Production Traits In Dairy Cattle Research Articles

The Number of Loci That Affect Milk Production Traits in Dairy Cattle

We have used the results of an experiment mapping quantitative trait loci (QTL) affecting milk yield and composition to estimate the total number of QTL affecting these traits. We did this by estimating the number of segregating QTL within a half-sib daughter design using logic similar to that used to estimate the "false discovery rate" (FDR). In a half-sib daughter design with six sire families we estimate that the average sire was heterozygous for approximately 5 QTL per trait. Also, in most cases only one sire was heterozygous for any one QTL; therefore at least 30 QTL were likely to be segregating for these milk production traits in this Holstein population.

Statistical Modeling of Candidate Gene Effects on Milk Production Traits in Dairy Cattle

A major objective of dairy cattle genomic research is to identify genes underlying the variability of milk production traits that could be useful in breeding programs. The candidate gene approach provides tools for searching for causative polymorphisms affecting quantitative traits. Genes with a possible effect on milk traits in cattle can be involved in different physiological pathways, such as triglyceride synthesis [acyl-CoA:diacylglycerol acyltransferase 1 gene (DGAT1)], fat secretion from the mammary epithelial tissue (butyrophilin), or entire-body energy homeostasis regulation (leptin and leptin receptor). In this study, based on data from 252 Black and White bulls from the active Polish dairy population, effects and potential interactions of 9 single nucleotide polymorphisms in the butyrophilin, DGAT1, leptin, and leptin receptor genes were investigated. Additionally, the effect of the number of additive, dominance, and epistatic genetic effects fitted into the model on the estimates of model parameters and model selection was illustrated. Phenotypic records were daughter yield deviations for milk, fat, and protein yields, obtained from a routine national genetic evaluation. Out of all the analyzed polymorphisms, DGAT1 K232A had a much larger effect on milk traits than the other single nucleotide polymorphisms considered. Estimates of the additive genetic effect of K232A expressed as half of the difference between Lys- and Ala-encoding variants were −107.4kg of milk, 5.4kg of fat, and −1.6kg of protein at first parity, as well as −120kg of milk and 6.8kg of fat at second parity. In terms of model selection, it was demonstrated that the modified version of Bayesian information criterion selects models with the parameterization reflecting the genetic background of the analyzed trait, while the Bayesian information criterion chooses models that are too highly parameterized.

The Association of Bovine PPARGC1A and OPN Genes with Milk Composition in Two Independent Holstein Cattle Populations

Many studies have reported quantitative trait loci on chromosome 6 that affect milk production traits in dairy cattle. Osteopontin (OPN) and peroxisome proliferator activated receptor gamma coactivator 1 alpha (PPARGC1A) are located in the middle of chromosome 6 about 6 Mb apart, which is approximately 12 cM. The objective of this study was to investigate the association of OPN and PPARGC1A variants with milk production traits in 2 independent Holstein cattle populations: the University of Wisconsin (UW) daughter design and the Cooperative Dairy DNA Repository (CDDR) granddaughter design resource populations. For OPN, 891 cows from the UW resource population were genotyped for the C/T polymorphism reported previously in the CDDR population. Additive effects were significant for fat percentage, protein percentage, and fat yield in the UW resource population. These results are consistent with previous studies that have shown significant association of OPN variants with milk composition traits. The association between PPARC1A variants was investigated in UW and CDDR resource populations using 2 single nucleotide polymorphisms. For the UW resource population, additive effects were significantly increased for protein percentage and decreased for milk yield. Dominance effects were not significant for any of the examined traits. For the CDDR population, PPARGC1A was associated with a significant increase in milk protein percentage and in SCS. Thus, in UW and CDDR populations, PPARGC1A was associated with a significant increase in milk protein percentage in contrast to association results previously reported for the German Holstein population.

Association of genetic variants of the prolactin gene with milk production traits in Russian Red Pied cattle

Prolactin plays an important regulatory function in mammary gland development, milk secretion, and expression of milk protein genes. Hence the PRL gene is a potential genetic marker of production traits in dairy cattle. The gene was mapped on chromosome 23 by Hallerman et al. (1988). It consists of 5 exons and four introns (Camper et al. 1984) encoding the 199-amino-acid mature protein (Wallis 1974). On the basis of sequence analysis of four different cDNA clones, seven possible nucleotide substitutions were described by Sasavage et al. (1982). One of them, recognized by RsaI endonuclease, has become a popular genetic marker used for genetic characterization of cattle populations by means of PCR-RFLP (Mitra et al., 1995). Two allelic variants (B and b) have been distinguished at the DNA level, based on RsaI polymorphism in the third exon of the coding region. It has been suggested that prolactin alleles correlate with milk yield (Lewin et al., 1992).

Evaluation of association between polymorphism within the thyroglobulin gene and milk production traits in dairy cattle

In dairy cattle, many studies have reported quantitative trait loci (QTL) on the centromeric end of chromosome 14 that affect milk production traits. One of the candidate genes in this QTL region - thyroglobulin (TG) - was previously found to be significantly associated with marbling in beef cattle. Thus, based on QTL studies in dairy cattle and because of possible effects of this gene on fat metabolism, we investigated the association of TG with milk yield and composition in Holstein dairy cattle. A total of 1279 bulls from the Cooperative Dairy DNA Repository Holstein population were genotyped for a single nucleotide polymorphism in TG used previously in beef cattle studies. Analysis of 29 sire families showed no significant association between TG variants and milk production traits. Within-sire family analysis suggests that TG is neither the responsible gene nor a genetic marker in association with milk production traits.

Single nucleotide polymorphism in the promoter region of the lactoferrin gene and its associations with milk performance traits in Polish Holstein-Friesian cows

Bovine lactoferrin (LTF) is a multifunctional small glycoprotein found in milk acting mainly as a defense factor in the mammary gland. Many polymorphisms have been found in the bovine LTF gene but almost none were considered as genetic markers of production traits in dairy cattle. In this study, the promoter fragment of LTF gene containing mutation (G/C) in position +32 has been amplified by PCR followed by genotyping by the SSCP and RFLP method. 358 Polish Holstein-Friesian cows were screened, giving the following frequency of genotypes: 0.628, 0.313 and 0.059 for GG, GC and CC, respectively. GLM (General Linear Model) analysis was applied to evaluate the associations of lactoferrin with milk performance traits, including SCC - somatic cell count. It was found that CC cows show significantly higher (P < or = 0.01) protein content in milk in comparison with GG cows. The values of other milk performance traits were also higher but at non-significant levels. SCC in milk was the lowest in CC cows, but also at a non-significant level.

Genetic mapping of loci responsible for milk production traits in dairy cattle

The review presents a definition of loci controlling quantitative traits (quantitative trait loci, QTLs) and localization of all currently known QTLs responsible for milk production traits in dairy cattle. The QTL number and chromosome localization are verified, with special reference to chromosomes 1, 3, 6, 14, 20, and 23. In a number of cases, close location of QTLs for mastitis and for milk production traits was found. Some aspects of QTL pleiotropy and epistasis are discussed and mapping methods of major QTLs are listed.

Random Herd Curves in a Test-Day Model for Milk, Fat, and Protein Production of Dairy Cattle in The Netherlands

With random regression models, genetic parameters of test-day milk production records of dairy cattle can be estimated directly from the data. However, several researchers that used this method have reported unrealistically high variances at the borders of the lactation trajectory and low genetic correlations between beginning and end of lactation. Recently, it has been proposed to include herd-specific regression curves in the random regression model. The objective was to study the effect of including random herd curves on estimated genetic parameters. Genetic parameters were estimated with 2 models; both included random regressions for the additive genetic and permanent environmental effect, whereas the second model also included a random regression effect for herd×2-yr period of calving. All random regressions were modeled with fourth-order Legendre polynomials. Bayesian techniques with Gibbs sampling were used to estimate all parameters. The data set comprised 857,255 test-day milk, fat, and protein records from lactations 1, 2, and 3 of 43,990 Holstein cows from 544 herds. Genetic variances estimated by the second model were lower in the first 100 d and at the end of the lactation, especially in lactations 2 and 3. Genetic correlations between d 50 and the end of lactation were around 0.25 higher in the second model and were consistent with studies where lactation stages are modeled as different traits. Subsequently, estimated heritabilities for persistency were up to 0.14 lower in the second model. It is suggested to include herd curves in a random regression model when estimating genetic parameters of test-day production traits in dairy cattle.

Testday models for production traits in dairy cattle

Methodology has been developed to implement testday models in the national genetic evaluation of dairy breeds for production traits in South Africa. Positive definite covariance matrices have been estimated, using multitrait, multi-lactation, fixed regression testday BLUP animal models, including testday records of the first three lactations as repeated measures for the Holstein and Jersey breeds. Heritability estimates were 0.40 + 0.007 for milk yield, 0.25 + 0.006 for butterfat yield and 0.37 + 0.006 for protein yield for the Holstein breed and 0.39 + 0.004 for milk yield, 0.21 + 0.002 for butterfat yield and 0.34 + 0.002 for protein yield for the Jersey breed. These estimates are well in the range of estimates reported by countries participating in INTERBULL evaluations. South African Journal of Animal Science Supp 2 2004: 35-37

Studies on the Development of Novel 305 day Adjustment Factors for Production Traits in Dairy Cattle

This study was conducted to develop a novel adjustment factors for 305 days using 138,103 lactation records and 1,770,764 daily records, which were based on environmental circumstances such as herd year, season, age at calving, dry period and lactating stages. The present study showed that the change of persistency of cows at the first parity from total lactacting characteristics was slowly processed, while it was rapidly changed in cows at the second parity stage. Particularly, there was an outstanding difference between the first and second parity cows. Milk yield and composition increased as the age at calving was increased. In addition, milk yield and composition were higher at the first parity on fall compared with others, and those were higher at the more than second parity on fall and winter compared with other parity stages and seasons. The cow of dry group was included into lactating records of more than second parity stage. The data indicated that optimal results arose from 45-70 days of dry period. Milk yield was decreased when dry period was longer or shorter than 45-70 days. The lactating days were divided into 17, 28 and 38 stages to compare the multiplicative correction factors. The factor was effective at 28 stages on the first parity. The total correlation coefficients were 0.93832, 0.95058 and 0.95076 at the present correction factor, 17 stage and 28 stage, respectively. At second parity, the factor was higher in dry group 1 and 3 at 17 stage, and it was higher in dry group 2 at 28 stage compared with others. Therefore, the present study showed that the percent squared bias (PSB), which was calculated from the novel correction factor, was better than previously used correction factors. Also, the present study indicated that the bias of the novel correction factor was improved, and this factor could be more accurate compared with others.

Comparison of test-day models for genetic evaluation of production traits in dairy cattle

A multiple-trait reduced rank random regression test-day model was applied for the breeding value estimation for first parity milk, protein, and fat yield of Finnish dairy cattle. This model was compared with three other models: a similar multiple-trait random regression test-day model without rank reduction, a multiple-trait repeatability test-day model, and a multiple-trait 305-d lactation yield model. Required (co)variance parameters were derived from the same covariance functions for all four models. For both random regression models, standard deviations of breeding values were the same and correlations between breeding values were between 0.995 and 0.998, resulting in only slight differences in the ranking of animals. Genetic trends were identical for the random regression test-day models and very similar to those estimated by the 305-d lactation yield model. The repeatability test-day model gave a slightly different genetic trend and inflated standard deviations for breeding values of cows with lactations in progress. Reduction of rank in the random regression test-day model decreased memory requirements and improved convergence in iteration when solving the mixed model equations.

An Autoregressive Repeatability Animal Model for Test-Day Records in Multiple Lactations

Test-day (TD) models are becoming a standard for genetic evaluation of production traits in dairy cattle. Various approaches to model covariances between TD records include random regression, autoregressive repeatability, orthogonal polynomials, and models based on character processing. The applicability of these models is mainly associated with the number of parameters to estimate, incorporation of multiple lactations, and the accuracy of correlations generated by the cow's repeated expression of milking performance (TD yields) within and across lactations. We define and evaluate a multiple-lactation, autoregressive-repeatability model that disentangles environmental effects due to cow within and between lactations. Simulated records either included or ignored a long-term environmental effect between lactations. Our autoregressive TD animal model correctly detected presence and the absence of this effect and accurately recovered the assumed variance components and correlations underlying the data (10 parameters for three lactations). Estimates of variance components and autocorrelation coefficients were obtained using DFREML-simplex methodology. Given the value of this approach to reduce the size of residual variance components, autoregressive animal models are a preferable alternative to classical methods based on cumulative lactation yield to improve milk production in dairy cattle.

Contrasting Models for Lactation Curve Analysis

Several statistical models have been proposed for the genetic evaluation of production traits in dairy cattle based on test-day records. Three main approaches have been put forward in the literature: random regression, orthogonal polynomials, and, more recently, character process models. The aim of this paper is to show how these different approaches are related, to compare their performance for the genetic analysis of lactation curves, and to assess equivalence between sire and animal models for repeated measures analyses. It was found that, with an animal model, a character process model with 11 parameters performed better, regarding the likelihood criterion, than a quartic random regression model (with 31 parameters). However, although the likelihood was higher, the genetic variance was very different with the character process model from the unstructured model, which raises important issues concerning model selection criteria. There are advantages in combining methodologies. A quadratic random regression model for the environmental part, combined with a character process model for the residual, performed better than the quartic random regression model and had fewer parameters. A character process structure allowing for a correlation pattern modeled the residual better than a simple quadratic variance, and had only one extra parameter.

Influence of Genetic Merit on 305-day Milk Production of Dairy Cattle on Commercial Farms at Three Levels of Feeding

The objective was to estimate the influence of genetic merit on milk production at different levels of concentrate feeding. The presence of an interaction between genetic merit and level of feeding would mean that cows with a high potential for milk production are unable to express their potential under all conditions. Oldham and Simm, (1998) showed there was a significant interaction between level of concentrate feeding and PTAf+p under experimental conditions, and that the milk yield response to genetic merit increased with increased concentrate feeding. The current work estimated the value of genetic merit (PIN95 and PTAf+p) on 305-day milk production traits of dairy cattle on commercial farms, where farms were categorised by the level of concentrate (Cg) fed.

Linear and threshold model genetic parameters for disease, fertility and milk production in dairy cattle

AbstractThis study provides estimates of genetic parameters for various diseases, fertility and 305-day milk production traits in dairy cattle using data from a UK national milk recording scheme. The data set consisted of 63891 multiple lactation records on diseases (mastitis, lameness, milk fever, ketosis and tetany), fertility traits (calving interval, conception to first service, number of services for a conception, and number of days to first service), dystocia and 305-day milk, fat and protein yield. All traits were analysed by multi-trait repeatability linear animal models (LM). Binary diseases and fertility traits were further analysed by threshold sire models (TM). Both LM and TM analyses were based on the generalized linear mixed model framework. The LM included herd-year-season of calving (HYS), age at calving and parity as fixed effects and genetic, permanent environmental and residual effects as random. The TM analyses included the same effects as for LM, but HYS effects were treated as random to avoid convergence problems when HYS sub-classes had 0 or 100% incidence. Because HYS effects were treated as random, herd effects were fitted as fixed effects to account for effect of herds in the data. The LM estimates of heritability ranged from 0•389 to 0•399 for 305-day milk production traits, 0•010 to 0•029 for fertility traits and 0•004 to 0•038 for diseases. The LM estimates of repeatability ranged from 0•556 to 0•586 for 305-day milk production traits, 0•029 to 0•086 for fertility traits and 0•004 to 0•100 for diseases. The TM estimates of heritabilities and repeatabilities were greater than LM estimates for binary traits and were in the range 0•012 to 0•126 and 0•013 to 0•168, respectively. Genetic correlations between milk production traits and fertility and diseases were all unfavorable: they ranged from 0•07 to 0•37 for milk production and diseases, 0•31 to 0•54 for milk production and poor fertility and 0•06 to 0•41 for diseases and poor fertility. These results show that future selection programmes should include disease and fertility for genetic improvement of health and reproduction and for sustained economic growth in the dairy cattle industry.

Effects of cow families on production traits in dairy cattle

AbstractThe components of phenotypic variance attributable to maternal lineage for production traits of the UK Holstein Friesian dairy population were estimated. First lactation production records of 55 230 cows calving between 1996-1998 in the UK Holstein Friesian population were used in the analysis. Maternal pedigree records were traced back to 1960 to establish maternal lineages. The tracing resulted in 36 320 cows being assigned to 11 786 cow families with more than one cow per maternal lineage. Using test day records it was possible to explore aspects of the lactation curve in terms of persistency and different periods of production. The traits analysed were 305-day milk yield and composition traits, the first three milk yield tests of lactation and two measures of persistency. A contemporary record design was used to minimize pair-wise additive direct genetic relationships between cows within a maternal lineage and to remove both the effect of heterogeneous variance over time and the complications of permanent environment effects. No significant component of variance attributable to maternal lineage was found for yield traits. When data were restricted to maternal lineages with five or more records, persistency, as a ratio of cumulative yield in the last third to that in the first third of a 300-day lactation, was estimated to have a 4·4% component due to maternal lineage variance significant at the 5% level. The study also investigated the preferential treatment of cow families. Some evidence of maternal lineage×herd interaction was found.

Relative weights on pedigree information and performance in evaluations based on a test day model

Genetic evaluations for milk production traits in dairy cattle based on test day yields (TD) have generated much interest recently due to a number of advantages, including improved accuracy of predicting breeding values and estimation of persistency evaluations. One of the methods proposed for a test day evaluation system is the random regression model (RRM) approach fitting covariance functions such as the Legendre Polynomials. Preliminary genetic evaluations using this approach have been produced by the Animal Data Centre in the United Kingdom based on the research of Brotherstone et al (1999). The objective of this paper is to estimate relative weights on pedigree and TD in evaluations of cows using RRM fitting Legendre Polynomials and compare with 305-day yield evaluations. Secondly to examine the efficiency of using part lactation TD information to predict breeding values for cows.

Association of Class I Bovine Lymphocyte Antigen Complex Alleles with Health and Production Traits in Dairy Cattle

Ninety-eight Holstein cows from the I-O-State breeding research herd were serologically typed for class I bovine lymphocyte antigens. After exclusion of animals carrying alleles that occurred at frequencies lower than 4%, records from 82 cows that had 161 lactations remained for analysis of major histocompatibility complex allelic effects. A gene substitution model was used to evaluate the additive effects of nine alleles (frequencies of 4 to 20%) at the bovine lymphocyte antigen complex A locus on general health, udder health, and production traits. Allele w14(w8) was associated with decreased quarter milk sample California Mastitis Test scores, composite milk sample California Mastitis Test and Wisconsin Mastitis Test scores, decreased total health costs, and with increased milk yield, fat yield, fat percentage, and income over feed costs. Allele w11 was associated with decreased clinical mastitis, discarded milk, and udder health costs but was also associated with decreased fat yield, fat percentage, and income over feed costs. Allele w31(w30) was associated with decreased fat percentage. These relationships suggest that alleles at the bovine lymphocyte antigen complex A locus may serve as markers for health and production traits. Thus, the potential may exist for enhancement of disease resistance or production in cattle via marker-assisted selection and genetic manipulation techniques using class I genes of the bovine major histocompatibility complex.

Potential Bias in Genetic Evaluations from Differences in Variation Within Herds

Evidence is reviewed that indicates substantial differences among herds in variation for production traits in dairy cattle. Examples are given to illustrate the overevaluation and selection of higher proportions of individuals from more variable herds, which reduces response to selection if greater variability is not due in part to greater additive genetic variance. Differences in heritability according to herd mean and variance are examined. Potential bias is substantial in genetic evaluations of cows and may increase over generations where, if the model is realistic, evaluations of dams are used in evaluations of daughters. Possible methods of adjustment are discussed.

Lack of Evidence of Cytoplasmic Inheritance in Milk Production Traits of Dairy Cattle

Heritability estimates, by year of freshening of daughter, were obtained from daughter-dam and granddaughter-granddam regressions using 61,482 triply matched first lactations of artificially sired Holstein cows obtained from the Northeast Dairy Records Processing Laboratory. After adjusting for herd-year-season effects, residual effects may include additive and other genetic effects of the animal, maternal effects, cytoplasmic effects, and other environmental effects. Analysis of residuals showed that cytoplasmic effects accounted for no variation in milk and fat yield and fat percent. Weighted yearly heritability estimates and standard errors from daughter on dam regressions were .35±.01 for milk yield, .30±.01 for milk fat yield, and .63±.01 for milk fat percent and from daughter on granddam regressions were .34±.03 for milk production, .28±.03 for milk fat production, and .55±.03 for milk fat percent. The differences between daughter-dam and daughter-granddam heritability estimates, which estimate twice the fraction of variance due to cytoplasmic effects, were negative and not statistically significant for milk fat yield and also were negative but highly significant for milk fat percent.