and QTL. In general, the gamma distribution led to the increased accuracy of the estimations in both Lasso and Ridge methods.Conclusion: Marker density, method to estimate marker effects, QTL distribution, number of QTL, number of generations and trait heritability are some effective factors on accuracy of estimated genomic breeding value. The accuracy of estimated genomic breeding value is output of these factors and the distribution of genes is an important factor for accuracy of estimated genomic breeding value. We can conclude that, accuracy is reduced with increasing number of generations from base population to training population while the accuracy of estimated genomic breeding value is increased when breeding value of the reference group is used in lieu of the phenotypic records. In addition, accuracy of estimated genomic breeding value is enhanced by increasing heritability, so that, between three the distributions simulated in high heritability scenario, gamma 2 distribution increased accuracy of the estimates. Although, the size and distribution of QTL effects still greatly influence the effectiveness of the genomic prediction methods, but as a suggestion, models of genetic variation of genomic assessment should be considered, since a method of estimating breeding value may have (or produce) a better estimation with a specific model.

This research was conducted to validate and fine-map the region attributed to salinity tolerance (Saltol) on chromosome1 in rice at International Rice Research Institute (IRRI) during years 2005 to 2007. A major effect QTL (Saltol) which is responsible for Na+ and K+ uptake and Na/K ratio was identified using F8 recombinant inbred lines (RILs) of Pokkali/ IR29 cross on chromosome 1. This QTL explained around 64.3 to 80.2% of the phenotypic variation for the mentioned traits. Fine-mapping was done using 10 SSR and EST-SSR markers and near isogenic lines (BC3F4), derived from IR29 × Pokkali produced for salinity tolerance at seedling stage. Random BC3F4 individuals were genotyped and phenotyped under two different electrical conductivities at seedling stage. QTL responsible for salinity tolerance at both ECs, were found in the Saltol region, which explained 18 and 24% of the phenotypic variation for SES scores, respectively. According to the present results, possible location of Saltol was found in the interval around 1.2 cM on chromosome 1 that was around 350Kb based on physical map. This QTL was mapped at the intervals of RM8094, RM3412 and CP6224. Therefore, molecular breeding for salinity tolerance in Iranian genotypes could be done using the mentioned markers.

One of the obvious reasons for most disorders in network service provisioning is network path congestion. Congestion avoidance in today's networks is too costly and sometimes impossible. With the introduction of SDN, centralizing the equipment's control plane has become possible. This paper presents an enhanced method named ESV-DBRA to avoid congestion in multi-tenant SDN networks. At first, ESV-DBRA monitors the traffic load and delay of all network paths for each tenant individually. Then, by merging the parameters obtained from the monitoring, the Service Level Agreements (SLA), and a novel proposed cost function, it calculates the cost of the network paths per tenant. As a result, traffic for each tenant is routed through the path/paths at the lowest possible cost from the tenant's perspective. Next, the bandwidth quotas will be calculated and assigned to the tenants over their optimal routes. Afterward, whenever congestion is likely to occur in a path, ESV-DBRA automatically changes the route or bandwidth of the tenants' traffic related to this path to avoid congestion. Related algorithms are also proposed.Eventually, simulations show that the proposed method effectively increases bandwidth utilization by 10.76%.

The aim of this study was to discover QTL for body weight traits on chromosome 3 of Japanese quail in a four-generation design based on diallel crosses. For this purpose, four strains of Wild, AandM Texas, Italian and Tuxedo Japanese quail were crossed in reciprocal and diallel pattern. The first generation of hybrid birds was then used to produce the next generations, including the second, third, and fourth generations. Phenotypic data included body weight gain from hatching to 45 days with an interval of 5 days in the offspring of the selected fourth generation parents. Third and fourth generation parents and all offspring of selected fourth-generation parents (369 birds) were genotyped for three microsatellite markers on chromosome 3. Marker effects and variance components were estimated using three models for additive, dominant, and additive-dominant markers using GVCBLUP software For marker effects estimation, the point with the highest F statistic was considered as the QTL position. The results of this study indicate the presence of at least one QTL with additive effects related to body weight traits at 5, 10, 20 and 40 days at the beginning of the chromosome and for hatching traits, 15, 25, 30, 35 and 45 days at the 38cM of chromosome 3. Significant QTLs were also found in the dominance model for most traits, except for 25 and 35 days at the beginning of chromosome 3. The percentage of changes due to additive and dominance effects of markers in the phenotypic expression ranged from 1.3 to 8.7%.