• Journal of Animal Science and Technology
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• v.58 no.10
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• pp.38.1-38.6
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• 2016

Background: Despite the importance of relationships between somatic cell score (SCS) and currently selected traits (milk, fat and protein yield) of Holstein cows, there was a lack of comprehensive literature for it in Iran. Therefore we tried to examine heritabilities and relationships between these traits using a fixed-regression animal model and Bayesian inference. The data set consisted of 1,078,966 test-day observations from 146,765 primiparous daughters of 1930 sires, with calvings from 2002 to 2013. Results: Marginal posterior means of heritability estimates for SCS ($0.03{\pm}0.002$) were distinctly lower than those for milk ($0.204{\pm}0.006$), fat ($0.096{\pm}0.004$) and protein ($0.147{\pm}0.005$) yields. In the case of phenotypic correlations, the relationships between production and SCS were near zero at the beginning of lactation but become increasingly negative as days in milk increased. Although all environmental correlations between production and SCS were negative ($-0.177{\pm}0.007$, $-0.165{\pm}0.008$ and $-0.152{\pm}0.007$ between SCS and milk, fat, and protein yield, respectively), slightly antagonistic genetic correlations were found; with posterior mean of relationships ranging from $0.01{\pm}0.039$ to $0.11{\pm}0.036$. This genetic opposition was distinctly higher for protein than for fat. Conclusion: Although small, the positive genetic correlations suggest some genetic antagonism between desired increased milk production and reduced SCS (i.e., single-trait selection for increased milk production will also increase SCS).

• Asian-Australasian Journal of Animal Sciences
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• v.31 no.5
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• pp.636-642
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• 2018

Objective: The objective of this study was to estimate genetic parameters of milk, fat, and protein yields within and across lactations in Tunisian Holsteins using a random regression test-day (TD) model. Methods: A random regression multiple trait multiple lactation TD model was used to estimate genetic parameters in the Tunisian dairy cattle population. Data were TD yields of milk, fat, and protein from the first three lactations. Random regressions were modeled with third-order Legendre polynomials for the additive genetic, and permanent environment effects. Heritabilities, and genetic correlations were estimated by Bayesian techniques using the Gibbs sampler. Results: All variance components tended to be high in the beginning and the end of lactations. Additive genetic variances for milk, fat, and protein yields were the lowest and were the least variable compared to permanent variances. Heritability values tended to increase with parity. Estimates of heritabilities for 305-d yield-traits were low to moderate, 0.14 to 0.2, 0.12 to 0.17, and 0.13 to 0.18 for milk, fat, and protein yields, respectively. Within-parity, genetic correlations among traits were up to 0.74. Genetic correlations among lactations for the yield traits were relatively high and ranged from $0.78{\pm}0.01$ to $0.82{\pm}0.03$, between the first and second parities, from $0.73{\pm}0.03$ to $0.8{\pm}0.04$ between the first and third parities, and from $0.82{\pm}0.02$ to $0.84{\pm}0.04$ between the second and third parities. Conclusion: These results are comparable to previously reported estimates on the same population, indicating that the adoption of a random regression TD model as the official genetic evaluation for production traits in Tunisia, as developed by most Interbull countries, is possible in the Tunisian Holsteins.

• 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.21 no.8
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• pp.1088-1096
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• 2008

Data from milk performance testing were used to analyze genetic and environmental trends for purebred Tsigai, Improved Valachian and Lacaune sheep. 103,715 (Tsigai), 212,962 (Improved Valachian) and 2,196 (Lacaune) test-day records gathered by the State Breeding Institute of the Slovak Republic entered the analyses. The respective pedigree data comprised 23,724 (Tsigai), 51,401 (Improved Valachian) and 438 (Lacaune) records. The multiple-trait, mixed model methodology was used to predict the breeding values for daily milk yield, fat and protein content and to estimate the fixed and remaining random effects assumed to affect the above mentioned traits, separately for each breed. The breeding values for daily milk yield were adjusted for 150-day standardized lactation length by multiplying with the constant 150, as the breeding goal of the selection scheme in Slovakian sheep is to increase 150-day milk production and constant heritability throughout the whole lactation is assumed. The genetic trends were expressed as changes in averages of breeding values across birth years of animals. For Tsigai and Lacaune breeds, cumulative genetic changes over the analyzed period were 3.8 and 5.1 kg for 150-day milk, 0 and -0.16% for fat content and 0 and -0.12% for protein content. For Improved Valachian breed, either a low (1.6 kg for 150-day milk yield) or zero (fat and protein content) cumulative genetic change was found. The environmental trends were calculated as averages of solutions for flock-test day effect across years and months in which measurements were taken. A distinctive cyclical pattern which reflected short-time variation in milk production traits was found. Possible explanations for this phenomenon are given and discussed.

• Journal of Animal Science and Technology
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• v.65 no.3
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• pp.588-595
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• 2023

The aim of this study was to investigate the effects of heat stress on milk traits in South Korea using comprehensive data (dairy production and climate). The dataset for this study comprised 1,498,232 test-day records for milk yield, fat- and protein-corrected milk, fat yield, protein yield, milk urea nitrogen (MUN), and somatic cell score (SCS) from 215,276 Holstein cows (primiparous: n = 122,087; multiparous: n = 93,189) in 2,419 South Korean dairy herds. Data were collected from July 2017 to April 2020 through the Dairy Cattle Improvement Program, and merged with meteorological data from 600 automatic weather stations through the Korea Meteorological Administration. The segmented regression model was used to estimate the effects of the temperature-humidity index (THI) on milk traits and elucidate the break point (BP) of the THI. To acquire the least-squares mean of milk traits, the generalized linear model was applied using fixed effects (region, calving year, calving month, parity, days in milk, and THI). For all parameters, the BP of THI was observed; in particular, milk production parameters dramatically decreased after a specific BP of THI (p < 0.05). In contrast, MUN and SCS drastically increased when THI exceeded BP in all cows (p < 0.05) and primiparous cows (p < 0.05), respectively. Dairy cows in South Korea exhibited negative effects on milk traits (decrease in milk performance, increase in MUN, and SCS) when the THI exceeded 70; therefore, detailed feeding management is required to prevent heat stress in dairy cows.

• Asian-Australasian Journal of Animal Sciences
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• v.9 no.6
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• pp.685-693
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• 1996

A total of 3,610 Ayrshire and 1,711 Jersey cows were phenotyped for the genetic variants of ${\alpha}_{s1}$-casein, ${\beta}$-casein, $\chi$-casein, ${\beta}$-lactoglobulin and ${\alpha}$-lactalbumin. Least squares analyses showed possible associations between milk protein phenotypes and lactational production traits. Depending on lactation number, ${\beta}$-casein phenotypes in Ayrshires were associated with milk production ($A^2A^2$ > $A^1A^2$ > $A^1A^1$), and with milk protein content. In the third lactation, Ayrshire cows with ${\beta}$-casein $A^1A^1$ produced milk with 3.43% fat compared to 3.37% fat for ${\beta}$-casein $A^2A^2$. In Ayrshire, $\chi$-casein phenotypes affected the protein content during the three lactations (BB > AB > AA) and ${\beta}$-lactoglobulin phenotypes significantly influenced the milk fat during the first lactation (4.06% for AA and 3.97% for BB). In Jerseys, protein content of milk was influenced by phenotypes of ${\alpha}_{s1}$-casein(3.98% for CC v/s 3.86% for BB in the first lactation). In the third lactation, $\chi$-casein AA of Jersey milk contained 5.35% fat compared to 4.82% for phenotype BB. The effects of ${\beta}$-lactoglobulin phenotypes on protein content were apparent in Jerseys during the second lactation with the A variant being superior to the B (4.00% for AA v/s 3.87% for BB).

• Asian-Australasian Journal of Animal Sciences
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• v.19 no.10
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• pp.1384-1389
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• 2006

Prolactin plays an important role in mammary gland development, milk section initiation and maintenance of lactation, so the bovine prolactin gene is considered as a potential quantitative trait locus affecting milk performance traits in dairy cattle. In this study, to determine the association between prolactin and milk performance traits, the genetic polymorphisms of a part of the prolactin gene were detected in a population of 649 cows of Chinese Holstein Dairy Cattle. Three SNPs in the promoter and one SNP in the intron1 of prolactin were identified, which was A/C (-767), G/T (-485), C/A (-247), and C/T (427), respectively. Statistical results indicated that one of SNP within promote, CHBP2, was significantly associated with milk yield (p<0.01), fat yield (p<0.05), protein yield (p<0.01), and protein percentage (p<0.05). The cows with genotype BB of CHBP2 had significantly higher milk yield (p<0.01), fat yield (p<0.05), and protein yield (p<0.01) than those of cows with genotype AA, while cows with genotype AA showed the highest protein percentage (p<0.05). In addition, based on the nine major haplotypes constructed from the four SNPs, the association analysis between diplotypes and milk performance trait was carried out. Results showed that the least square mean for fat yield of diplotype H2H8 was significantly higher than those of other eleven diplotypes (p<0.05). Our findings implied that CHBP2 and H2H8 of prolactin would be useful genetic markers in selection program on milk performance traits in Holstein Dairy Cattle.

• Asian-Australasian Journal of Animal Sciences
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• v.11 no.3
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• pp.311-317
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• 1998

A total of 1033 Brown Swiss and 610 Canadienne cows were phenotyped for the genetic variants ${\alpha}_{s1}$-casein, ${\beta}$-casein, ${\kappa}$-casein, ${\beta}$-lactoglobulin and ${\alpha}$-lactalbumin. In Brown Swiss, frequency distributions were: 97.3% B and 2.7% C variant of ${\alpha}_{s1}$-casein; 31.6% $A^1$, 51.8% $A^2$, 0.5% $A^3$ and 16.1% B variant of ${\beta}$-casein; 70.4% A, 29.3% B, and 0.3% C variant of ${\kappa}$-casein; 41.7% A and 58.3% B variant of ${\beta}$-lactoglobulin; and 100% B variant of ${\alpha}$-lactalbumin. Corresponding frequencies in Canadienne for those five milk proteins were: 98.6 and 1.4%;58.5, 33.5, 0.08 and 7.9%; 78.8, 21.1 and 0.1%, 42.4 and 57.6%; and 100%. Analysis of variance by least squares showed possible association between milk protein phenotypes and some lactational production traits. There were no significant association of phenotypes of ${\alpha}_{s1}$-casein, ${\beta}$-casein and ${\beta}$-lactoglobulin with milk yield, fat yield, protein yield, fat percentage and protein percentage in both breeds during the three lactations. In the Brown Swiss, ${\kappa}$-casein phenotype was associated with 305-day fat yield and protein yield during the first lactation. ${\kappa}$-Casein AB was associated with higher milk, fat and protein yield during the second lactation. During the third lactation, ${\beta}$-lactoglobulin AA in Canadienne cows was associated with higher protein content in the milk (3.70%) when compared to phenotypes AB (3.54%) and BB (3.64%).

• 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.

• 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.