ASReml-based estimation of early genetic parameters in hybrid mutton sheep populations.

The aim of this study was to analyze the effects of non-genetic factors on the estimation of genetic parameters of early growth traits in hybrid mutton sheep using ASReml software, in order to provide theoretical basis for screening the optimal hybriding combinations and accelerating the breeding process of new breeds of specialized housed-feeding mutton sheep. We selected the wellgrown hybrid Southhu (Southdown × Hu sheep) and Dorhu (Dorset × Hu sheep) sheep as the research objects, constructed weight correction formulae for SH and DH sheep at 60 and 180 days; and used ASReml software to investigate the effects of non-genetic factors on the estimation of genetic parameters of early growth traits in hybrid sheep. The results showed that the birth month and birth type were found significant for all traits (p < 0.001); the heritability of maternal effects ranged from 0.0709 to 0.1859. It was found that both SH and DH sheep emerged the potential for rapid early growth and development, early growth traits are significantly affected by maternal genetic effects, thereby the maternal effect should be taken into consideration for the purpose of improving accuracy in parameter estimations and therefore increasing the success of breeding programs.

Genetic aspects of immunoglobulins and cyclophilin A in milk as potential indicators of mastitis resistance in Holstein cows.

Mastitis is one of the most frequent and costly diseases affecting dairy cattle. Natural antibodies (immunoglobulins) and cyclophilin A (CyPA), the most abundant member of the family of peptidyl prolyl cis/trans isomerases, in milk may serve as indicators of mastitis resistance in dairy cattle. However, genetic information for CyPA is not available, and knowledge on the genetic and nongenetic relationships between these immune-related traits and somatic cell score (SCS) and milk yield in dairy cattle is sparse. Therefore, we aimed to comprehensively evaluate whether immune-related traits consisting of 5 Ig classes (IgG, IgG1, IgG2, IgA, and IgM) and CyPA in the test-day milk of Holstein cows can be used as genetic indicators of mastitis resistance by evaluating the genetic and nongenetic relationships with SCS in milk. The nongenetic factors affecting immune-related traits and the effects of these traits on SCS were evaluated. Furthermore, the genetic parameters of immune-related traits according to health status and genetic relationships under different SCS environments were estimated. All immune-related traits were significantly associated with SCS and directly proportional. Additionally, evaluation using a classification tree revealed that IgA, IgG2, and IgG were associated with SCS levels. Genetic factor analyses indicated that heritability estimates were low for CyPA (0.08) but moderate for IgG (0.37), IgA (0.44), and IgM (0.44), with positive genetic correlations among Ig (0.25-0.96). We also evaluated the differences in milk yield and SCS of cows between the low and high groups according to their sires' estimated breeding value for immune-related traits. In the high group, IgA had a significantly lower SCS in milk at 7 to 30 d compared with that in the low group. Furthermore, the Ig in milk had high positive genetic correlations between healthy and infected conditions (0.82-0.99), suggesting that Ig in milk under healthy conditions could interact with those under infected conditions, owing to the genetic ability based on the level of Ig in milk. Thus, Ig in milk are potential indicators for the genetic selection of mastitis resistance. However, because only the relationship between immune-related traits and SCS was investigated in this study, further study on the relationship between clinical mastitis and Ig in milk is needed before Ig can be used as an indicator of mastitis resistance.

Optimizing selection strategy for enhancing reproduction efficiency in Indian crossbred goats using milk productivity as a selection criterion.

The present study was aimed at optimizing the selection strategy for enhancing reproductive efficiency and milk productivity of Alpine × Beetal crossbred goats. The data set included 2949 milk trait records across parities and 1389 milk records from first parity and corresponding reproductive traits. The traits included for analysis were 150-day milk yield (150DMY), days in milk (DIM), peak yield (PY) and total milk yield (TMY). The litter size (LS) and litter weight (LW) were used for specifically formulating selection plan using indirect selection. The least squares mean for lactation traits during the first parity were 150DMY: 195.32 ± 2.09 kg, DIM: 236.42 ± 3.04 days, PY: 1.82 ± 0.02 kg, TMY: 269.62 ± 4.52 kg. Notably, Alpine × Beetal goats demonstrated genetic superiority pan India for milk productivity as compared to other native goat breeds. The least squares mean for 150DMY across all parities was 236 ± 3.13 kg. An animal model employing average information restricted maximum likelihood was used for (co)variance component estimation to get the genetic parameters. The analysis revealed total heritability estimates for 150DMY, DIM, PY and TMY as 0.18 ± 0.06, 0.04 ± 0.04, 0.12 ± 0.06 and 0.08 ± 0.05, respectively. Repeatability estimates for 150DMY, DIM, and TMY were 0.28 ± 0.04, 0.21 ± 0.03 and 0.37 ± 0.03, respectively. Bivariate analysis of 150DMY with reproductive traits revealed heritability for LS and LW as 0.05 ± 0.01 and 0.10 ± 0.01, respectively using Gibbs sampling. Strong and positive genetic correlations of 150DMY with other production and reproduction traits was observed, such as DIM (0.72), PY (0.98), TMY (0.88), LS (0.57) and LW (0.33). Moderate heritability and repeatability estimate of 150DMY, along with its positive correlation with production and reproductive traits suggested it as a suitable selection criterion for early selection and overall genetic progress of lactation traits. The genetic trend analysis showed an overall improvement in all these traits, with observed gain of 98.4 g per year for 150DMY, 0.04 days per year for DIM, 0.5 g per year for PY and 220.5 g per year for TMY. We observed that selecting based on 150DMY would lead to a favourable indirect improvement for LW as 79 g and LS 0.04 units per generation. We, therefore, recommend employing 150DMY as the single trait selection criteria to enhance both milk productivity and reproductive potential of Alpine × Beetal goats.

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.

Estimation of heritability for milk urea and genetic correlations with milk production traits in 3 Danish dairy breeds.

The aim of this study was to estimate genetic parameters for milk urea (MU) content in 3 main Danish dairy breeds. As a part of the Danish milk recording system, milk samples from cows on commercial farms were analyzed for MU concentration (mmol/L) and the percentages of fat and protein. There were 323,800 Danish Holstein, 70,634 Danish Jersey, and 27,870 Danish Red cows sampled with a total of 1,436,580, 368,251, and 133,922 test-day records per breed, respectively, included in the data set. Heritabilities for MU were low to moderate (0.22, 0.18, and 0.24 for the Holstein, Jersey, and Red breeds, respectively). The genetic correlation was close to zero between MU and milk yield in Jersey and Red, and -0.14 for Holstein. The genetic correlations between MU and fat and protein percentages, respectively, were positive for all 3 dairy breeds. Herd-test-day explained 51%, 54%, and 49% of the variation in MU in Holstein, Jersey, and Red, respectively. This indicates that MU levels in milk can be reduced by farm management. The current study shows that there are possibilities to influence MU by genetic selection as well as by farm management.

Genetic parameters of sole lesion recovery in Holstein cows.

Sole hemorrhage and sole ulcers, referred to as sole lesions, are important causes of lameness in dairy cattle. The objective of this study was to estimate the genetic parameters of a novel trait reflecting how well cows recovered from sole lesions and the genetic correlation of this trait with overall susceptibility to sole lesions. A cohort of Holstein dairy cows was prospectively enrolled on 4 farms and assessed at 4 timepoints: before calving, immediately after calving, in early lactation, and in late lactation. At each timepoint, sole lesions were recorded at the claw level by veterinary surgeons and used to define 2 binary traits: (1) susceptibility to sole lesions-whether animals were affected with sole lesions at least once during the study or were unaffected at every assessment, and (2) sole lesion recovery-whether sole lesions healed between early and late lactation. Animals were genotyped and pedigree details extracted from the national database. Analyses were conducted with BLUPF90 software in a single-step framework; genetic parameters were estimated from animal threshold models using Gibbs sampling. The genetic correlation between both traits was approximated as the correlation between genomic estimated breeding values, adjusting for their reliabilities. A total of 2,025 animals were used to estimate the genetic parameters of sole lesion susceptibility; 44% of animals recorded a sole lesion at least once during the study period. The heritability of sole lesion susceptibility, on the liability scale, was 0.25 (95% highest density interval = 0.16-0.34). A total of 498 animals were used to estimate the genetic parameters of sole lesion recovery; 71% of animals had recovered between the early and late lactation assessments. The heritability of sole lesion recovery, on the liability scale, was 0.27 (95% highest density interval = 0.02-0.52). The approximate genetic correlation between each trait was -0.11 (95% confidence interval = -0.20 to -0.02). Our results indicate that recovery from sole lesions is heritable. If this finding is corroborated in further studies, it may be possible to use selective breeding to reduce the frequency of chronically lame cows. As sole lesion recovery appears to be weakly genetically related to sole lesion susceptibility, successful genetic improvement of sole lesion recovery would benefit from selection on this trait directly.

Genomic-based genetic parameters for resilience across lactations in North American Holstein cattle based on variability in daily milk yield records.

Considering the increasing challenges imposed by climate change and the need to improve animal welfare, breeding more resilient animals capable of better coping with environmental disturbances is of paramount importance. In dairy cattle, resilience can be evaluated by measuring the longitudinal occurrences of abnormal daily milk yield throughout lactation. Aiming to estimate genetic parameters for dairy cattle resilience, we collected 5,643,193 daily milk yield records on automatic milking systems (milking robots) and milking parlors across 21,350 lactations 1 to 3 of 11,787 North American Holstein cows. All cows were genotyped with 62,029 SNPs. After determining the best fitting models for each of the 3 lactations, daily milk yield residuals were used to derive 4 resilience indicators: weighted occurrence frequency of yield perturbations (wfPert), accumulated milk losses of yield perturbations (dPert), and log-transformed variance (LnVar) and lag-1 autocorrelation (rauto) of daily yield residuals. The indicator LnVar presented the highest heritability estimates (±standard error), ranging from 0.13 ± 0.01 in lactation 1 to 0.15 ± 0.02 in lactation 2; the other 3 indicators had relatively lower heritabilities across the 3 lactations (0.01-0.06). Based on bivariate analyses of each resilience indicator across lactations, stronger genetic correlations were observed between lactations 2 and 3 (0.88-0.96) than between lactations 1 and 2 or 3 (0.34-0.88) for dPert, LnVar, and rauto. For the pairwise comparisons of different resilience indicators within each lactation, dPert had the strongest genetic correlations with wfPert (0.64) and rauto (0.53) in lactation 1, whereas the correlations in lactations 2 and 3 were more variable and showed relatively high standard errors. The genetic correlation results indicated that different resilience indicators across lactations might capture additional biological mechanisms and should be considered as different traits in genetic evaluations. We also observed favorable genetic correlations of these resilience indicators with longevity and Net Merit index, but further biological validation of these resilience indicators is needed. In conclusion, this study provided genetic parameter estimates for different resilience indicators derived from daily milk yields across the first 3 lactations in Holstein cattle, which will be useful when potentially incorporating these traits in dairy cattle breeding schemes.

Single-step genome-wide association for selected milk fatty acids in Dual-Purpose Belgian Blue cows.

The aim of this study was to estimate genetic parameters and identify genomic regions associated with selected individual and groups of milk fatty acids (FA) predicted by milk mid-infrared spectrometry in Dual-Purpose Belgian Blue cows. The used data were 69,349 test-day records of milk yield, fat percentage, and protein percentage along with selected individual and groups FA of milk (g/dL milk) collected from 2007 to 2020 on 7,392 first-parity (40,903 test-day records), and 5,185 second-parity (28,446 test-day records) cows distributed in 104 herds in the Walloon Region of Belgium. Data of 28,466 SNPs, located on 29 Bos taurus autosomes (BTA), of 1,699 animals (639 males and 1,060 females) were used. Random regression test-day models were used to estimate genetic parameters through the Bayesian Gibbs sampling method. The SNP solutions were estimated using a single-step genomic best linear unbiased prediction approach. The proportion of genetic variance explained by each 25-SNP sliding window (with an average size of ~2 Mb) was calculated, and regions accounting for at least 1.0% of the total additive genetic variance were used to search for candidate genes. Average daily heritability estimated for the included milk FA traits ranged from 0.01 (C4:0) to 0.48 (C12:0) and 0.01 (C4:0) to 0.42 (C12:0) in the first and second parities, respectively. Genetic correlations found between milk yield and the studied individual milk FA, except for C18:0, C18:1 trans, C18:1 cis-9, were positive. The results showed that fat percentage and protein percentage were positively genetically correlated with all studied individual milk FA. Genome-wide association analyses identified 11 genomic regions distributed over 8 chromosomes [BTA1, BTA4, BTA10, BTA14 (4 regions), BTA19, BTA22, BTA24, and BTA26] associated with the studied FA traits, though those found on BTA14 partly overlapped. The genomic regions identified differed between parities and lactation stages. Although these differences in genomic regions detected may be due to the power of quantitative trait locus detection, it also suggests that candidate genes underlie the phenotypic expression of the studied traits may vary between parities and lactation stages. These findings increase our understanding about the genetic background of milk FA and can be used for the future implementation of genomic evaluation to improve milk FA profile in Dual-Purpose Belgian Blue cows.

Genetic parameter estimation for pork production and litter performance traits of Landrace, Large White, and Duroc pigs in Japan.

We estimated genetic parameters for two pork production and six litter performance traits of Landrace, Large White, and Duroc pigs reared in Japan. Pork production traits were average daily gain from birth to end of performance testing and backfat thickness at end of testing (46,042 records for Landrace, 40,467 records for Large White, and 42,920 records for Duroc). Litter performance traits were number born alive, litter size at weaning (LSW), number of piglets dead during suckling (ND), survival rate of piglets during suckling (SV), total piglet weight at weaning (TWW), and average piglet weight at weaning (AWW) (27,410, 26,716, and 12,430 records for Landrace, Large White, and Duroc, respectively). ND was calculated as the difference between LSW and litter size at start of suckling (LSS). SV was calculated as LSW/LSS. AWW was calculated as TWW/LSW. Pedigree data for Landrace, Large White, and Duroc breeds contained 50,193, 44,077, and 45,336 pigs, respectively. Trait heritability was estimated via single-trait analysis and genetic correlation between two traits was estimated via two-trait analysis. When considering the linear covariate of LSS in the statistical model for LSW and TWW, for all breeds, the heritability was estimated to be 0.4-0.5 for pork production traits and below 0.2 for litter performance traits. Estimated genetic correlation between average daily gain and backfat thickness was small, ranging from 0.057 to 0.112, and those between pork production traits and litter performance traits were negligible to moderate, ranging from -0.493 to 0.487. A wide range of genetic correlation values among the litter performance traits was estimated, while that between LSW and ND could not be obtained. The results of genetic parameter estimation were affected by whether the linear covariate of LSS was included in the statistical model for LSW and TWW or not. This finding implies the necessity of carefully interpreting the results according to the choice of statistical model. Our results could give fundamental information on simultaneously improving productivity and female reproductivity for pigs.

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.

Genetic Parameter and Hyper-Parameter Estimation Underlie Nitrogen Use Efficiency in Bread Wheat.

Estimation and prediction play a key role in breeding programs. Currently, phenotyping of complex traits such as nitrogen use efficiency (NUE) in wheat is still expensive, requires high-throughput technologies and is very time consuming compared to genotyping. Therefore, researchers are trying to predict phenotypes based on marker information. Genetic parameters such as population structure, genomic relationship matrix, marker density and sample size are major factors that increase the performance and accuracy of a model. However, they play an important role in adjusting the statistically significant false discovery rate (FDR) threshold in estimation. In parallel, there are many genetic hyper-parameters that are hidden and not represented in the given genomic selection (GS) model but have significant effects on the results, such as panel size, number of markers, minor allele frequency, number of call rates for each marker, number of cross validations and batch size in the training set of the genomic file. The main challenge is to ensure the reliability and accuracy of predicted breeding values (BVs) as results. Our study has confirmed the results of bias-variance tradeoff and adaptive prediction error for the ensemble-learning-based model STACK, which has the highest performance when estimating genetic parameters and hyper-parameters in a given GS model compared to other models.

Genetic evaluation of growth rate and efficiency-related traits in Dorper × local crossbred sheep population.

The present study was carried out to estimate genetic and phenotypic parameters for growth rate and efficiency-related traits in Dorper crossbred sheep population. Data on body weight collected from 2012 to 2021 at Debre Birhan Agricultural Research Center, Amhara Regional State, Ethiopia, were used to estimate phenotypic and genetic parameters for daily gain from birth to weaning (DG0-3), daily gain from weaning to 6 months (DG3-6), and daily gain from 6 months to yearling (DG6-12) and corresponding Kleiber ratios (KR0-3, KR3-6, KR6-12), efficiency of growth (GE0-3, GE3-6, GE6-12), and relative growth rate (RG0-3, RG3-6, RG6-12). Genetic parameters were estimated by restricted maximum likelihood (REML) procedure fitting six different univariate animal models and the most appropriate model for each trait was determined by log-likelihood ratio test. Multivariate analysis was carried out to estimate correlations between traits. Year and season of birth had a significant effect (p<0.001) in all studied traits. Direct heritability estimates for DG0-3, DG3-6, DG6-12, KR0-3, KR3-6, KR6-12, GE0-3, GE3-6, GE6-12, GR0-3, GR3-6, and GR6-12 were 0.45±0.15, 0.04±0.06, 0.15±0.11, 0.30±0.08, 0.13±0.11, 0.14±0.12, 0.34±0.15, 0.39±0.17, 0.31±0.14, 0.25±0.08, 0.23±0.13, and 0.23±0.13 respectively. Genetic correlation estimates between DG3-6 and other traits were positive and high in magnitude to their respective growth phase (0.95, 0.86, and 0.91 for KR3-6, GE3-6, and GR3-6 respectively). As the Dorper crossbred sheep are reaches market weight at about 6 months of age, focusing on improving traits measured during weaning to 6 months of age is more feasible. Selection based on DG3-6 is recommended to improve efficiency-related traits.

Genetic analysis of fetal loss in Holstein cattle.

Pregnancy loss is recognized as one of the major factors contributing to poor reproductive performance in dairy cattle. Here, we performed a comprehensive genetic analysis of fetal loss, defined as a pregnancy loss that occurs after detection of a viable embryo around 42 d of gestation. The objectives of this study were to reveal (1) whether fetal loss is heritable and, hence, whether it will respond to selection, and (2) to what extent current fertility traits, such as daughter pregnancy rate, are associated with fetal loss. Data consisted of 59,308 confirmed pregnancy or fetal loss records distributed across nulliparous heifers and primiparous and multiparous cows. We defined fetal loss as a binary trait (yes vs. no) or as an ordinal trait (pregnancy maintenance, early fetal loss ≤150 d of gestation, and late fetal loss >150 d of gestation), and we assessed both linear and threshold models. Heritability estimates for fetal loss ranged from 1 to 18%, depending upon parity, trait definition, and statistical model used. Heritability estimates were greater for lactating cows than for nonlactating nulliparous heifers. Threshold models were able to capture more additive genetic variance and, thus, yielded higher heritability estimates than linear models. Notably, fetal loss traits were highly genetically correlated with each other but only weakly correlated with current fertility traits included in the national genetic evaluation. Overall, our study provides evidence that fetal loss is heritable enough to make genetic selection for reducing fetal loss and improving pregnancy maintenance feasible. In addition, our results suggest that fetal loss is largely independent from current traits used to select for cow fertility, and thus current breeding efforts have unfortunately little effect on reducing the incidence of fetal loss.

Genomic loci and genetic parameters for uterine diseases in first-parity Holstein cows and associations with milk production and fertility.

Based on the clinical stage (e.g., vaginal discharge) and bacterial species, several forms of uterine diseases (UD) exist and can be classified as different traits [i.e., different stages of endometritis (EM) and metritis (MET)], which may differ in their genetic background and causal physiological mechanisms. Consequently, the present study aimed to study (1) the effect of UD on 305-d lactation and fertility, (2) the estimation of heritabilities for UD traits using pedigree- and SNP-based relationships, and (3) genome-wide associations to detect significant SNP markers and to infer candidate genes for UD traits. The data set contained herd manager and veterinarian recorded UD traits of 14,810 first-lactating genotyped Holstein cows from 63 large-scale contract herds. Binary defined UD traits (healthy or diseased) according to the clinical stage were endometritis catarrhalis (EM I), endometritis mucopurulenta (EM II), endometritis purulenta (EM III), pyometra (EM IV), endometritis (EM_SOD; superordinate diagnosis = no specific clinical stage defined), and MET. The binary defined trait UDall included all EM and MET diagnoses. The prevalence of UDall was 26.7%. The effect of UD on 305-d lactation and fertility was estimated via linear and generalized linear mixed models. We applied linear single-trait animal models and threshold models to estimate pedigree- and SNP-based heritabilities for UD traits, and bivariate linear models for genetic correlation estimations between UDall with 305-d lactation and fertility traits. A diagnosis for UDall had significant unfavorable effects on the female fertility traits calving interval, interval from calving to first service, days open, and nonreturn rate after 90 d, but was unrelated to 305-d lactation records for production traits milk yield, protein yield, and fat yield. Heritabilities for UDall and EM stages were close to zero, displaying maximal values of 0.05 for pedigree and 0.07 for SNP-based relationship matrices. For MET, pedigree- and SNP-based heritabilities were <0.001 and 0.07, respectively. Genetic correlations ranged from 0.20 to 0.31 between UDall with 305-d milk, protein, and fat yield, and from 0.17 to 0.40 with fertility traits. The GWAS revealed 5 SNP on bovine chromosomes (BTA) 1, 8, 10, 23 for UDall, 5 SNP on BTA 26 for EM I, 1 SNP on BTA 19 for EM II, 4 SNP on BTA 2, 18, 20, 25 for EM III, and 4 SNP on BTA 4, 16, 20 for EM IV above the significance threshold. For EM_SOD, we identified 15 significantly associated SNP on 4 chromosomes, and 4 significant SNP on BTA 3, 20, 22, 28 for MET. Marker associations for UD traits were annotated to 24 potential candidate genes using the ENSEMBL database. Six of these genes were previously reported to be involved in uterine defense mechanisms or in endometritis. Further detected genes contribute to immune response mechanisms during bacterial infections. Different SNP significantly influenced different UD stages, explaining the inter-individual variations in clinical severity of uterine infections.

Genome-wide association study for selected cheese-making properties in Dual-Purpose Belgian Blue cows.

This study aimed to estimate genetic parameters and identify genomic region(s) associated with selected cheese-making properties (CMP) in Dual-Purpose Belgian Blue (DPBB) cows. Edited data were 46,301 test-day records of milk yield, fat percentage, protein percentage, casein percentage, milk calcium content (CC), coagulation time (CT), curd firmness after 30 min from rennet addition (a30), and milk titratable acidity (MTA) collected from 2014 to 2020 on 4,077 first-parity (26,027 test-day records), and 3,258 second-parity DPBB cows (20,274 test-day records) distributed in 124 herds in the Walloon Region of Belgium. Data of 28,266 SNP, located on 29 Bos taurus autosomes (BTA) of 1,699 animals were used. Random regression test-day models were used to estimate genetic parameters through the Bayesian Gibbs sampling method. The SNP solutions were estimated using a single-step genomic BLUP approach. The proportion of the total additive genetic variance explained by windows of 25 consecutive SNPs (with an average size of ∼2 Mb) was calculated, and regions accounting for at least 1.0% of the total additive genetic variance were used to search for candidate genes. Heritability estimates for the included CMP ranged from 0.19 (CC) to 0.50 (MTA), and 0.24 (CC) to 0.41 (MTA) in the first and second parity, respectively. The genetic correlation estimated between CT and a30 varied from -0.61 to -0.41 and from -0.55 to -0.38 in the first and second lactations, respectively. Negative genetic correlations were found between CT and milk yield and composition, while those estimated between curd firmness and milk composition were positive. Genome-wide association analyses results identified 4 genomic regions (BTA1, BTA3, BTA7, and BTA11) associated with the considered CMP. The identified genomic regions showed contrasting results between parities and among the different stages of each parity. It suggests that different sets of candidate genes underlie the phenotypic expression of the considered CMP between parities and lactation stages of each parity. The findings of this study can be used for future implementation and use of genomic evaluation to improve the cheese-making traits in DPBB cows.

Genetic parameters for dairy calf and replacement heifer wellness traits and their association with cow longevity and health indicators in Holstein cattle.

High mortality and involuntary culling rates cause great economic losses to the worldwide dairy cattle industry. However, there is low emphasis on wellness traits in replacement animals (dairy calves and replacement heifers) during their development stages in modern dairy cattle breeding programs. Therefore, the main objectives of this study were to estimate genetic parameters of wellness traits in replacement cattle (replacement wellness traits) and obtain their genetic correlations with 12 cow health and longevity traits in the Chinese Holstein population. Seven replacement wellness traits were analyzed, including birth weight, survival from 3 to 60 d (Sur1), survival from 61 to 365 d (Sur2), survival from 366 d to the first calving (Sur3), calf diarrhea, calf pneumonia, and calf serum total protein (STP). Single and bivariate animal models were employed to estimate (co)variance components using the data from 189,980 Holstein cattle. The genetic correlations between replacement wellness traits and cow longevity, health traits were calculated by employing bivariate models, including 6 longevity traits and 6 health traits (clinical mastitis, metritis, ketosis, displaced abomasum, milk fever, and hoof health or hoof disease). The estimated heritabilities (± SE) were 0.335 (± 0.008), 0.088 (± 0.005), 0.166 (± 0.006), 0.102 (±0 .006), 0.048 (± 0.003), 0.063 (± 0.004), and 0.170 (± 0.019) for birth weight, Sur1, Sur2, Sur3, pneumonia, diarrhea, and STP, respectively. The majority of the genetic correlations among the 7 replacement wellness traits were negligible. The genetic correlations among Sur1, Sur2, and Sur3 ranged from 0.112 (Sur1 and Sur3) to 0.445 (Sur1 and Sur2) when fitting a linear model (estimates in the observed scale), and from 0.560 (Sur1 and Sur3) to 0.773 (Sur1 and Sur2) when fitting a threshold model (estimates in the liability scale). The genetic correlations between replacement wellness and cow longevity were low (absolute value lower than 0.30), but some of them were significantly different from zero. Compared with other replacement wellness traits, Sur3 and STP had relatively high genetic correlations with cow longevity. Replacement wellness traits are heritable and can be improved through direct genetic and genomic selection. The results from the current study will contribute for better balancing dairy cattle breeding goals to genetically improve dairy cattle wellness in the period from birth to first calving.

Role of CSN2, CSN3, and BLG genes and the polygenic background in the cattle milk protein profile.

To devise better selection strategies in dairy cattle breeding programs, a deeper knowledge of the role of the major genes encoding for milk protein fractions is required. The aim of the present study was to assess the effect of the CSN2, CSN3, and BLG genotypes on individual protein fractions (αS1-CN, αS2-CN, ß-CN, κ-CN, ß-LG, α-LA) expressed qualitatively as percentages of total nitrogen content (% N), quantitatively as contents in milk (g/L), and as daily production levels (g/d). Individual milk samples were collected from 1,264 Brown Swiss cows reared in 85 commercial herds in Trento Province (northeast Italy). A total of 989 cows were successfully genotyped using the Illumina Bovine SNP50 v.2 BeadChip (Illumina Inc.), and a genomic relationship matrix was constructed using the 37,519 SNP markers obtained. Milk protein fractions were quantified and the ß-CN, κ-CN, and ß-LG genetic variants were identified by reversed-phase HPLC (RP-HPLC). All protein fractions were analyzed through a Bayesian multitrait animal model implemented via Gibbs sampling. The effects of days in milk, parity order, and the CSN2, CSN3, and BLG genotypes were assigned flat priors in this model, whereas the effects of herd and animal additive genetic were assigned Gaussian prior distributions, and inverse Wishart distributions were assumed for the respective co-variance matrices. Marginal posterior distributions of the parameters of interest were compared before and after the inclusion of the effects of the 3 major genes in the model. The results showed that a high portion of the genetic variance was controlled by the major genes. This was particularly apparent in the qualitative protein profile, which was found to have a higher heritability than the protein fraction contents in milk and their daily yields. When the genes were included individually in the model, CSN2 was the major gene controlling all the casein fractions except for κ-CN, which was controlled directly by the CSN3 gene. The BLG gene had the most influence on the 2 whey proteins. The genetic correlations showed the major genes had only a small effect on the relationships between the protein fractions, but through comparison of the correlation coefficients of the proteins expressed in different ways they revealed potential mechanisms of regulation and competitive synthesis in the mammary gland. The estimates for the effects of the CSN2 and CSN3 genes on protein profiles showed overexpression of protein synthesis in the presence of the B allele in the genotype. Conversely, the ß-LG B variant was associated with a lower concentration of ß-LG compared with the ß-LG A variant, independently of how the protein fractions were expressed, and it was followed by downregulation (or upregulation in the case of the ß-LG B) of all other protein fractions. These results should be borne in mind when seeking to design more efficient selection programs aimed at improving milk quality for the efficiency of dairy industry and the effect of dairy products on human health.

Genome-wide association for milk urea concentration in Dual-Purpose Belgian Blue cows.

The objectives of this study were to estimate genetic parameters and identify genomic regions associated with milk urea concentration (MU) in Dual-Purpose Belgian Blue (DPBB) cows. The data were 29,693 test-day records of milk yield (MY), fat yield (FY), protein yield (PY), fat percentage (FP), protein percentage (PP) and MU collected between 2014 and 2020 on 2498 first parity cows (16,935 test-day records) and 1939 second-parity cows (12,758 test-day records) from 49 herds in the Walloon Region of Belgium. Data of 28,266 single nucleotide polymorphisms (SNP), located on 29 Bos taurus autosomes (BTA), on 1699 animals (639 males and 1060 females) were used. Random regression test-day models were used to estimate genetic parameters through the Bayesian Gibbs sampling method using a single chain of 100,000 iterations after a burn-in period of 20,000. SNP solutions were estimated using a single-step genomic best linear unbiased prediction approach. The proportion of genetic variance explained by windows of 25 consecutive SNPs (with an average size of ~2 Mb) was calculated, and regions accounting for at least 1.0% of the total additive genetic variance were used to search for candidate genes. The mean (SD) of MU was 22.89 (10.07) and 22.35 (10.07) mg/dl for first and second parity, respectively. The mean (SD) heritability estimates for daily MU were 0.18 (0.01) and 0.22 (0.02), for first and second parity, respectively. The mean (SD) genetic correlations between daily MU and MY, FY, PY, FP and PP were -0.05 (0.09), -0.07 (0.11), -0.03 (0.13), -0.05 (0.08) and -0.03 (0.11) for first parity, respectively. The corresponding values estimated for second parity were 0.02 (0.10), -0.02 (0.09), 0.02 (0.08), -0.08 (0.06) and -0.05 (0.05). The genome-wide association analyses identified three genomic regions (BTA2, BTA3 and BTA13) associated with MU. The identified regions showed contrasting results between parities and among different stages within each parity. This suggests that different groups of candidate genes underlie the phenotypic expression of MU between parities and among different lactation stages within a parity. The results of this study can be used for future implementation and use of genomic evaluation to reduce MU in DPBB cows.

Estimates of genetic parameters for linear body measurements and prediction of body weight in goat.

The objective of this study is to estimate genetic parameters for linear body measurements along with their correlation to live weight with a focus on devising a scale to predict live weights from body measurement. A total of 142,564 records on body measures and live weights were collected from 8701 Jamunapari goats. Genetic parameters were obtained for body length (L), height at withers (H) and heart girth (G) from birth to adult stage by univariate and multivariate analysis using the average information restricted maximum likelihood method. The best model for body measures at birth included the additive effect of animal and dam along with their covariance and maternal environment, whereas for traits measured later in life, the maternal environment was not significant. After accounting for the direct maternal correlation (ram ), the total heritability estimates for linear body measurements (L,H and G) at the preweaning and postweaning stages of growth ranged from 0.14 to 0.20. Significant genetic variability implies further scope for selection. The genetic correlations of live weight at birth, 3, 6, 9 and 12 months with corresponding L,H and G were high in magnitude indicating scope to select animals for higher weight using morphometric measurements. When weighing scales are unavailable in the field, prediction of weight using L and G was recommended [live weight = (0.291 × L) + (0.306 × G) - 16.8]. We recommend the use of body measurements in the Jamunapari goat breeding program owing to their high genetic correlation with corresponding live weights.

Genetic analysis of semen characteristic traits in Norwegian Red bulls at the artificial insemination center.

Compared with cow fertility, genetic analyses of bull fertility are limited and based on relatively few animals. The aim of the present study was to estimate genetic parameters for semen characteristics of Norwegian Red bulls at the artificial insemination (AI) center (Geno AI station, Stange, Norway) and to estimate genetic correlations between some of these traits and andrology traits measured at the performance test station. The data from the AI center consisted of records from 137,919 semen collections from 3,145 bulls with information on semen weight, sperm concentration, motility before and after cryopreservation, motility change during cryopreservation, and number of accepted straws made. Data from the performance test station included 12,522 observations from 3,219 bulls on semen volume, concentration, and motility (%) when fresh and after storing for 24 and 48 h. Genetic parameters were estimated using linear animal repeatability models that included fixed effects of year-month of observation, age of bull, interaction between semen collection number, and interval between collections for all traits and type of diluter for postcryopreservation traits. The random effects included test-day, permanent environmental, and additive genetic effects of the bull. Based on records from the AI center, we found that semen weight, sperm concentration, and number of straws were moderately heritable (0.18-0.20), whereas motility had a lower heritability (0.02-0.08). Heritability of motility (%) was higher after cryopreservation than before. Genetic correlations among the semen characteristics ranged from unfavorable (-0.35) to favorable (0.93), with standard errors ranging from 0.02 to 0.22. Among the most precise genetic correlation estimates, number of straws made from a batch correlated favorably with semen weight (0.62 ± 0.06) and sperm concentration (0.44 ± 0.08), whereas sperm concentration was negatively correlated with weight (-0.33 ± 0.09). The genetic correlation between motility (%) before and after cryopreservation was 0.64 ± 0.14, and motility change during cryopreservation had a strong favorable genetic correlation with motility after cryopreservation (-0.93 ± 0.02). The estimated genetic correlation (standard error) between the traits volume, concentration, and motility when fresh measured at the performance test station and their respective corresponding traits at the AI center were 0.83 (0.05), 0.78 (0.09), and 0.49 (0.31). The final product at the AI center (number of accepted straws) correlated genetically favorably with all semen characteristic traits recorded at the performance test station (ranging from 0.51 to 0.67). Our results show that the andrology testing done at the performance test station is a resource to identify the genetically best bulls for AI production.