Identification of hub genes associated with somatic cell score in dairy cow.

CONTEXT: Somatic cell count (SCC) is used as an indicator of udder health. The log transformation of SCC is called somatic cell score (SCS). AIM: Several QTL and genes have been identified that are associated with SCS. This study aimed to identify the most important genes associated with SCS. METHODS: This study compiled 168 genes that were reported to be significantly linked to SCS. Pathway analysis and network analysis were used to identify hub genes. KEY RESULTS: Pathway analysis of these genes identified 73 gene ontology (GO) terms associated with SCS. These GO terms are associated with molecular function, biological processes, and cellular components, and the identified pathways are directly or indirectly linked with the immune system. In this study, a gene network was constructed, and from this network, the 17 hub genes (CD4, CXCL8, TLR4, STAT1, TLR2, CXCL9, CCR2, IGF1, LEP, SPP1, GH1, GHR, VWF, TNFSF11, IL10RA, NOD2, and PDGFRB) associated to SCS were identified. The subnetwork analysis yielded 10 clusters, with cluster 1 containing all identified hub genes (except for the VWF gene). CONCLUSION: Most hub genes and pathways identified in our study were mainly involved in inflammatory and cytokine responses. IMPLICATIONS: Result obtained in current study provides knowledge of the genetic basis and biological mechanisms controlling SCS. Therefore, the identified hub genes may be regarded as the main gene for the genomic selection of mastitis resistance.

Genetic Parameters for Methane Emissions Using Indirect Prediction of Methane and Its Association with Milk and Milk Composition Traits.

The study covers milk yield and composition data for 17,468 Polish Holstein-Friesian cows. Methane production (g/lactation per cow, MP) for dairy cow were predicted using three methane production equations (MPE) that took into account: milk yield (MPE1), energy corrected milk (MPE2) and both milk protein concentration (%), and energy-corrected milk (MPE3). The average amounts of methane produced for each cow per lactation were 31,089 g, 46,487 g, and 51,768 g for MPE1, MPE2, and MPE3, respectively. Repeatability models were used to estimate genetic parameters for MP. The estimated heritabilities for MPE1, MPE2, and MPE3 were 0.30, 0.24, and 0.24, respectively, with a standard error of 0.01. High genetic correlations (>0.76) were obtained between methane and milk yield, protein, fat, lactose and dry matter contents in milk for MPE1, MPE2 and MPE3. Still, a moderate genetic correlation (0.34) was obtained between methane and fat content (MPE1); the standard error of the estimated genetic correlation was less than 0.05. The results of the current study indicate that genetic selection aimed to reduce MP in dairy cows is possible. However, such direct genetic selection could cause a negative genetic response in milk yield and composition due to negative genetic correlations between MP and milk yield and composition.

Genetic analysis of retained placenta and its association with reproductive disorder, production, and fertility traits of Iranian Holstein dairy cows.

To facilitate breeding for improved resistance to the reproductive disorder of retained placenta (RP), genetic parameters were estimated for RP and its genetic correlation with other reproductive disorders as well as with production and fertility traits of Iranian Holstein dairy cows. Data were 154,048 lactation records collected between 2011 and 2018 from 59,610 Holstein dairy cows in 9 Iranian herds. Other reproductive disorders included dystocia, stillbirth, and twinning. Fertility records were available for days from calving to first service (DFS), days open (DO), number of inseminations per conception (NIC), and success of first insemination (SFI). Genetic parameters for RP were estimated using univariate linear and logistic animal models with ASREML software. The univariate linear animal model was used to implement bivariate analysis to investigate potential genetic correlations of RP with other reproductive disorders and with production and fertility traits. Heritability estimates for RP were low from both linear (0.031) and logistic (0.092) animal models. Estimated genetic correlations with RP were -0.04 for twinning, 0.32 for stillbirth, and 0.34 for dystocia, which indicates that selection against RP could indirectly select against dystocia and stillbirth. Estimated genetic correlations between RP and production traits (milk, fat, and protein yields) at 100, 200, and 305 d in milk ranged from -0.12 to -0.29; the greatest correlation (-0.29) was for the first 100 d in milk. A moderate positive genetic correlation (0.25) was found for RP and DO, DFS, and NIC, whereas a low negative genetic correlation (-0.09) was found between RP and SFI. The pedigree-based genetic analysis of RP showed that this trait has a low heritability, is linked to other reproductive disorders, and generally has an unfavorable relationship with production and fertility traits. Selection against RP can reduce the incidence of reproductive disorders and improve fertility and production traits.

Genetic Parameters Estimation of Milking Traits in Polish Holstein-Friesians Based on Automatic Milking System Data.

The automatic milking system (AMS) provides a large amount of information characterizing the course of each milking cow, which is not available in the conventional system. The aim of our study was to estimate heritability and genetic correlations for milk yield (MY), milking frequency (MF), and speed (MS) for 1713 Polish Holstein-Friesian primiparous cows milked in barns with an AMS. Daily heritability indicators estimated using second-order Legendre polynomials and Random Regression Models showed high variation during lactation, ranging 0.131-0.345 for MY, 0.153-0.322 for MF, and 0.336-0.493 for MS. The rates of genetic correlation between traits ranged: 0.561-0.929 for MY-MF, (-0.255)-0.090 for MF-MS, (-0.174)-0.020 for MY-MS. It is possible to carry out effective selection for milking speed, which provides an opportunity to increase the number of cows per milking robot, and thus increase the profitability of production in the herd. The results proved that selection for milk yield and daily milking frequency is also feasible. The research showed a high, positive genetic correlation between milking frequency and milk yield, which allows us to conclude that preferring breeding cows with a natural tendency to frequent visits to the milking robot should indirectly improve the genetic basis of milking.

New composite traits for joint improvement of milk and fertility trait in Holstein dairy cow.

OBJECTIVE: The objective of this study was to define a new composite trait for Holstein dairy cows and evaluate the possibility of joint improvement in milk and fertility traits. METHODS: A data set consisting 35,882 fertility related records (days open [DO], calving interval [CI], and number of services per conception [NSC], and total milk yield in each lactation [TMY]) was collected from 1998 to 2016 in Polish Holstein-Friesian breed herds. In this study TMY, DO, CI, and lactation length of each cow was used to obtain composite milk and fertility traits (CMF). RESULTS: Moderate heritability (0.15) was estimated for composite trait that was higher than heritability of female fertility related traits: DO 0.047, CI 0.042, and NSC 0.014, and slightly lower than heritability of TMY 0.19. Favourable genetic correlations (-0.87) were estimated between CMF with TMY. Spearman rank correlation coefficients between breeding value of CMF with DO, CI, and TMY were high (>0.94) but with NSC were moderate (0.64). Selection on CMF caused favourable correlated genetic gains for DO, CI, and TMY. Different selection indices with different emphasis on fertility and milk production were constructed. The amount of correlated genetic gains obtained for DO and total milk production according to selection in CMF were higher than of genetic gains obtained for DO and TMY in selection indices with different emphasis on milk and fertility. CONCLUSION: The animal selection only based on a composite trait - CMF proposed in current study would simultaneously lead to favourable genetic gains for both milk and fertility related traits. In this situation CMF introduced in current study can be used to overcome to limitations of selection index and CMF could be useful for countries that have problems in recording traits, especially functional traits.

Possibility of modifying the growth trajectory in Raeini Cashmere goat.

The objective of this study was to investigate the possibility of modifying the growth trajectory in Raeini Cashmere goat breed. In total, 13,193 records on live body weight collected from 4788 Raeini Cashmere goats were used. According to Akanke's information criterion (AIC), the sing-trait random regression model included fourth-order Legendre polynomial for direct and maternal genetic effect; maternal and individual permanent environmental effect was the best model for estimating (co)variance components. The matrices of eigenvectors for (co)variances between random regression coefficients of direct additive genetic were used to calculate eigenfunctions, and different eigenvector indices were also constructed. The obtained results showed that the first eigenvalue explained 79.90% of total genetic variance. Therefore, changing the body weights applying the first eigenfunction will be obtained rapidly. Selection based on the first eigenvector will cause favorable positive genetic gains for all body weight considered from birth to 12 months of age. For modifying the growth trajectory in Raeini Cashmere goat, the selection should be based on the second eigenfunction. The second eigenvalue accounted for 14.41% of total genetic variance for body weights that is low in comparison with genetic variance explained by the first eigenvalue. The complex patterns of genetic change in growth trajectory observed under the third and fourth eigenfunction and low amount of genetic variance explained by the third and fourth eigenvalues.

Fertility subindex for improving fertility performance in Iranian Holstein cows.

Different fertility indices were constructed for improving fertility performance in Iranian Holstein dairy cows. Number of inseminations per conception and days from calving to first insemination, each weighted by its economic value, were included as breeding goals in the aggregate genotype definition. Different fertility indices (FI) were constructed with different combinations of available fertility traits: number of inseminations to conception (INS), days from calving to first service (DFS), interval between first and last insemination (IFL), and days open (DO). The fertility index (FI1) that included INS and DFS had the greatest genetic gain for INS (-0.39 insemination), DFS (-7.47 days), and profit ($4.3) per generation. Genetic gain for profit, DFS, and INS including only DO showed slight differences regarding FI1. A selection index that included only INS (DFS) presented the larger (smaller) genetic gains for INS and smaller (larger) for DFS, which were -0.40 (-0.034) and -0.975 (-11.18) inseminations and days, respectively. The result of this study showed that recording INS and DFS are preferable traits for including in a fertility subindex. DO can be used in the absence of other fertility traits.