Genotype and Diet Effects on Energy Balance in the First Three Lactations of Dairy Cows

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Genome-wide association studies for difficult-to-measure traits are generally limited by the sample size with accurate phenotypic data. The objective of this study was to utilise data on primiparous Holstein-Friesian cows from experimental farms in Ireland, the United Kingdom, the Netherlands and Sweden to identify genomic regions associated with the feed utilisation complex: fat and protein corrected milk yield (FPCM), dry matter intake (DMI), body condition score (BCS) and live-weight (LW). Phenotypic data and 37 590 single nucleotide polymorphisms (SNPs) were available on up to 1629 animals. Genetic parameters of the traits were estimated using a linear animal model with pedigree information, and univariate genome-wide association analyses were undertaken using Bayesian stochastic search variable selection performed using Gibbs sampling. The variation in the phenotypes explained by the SNPs on each chromosome was related to the size of the chromosome and was relatively consistent for each trait with the possible exceptions of BTA4 for BCS, BTA7, BTA13, BTA14, BTA18 for LW and BTA27 for DMI. For LW, BCS, DMI and FPCM, 266, 178, 206 and 254 SNPs had a Bayes factor .3, respectively. Olfactory genes and genes involved in the sensory smell process were overrepresented in a 500 kbp window around the significant SNPs. Potential candidate genes were involved with functions linked to insulin, epidermal growth factor and tryptophan.

Section: Methodsmentioning

confidence: 99%

Genome-wide associations for feed utilisation complex in primiparous Holstein–Friesian dairy cows from experimental research herds in four European countries

Veerkamp

Coffey

Berry

et al. 2012

Self Cite

“…110 records) were selected to maximize the spectral variability from the 1609 samples collected in a previous study (Soyeurt et al, 2007) (Coleman et al, 2009). Cows from Scotland were from two genetically divergent lines (divergent for milk solids) and were fed two different diets (Coffey et al, 2004). Approximately half were fed a predominantly forage only diet and the other half a diet consisting of ,60% silage and 40% concentrate.…”

Section: Methodsmentioning

confidence: 99%

Mid-infrared prediction of lactoferrin content in bovine milk: potential indicator of mastitis

Soyeurt

Bastin

Colinet

et al. 2012

Lactoferrin (LTF) is a milk glycoprotein favorably associated with the immune system of dairy cows. Somatic cell count is often used as an indicator of mastitis in dairy cows, but knowledge on the milk LTF content could aid in mastitis detection. An inexpensive, rapid and robust method to predict milk LTF is required. The aim of this study was to develop an equation to quantify the LTF content in bovine milk using mid-infrared (MIR) spectrometry. LTF was quantified by enzyme-linked immunosorbent assay (ELISA), and all milk samples were analyzed by MIR. After discarding samples with a coefficient of variation between 2 ELISA measurements of more than 5% and the spectral outliers, the calibration set consisted of 2499 samples from Belgium (n 5 110), Ireland (n 5 1658) and Scotland (n 5 731). Six statistical methods were evaluated to develop the LTF equation. The best method yielded a cross-validation coefficient of determination for LTF of 0.71 and a cross-validation standard error of 50.55 mg/l of milk. An external validation was undertaken using an additional dataset containing 274 Walloon samples. The validation coefficient of determination was 0.60. To assess the usefulness of the MIR predicted LTF, four logistic regressions using somatic cell score (SCS) and MIR LTF were developed to predict the presence of mastitis. The dataset used to build the logistic regressions consisted of 275 mastitis records and 13 507 MIR data collected in 18 Walloon herds. The LTF and the interaction SCS 3 LTF effects were significant (P , 0.001 and P 5 0.02, respectively). When only the predicted LTF was included in the model, the prediction of the presence of mastitis was not accurate despite a moderate correlation between SCS and LTF (r 5 0.54). The specificity and the sensitivity of models were assessed using Walloon data (i.e. internal validation) and data collected from a research herd at the University of Wisconsin -Madison (i.e. 5886 Wisconsin MIR records related to 93 mastistis events -external validation). Model specificity was better when LTF was included in the regression along with SCS when compared with SCS alone. Correct classification of non-mastitis records was 95.44% and 92.05% from Wisconsin and Walloon data, respectively. The same conclusion was formulated from the Hosmer and Lemeshow test. In conclusion, this study confirms the possibility to quantify an LTF indicator from milk MIR spectra. It suggests the usefulness of this indicator associated to SCS to detect the presence of mastitis. Moreover, the knowledge of milk LTF could also improve the milk nutritional quality.

“…In the first data set of Cutullic et al (2011) and Delaby (personal communication, 2014), recorded at the Le Pin research farm, two breeds of cows (Holstein Friesian selected for MY and dual-purpose Normande cows) fed a high-concentrate diet were used to evaluate the effect of genetic selection on reproductive performance and LTE. The second data set was taken from the long-term experiment carried out at the Langhill Dairy Cattle Research Centre (Pryce et al, 1999;Coffey et al, 2004). In the Langhill data, high-and low-feeding systems were applied to two lines of Holstein Friesian cows: a control line and the line that had been selected for kilograms of fat plus protein yield.…”

Section: Model Implementation and Evaluationmentioning

confidence: 99%

Modelling impacts of performance on the probability of reproducing, and thereby on productive lifespan, allow prediction of lifetime efficiency in dairy cows

Phuong

Blavy

Martin

et al. 2016

Reproductive success is a key component of lifetime efficiency -which is the ratio of energy in milk (MJ) to energy intake (MJ) over the lifespan, of cows. At the animal level, breeding and feeding management can substantially impact milk yield, body condition and energy balance of cows, which are known as major contributors to reproductive failure in dairy cattle. This study extended an existing lifetime performance model to incorporate the impacts that performance changes due to changing breeding and feeding strategies have on the probability of reproducing and thereby on the productive lifespan, and thus allow the prediction of a cow's lifetime efficiency. The model is dynamic and stochastic, with an individual cow being the unit modelled and one day being the unit of time. To evaluate the model, data from a French study including Holstein and Normande cows fed high-concentrate diets and data from a Scottish study including Holstein cows selected for high and average genetic merit for fat plus protein that were fed high-v. low-concentrate diets were used. Generally, the model consistently simulated productive and reproductive performance of various genotypes of cows across feeding systems. In the French data, the model adequately simulated the reproductive performance of Holsteins but significantly under-predicted that of Normande cows. In the Scottish data, conception to first service was comparably simulated, whereas interval traits were slightly under-predicted. Selection for greater milk production impaired the reproductive performance and lifespan but not lifetime efficiency. The definition of lifetime efficiency used in this model did not include associated costs or herd-level effects. Further works should include such economic indicators to allow more accurate simulation of lifetime profitability in different production scenarios.Keywords: dairy cow, lifetime efficiency, productive lifespan, reproduction, nutrient partitioning Implications This paper extended an existing lifetime performance model to incorporate the impacts that varying milk yield, energy balance and body condition have on the probability of reproducing and the productive lifespan, thereby allowing prediction of a cow's lifetime efficiency. The model adequately simulated the productive and reproductive performance of various genotypes of cows across feeding systems. Thus, it can be used to study the effect of future selection and management on animal performance and efficiency.