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.

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.

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.

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.

Genome-wide association study on abdomen depth, head width, hip width, and withers height in native cattle of Guilan (Bos indicus).

Native breeds in any country are a national capital, and their preservation is of great importance. Native Cattle of Guilan (NCG) is one of the few pure native breeds in Iran and the West Asia region. During the last decade, NCG population has decreased by more than 40%. This study aimed to identify significant single nucleotide polymorphisms (SNPs) and candidate genes associated with meat production traits in NCG using a genome-wide association study (GWAS). The blood and hair samples were collected from 72 NCG individuals and genotyped using the Illumina Bovine SNP50 chip. The results of the genomic scan showed that several SNPs were associated with abdominal depth, head width, hip width, and withers height in NCG. Several candidate genes were identified, including multiple epidermal growth factor-like domains 11 (MEGF11), Methionine Sulfoxide Reductase A (MSRA), chondroitin sulfate synthase 3 (CHSY3), Cyclin-Dependent Kinase 7 (CDK7), and Parkin (PRKN) genes, which are involved in muscle growth, meat tenderness, differentiation of fat cells, fat metabolism, and adipogenesis. These genes can contribute to meat quantity and quality in NCG. This study provided valuable insights into the genetics of NCG and the identification of effective genes associated with meat production traits. The results of this study could be used for the preservation and sustainable use of this breed of native cattle, as an important genetic resource in Iran.

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.

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.

A meta-analysis of the genetic contribution estimates to major indicators for ketosis in dairy cows.

The present study aimed to perform a meta-analysis using the random-effects model to merge published genetic parameter estimates for major indicators of ketosis [milk concentrations of acetone (ACETm) and ß-hydroxybutyrate (BHBAm), and blood concentration of ß-hydroxybutyrate (BHBAb)] in dairy cows. Overall, 51 heritability estimates and 130 genetic correlations from 19 papers published between 2012 and 2022 were used in this study. The average heritability estimates for ACETm, BHBAm, and BHBAb were 0.164, 0.123, and 0.141, respectively. The genetic correlation estimates between BHBAm and milk yield (MY), milk protein percentage (PP), and body condition score (BCS) were negative and moderate (-0.252, -0.200, and - 0.314, respectively). Genetic correlation estimates between BHBAm and milk fat percentage (FP), milk fat to protein ratio (FPR), and ketosis (KET) were moderate to high (0.411, 0.512, and 0.614, respectively). The genetic correlation estimates between BHBAb and MY and FP were low and equal to 0.128 and 0.035, respectively. The genetic correlation estimates between ACETm-MY and ACETm-PP were negative and moderate (-0.374 and - 0.398, respectively). Estimates of genetic correlation between ACETm and FP, FPR, and KET were moderate to high (0.455, 0.626, and 0.876, respectively). The results of this meta-analysis indicated the existence of additive genetic variation for ketosis indicator metabolites which could be exploited in genetic selection programs to reduce ketosis in dairy cows. Moreover, the results propose that selection for lower concentrations of indicator traits could be an effective plan for indirect improvement of production and reproduction performance, and health in dairy cows.

Estimates of the genetic contribution to methane emission in dairy cows: a meta-analysis.

The present study aimed to perform a meta-analysis using the three-level model to integrate published estimates of genetic parameters for methane emission traits [methane yield (METY), methane intensity (METINT), and methane production (METP)] in dairy cows. Overall, 40 heritability estimates and 32 genetic correlations from 17 papers published between 2015 and 2021 were used in this study. The heritability estimates for METY, METINT, and METP were 0.244, 0.180, and 0.211, respectively. The genetic correlation estimates between METY and METINT with corrected milk yield for fat, protein, and or energy (CMY) were negative (- 0.433 and - 0.262, respectively). Also, genetic correlation estimates between METINT with milk fat and protein percentages were 0.254 and 0.334, respectively. Although the genetic correlation estimate of METP with daily milk yield was 0.172, its genetic correlation with CMY was 0.446. All genetic correlation estimates between METP with milk fat and protein yield or percentage ranged from 0.005 (between METP-milk protein yield) to 0.185 (between METP-milk protein percentage). The current meta-analysis confirmed the presence of additive genetic variation for methane emission traits in dairy cows that could be exploited in genetic selection plans.

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.

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.

Genetic Analysis of Persistency for Milk Fat Yield in Iranian Buffaloes (Bubalus bubalis).

This study aimed to estimate heritabilities and genetic trends for different persistency measures for milk fat yield and their genetic correlations with 270-day milk yield in Iranian buffaloes. The records of test-day milk fat yield belonging to the first three lactations of buffaloes within 523 herds consisting of 43,818 records were got from the Animal Breeding Center and Promotion of Animal Products of Iran from 1996 to 2012. To fit the lactation curves based on a random regression test-day model, different orders of Legendre polynomial (LP) functions were selected. Three persistency measures were altered according to the specific condition of the lactation curve in buffaloes: (1) The average of estimated breeding values (EBVs) for test day fat yield 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), and (3) The difference between EBVs for day 257 and day 80 (PM3). The estimates of heritability for PM1, PM2, and PM3 ranged from 0.20 to 0.48, from 0.36 to 0.47, and from 0.19 to 0.35 over the first three lactations, respectively. The estimate of genetic trends for different persistency measures of milk fat yield was not significant over the lactations (P > 0.05). Genetic correlation estimates between various measures of persistency were generally high over the first three lactations. Also, genetic correlations estimates between persistency measures and 270-day milk yield were mostly low and varied from 0.00 to 0.24 (between PM1 and 270-day milk yield), from -0.19 to 0.13 (between PM2 and 270-day milk yield), and from -0.02 to 0.00 (between PM1 and 270-day milk yield) over the first three lactations, respectively. Persistency measures that showed low genetic correlations with milk fat yield were considered the most suitable measures in selection schemes. Besides, medium to high heritability estimates for different persistency measures for milk fat yield indicated that relevant genetic variations detected for these characters could be regarded in outlining later genetic improvement programs of Iranian buffaloes.

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.

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.

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.

Genetic aspects of some reproductive, udder health and energy status traits in Holstein cows.

The objective of this study was to estimate heritability as well as genetic and environmental relationships between days to first heat (DFH), days to first service (DFS), interval from calving to conception (ICC), calving interval (CI) and number of inseminations per conception (NIS) with mastitis (Mast), number of mastitis occurrence (NumMast), different measures of somatic cell count (SCC) and fat to protein ratio (F:P) in the first lactation of Holstein cows in Iran using linear and threshold animal and sire models. The 33851 first lactation records of Holstein cows from five large dairy herds with calving dates from March 2002 to September 2014 were analyzed with univariate and bivariate linear and threshold animal and sire models using Gibbs sampling methodology. Data from parity one to nine comprising 62483 records were used to conduct repeatability model analysis for reproductive traits. Heritabilities of the reproduction traits varied from 0.067 (for ICC) to 0.105 (for DFH) using linear animal models. Also, the heritabilities of udder health traits varied from 0.005 to 0.102 using different models. The repeatabilities of reproductive traits ranged from 0.110 to 0.307. In general, the genetic correlations (rg) between reproduction traits were positive and high (with the exception of rg between DFH-NIS). The rg between reproduction traits with udder health traits ranged from -0.029 to 0.359 and 0.151 to 0.584 using linear-linear and threshold-linear animal models, respectively. Generally, there was favorable rg between reproduction traits with udder health traits; therefore, selection for one set of these traits would improve the correlated traits. However, due to different (co)variance components and economic weights in each country/region, it can be recommended to investigate inclusion of both sets of these traits in breeding objectives.

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.