Heritability and Correlations for Body Condition Score and Dairy Form Within and Across Lactation and Age

Alterations of the acylcarnitine profiles in blood serum and in muscle from periparturient cows with normal or elevated body condition

The objective of the current study was to characterize muscle and blood serum acylcarnitine (AcylCN) profiles and to determine the mRNA abundance of muscle carnitine acyltransferases in periparturient dairy cows with high (HBCS) and normal body condition (NBCS). Fifteen weeks antepartum, 38 pregnant multiparous Holstein cows were assigned to 2 groups that were fed differently to reach the targeted BCS and backfat thickness (BFT) until dry-off at -49 d before calving (HBCS: BCS >3.75 and BFT >1.4 cm; NBCS: <3.5 and <1.2 cm). Thereafter, both groups were fed identical diets. Blood samples and biopsies from the semitendinosus muscle were collected on d -49, 3, 21, and 84 relative to calving. Actual BCS at d -49 were 3.02 ± 0.24 and 3.82 ± 0.33 (mean ± SD) for NBCS and HBCS, respectively. In both groups, serum profiles showed marked changes during the periparturient period, with decreasing concentrations of free carnitine and increasing concentrations of long-chain AcylCN. Compared with NBCS, HBCS had greater serum long-chain AcylCN in early lactation, which may point to an insufficient adaptation of their metabolism in response to the metabolic load of fatty acids around parturition. The muscle concentrations of C5-, C9-, C18:1-, and C18:2-AcylCN were lower and those of C14:2-AcylCN were greater in HBCS than in NBCS cows. The mRNA abundance of carnitine palmitoyltransferase (CPT)1, muscle isoform (CPT1b) and CPT2 increased from d -49 to early lactation (d 3, d 21), followed by a decline to nearly antepartum values by d 84; this change was not affected by group. In conclusion, over-conditioning around calving seems to be associated with mitochondrial overload, which can result in incomplete fatty acid oxidation in dairy cows.

Genetic parameters for body condition score, locomotion, angularity, and production traits in Italian Holstein cattle

The objectives of this research were to estimate genetic parameters for body condition score (BCS) and locomotion (LOC), and to assess their relationships with angularity (ANG), milk yield, fat and protein content, and fat to protein content ratio (F:P) in the Italian Holstein Friesian breed. The Italian Holstein Friesian Cattle Breeders Association collects type trait data once on all registered first lactation cows. Body condition score and LOC were introduced in the conformation scoring system in 2007 and 2009, respectively. Variance (and covariance) components among traits were estimated with a Bayesian approach via a Gibbs sampling algorithm and an animal model. Heritability estimates were 0.114 and 0.049 for BCS and LOC, respectively. The genetic correlation between BCS and LOC was weak (-0.084) and not different from zero; therefore, the traits seem to be genetically independent, but further investigation on possible departures from linearity of this relationship is needed. Angularity was strongly negatively correlated with BCS (-0.612), and strongly positively correlated with LOC (0.650). The genetic relationship of milk yield with BCS was moderately negative (-0.386), and was moderately positive (0.238) with LOC. These results indicate that high-producing cows tend to be thinner and tend to have better locomotion than low-producing cows. The genetic correlation of BCS with fat content (0.094) and F:P (-0.014) was very weak and not different from zero, and with protein content (0.173) was weak but different from zero. Locomotion was weakly correlated with fat content (0.071), protein content (0.028), and F:P (0.074), and correlations were not different from zero. Phenotypic correlations were generally weaker than their genetic counterparts, ranging from -0.241 (BCS with ANG) to 0.245 (LOC with ANG). Before including BCS and LOC in the selection index of the Italian Holstein breed, the correlations with other traits currently used to improve type and functionality of animals need to be investigated.

Accuracy of direct genomic values in Holstein bulls and cows using subsets of SNP markers

Background

At the current price, the use of high-density single nucleotide polymorphisms (SNP) genotyping assays in genomic selection of dairy cattle is limited to applications involving elite sires and dams. The objective of this study was to evaluate the use of low-density assays to predict direct genomic value (DGV) on five milk production traits, an overall conformation trait, a survival index, and two profit index traits (APR, ASI).

Methods

Dense SNP genotypes were available for 42,576 SNP for 2,114 Holstein bulls and 510 cows. A subset of 1,847 bulls born between 1955 and 2004 was used as a training set to fit models with various sets of pre-selected SNP. A group of 297 bulls born between 2001 and 2004 and all cows born between 1992 and 2004 were used to evaluate the accuracy of DGV prediction. Ridge regression (RR) and partial least squares regression (PLSR) were used to derive prediction equations and to rank SNP based on the absolute value of the regression coefficients. Four alternative strategies were applied to select subset of SNP, namely: subsets of the highest ranked SNP for each individual trait, or a single subset of evenly spaced SNP, where SNP were selected based on their rank for ASI, APR or minor allele frequency within intervals of approximately equal length.

Results

RR and PLSR performed very similarly to predict DGV, with PLSR performing better for low-density assays and RR for higher-density SNP sets. When using all SNP, DGV predictions for production traits, which have a higher heritability, were more accurate (0.52-0.64) than for survival (0.19-0.20), which has a low heritability. The gain in accuracy using subsets that included the highest ranked SNP for each trait was marginal (5-6%) over a common set of evenly spaced SNP when at least 3,000 SNP were used. Subsets containing 3,000 SNP provided more than 90% of the accuracy that could be achieved with a high-density assay for cows, and 80% of the high-density assay for young bulls.

Conclusions

Accurate genomic evaluation of the broader bull and cow population can be achieved with a single genotyping assays containing ~ 3,000 to 5,000 evenly spaced SNP.

Genetic correlations between body condition scores and fertility in dairy cattle using bivariate random regression models

Genetic correlations between body condition score (BCS) and fertility traits in dairy cattle were estimated using bivariate random regression models. BCS was recorded by the Swiss Holstein Association on 22,075 lactating heifers (primiparous cows) from 856 sires. Fertility data during first lactation were extracted for 40,736 cows. The fertility traits were days to first service (DFS), days between first and last insemination (DFLI), calving interval (CI), number of services per conception (NSPC) and conception rate to first insemination (CRFI). A bivariate model was used to estimate genetic correlations between BCS as a longitudinal trait by random regression components, and daughter's fertility at the sire level as a single lactation measurement. Heritability of BCS was 0.17, and heritabilities for fertility traits were low (0.01-0.08). Genetic correlations between BCS and fertility over the lactation varied from: -0.45 to -0.14 for DFS; -0.75 to 0.03 for DFLI; from -0.59 to -0.02 for CI; from -0.47 to 0.33 for NSPC and from 0.08 to 0.82 for CRFI. These results show (genetic) interactions between fat reserves and reproduction along the lactation trajectory of modern dairy cows, which can be useful in genetic selection as well as in management. Maximum genetic gain in fertility from indirect selection on BCS should be based on measurements taken in mid lactation when the genetic variance for BCS is largest, and the genetic correlations between BCS and fertility is strongest.

Phenotypic associations between traits other than production and longevity in New Zealand dairy cattle

A proportional hazards model was used to investigate the phenotypic effect of traits other than production (TOP) on true and functional longevity across purebred and crossbred Holstein-Friesian and Jersey dairy cattle in registered and commercial herds in New Zealand. The hazard function was described as the product of a baseline hazard function and the time-independent effects of age at first calving, heterosis, proportion of breed, period of last calving relative to herdmates, and TOP; a time-dependent effect of herd-year was also included. The influence of TOP on functional longevity was assessed by adjusting true longevity for the time-independent effects of production values as well as the time-dependent effects of deviation of milk, fat, and protein yield within contemporary group. All analyses were stratified by breed, and separate analyses were carried out for registered or commercial herds. All TOP were significantly related to true and functional longevity. Obvious differences existed in the relative influence of individual TOP on longevity in registered or commercial herds. Of the individual TOP describing the physical characteristics of the cow, the udder-related TOP exhibited the largest influence on functional longevity. Farmer opinion explained the largest proportion of variation in true and functional longevity among cows. In commercial herds, the risk of culling in cows with very low farmer opinion was 1.5 to 2.0 times that in cows with average or high farmer opinion.

Non-lactational traits of importance in dairy cows and applications for emerging biotechnologies

Dairy cattle have traditionally been selected for their ability to produce milk and milk components. The traditional single-minded approach to selection of dairy cattle has now changed and secondary traits are being included in selection indices by decreasing the emphasis on production. Greater emphasis on non-production traits reflects the industry's desire for functional dairy cattle. Six broad categories of non-lactational traits are discussed in this review. They are: type; growth, body size and composition; efficiency of feed utilisation; disease resistance, e.g. udder health as measured by somatic cell score; reproduction; and management. Most of these traits can be found within selection indices worldwide, although relative emphasis varies. The non-lactational traits mentioned above are quantitative, meaning that the phenotype in the whole animal represents the sum of lesser traits that cannot be easily measured. The physiological mechanisms that underlie quantitative traits are extremely complex. Genetic selection can be applied to quantitative traits but it is difficult to link successful genetic selection with the underlying physiological mechanisms. The importance that the bovine genome sequence will play in the future of the genetics of dairy cattle cannot be understated. Completing the bovine genome sequence is the first step towards modernising our approach to the genetics of dairy cattle. Finding genes in the genome is difficult and scanning billions of base pairs of DNA is an imperfect task. The function of most genes is either unknown or incompletely understood. Combining all of the information into a useable format is known as bioinformatics. At the present time, our capacity to generate information is great but our capacity to understand the information is small. The important information resides within subtle changes in gene expression and within the cumulative effect that these have. Traditional methods of genetic selection in dairy cattle will be used for the foreseeable future. Most non-lactational traits are heritable and will be included in selection indices if the traits have value. The long-term prognosis for genome science is good but advances will take time. Genetic selection in the genome era will be different because DNA sequence analysis may replace traditional methods of genetic selection.

Body Trait Profiles in Holstein-Friesians Modeled Using Random Regression

Legendre polynomial and cubic spline functions were used in random regression models to model the change in body traits over the course of the first lactation for daughters of 954 sires. Both functions estimated similar genetic variances for d 50 to 250 across lactation for the majority of traits. The heritability of the traits was similar to other studies using univariate models as well as random regression models. There was little difference between the 2 functions in their predictive power for each of the body type traits, as measured by the absolute difference between the predicted and actual type traits and the proportion of the total phenotypic variance explained by the model. Overall, the Legendre polynomial appeared to model these traits slightly better. Plots of the fixed curves and daily sire solutions obtained from the random regression models showed that there were differences in how the traits and sires changed across lactation. The daily sire solutions were then used to predict differences in liveweight of sires' daughters across first lactation and showed that the daughters of some sires grew faster during first lactation than others. The spatial differences in the body traits that are displayed by this study could be an important indicator of the physical and biological changes that cows are undergoing in their first lactation. Information from these sire profiles could be harnessed to indicate production and functional traits later in life.

Changing Definition of Productive Life in US Holsteins: Effect on Genetic Correlations

Data included 392,800 records for cows born between 1995 and 1997. Traits analyzed were milk, fat, and protein yields, somatic cell score, days open (DO), 18 linear type traits, final score, and several measures of longevity. Productive life (PL) was defined as the total number of days in milk up to 84 mo of age with a restriction of 305, 500, or 999 d per lactation (PL(305), PL(500), or PL(999), respectively). Herd life was defined as the total number of days from the first calving date to the last (culling) date. A multiple-trait sire model including the effects of registration status, herd-year, age group, month of calving and stage of lactation, sire, and residual was used for parameter estimation. The average duration of the first lactation was 365 d for survivors and 386 d for culled cows. Lactation lengths for the survivors in the next 3 parities all exceeded 330 d. Heritability estimates of between 0.08 and 0.10 were obtained for all definitions of longevity. As maximum recordable PL was increased from 305 to 999 d per lactation, the genetic correlations with milk production increased (from -0.11 to +0.14) and with DO decreased (-0.62 to -0.27). Formulas for an indirect prediction of PL from correlated traits were developed. As maximum PL per lactation was increased, little change in the weights used to predict the various measures of PL, with the exception of DO was found. As the currently used value of PL(305) does not properly account for the longer lactation lengths that are routinely occurring with today's cows, PL with longer lactations may be preferable in routine evaluation.