رستم عبدالهی آرپناهی

Genetic selection for increasing milk production and economic profitability in the dairy industry has been associated with reduction in reproductive performance, including lower embryo survival and pregnancy loss. The main purpose of this study was to use the transcriptome profiles of endometrial tissue and Corpus luteum of two groups of high and low fertility Holstein dairy cows to identify genes that are effective in reproductive rate, especially in early pregnancy. By the analysis of RNA-Seq data to express the gene differences, 4538 genes were extracted, which a total of 1466 genes showed significant expression differences (P<0.000001, Fold change<0.5). Then, by comparing the relevant genes among transcriptome profiles, common genes between endometrial tissue (on days 7 and 13 of the estrous cycle) and Corpus luteum (on day 13 of the estrous cycle) including SYNM, PARM1, NXPE2, NT5DC3, COL4A3, COL12A1, ALPK3, ADAMDEC1, SERPINA14, S100A9, PI16, OAS1X, MSTN, MASP1, CD83, CA2, C2, C5, JSP.1 and SAA3 were identified. Annotation results of these genes indicated that they have a role in the main process of metabolic and signaling pathways related to the ion transport system, inflammation, immune system function, and cell-matrix structure. Overall, the present study can provide new insights into the molecular evidence for the biological mechanisms of transcriptome profiling in the uterine environment and biomarkers related to fertility in dairy cows.

This study aimed to compare different methods of genomic evaluation using the criteria of correlation (ρ), regression (β), mean square error (MSE), and selection effectiveness (SE). For this purpose, nine different scenarios were designed based on different levels of heritability (0.1, 0.3, and 0.5) and different numbers of QTLs (20, 200, and 1000). To simulate different scenarios, five generations with a size of 1000 animals were simulated, of which the first two generations were considered as the reference population and the next three generations as the validation population. For each animal, a genome of 500 centimorgans with a marker density of 10000 SNP consisting of five chromosomes was simulated. Genomic breeding values were predicted using three statistical methods GBLUP, Bayes A, and Bayes B. The results showed that with increasing generation interval from the reference population, the accuracy of genomic breeding values decreased for three statistical methods, although Bayesian methods performed better in terms of continuity of accuracy. Based on the criteria of correlation, regression, and selection effectiveness, with increasing the levels of heritability, improved accuracy was observed, but the criterion of mean squares error showed the opposite trend. Bayesian methods performed better in low QTLs, but differences in different genomic evaluation methods were minimized in high QTLs. The results of selection effectiveness in comparison with the accuracy of genomic breeding value showed that accuracy can’t always be a suitable criterion for determining the superior method of genomic evaluation.

In order to evaluate the effect of genotype by environment interaction on production traits of Holstein cattle of Iran, first lactation test day records of 344170, 135000 and 156840 of milk, fat and protein yield on 34417, 13500 and 15684 cows and SNP markers of 1935 genotyped bulls were used. The production data were retrieved from the Animal Breeding Center and Productions Improvement of Iran’s database which were collected from 2013 to 2018. To consider the interaction of genotype and environment, mean of temperature-humidity index (THI) in three days before each test day records as continuous environmental effect were retrieved from the 35 closest meteorological stations in the vicinity of 139 Holstein herds from 13 provinces. Variance and covariance components were estimated through a single-trait random regression model with orthogonal Legendre polynomials of second order for days in milk and THI using AIREMLF90 software. The results showed that changes in THI across lactation led tofluctuations in additive genetic variance over time. The change in heritability of milk production traits over lactation followed the same trend as additive genetic variance. The results from cross-validation analysis showed that including genomic information into the predictive model, increased prediction accuracy and including THI information increased unbiasedness. Due to the changes in milk production of daughters of bulls across days and THI , genotype by environment interaction should be considered when selecting bulls under different conditions.

The aim of this study was to compare three selection strategies and two properties of breeding value estimations using computer simulation. Simulated traits were weights at birth (BW1), eight weeks (BW8), twelve weeks (BW12), maturation (BWM) and also age at first laying (AFL), weight of first egg (EWM), average egg weight (EW) and egg number (EN). The first strategy was to select cockerels based on breeding value of BW12 and selection of hens based on a selection index with four traits including BW12, AFL, EW and EN. In the second strategy, cockerels and hens were selected using a selection index, as already said. But in the third strategy, cockerels were selected based on breeding value of BW12 and hens based on breeding value of EN. The individual's breeding values for three schemes were estimated by BLUP and ssGBLUP. Matings were performed based on the optimal genetic contribution. The results showed that the total economic values of the first and second programs using ssGBLUP estimations were 450, 460 and 421, and using BLUP were 434, 349 and 418, respectively. The rate of true inbreeding coefficient for ssGBLUP estimations were more than BLUP estimations (0.083, 0.287 and 0.046 vs. 0.072, 0.116 and 0.024, respectively). The results showed that the first strategy for a breeding flock of broiler production, the second strategy for a dual-purpose flock for producing egg and meat and the third strategy for a flock to achieve the highest total economic value with the lowest possible rate of true inbreeding were desirable.

The aim of current study was to evaluate the accuracy of genomic breeding values (GEBV) for two important economical traits of milk yield and somatic cell score using SNP markers and LD-based haplotype blocks (haploblocks) by two statistical methods of GBULP and Bayes B. The data set consisted of 1654 bulls genotyped with different marker densities. When SNPs were used, the accuracy of breeding values obtained by Bayes B was better than GBLUP. In other words, for milk yield and somatic cell score traits, the prediction accuracy of GBLUP was 0.54 and 0.44 and by Bayes B was 0.58 and 0.44,respectively. For milk yield, the prediction accuracy of using haploblocks in both statistical methods was higher than the prediction accuracy using SNPs, while for the somatic cell score, this increase was more pronounced when GBLUP was used. However, when Bayes B was used this superiority was only obtained when the r2 statistic used to build the haploblocks was higher than 0.2. The results showed that the optimum level of r2 for building haploblocks depends on the trait type and its heritability. As a result, using r2 statistic more than 0.2 for building haploblocks can increase the accuracy of breeding valuesfoe both traits compared to SNP markers.

The objective of this study was to compare introgression and synthetic breed strategies for litter size trait improvement in sheep using computer simulation. For this purpose, a trait with heritability of 0.1, consisting of two chromosomes was simulated. On chromosome 1, a single QTL as the major gene was created that accounted for 40% of the total genetic variance. The effect of favorable and unfavorable alleles for the QTL was fixed after seven generations in both A and B breeds, respectively. The introgression and synthetic breed strategies were compared using Classical and Classical with gene-assisted selection (GasClassical) methods. The genetic gain in introgression and synthetic breed strategies using GasClassical method was 39% and 16% higher than that of Classical method, respectively. The mean of inbreeding coefficient in the fifth generation in introgression strategy was 0.049 and 0.077 using the Classical and GasClassical methods, respectively, and in synthetic breed strategy was 0.11 and 0.008, respectively. The results of this study showed that the GasClassical method in comparison with the Classical method led to an increasing frequency of favorable allele (major gene) and genetic gain in both introgression and synthetic breed strategies. However, the genetic gain for one percent increase in inbreeding in the synthetic breed strategy was greater than that of introgression strategy, and as a result, the synthetic breed strategy performs better than introgression strategy.

Baluchi sheep is one ofthe indigenous sheep breeds originating from the eastern part of Iran and it isadapted to the severe weather conditions. The economic efficiency of sheepbreeding is clearly related to the reproduction efficiency of the ewes. In thisstudy, twinning trait was studied as an important reproductive trait. Toidentify the genes associated with twinning trait, genome-wide association wasdone using 91 Baluchi ewes genotyped with 50k Illumina SNP chips. For thispurpose, the extended repeatability model was used. In the next step, a geneset enrichment analysis was implemented to identify the biological pathwaysassociated with the twinning. Six SNP markers on chromosomes 1, 3, 10, 15 and25 located in CTH, ANKRD13C, SRSF11, PTGER3, LDHB, LRRC40, and KCNMA1 geneswere identified. According to pathway analysis, 25 pathways were associatedwith the twinning trait. Among those pathways, the defense response biologicalpathway has an important role in the ovulation. Interferons and interleukinsare the main cytokine proteins in this pathway. These cytokines create aprocess such as inflammation that causes oocytes releasefrom the follicle into the oviductfor transport and fertilization. Cell adhesion andintercellular bridge pathways were other significant pathways. Cell junction pathwayvia the CX43 protein can affect the transfer of GDF9 effects togranulosa cells. To the best of our knowledge, this is the first study thatreports indirect effects of GDF9 on twinning trait in Iranian sheep.

Genetic evaluations are computed to assess the genetic merit of animals based on mixed model equations. An important assumption for setting up these equations is that all genetic relationships among animals are available and correct. The objectives of the present study were to estimate the effects of incomplete pedigree and paternity errors on genetic evaluation. Data and pedigree of 100 dairy herds were obtained from Animal Breeding Center of Iran. Final data edited included milk yield records from 302860 first lactation Holstein cows. DMU Trace program was used for tracing ancestors and creating the full pedigree of animals. To simulate incomplete and wrong pedigrees, different scenarios including 8, 12, 16, 20, 24 percent of paternal identification numbers were removed or replaced using R program. Breeding values for milk yield was predicted by animal model using DMU program. Spearman's rank correlation was estimated for superior animals in different scenarios using SAS software. Estimates of heritability for full, incomplete and wrong pedigrees were 0.29, 0.26 and 0.27, respectively. The results showed a high variation in ranking of animals and determination of superior animals (P<0.01). As an example, at 12% level scenario, Spearman's rank correlation of BVs predicted from full pedigree with incomplete and wrong pedigrees were 0.65 and 0.60, respectively. Selection effectiveness, defined as the ratio of common superior animals in alternative scenarios, was decreased by increasing the rate of misidentification and errors (P<0.01). Incorrect pedigree and misidentification of animals could reduce accuracy of breeding values and consequently bias in animals ranking.

In order to determine the optimum number of test-day records for the progeny test program of Holstein bulls, 732,140 milk yield test-day were used. These milk yield test-days, which were related to 73,214 first parity dairy cows belonging to 62 herds, had been collected by the Animal Breeding Center of Iran from 1992 to 2016. The correlation of predicted breeding value (EBV) of bulls from ten test-day of their daughters compared with EBV predicted from different number of recorded test-days. The Correlation of predicted EBV from ten test-days with EBV from even, odd, (second, fifth, seventh), (second, fifth, tenth) and (second, sixth) test-day records were estimated to be 0.99, 0.98, 0.98, 0.97 and 0.94 respectively. The results showed that to reduce cost of recording, number of records and generation interval in EBV prediction of bulls with random regression model it is possible to use only second, fifth and seventh test-days instead of ten test-days.

The aim of this study was to determine genomewide linkage disequilibrium (LD), Haplotype block and effective population size using the information obtained from 243 Azarbaijani breed buffalo using a high density SNP panel (Axiom® Buffalo Genotyping 90K). After quality control of SNP markers data, 62,141 SNP markers remained for identification of linkage disequiliberum, haplotype blocks and effective population size. LD was measured by the square of the correlation coefficient (r2) between alleles. The maximum LD measured by r2 varied from 0.25 to 0.29 at a distance of < 2.5 kb, and the minimum average values of r2 varied from 0.012 to 0.014 at distances ranging from 900 to 1000 kb, clearly showing that the average r2 reduced with the increase in SNP pair distances. Overall, 1693 blocks were observed through the genome. Eleven percent of all SNPs were clustered into haplotype blocks, covering 202 ±3.4 Mb of the total autosomal genome size. Effective population size (Ne) was estimated based on expected linkage disequilibrium. Ne was estimated to be 422 in our population.

In order to estimate genetic parameters and genetic trends of growth traits in Lori Bakhtiari sheep using structural equation models and standard multivariate models, pedigree and phenotypic data which were collected from 1994 to 2011 in Lori Bakhtiari sheep breeding station were used. The studied growth traits included birth weight (BW), average daily gain from birth to weaning (ADG1), weaning weight (WW), average daily gain from weaning to six-month weight (ADG2) and six-month weight (6MW). Three different models including standard multivariate model (SMM), multivariate temporal recursive model (TRM) and multivariate fully recursive model (FRM) were considered. Based on DIC values, TRM had the highest plausibility. Under TRM, structural coefficients between BW-ADG1, ADG1-WW, WW-ADG2 and ADG2-6MW were estimated to be 9.343, 0.03, 10.632 and 0.14, respectively. Direct heritability estimates for mentioned traits were 0.32-0.29, 0.2-0.22, 0.17-0.18, 0.11-0.11 and 0.16-0.16, respectively. The estimated values for genetic trends under SMM and TRM were 6-23, 0.7-3, 69-129, 0.4-1.2 and 110-187 grams per year for BW, ADG1, WW, ADG2 and 6MW, respectively. The results of genetic analyses of growth traits indicated the necessity of considering causal relationships between the studied traits for developing efficient breeding program in Lori Bakhtiari sheep

Single step genomic selection method (SS-GBLUP) with combining genomic relationship matrix and pedigree relationship matrix and simultaneous use of all genotyped and ungenotyped animals has been used widely in estimating genomic breeding values (GEBV) in recent years. The purpose of the present study was to compare genomic selection accuracy of traditional BLUP, SNP-BLUP, G-BLUP and SS-GBLUP for carcass weight in beef cattle. Results showed that genomic prediction accuracy for the above methods were 0.77±0.11, 0.88±0.21, 0.87±0.23 and 0.95±0.25 respectively. Genomic prediction accuracy of SS_BLUP was 0.08, 0.07 and 0.18 higher than SNP- BLUP, G_BLUP and traditional BLUP. Mean squared error of above methods was 3.46, 2.21, 2.22 and 1.76 respectively. In general, the performance of single step method was better than other studied genomic evaluation methods. Moreover, the regression coefficient of corrected phenotype on GEBV was 0.81, 0.82 and 0.90 for SNP- BLUP, G-BLUP and single step method, respectively. Thus, the bias of GEBV for single step method was less than G-BLUP and SNP-BLUP.

In order to estimate the genetic parameters for days from calving to first service (DFS) in Iranian Holstein cattle by using repeatability, multiple-trait (MT) and random regression (RR) models, 159,482 records of parities 1 to 6 collected during 1981 to 2013 and distributed over 33 large Holstein herds were used. Bayesian information criterion of MT model was lower than other models and Bayesian factor of MT model was significant compared to other models (P

The objective of this study was to compare three parametric (GBLUP, BayesB and RKHS) and two resampling (Bagging GBLUP and Random Forest) statistical methods in genomic prediction of traits with different genetic architecture. A genome consisting of three chromosomes, 1 Morgan each, was simulated on which 5000 SNPs and 50, 100 and 200 QTLs were distributed. The substitutions effects of QTLs were modeled with normal, gamma and uniform distributions with a level of heritability equal to 0.30. The predictive performance of statistical models was evaluated using the correlation between predicted and true breeding values as well as the regression of predicted values on true breeding values. In the target population, Random Forest resulted in overestimation of estimated regression coefficients while GBLUP, BayesB and RKHS led to an underestimation of regression coefficients of true breeding values on predicted breeding values. In exception of Bagging GBLUP, the performance of all statistical methods was the same in three gene effect distributions. However, the performance of GBLUP and BayesB was better than other statistical methods. A reason for this superiority could be the additive architecture of simulated traits. In conclusion, GBLUP and BayesB were superior over resampling methods in genomic predictions.

The objective followed in this study was to compare six software packages (DFREML, WOMBAT, ASReml, MATVEC, DMU and Thrgibbsf90) as based on some such features as being user friendly, easing computational facilities as well as accuracy and precision of the estimated genetic vs. environmental parameters. For conduction of the study, simulated and field data (Birth weights and weaning weight of Baluchi lambs) were utilized. In all the softwares, except for Thrgibbsf 90, estimates were obtained with restricted Maximum likelihood method via AI algorithm, but in Thrgibbsf 90, the estimates were obtained using Bayesian Gibbs Sampling. The results showed that the choice of software depends on user interest and the goal followed in the research. Since the estimated parameters come out to be similar, even in confidence intervals, there seems no concern to exist in estimation of genetic and environmental parameters (for continous variables) using linear models,and through an application of different softwares.