• Animal Bioscience
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• v.35 no.7
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• pp.955-963
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• 2022

Objective: The aim of this study was to estimate genetic parameters for 305-day cumulative milk yield and components, growth, and reproductive traits in Guzerá cattle. Methods: The evaluated traits were 305-day first-lactation cumulative yields (kg) of milk (MY305), fat (FY305), protein (PY305), lactose (LY305), and total solids (SY305); age at first calving (AFC) in days; adjusted scrotal perimeter (cm) at the ages of 365 (SP365) and 450 (SP450) days; and adjusted body weight (kg) at the ages of 210 (W210), 365 (W365), and 450 (W450) days. The (co)variance components were estimated using the restricted maximum likelihood method for single-trait, bi-trait and tri-trait analyses. Contemporary groups and additive genetic effects were included in the general mixed model. Maternal genetic and permanent environmental effects were also included for W210. Results: The direct heritability estimates ranged from 0.16 (W210) to 0.32 (MY305). The maternal heritability estimate for W210 was 0.03. Genetic correlation estimates among milk production traits and growth traits ranged from 0.92 to 0.99 and from 0.92 to 0.99, respectively. For milk production and growth traits, the genetic correlations ranged from 0.33 to 0.56. The genetic correlations among AFC and all other traits were negative (-0.43 to -0.27). Scrotal perimeter traits and body weights showed genetic correlations ranging from 0.41 to 0.46, and scrotal perimeter and milk production traits showed genetic correlations ranging from 0.11 to 0.30. The phenotypic correlations were similar in direction (same sign) and lower than the corresponding genetic correlations. Conclusion: These results suggest the viability and potential of joint selection for dairy and beef traits in Guzerá cattle, taking into account reproductive traits.

• Asian-Australasian Journal of Animal Sciences
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• v.24 no.11
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• pp.1514-1524
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• 2011

A total of 4,752 monthly lactation records of Friesian cows during the period from 2000 to 2005 were used to estimate genetic parameters and to determine the effect of udder health on milk production traits. Three milk production traits were studied: 305-day milk yield (305-dMY), 305-day fat yield (305-dFY) and 305-day protein yield (305-dPY). Four udder health traits were studied: somatic cell count (SCC), mastitis (MAST), udder health status (UDHS) with 10 categories and udder quarter infection (UDQI) with 7 categories. Mixed model least square analysis was used to estimate the fixed effects of month and year of calving and parity (P) on different studied traits. Sire and dam within sire were included in the model as random effects. Data were analyzed using Multi-trait Derivative Free Restricted Maximum Likelihood methodology (MTDFREML) to estimate genetic parameters. Unadjusted means of 305-dMY, 305-dFY, 305-dPY and SCC were 3,936, 121, 90 kg and 453,000 cells/ml, respectively. Increasing SCC from 300,000 to 2,000,000 cells/ml increased UDQI from 5.51 to 23.2%. Losses in monthly and lactationally milk yields per cow ranged from 17 to 93 and from 135 to 991 kg, respectively. The corresponding losses in monthly and lactationally milk yields return per cow at the same level of SCC ranged from 29.8 to 163 and from 236 to 1,734 Egyptian pounds, respectively. Heritability estimates of 305-dMY, 305-dFY, 305-dPY, SCC, MAST, UDHS, UDQI were 0.31${\pm}$0.4, 0.33${\pm}$0.03, 0.35${\pm}$0.05, 0.23${\pm}$0.02, 0.14${\pm}$0.02, 0.13${\pm}$0.03, and 0.09${\pm}$0.01, respectively. All milk production traits showed slightly unfavorable negative phenotypic and genetic correlations with SCC, MAST, UDHS and UDQI. There were positive and high genetic correlations between SCC and each of MAST (0.85${\pm}$0.7), UDHS (0.87${\pm}$0.10) and UDQI (0.77${\pm}$0.06) and between MAST and each of UDHS (0.91${\pm}$0.11) and UDQI (0.83${\pm}$0.07). It could be concluded that the economic losses from mastitis and high SCC are considerable. The high genetic correlation between SCC and clinical mastitis (CM) suggest that the selection for lower SCC would help to reduce or eliminate the undesirable correlated responses of clinical mastitis associated with selection for increasing milk yield. Additionally, it is recommended also that if direct information on under health traits is not available, measures of SCC can be inclusion in a selection criteria to improve the income from dairy cows.

• Asian-Australasian Journal of Animal Sciences
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• v.20 no.2
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• pp.166-171
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• 2007

The objective of this study was to assess the association of polymorphisms in ${\kappa}$-cn, ${\beta}$-lg, ${\beta}$-lg 5′ flanking region, CSN1S2, and IGFBP-3 genes with milk production traits and mastitis-related traits in Chinese Holstein. Traits analyzed were 305 day standard milk yield, protein percentage, fat percentage, the ratio of fat percentage and protein percentage, pre-somatic cell count, somatic cell count, and somatic cell score, respectively. CSN1S2 locus was uninformative because only one genotype BB was found in Chinese Holstein. Allele frequencies of A and B in IGFBP-3 gene were 0.5738 and 0.4262 in Chinese Holstein population, which was different from reported Qinchuan cattle population. The genotypes of animals at IGFBP-3 locus significantly affected 305 day standard milk yield, protein percentage, and somatic cell score. The ${\beta}$-lg genotypes had a significant effect on protein percentage and the ratio of fat percentage and protein percentage. Polymorphism in ${\beta}$-lg 5′ flanking region was associated with 305 day standard milk yield, protein percentage, fat percentage, pre-somatic cell count, and somatic cell count. No significant associations of the polymorphism in ${\kappa}$-cn gene were observed for any trait.

• Asian-Australasian Journal of Animal Sciences
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• v.33 no.9
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• pp.1387-1399
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• 2020

Objective: The objective of this study was to estimate the genetic parameters and trends for milk, fat, and protein yields in the first three lactations of Thai dairy cattle using a 3-trait,-3-lactation random regression test-day model. Methods: Data included 168,996, 63,388, and 27,145 test-day records from the first, second, and third lactations, respectively. Records were from 19,068 cows calving from 1993 to 2013 in 124 herds. (Co) variance components were estimated by Bayesian methods. Gibbs sampling was used to obtain posterior distributions. The model included herd-year-month of testing, breed group-season of calving-month in tested milk group, linear and quadratic age at calving as fixed effects, and random regression coefficients for additive genetic and permanent environmental effects, which were defined as modified constant, linear, quadratic, cubic and quartic Legendre coefficients. Results: Average daily heritabilities ranged from 0.36 to 0.48 for milk, 0.33 to 0.44 for fat and 0.37 to 0.48 for protein yields; they were higher in the third lactation for all traits. Heritabilities of test-day milk and protein yields for selected days in milk were higher in the middle than at the beginning or end of lactation, whereas those for test-day fat yields were high at the beginning and end of lactation. Genetics correlations (305-d yield) among production yields within lactations (0.44 to 0.69) were higher than those across lactations (0.36 to 0.68). The largest genetic correlation was observed between the first and second lactation. The genetic trends of 305-d milk, fat and protein yields were 230 to 250, 25 to 29, and 30 to 35 kg per year, respectively. Conclusion: A random regression model seems to be a flexible and reliable procedure for the genetic evaluation of production yields. It can be used to perform breeding value estimation for national genetic evaluation in the Thai dairy cattle population.

• Asian-Australasian Journal of Animal Sciences
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• v.18 no.2
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• pp.165-169
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• 2005

This study was performed to offer effective basic data for selection and improvement of Korean dairy cattle through identifying distributional properties among candidate genes (bovine butyrophilin, signal transducers and activators of transcription 5a, and prolactin hormone). In this study, polymorphisms of candidate genes were identified and the relationships between loci and production traits of each gene were analyzed using frozen semen of Holstein bulls (19 proven and 77 candidates). In butyrophilin (BTN) locus, polymorphisms information contents (PIC) value of BTN2 (0.372) was higher than those of others (BTN1; 0.155, BTN3; 0.254, BTN4; 0.169). As a result of analysis of genotyping STAT5a, using single stranded conformational polymorphism (SSCP) method and microsatellite locus, PIC values were 0.189 and 0.457, respectively. And PIC value of prolactin hormone gene was 0.176. In the relationships between genotypes and production traits, BTN3 was associated with 305-day production traits (p<0.05). PTAs for B allele were such as 110.43, 88.28 and 75.25 in BTN1, 3, 4 and these values were higher than those of A allele, but in the case of BTN2, A allele with 154.19 was higher than that of B allele. The results obtained from using candidate genes may be used as an useful index for the genetic improvement of dairy cattle population in Korea, and further studies are needed.

• Asian-Australasian Journal of Animal Sciences
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• v.32 no.7
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• pp.913-921
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• 2019

Objective: The objectives of this study were to compare identified informative regions through two genome-wide association study (GWAS) approaches and determine the accuracy and bias of the direct genomic value (DGV) for milk production traits in Korean Holstein cattle, using two genomic prediction approaches: single-step genomic best linear unbiased prediction (ss-GBLUP) and Bayesian Bayes-B. Methods: Records on production traits such as adjusted 305-day milk (MY305), fat (FY305), and protein (PY305) yields were collected from 265,271 first parity cows. After quality control, 50,765 single-nucleotide polymorphic genotypes were available for analysis. In GWAS for ss-GBLUP (ssGWAS) and Bayes-B (BayesGWAS), the proportion of genetic variance for each 1-Mb genomic window was calculated and used to identify informative genomic regions. Accuracy of the DGV was estimated by a five-fold cross-validation with random clustering. As a measure of accuracy for DGV, we also assessed the correlation between DGV and deregressed-estimated breeding value (DEBV). The bias of DGV for each method was obtained by determining regression coefficients. Results: A total of nine and five significant windows (1 Mb) were identified for MY305 using ssGWAS and BayesGWAS, respectively. Using ssGWAS and BayesGWAS, we also detected multiple significant regions for FY305 (12 and 7) and PY305 (14 and 2), respectively. Both single-step DGV and Bayes DGV also showed somewhat moderate accuracy ranges for MY305 (0.32 to 0.34), FY305 (0.37 to 0.39), and PY305 (0.35 to 0.36) traits, respectively. The mean biases of DGVs determined using the single-step and Bayesian methods were $1.50{\pm}0.21$ and $1.18{\pm}0.26$ for MY305, $1.75{\pm}0.33$ and $1.14{\pm}0.20$ for FY305, and $1.59{\pm}0.20$ and $1.14{\pm}0.15$ for PY305, respectively. Conclusion: From the bias perspective, we believe that genomic selection based on the application of Bayesian approaches would be more suitable than application of ss-GBLUP in Korean Holstein populations.

• Animal Bioscience
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• v.35 no.10
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• pp.1499-1511
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• 2022

Objective: This study was aimed to estimate the genetic parameters, including genetic and phenotypic correlations, of milk yield, lactation curve traits and milk composition of Thai dairy cattle from three government research farms. Methods: The data of 25,789 test-day milk yield and milk composition records of 1,468 cattle from lactation 1 to 3 of Holstein Friesian (HF) and crossbred HF dairy cattle calved between 1990 and 2015 from three government research farms in Thailand were analysed. 305-day milk yield was estimated by the Wood model and a test interval method. The Wood model was used for estimating cumulative 305-day milk yield, peak milk yield, days to peak milk yield and persistency. Genetic parameters were estimated using linear mixed models with herd, breed group, year and season of calving as fixed effects, and animals linked to a pedigree as random effects, together with a residual error. Univariate models were used to estimate variance components, heritability, estimated breeding values (EBVs) and repeatability of each trait, while pairwise bivariate models were used to estimate covariance components and correlations between traits in the same lactation and in the same trait across lactations. Results: The heritability of 305-day milk yield, peak milk yield and protein percentage have moderate to high estimates ranging from 0.19 to 0.45 while days to peak milk yield, persistency and fat percentage have low heritability ranging from 0.08 to 0.14 in lactation 1 cows. Further, heritability of most traits considered was higher in lactation 1 compared with lactations 2 and 3. For cows in lactation 1, high genetic correlations were found between 305-day milk yield and peak milk yield (0.86±0.07) and days to peak milk yield and persistency (0.99±0.02) while estimates of genetic correlations between the remaining traits were imprecise due to the high standard errors. The genetic correlations within the traits across lactation were high. There was no consistent trend of EBVs for most traits in the first lactation over the study period. Conclusion: Both the Wood model and test interval method can be used for milk yield estimates in these herds. However, the Wood model has advantages over the test interval method as it can be fitted using fewer test-day records and the estimated model parameters can be used to derive estimates of other lactation curve parameters. Milk yield, peak milk yield and protein percentage can be improved by a selection and mating program while days to peak milk yield, persistency and fat percentage can be improved by including into a selection index.

• Asian-Australasian Journal of Animal Sciences
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• v.19 no.2
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• pp.189-197
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• 2006

Twenty small-scale farms of two villages (A and B) were surveyed to identify the feeding traits, milk productivity and nutritional status of lactating cattle and buffalo in Terai, Nepal. Constituents and dry matter (DM) of feed supplied, body condition score (BCS), heart girth (HG), bodyweight (BW), milk yield (MY) and plasma metabolites were obtained in the pasture-sufficient, pasture-decreasing and fodder-shortage periods. Milk yield of 305-day lactation was estimated by the daily MY. The supplies of rice straw and native grass were lower and higher in the pasture-sufficient period than in the other periods, respectively (5.5 kg/day vs. 9.8 kg/day and 3.2 kg/day vs. 0.4 kg/day, respectively, p<0.01). The roughage-supplement rates of the animals were higher in village A than in village B (5.0 vs. 2.2 in cattle and 9.3 vs. 1.8 in buffalo, p<0.01). The variance of feed constituents among the periods and between the villages induced different supplies of CP, NDF and TDN. The concentrations of CP and TDN in the cattle feed were higher in the pasture-sufficient period than in the other periods (9.1% vs. 7.3% and 57.4% vs. 51.0%, respectively, p<0.01). The supplies of CP for cattle and buffalo, and of TDN for buffalo were lower in village A than in village B (7.5% vs. 8.7% and 6.6% vs. 9.1% [p<0.01], and 53.1% vs. 56.2% [p<0.05], respectively). The BCS, HG and BW of the animals were lower in village A than in village B (2.51 vs. 2.86, 156 cm vs. 170 cm and 300 kg vs. 318 kg, respectively in cattle, 2.83 vs. 4.00, 186 cm vs. 216 cm and 429 kg vs. 531 kg, respectively in buffalo, p<0.01). The cattle yielded more milk in the pasture-sufficient period than in the other periods (7.9 liters/day vs. 6.6 liters/day, p<0.01). The 305-day MY of cattle that calved in the fodder-shortage period was lower than that of cattle that calved in the other periods (1,900 liters vs. 2,251 liters, p<0.01). The MYs of cattle and buffalo were lower in village A than in village B (6.2 liters/day vs. 8.1 liters/day and 3.7 liters/day vs. 7.7 liters/day, respectively, p<0.01). The 305-day MY of cattle was lower in village A than in village B (1,935 liters vs. 2,409 liters, p<0.01). The concentrations of plasma albumin and urea nitrogen in cattle were lower in village A than in village B (3.2 g/dl vs. 3.4 g/dl [p<0.01] and 7.4 mg/dl vs. 10.2 mg/dl [p<0.05], respectively). The different supplies of CP, NDF and TDN among the periods and between the villages might have affected MY and nutritional status in cattle and buffalo. It was likely that the lower supplies of CP and TDN for cattle that calved in the fodder-shortage period and in village A lowered the 305-day MY of cattle.

• Asian-Australasian Journal of Animal Sciences
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• v.17 no.2
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• pp.159-163
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• 2004

Data pertaining to first lactation records of 316 Murrah buffaloes, progeny of 47 sires, maintained at NDRI Farm for a period of 18 years were analysed to construct selection indices and to examine their robustness by changing the relative economic values of different economic traits. A total of 120 selection indices were constructed for three sets of relative economic values ( 40 for each set) considering different combinations of seven first lactation traits viz. age at first calving (AFC), first lactation 305 day or less milk yield (FLMY), first lactation length (FLL), first calving interval (FCI), milk yield per day of first lactation length (MY/FLL), milk yield per day of first calving interval (MY/FCI) and milk yield per day age at second calving (MY/ASC). The three sets of relative economic values were based on economic values of different traits, 1% standard deviation of different traits and regression of different traits on FLMY. The 'optimum' indices for the first two sets had five traits each namely AFC, FLMY, FLL, FCI and MY/ASC giving improvement in aggregate genotype of Rupees 269.11 and Rs. 174.88, respectively. The accuracy of selection from both indices was 70.79 and 69.39%, respectively. The 'best' selection index from the third set of data again had five traits (AFC, FLMY, FLL, FCI and MY/FLL) giving genetic gain of Rs. 124.16 and accuracy of selection of 71.81%. The critcal levels or break-even points for FLMY for varying levels of AFC and FCI estimated from the "optimum index" suggested the need of enhancement of present production level of the herd or reduction of AFC or FCI. It was concluded that economic values of various first lactation traits were the most appropriate to construct selection indices as compared to other criteria of assigning relative economic weights in Murrah buffaloes.

• Asian-Australasian Journal of Animal Sciences
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• v.13 no.5
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• pp.575-579
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• 2000

First lactation records of 683 Murrah buffaloes maintained at NDRI, Karnal which were progeny of 84 sires used for comparing the heritability estimates of age at first calving, first lactation milk yield and first service period under single and multiple trait models using restricted maximum likelihood (REML) method of estimation under an individual animal model. The results indicated that the heritability estimates may vary under single and multiple trait models depending upon the magnitude of genetic and environmental correlation among the traits being considered. Therefore, a single or multiple trait model is recommended for estimation of variance components depending upon the goal of breeding programme. However, there may not be any advantage of considering a trait with zero or near zero heritability and having no or very low genetic correlation with other traits in the model. Lower heritability estimates of part lactation yield (120-day milk yield) implied that there may not be any advantage of considering this trait in place of actual 305-day milk yield, whereas, comparable heritability estimates of predicted 305-day milk yield suggested that it could be used for sire evaluation to reduce the cost of milk recording under field conditions.