Comparison of nonlinear functions to describe lactation curves for cumulative milk production in buffalo.

The aim of this study was to examine the suitability of different growth functions (linear, sinusoidal, Gompertz, Schumacher and Richards) to fit cumulative milk production data from buffalo cows. Cumulative milk production at each day in milk was calculated from two published datasets reporting (i) fortnightly test-day milk yield records of the first lactation of Murrah buffalo that had calved during 1977-2012 and (ii) the first lactation records of Jaffarabadi buffalo collected from history-cum-pedigree registers for each quinquennium between 1991 and 2010. Each function was fitted to the lactation curves using nonlinear regression procedures. The Richards and sinusoidal equations provided the smallest root mean square error values, Akaike's and Bayesian information criteria and, therefore, the best fit for the cumulative lactation curves for milk yield. The Richards equation appeared to provide the most accurate estimate of the cumulative milk production at peak milk yield. Sinusoidal and flexible classical growth functions are appropriate to describe cumulative milk production curves and estimate lactation traits in buffalo.

Evidence of additive genetic variation for major milk proteins in dairy cows: A meta-analysis.

In the past, there have been reports of genetic parameters for milk proteins in various dairy cattle populations. The high variability among genetic parameter estimates has been caused by this. This study aimed to use a random-effects meta-analysis model to compile published estimates of genetic parameter for major milk proteins of α-lactalbumin, ß-lactoglobulin, sum of whey proteins, casein, αs1-casein, αs2-casein, ß-casein, and κ-casein in dairy cows. The study used a total of 140 heritability and 256 genetic correlation estimates from 23 papers published between 2004 and 2022. The estimated range of milk protein heritability is from 0.284 (for α-lactalbumin in milk) to 0.596 (for sum of whey proteins). The genetic correlation estimates between casein and milk yield, milk fat and protein percentages were -0.461, 0.693, and 0.976, respectively (p < 0.05). The genetic correlation estimates between milk proteins expressed as a percentage of milk were significant and varied from 0.177 (between ß-lactoglobulin and κ-casein) to 0.892 (between αs1-casein and αs2-casein). Moderate-to-high heritability estimates for milk proteins and their low genetic associations with milk yield and composition indicated the possibility for improving milk proteins in a genetic selection plan with negligible correlated effects on production traits in dairy cows.

Runs of homozygosity and cross-generational inbreeding of Iranian fat-tailed sheep.

The Lori-Bakhtiari fat-tailed sheep is one of the most important heavyweight native breeds of Iran. The breed is robust and well-adapted to semi-arid regions and an important resource for smallholder farms. An established nucleus-based breeding scheme is used to improve their production traits but there is an indication of inbreeding depression and loss of genetic diversity due to selection. Here, we estimated the inbreeding levels and the distribution of runs of homozygosity (ROH) islands in 122 multi-generational female Lori-Bakhtiari from different half-sib families selected from a breeding station that were genotyped on the 50k array. A total of 2404 ROH islands were identified. On average, there were 19.70 ± 1.4 ROH per individual ranging between 6 and 41. The mean length of the ROH was 4.1 ± 0.14 Mb. There were 1999 short ROH of length 1-6 Mb and another 300 in the 6-12 Mb range. Additionally long ROH indicative of inbreeding were found in the ranges of 12-24 Mb (95) and 24-48 Mb (10). The average inbreeding coefficient (FROH) was 0.031 ± 0.003 with estimates varying from 0.006 to 0.083. Across generations, FROH increased from 0.019 ± 0.012 to 0.036 ± 0.007. Signatures of selection were identified on chromosomes 2, 6, and 10, encompassing 55 genes and 23 QTL associated with production traits. Inbreeding coefficients are currently within acceptable levels but across generations, inbreeding is increasing due to selection. The breeding program needs to actively monitor future inbreeding rates and ensure that the breed maintains or improves on its current levels of environmental adaptation.

Milk coagulation properties are moderately heritable in dairy cows: a meta-analysis using the random-effects model.

This study aimed to conduct a meta-analysis using the random-effects model to merge published genetic parameter estimates for milk coagulation properties (MCP: comprising rennet coagulation time (RCT), curd-firming time (k20), curd firmness 30 min after rennet addition (a30), titrable acidity (TA) and milk acidity or pH) in dairy cows. Overall, 80 heritability estimates and 157 genetic correlations from 23 papers published between 1999 and 2020 were used. The heritability estimates for RCT, a30, k20, TA, and pH were 0.273, 0.303, 0.278, 0.189 and 0.276, respectively. The genetic correlation estimates between RCT-a30, RCT-pH, and RCT-TA were 0.842, 0.549 and -0.565, respectively. Genetic correlation estimates between RCT and production traits were generally low and ranged from -0.142 (between RCT and casein content) to 0.094 (between RCT and somatic cell score). Moderate and significant genetic correlations were observed between a30-pH (-0.396) and a30-TA (0.662). Also, the genetic correlation estimates between a30 and production traits were low to moderate and varied from -0.165 (between a30 and milk yield) to 0.481 (between a30 and casein content). Genetic correlation estimates between pH and production traits were low and varied from -0.190 (between pH and milk protein percentage) to 0.254 (between pH and somatic cell score). The results of this meta-analysis indicated the existence of additive genetic variation for MCP that could be used in genetic selection programs for dairy cows. Because of the moderate heritability of MCP and small genetic correlations with production traits, it could be possible to improve MCP with negligible correlated effects on production traits.

A meta-analysis of the genetic contribution to greenhouse gas emission in sheep.

The objective of this study was to use a random-effects model of meta-analysis to merge various heritability estimates of different gas emission traits (methane yield [METY], methane production [METP], carbon dioxide production [CO2 ], the sum of carbon dioxide and methane production [METP + CO2 ], METP METP + CO 2 ratio, and oxygen consumption [O2 ]) and their genetic association with growth and partial efficiency traits in sheep. A total of 53 genetic correlations and 47 heritability estimates from 13 scientific articles were used in the meta-analysis. The included papers were published between 2010 and 2022. To measure heterogeneity, Chi-square (Q) test was performed, and the I2 statistic was determined. The average heritability estimates for the studied traits were low to moderate and ranged from 0.137 (for METY) to 0.250 (for METP + CO2 ). The heterogeneity test of heritability estimates indicated that heritability estimates for METY, O2 consumption, and METP METP + CO 2 had low Q values and non-significant heterogeneity (p > 0.10). However, the average heritability estimates for other traits experienced significant heterogeneities (p < 0.10). The genetic correlation estimate between METP with O2 was -0.597 (p < 0.05), but its genetic correlations with other gas traits ranged from 0.593 (with METP + CO2 ) to 0.653 (CO2 ; p < 0.05). Also, mean estimates of genetic correlation between METP with live weight (LW), feed intake (FI), and residual feed intake (RFI) were 0.719, 0.598, and 0.408, respectively. The genetic correlations of CO2 with performance traits varied from 0.641 (with RFI) to 0.833 (with FI; p < 0.05). This meta-analysis showed gas emission traits in sheep are under low-to-moderate genetic control. The average genetic parameter estimates obtained in this study could be considered in the genetic selection programmes for sheep, especially when there is no access to accurate phenotypic records or genetic parameter estimates for gas emission traits.

The statistical power of genome-wide association studies for threshold traits with different frequencies of causal variants.

This study aimed to investigate the effects of incidence rate, heritability, and polygenic variance on the statistical power of genome-wide association studies (GWAS) for threshold traits. Different incidence rates of threshold trait (1, 3, 5, 10, 25, 40, 50, 60, 75 and 90%), heritability (10 and 25%), and polygenic variance ratio (0 and 25%) were simulated separately for common (MAF ≥ 0.05), low-frequency (0.05 > MAF ≥ 0.01), and rare (MAF < 0.01) variants. Association studies were performed by logistic and linear mixed models. The highest statistical powers were observed in common and low-frequency variants with an incidence of 25-50% and 10-40%, respectively, but for rare variants, the highest statistical power was observed at low incidence. For all causal variant frequencies, the estimated heritability decline with an increase in incidence rate. We found high statistical power for traits with high heritability. In contrast, those with a high polygenic variance ratio have lower statistical power to detect common causal variants using a linear mixed model. These results demonstrate that the incidence rate of threshold traits, heritability, and polygenic variance may affect the statistical power of GWAS. Therefore, it is recommended that the effect of incidence rate, heritability, and polygenic variance be considered in designing GWAS for threshold traits.

A meta-analysis of genetic parameter estimates for milk and serum minerals in dairy cows.

This study aimed to conduct a meta-analysis based on a random-effects model to combine different published heritability estimates and genetic correlations for milk and serum minerals in dairy cows. In total, 59 heritability and 25 genetic correlation estimates from 12 articles published between 2009 and 2021 were used. The heritability estimates for milk macro-minerals were moderate to high and ranged from 0.311 (for Na) to 0.420 (for Ca). On the other hand, milk micro-minerals had lower heritabilities with a range from 0.013 (for Fe) to 0.373 (for Zn). The heritability estimates for serum macro-minerals were generally low and varied from 0.126 (for K) to 0.206 (for Mg). The estimates of genetic correlation between milk macro-minerals varied from -0.024 (between Na and K) to 0.625 (between Mg and P). The genetic correlations of milk Ca and P with milk yield were -0.171 and -0.211, respectively. The estimates of genetic parameters reported in this meta-analysis study are appropriate to utilize in breeding plans when valid estimates are not available for milk minerals in dairy cow populations.

Marker genotyping error effects on genomic predictions under different genetic architectures.

This study aimed to determine the effect of different rates of marker genotyping error on the accuracy of genomic prediction that was examined under distinct marker and quantitative trait loci (QTL) densities and different heritability estimates using a stochastic simulation approach. For each scenario of simulation, a reference population with phenotypic and genotypic records and a validation population with only genotypic records were considered. Marker effects were estimated in the reference population, and then their genotypic records were used to predict genomic breeding values in the validation population. The prediction accuracy was calculated as the correlation between estimated and true breeding values. The prediction bias was examined by computing the regression of true genomic breeding value on estimated genomic breeding value. The accuracy of the genomic evaluation was the highest in a scenario with no marker genotyping error and varied from 0.731 to 0.934. The accuracy of the genomic evaluation was the lowest in a scenario with marker genotyping error equal to 20% and changed from 0.517 to 0.762. The unbiased regression coefficients of true genomic breeding value on estimated genomic breeding value were obtained in the reference and validation populations when the rate of marker genotyping error was equal to zero. The results showed that marker genotyping error can reduce the accuracy of genomic evaluations. Moreover, marker genotyping error can provide biased estimates of genomic breeding values. Therefore, for obtaining accurate results it is recommended to minimize the marker genotyping errors to zero in genomic evaluation programs.

Bayesian estimation of the genetic relationship of survival traits from birth to yearling age with birth weight in Guilan sheep.

This study aimed to estimate genetic parameters for cumulative survival traits from birth to yearling age and to estimate their genetic relationship with birth weight in Guilan sheep. The dataset used in this study comprised 41,037 survival records of Guilan lambs born from 496 sires and 10,256 dams, collected by the Guilan Province Agricultural Jihad Organization (in Rasht, Iran) during 1990-2013. The data included complete pedigree information; gender; year, month, and day of death; dam age; year, month, and day of birth; birth type; and birth weight. Cumulative survival traits from birth to yearling age were analyzed using threshold animal models via the Bayesian method. Also, linear-threshold animal models were used to study the genetic relationship between survival at different ages and birth weight. Direct heritability estimates of cumulative survival from birth to 60, 90, 180, 270, and 365 days of age were low and equal to 0.17, 0.16, 0.08, 0.04, and 0.04, respectively, and the corresponding maternal heritability estimates were 0.21, 0.18, 0.15, 0.08 and 0.08, respectively. Mean estimates of direct genetic correlations between birth weight and survival traits were medium (from 0.22 to 0.28). To improve the survival traits, more emphasis must be put on the amelioration of the non-genetic factors affecting it. Indirect selection based on traits high genetically correlated with survival could increase the survival rate in Guilan lambs.

Introducing a sinusoidal equation to describe lactation curves for cumulative milk yield and composition in Holstein cows.

The aim of the work reported here was to investigate the appropriateness of a sinusoidal function by applying it to model the cumulative lactation curves for milk yield and composition in primiparous Holstein cows, and to compare it with three conventional growth models (linear, Richards and Morgan). Data used in this study were 911 144 test-day records for milk, fat and protein yields, which were recorded on 834 dairy herds from 2000 to 2011 by the Animal Breeding Centre and Promotion of Animal Products of Iran. Each function was fitted to the test-day production records using appropriate procedures in SAS (PROC REG for the linear model and PROC NLIN for the Richards, Morgan and sinusoidal equations) and the parameters were estimated. The models were tested for goodness of fit using adjusted coefficient of determination $\lpar {R_{{\rm adj}}^2 } \rpar $, root mean square error (RMSE), Akaike's information criterion (AIC) and the Bayesian information criterion (BIC). $R_{{\rm adj}}^2 $ values were generally high (>0.999), implying suitable fits to the data, and showed little differences among the models for cumulative yields. The sinusoidal equation provided the lowest values of RMSE, AIC and BIC, and therefore the best fit to the lactation curve for cumulative milk, fat and protein yields. The linear model gave the poorest fit to the cumulative lactation curve for all production traits. The current results show that classical growth functions can be fitted accurately to cumulative lactation curves for production traits, but the new sinusoidal equation introduced herein, by providing best goodness of fit, can be considered a useful alternative to conventional models in dairy research.

Estimation of genetic variation for macro- and micro-environmental sensitivities of milk yield and composition in Holstein cows using double hierarchical generalized linear models.

The aim of this study was to estimate genetic parameters for environmental sensitivities in milk yield and composition of Iranian Holstein cows using the double hierarchical generalized linear model (DHGLM) method. Data set included test-day productive records of cows which were provided by the Animal Breeding Center and Promotion of Animal Products of Iran during 1983 to 2014. In the DHGLM method, a random regression model was fitted which included two parts of mean and residual variance. A random regression model (mean model) and a residual variance model were used to study the genetic variation of micro-environmental sensitivities. In order to consider macro-environmental sensitivities, DHGLM was extended using a reaction norm model, and a sire model was applied. Based on the mean model, additive genetic variances for the mean were 38.25 for milk yield, 0.23 for fat yield and 0.03 for protein yield in the first lactation, respectively. Based on the residual variance model, additive genetic variances for residual variance were 0.039 for milk yield, 0.030 for fat yield and 0.020 for protein yield in the first lactation, respectively. Estimates of genetic correlation between milk yield and macro- and micro-environmental sensitivities were 0.660 and 0.597 in the first lactation, respectively. The results of this study indicated that macro- and micro-environmental sensitivities were present for milk production traits of Iranian Holsteins. High genetic coefficient of variation for micro-environmental sensitivities indicated the possibility of reducing environmental variation and increase in uniformity via selection.

Appropriate selection indices for functional traits in dairy cattle breeding schemes.

The objective of this study was to establish different single or multiple trait selection indices to calculate genetic and economic gains by combining some production, reproduction and udder health traits in a population similar to the overall practical situation in Iran, with and without imposing restrictions on genetic change for some traits. The SelAction software was used to perform the analyses based on selection index theory through a deterministic model. Results indicated that among established indices, the index that showed the highest genetic gain for milk yield did not maximize the total genetic and economic gains. Rather, the index that included all production, reproduction and udder health traits yielded the highest genetic and economic gains. When we placed restriction on the selection indices, the economic gain decreased and the amount of reduction depended on the heritability and the correlation of restricted trait(s) with other traits. Generally, regarding the economic genetic gain per generation, the indices based on records of 200 offspring were 4.819% more efficient than those that used information of 100 offspring.

Economic values and selection indices for production and reproduction traits of Iranian buffaloes (Bubalus bubalis).

Iranian buffalo plays a critical role in supplying a portion of the income and the necessities of the rural population. The first step to design a breeding program is difinition of breeding goal (BG), a linear combination of breeding values for various traits and their economic values (EV). The current study was aimed at determining EVs for important traits of Iranian buffaloes, namely milk yield (MY), milk fat (MF), age at the first calving (AFC), and calving interval (CI), as well as at estimating the genetic response of applying various types of selection indices. Economic and management data of 50 buffalo herds from various main regions of buffalo rearing in Iran were collected. The EVs were estimated using a simple profit function. Five selection indices were constructed by combining information on various traits. The EVs for BG traits of MY, MF, AFC, and CI were 0.18, 4.66, - 0.36, and - 1.87 US$, respectively. The highest predicted genetic gain in BG was 16.95 and came from applying the selection index that included all traits. The smallest genetic gain (4.93) was predicted for the index with only AFC included. Predicted genetic gain from an index that included production traits and AFC as a reproduction trait (16.9) was higher than that from the index with only production traits (16.15). Results showed that inclusion of reproductive traits in the selection index had a positive effect on genetic gain for breeding goal.

A review on breeding and genetic strategies in Iranian buffaloes (Bubalus bubalis).

The aim of current study was to review breeding progress and update information on genetic strategies in Iranian buffaloes. Iranian buffalo is one of the vital domestic animals throughout north, north-west, south and south-west of Iran with measurable characteristics both in milk and meat production. The species plays an important role in rural economy of the country due to its unique characteristics such as resistance to diseases and parasites, having long productive lifespan and showing higher capability of consuming low-quality forage. In Iran, total production of milk and meat devoted to buffaloes are 293,000 and 24,700 tons, respectively. Selection activities and milk yield recording are carrying out by the central government through the Animal Breeding Centre of Iran. The main breeding activities of Iranian buffaloes included the estimation of genetic parameters and genetic trends for performance traits using different models and methods, estimation of economic values and selection criteria and analysis of population structure. Incorporating different aspects of dairy buffalo management together with improved housing, nutrition, breeding and milking, is known to produce significant improvements in buffalo production. Therefore, identifying genetic potential of Iranian buffaloes, selection of superior breeds, improving nutritional management and reproduction and developing the education and increasing the skills of practical breeders can be useful in order to enhance the performance and profitability of Iranian buffaloes.

Genetic perspective of milk yield persistency in the first three lactations of Iranian buffaloes (Bubalus bubalis).

The objectives of this study were to estimate the genetic parameters and genetic trends for different measures of persistency (PM) for milk yield in Iranian buffaloes using a random regression test day model. Test day records of the first three lactations of buffaloes within 715 herds comprising 43 014, 38 941 and 33 937 records, respectively, were obtained from the Animal Breeding Center of Iran during 1992 to 2012. Legendre polynomial functions were chosen to fit the lactation curves in the framework of a random regression test day model for estimating (co)variance components. Three measures of persistency were modified based on the lactation curve conditions of buffaloes: (1) The average of estimated breeding values (EBVs) for test day milk yields from day 226 to day 270 as a deviation from the average of EBVs from day 44 to day 62 (PM1), (2) A summation of contribution for each day from day 53 to day 247 as a deviation from day 248 (PM2), (3) The difference between EBVs for day 257 and day 80 (PM3). The highest heritability estimates were observed for PM2 over the first three lactations (0·29, 0·80 and 0·42, respectively). Estimates of genetic correlations between persistency measures and milk yield varied from -0·48 to 0·29 (first lactation), from -0·12 to 0·48 (second lactation), and from -0·62 to 0·63 (third lactation). Estimate of genetic trend for first lactation PM2 was negative (-0·0088 ± 0·0043; P < 0·05). It is essential to include milk yield persistency in the breeding goal of Iranian buffaloes along with other important traits such as milk yield or reproductive traits.

Inbreeding effects on reproductive traits in Iranian Guilan sheep.

The objective of the present study was to evaluate the effects of inbreeding on reproductive traits in Guilan sheep. Traits included were litter size at birth (LSB), litter size at weaning (LSW), litter mean weight per lamb born (LMWLB), litter mean weight per lamb weaned (LMWLW), total litter weight at birth (TLWB), and total litter weight at weaning (TLWW). Data and pedigree information used in this study were collected during 1994 to 2011 by the Agriculture Organization of Guilan Province in the north of Iran. Data were comprised of 14,534 records of lambs from 136 sires and 2021 dams. All the animals were grouped into three classes according to the inbreeding coefficients (F) obtained by their pedigree: the first class included non-inbred animals (F = 0%), and the second and third classes included inbred animals (0 < F ≤ 5% and F > 5%, respectively). The regression coefficients of LSB, LSW, LMWLB, LMWLW, TLWB, and TLWW on lamb inbreeding for a change of 1% in inbreeding were estimated to be 0.035 ± 0.0038 (P < 0.001), -0.029 ± 0.0077 (P < 0.05), -0.333 ± 0.009 (P < 0.001), -2.21 ± 0.071 (P < 0.001), -0.254 ± 0.013 (P < 0.001), and -1.95 ± 0.093 (P < 0.001), respectively. Effect of inbreeding on reproductive traits in Guilan sheep was very pronounced in the flock. The utilization of a program for planned mating system, in the present flock, suggested keeping the level of inbreeding under control and avoiding appearance of its detrimental effects.

Modeling the growth curve in ducks: a sinusoidal model as an alternative to classical nonlinear models.

The present study aimed to apply a sinusoidal model to duck body weight records in order to introduce it to the field of poultry science. Using 8 traditional growth functions as a guide (Bridges, Janoschek, logistic, Gompertz, Von Bertalanffy, Richards, Schumacher, and Morgan), this study looked at how well the sinusoidal equation described the growth patterns of ducks. By evaluating statistical performance and examining model behavior during nonlinear regression curve fitting, models were compared. The data used in this study came from 3 published articles reporting 1) body weight records of Kuzi ducks aged 1 to 70 d, 2) body weight records for Polish Peking ducks aged 1 to 70 d, and 3) average body weight of Peking ducks aged 1 to 42 d belonging to 5 different breeds. The general goodness-of-fit of each model to the various data profiles was assessed using the adjusted coefficient of determination, root mean square error, Akaike's information criterion (AIC), and Bayesian information criterion. All of the models had adjusted coefficient of determination values that were generally high, indicating that the models generally fit the data well. Duck growth dynamics are accurately described by the chosen sinusoidal equation. The sinusoidal equation was found to be one of the best functions for describing the age-related changes in body weight in ducks when the growth functions were compared using the goodness-of-fit criteria. To date, no research has been conducted on the use of sinusoidal equations to describe duck growth development. To describe the growth curves for a variety of duck strains/lines, the sinusoidal function employed in this study serves as a suitable substitute for conventional growth functions.

An overview of recent technological developments in bovine genomics.

Cattle are regarded as highly valuable animals because of their milk, beef, dung, fur, and ability to draft. The scientific community has tried a number of strategies to improve the genetic makeup of bovine germplasm. To ensure higher returns for the dairy and beef industries, researchers face their greatest challenge in improving commercially important traits. One of the biggest developments in the last few decades in the creation of instruments for cattle genetic improvement is the discovery of the genome. Breeding livestock is being revolutionized by genomic selection made possible by the availability of medium- and high-density single nucleotide polymorphism (SNP) arrays coupled with sophisticated statistical techniques. It is becoming easier to access high-dimensional genomic data in cattle. Continuously declining genotyping costs and an increase in services that use genomic data to increase return on investment have both made a significant contribution to this. The field of genomics has come a long way thanks to groundbreaking discoveries such as radiation-hybrid mapping, in situ hybridization, synteny analysis, somatic cell genetics, cytogenetic maps, molecular markers, association studies for quantitative trait loci, high-throughput SNP genotyping, whole-genome shotgun sequencing to whole-genome mapping, and genome editing. These advancements have had a significant positive impact on the field of cattle genomics. This manuscript aimed to review recent advances in genomic technologies for cattle breeding and future prospects in this field.

Genomic predictions under different genetic architectures are impacted by mating designs.

Mating in animal communities must be managed in a way that assures the performance increase in the progenies without increasing the rate of inbreeding. It has currently become possible to identify millions of single nucleotide polymorphisms (SNPs), and it is feasible to select animals based on genome-wide marker profiles. This study aimed to evaluate the impact of five mating designs among individuals (random, positive and negative assortative, minimized and maximized inbreeding) on genomic prediction accuracy. The choice of these five particular mating designs provides a thorough analysis of the way genetic diversity, relatedness, inbreeding, and biological conditions influence the accuracy of genomic predictions. Utilizing a stochastic simulation technique, various marker and quantitative trait loci (QTL) densities were taken into account. The heritabilities of a simulated trait were 0.05, 0.30, and 0.60. A validation population that only had genotypic records was taken into consideration, and a reference population that had both genotypic and phenotypic records was considered for every simulation scenario. By measuring the correlation between estimated and true breeding values, the prediction accuracy was calculated. Computing the regression of true genomic breeding value on estimated genomic breeding value allowed for the examination of prediction bias. The scenario with a positive assortative mating design had the highest accuracy of genomic prediction (0.733 ± 0.003 to 0.966 ± 0.001). In a case of negative assortative mating, the genomic evaluation's accuracy was lowest (0.680 ± 0.011 to 0.899 ± 0.003). Applying the positive assortative mating design resulted in the unbiased regression coefficients of true genomic breeding value on estimated genomic breeding value. Based on the current results, it is suggested to implement positive assortative mating in genomic evaluation programs to obtain unbiased genomic predictions with greater accuracy. This study implies that animal breeding programs can improve offspring performance without compromising genetic health by carefully managing mating strategies based on genetic diversity, relatedness, and inbreeding levels. To maximize breeding results and ensure long-term genetic improvement in animal populations, this study highlights the importance of considering different mating designs when evaluating genomic information. When incorporating positive assortative mating or other mating schemes into genomic evaluation programs, it is critical to consider the complex relationship between gene interactions, environmental influences, and genetic drift to ensure the stability and effectiveness of breeding efforts. Further research and comprehensive analyzes are needed to fully understand the impact of these factors and their possible complex interactions on the accuracy of genomic prediction and to develop strategies that optimize breeding outcomes in animal populations.

Estimation of genetic parameters for reproductive traits in Shall sheep.

The objective of this study was to estimate genetic parameters for reproductive traits in Shall sheep. Data included 1,316 records on reproductive performances of 395 Shall ewes from 41 sires and 136 dams which were collected from 2001 to 2007 in Shall breeding station in Qazvin province at the Northwest of Iran. Studied traits were litter size at birth (LSB), litter size at weaning (LSW), litter mean weight per lamb born (LMWLB), litter mean weight per lamb weaned (LMWLW), total litter weight at birth (TLWB), and total litter weight at weaning (TLWW). Test of significance to include fixed effects in the statistical model was performed using the general linear model procedure of SAS. The effects of lambing year and ewe age at lambing were significant (P<0.05). Genetic parameters were estimated using restricted maximum likelihood procedure, under repeatability animal models. Direct heritability estimates were 0.02, 0.01, 0.47, 0.40, 0.15, and 0.03 for LSB, LSW, LMWLB, LMWLW, TLWB, and TLWW, respectively, and corresponding repeatabilities were 0.02, 0.01, 0.73, 0.41, 0.27, and 0.03. Genetic correlation estimates between traits ranged from -0.99 for LSW-LMWLW to 0.99 for LSB-TLWB, LSW-TLWB, and LSW-TLWW. Phenotypic correlations ranged from -0.71 for LSB-LMWLW to 0.98 for LSB-TLWW and environmental correlations ranged from -0.89 for LSB-LMWLW to 0.99 for LSB-TLWW. Results showed that the highest heritability estimates were for LMWLB and LMWLW suggesting that direct selection based on these traits could be effective. Also, strong positive genetic correlations of LMWLB and LMWLW with other traits may improve meat production efficiency in Shall sheep.