• Asian-Australasian Journal of Animal Sciences
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• v.28 no.4
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• pp.476-484
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• 2015

The objective of this study was to estimate genetic and phenotypic parameters for linear type traits, as well as milk yield (MY), fat yield (FY) and protein yield (PY) in 18,831 Holstein cows reared in 495 herds in Brazil. Restricted maximum likelihood with a bivariate model was used for estimation genetic parameters, including fixed effects of herd-year of classification, period of classification, classifier and stage of lactation for linear type traits and herd-year of calving, season of calving and lactation order effects for production traits. The age of cow at calving was fitted as a covariate (with linear and quadratic terms), common to both models. Heritability estimates varied from 0.09 to 0.38 for linear type traits and from 0.17 to 0.24 for production traits, indicating sufficient genetic variability to achieve genetic gain through selection. In general, estimates of genetic correlations between type and production traits were low, except for udder texture and angularity that showed positive genetic correlations (>0.29) with MY, FY, and PY. Udder depth had the highest negative genetic correlation (-0.30) with production traits. Selection for final score, commonly used by farmers as a practical selection tool to improve type traits, does not lead to significant improvements in production traits, thus the use of selection indices that consider both sets of traits (production and type) seems to be the most adequate to carry out genetic selection of animals in the Brazilian herd.

• 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.

• Genomics & Informatics
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• v.13 no.4
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• pp.146-151
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• 2015

Previous studies in Holstein have shown 35% to 51.8% heritability in milk production traits, such as milk yield, fat, and protein, using pedigree data. Other studies in complex human traits could be captured by common single-nucleotide polymorphisms (SNPs), and their genetic variations, attributed to chromosomes, are in proportion to their length. Using genome-wide estimation and partitioning approaches, we analyzed three quantitative Holstein traits relevant to milk production in Korean Holstein data harvested from 462 individuals genotyped for 54,609 SNPs. For all three traits (milk yield, fat, and protein), we estimated a nominally significant (p = 0.1) proportion of variance explained by all SNPs on the Illumina BovineSNP50 Beadchip ($h^2_G$). These common SNPs explained approximately most of the narrow-sense heritability. Longer genomic regions tended to provide more phenotypic variation information, with a correlation of 0.46~0.53 between the estimate of variance explained by individual chromosomes and their physical length. These results suggested that polygenicity was ubiquitous for Holstein milk production traits. These results will expand our knowledge on recent animal breeding, such as genomic selection in Holstein.

• Asian-Australasian Journal of Animal Sciences
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• v.17 no.2
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• pp.153-158
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• 2004

Data containing 14,188 lactation and reproductive records of Korean Holstein cows at first parity distributed across 3,734 herd-year-season groups were analyzed to get genetic (co)variance estimates for milk yield, fat yield, calving ease, and days open. Milk and Fat yields were adjusted to 305 d. Heritabilities and genetic correlations were estimated in two different animal models on which were included direct genetic effects (Model 1) and direct+maternal genetic effects (Model 2) using REML algorithms. Milk and fat yields were affected by age at first calving as linear and quadratic. Heritability estimates of direct effects were 0.25 for milk yield, 0.17 for fat yield, 0.03 for calving ease and 0.03 for days open in Model 2. These estimates for maternal effects were 0.05, 0.08, 0.04 and less than 0.01 for each corresponding trait. Milk productions at first lactation were to show genetically favorable correlation with calving ease and days open for direct genetic effects (-0.24 - -0.11). Moreover, calving ease was correlated with days open of 0.30 for direct genetic effects. Correlations between direct and maternal effects for each trait were negatively correlated (-0.63 - -0.32). This study suggested that maternal additive genetic variance would be not ignorable for genetic evaluation of milk production as well as reproductive traits such as calving ease and days open at first parity. Furthermore, difficult calving would genetically influence the next conception.

• Asian-Australasian Journal of Animal Sciences
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• v.19 no.3
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• pp.315-318
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• 2006

Associations were analysed between polymorphisms localized in intron 3 of the growth hormone gene (GH-MspI) and milk production traits of 543 China Holstein cows. A PCR-RFLP method was used for identification of genotypes. The following frequencies of genotypes and alleles were found: 0.77, 0.21 and 0.02 for +/+, +/- and -/-, respectively, and 0.87 and 0.13 for $GH^+$ and $GH^-$, respectively. Significant differences between herds were observed in the frequencies of both genotypes and alleles. The results of least squares analysis showed that in all three lactation phases the GH +/+ cows yielded most milk (p<0.01 for lactation I and p<0.05 for lactations II and III), whereas +/- cows showed higher milk fat content than +/+ individuals (p<0.05 for lactation I and II, and p<0.01 for lactation III). The +/+ cows yielded more fat than +/- individuals (p<0.01 only in lactation I). The +/+ cows yielded more milk protein than +/- individuals (p<0.01 for lactation I, II, and III). The +/+ cows produced milk of higher protein content than that of +/- individuals (p<0.05 only in lactation II). Based on these results, we conclude that the +/+ of GH locus should be the favored genotype in China Holstein cow breeds for use in marker-assisted selection programmes.

• 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.23 no.10
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• pp.1261-1267
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• 2010

Milk production traits are important economic traits for dairy cattle. The aim of the present study was to refine the position of previously detected quantitative trait loci (QTL) on bovine chromosome 6 affecting milk production traits in Chinese Holstein dairy cattle. A daughter design with 918 daughters from 8 elite sire families and 14 markers spanning the previously identified QTL region were used in the analysis. We employed a combined linkage and linkage disequilibrium analysis (LDLA) approach with two options for calculating the IBD probabilities, one was based on haplotypes of all 14 markers (named Method 1) and the other based on haplotypes with sliding windows of 5 markers (named Method 2). For milk fat yield, the two methods revealed a highly significant QTL located within a 6.5 cM interval (Method 1) and a 4.0 cM interval (Method 2), respectively. For milk protein yield, a highly significant QTL was detected within a 3.0 cM interval (Method 1) or a 2.5 cM interval (Method 2). These results confirmed the findings of our previous study and other studies, and greatly narrowed down the QTL positions.

• 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.33 no.3
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• pp.382-389
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• 2020

Objective: This study was conducted to test the efficiency of genomic selection for milk production traits in a Korean Holstein cattle population. Methods: A total of 506,481 milk production records from 293,855 animals (2,090 heads with single nucleotide polymorphism information) were used to estimate breeding value by single step best linear unbiased prediction. Results: The heritability estimates for milk, fat, and protein yields in the first parity were 0.28, 0.26, and 0.23, respectively. As the parity increased, the heritability decreased for all milk production traits. The estimated generation intervals of sire for the production of bulls (L_SB) and that for the production of cows (L_SC) were 7.9 and 8.1 years, respectively, and the estimated generation intervals of dams for the production of bulls (L_DB) and cows (L_DC) were 4.9 and 4.2 years, respectively. In the overall data set, the reliability of genomic estimated breeding value (GEBV) increased by 9% on average over that of estimated breeding value (EBV), and increased by 7% in cows with test records, about 4% in bulls with progeny records, and 13% in heifers without test records. The difference in the reliability between GEBV and EBV was especially significant for the data from young bulls, i.e. 17% on average for milk (39% vs 22%), fat (39% vs 22%), and protein (37% vs 22%) yields, respectively. When selected for the milk yield using GEBV, the genetic gain increased about 7.1% over the gain with the EBV in the cows with test records, and by 2.9% in bulls with progeny records, while the genetic gain increased by about 24.2% in heifers without test records and by 35% in young bulls without progeny records. Conclusion: More genetic gains can be expected through the use of GEBV than EBV, and genomic selection was more effective in the selection of young bulls and heifers without test records.

• Animal Bioscience
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• v.34 no.5
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• pp.914-921
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• 2021

Objective: The objective of this study was to compare growth performance, carcass traits and meat quality in young bulls, steers and heifers produced by crossing Limousin bulls with Holstein-Friesian cows, fattened semi-intensively and slaughtered at 18 months of age. Methods: Thirty-one young calves were reared in a conventional production system, and were fed milk replacer, hay and concentrate. At 6 months of age, the animals were divided into groups based on gender, and were fed a total mixed ration composed of grass silage, concentrates I and II in a semi-intensive production system. At the end of the fattening period (18 months), the animals were slaughtered, carcass quality was evaluated, and samples of musculus longissimus thoracis were collected to determine the proximate composition and quality of meat. Results: Bulls were characterized by the highest percentage share of the most valuable cuts in the carcass, and three-rib sections from bull carcasses had the highest lean meat content with low intramuscular fat content (0.93%). No significant differences in carcass conformation, dressing percentage or the percentage share of round in the right half-carcass were found between bulls vs. steers and heifers. Heifers and steers had higher carcass fat content than bulls, which had a positive influence on the sensory properties of beef. In comparison with the meat of bulls, the meat of steers and heifers was characterized by more desirable physical properties and sensory attributes (water-holding capacity, shear force, color lightness, aroma, juiciness, tenderness, flavor). Conclusion: Under the semi-intensive production system, heifers and steers had higher carcass fat content than bulls, which had a positive effect on the sensory properties of beef. Bulls are better suited for intensive systems, which contribute to improving the quality of their meat. The results of this study may encourage producers to breed steers and heifers for beef.