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Variation in plasma growth hormone during first parity in lactating cows¹

Danish Institute of Agricultural Sciences, PO Box 50, DK-8830 Tjele, Denmark

	Abstract

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This study analyzed genetic and phenotypic variation in plasma GH during lactation in first parity dairy cows. The heritability and repeatability were examined using an algorithm for separation of basal and peak concentrations and different power transformations. Blood samples were obtained 17 times during first parity in 85 Holstein, 67 Red Dane, and 62 Jersey cows and assayed for GH. Each breed comprised 2 genetic groups; thus, a total of 6 genetic groups were defined. Across genetic groups, cows were assigned to 1 of 2 total mixed rations with a low or a normal energy concentration. The separation algorithm identified only 4.0% of the plasma GH concentrations as peaks. After excluding peak concentrations, the repeatability of GH during lactation was improved. A log-transformation was found appropriate for GH. The log-transformed GH concentrations for lactating dairy cows had a heritability ranging between 0.14 in early lactation to 0.08 in mid and late lactation. The repeatability was 0.24 in early lactation and increased to between 0.58 and 0.61 in mid and late lactation. We conclude that for GH concentrations in lactating cows that are sampled infrequently, the exclusion of peak values to obtain a basal GH concentration was not effective in clarifying phenotypic or genetic effects.

Key Words: breed • dairy cow • heritability • lactation • somatotropin

	INTRODUCTION

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Growth hormone plays a major role in the distribution of nutrients between tissues and is especially involved in metabolic regulation during lactation (Bauman, 2000). For example, the administration of exogenous GH as natural or synthetic analogs can stimulate milk production (Azimov and Krouze, 1937; Bauman, 2000). However, measurements of plasma GH are to a large extent affected by considerable measurement noise arising from this hormone being released to the circulation in randomly occurring, secretory spikes (e.g., Breier et al., 1986).

Two strategies have been taken to improve GH measurements. One is to control GH release by administration of GH-releasing hormone or thyrotropin releasing hormone and to sample blood over a response window lasting at least 1 h (e.g., Løvendahl et al., 1991). The other approach has been to describe naturally occurring spikes in blood sampled serially over time frames of 8 to 24 h at fixed, 10- to 30-min intervals. Next, the secretory spikes are detected by algorithms such as PULSAR (Merriam and Wachter, 1982) or by separating values into pools containing the baseline or the peak values, based on the distribution properties (Breier et al., 1986; Woolliams et al., 1993).

For purposes of studying individual or genetic variation in GH that is repeatedly measured during lactation, simpler approaches are appealing but prone to the limitations mentioned previously. However, a combined use of transformations and separation methods may offer a way to obtain reliable estimates of fixed effects and random variance.

To describe individual or genetic variation in plasma GH during first lactation, dairy cows from an experimental herd were blood sampled several times. Estimates of variance components and the effects of genetic groups and feed ration concentration were obtained by fitting covariance functions to data after transformation and separation of values into baseline and secretory spikes using an iterative, filtering algorithm.

	MATERIALS AND METHODS

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All procedures involving the animals were approved by the Danish Animal Experiments Inspectorate and complied with the Danish Ministry of Justice Law No. 382 (June 10, 1987) and Acts 739 (December 6, 1988) and 333 (May 19, 1990) concerning animal experimentation and care of experimental animals.

Animals
The design and production results of the experiment have been reported in detail by Nielsen et al. (2003). Briefly, the design included cows of 3 breeds: Red Dane (RD, n = 67), Holstein (HF, n = 85), and Jersey (n = 62). Each breed was composed of 2 genetic groups: the RD and HF each had a group bred for yield (Y group) and a dual-purpose group bred for a combination of milk yield and meat (C group); the Jerseys had a Danish Jersey and an American Jersey group.

The RD and HF yield-groups were established in 1989 to 1990 by selecting heifers sired by the greatest milk yield index, AI sires in the Danish sire summary. For the dual-purpose groups, heifers of sires with the greatest index for beef and average index for milk yield were chosen. In 1992, heifers with predominantly American or Danish pedigree were chosen for establishing the Jersey groups. Thus, a total of 6 genetic groups were defined, composed of 214 cows. Pedigrees for all cows were traced back as far as possible and included 2,646 relatives obtained from the national cattle database.

Housing and Feeding
The cows were housed in tie stalls with rubber mats at the research station Ammitsbøl Skovga °rd (Vejle, Denmark). Across genetic groups, the animals were randomly assigned to 1 of 2 total mixed rations: a normal (NTMR) or low (LTMR) energy density total mixed ration. The NTMR was formulated to meet the energy requirement of the animal, whereas the LTMR was formulated to limit the cow in production and to provoke a larger mobilization of body reserves. The energy content of the NTMR and LTMR rations was on average 13.55 and 12.88 MJ/kg of DM, respectively. The difference in energy content was obtained by substituting whole-crop, wheat silage with wheat straw in the LTMR. Both rations were available to the cows ad libitum. Before calving, all cows were on the LTMR. Further details as to feeding and production are given in Nielsen et al. (2003).

Blood Sampling and GH Assay
Blood samples were taken in the first week after calving on d 1, 3, and 7. Subsequently, samples were taken every week up to and including 8 wk postcalving, then every 2 wk from up to 12 wk postcalving and every 6 wk up to 42 wk postcalving. This resulted in total of 17 samples. After the first week, samples were taken on the Thursday closest to the precise sampling date. To control diurnal variation, the samples were taken by direct venipuncture using heparinized tubes (Vacuette, Greiner AG, Germany) between 0900 and 1030. Plasma was harvested by centrifugation (2,000 x g, 4°C, 20 min) and stored at –20°C until assayed by a time-resolved immunofluorometric assay (Løvendahl et al., 2003). The interassay CV was 0.105 and the intraassay CV was 0.044 at an average control of 3.54 ng/mL. Observations below the limit of detection (0.1 ng/mL) were set to 0.1 (47 out of 3,107 observations).

Statistical Methods
Random Regression Model.
Models allowing the measured trait and its variance to change continuously over a time frame (trajectory) have been applied to growth in cattle and other species (Kirkpatrick et al., 1990). Models using such variance functions often consist of a small set of orthogonal (Legendre) polynomials and because they model the random part of the variation are commonly called random regression models. Because they are continuous functions, they are effective for measurements obtained even with some deviation from the preplanned timing.

A random regression model was fitted to log-transformed GH concentrations for each animal. Each cow was fitted for up to fourth order Legendre polynomials. The model was reduced stepwise as the effects were found nonsignificant (P > 0.05). Up to third order Legendre polynomials within feed group by genetic group interaction were found to be significant (P < 0.05) and thus were included in the model. Higher order interactions between season of calving and calving year interaction were not significant and were not included in the final model. The model used was

[1]

where Y_ijlpo is the concentration of GH for cow i to time j; µ_lp is the intercept for each combination of genetic group_l, and feed group_p, where l is RD-Y, RD-C, HF-Y, HF-C, American Jersey or Danish Jersey, and p relates to normal or low feed regimen (NTMR, LTMR). The fixed regression, ß_lp, contains coefficients fitted within genetic group by feed group_lp. The mth Legendre polynomial, _m(j), was evaluated at time j, where j represent the days in milk (DIM), 1 DIM 294 standardized to –1 j 1. The random regression coefficients of cow i was included in P. Other random terms included were the error term e_ijlpo associated with observation Y_ijlpo, and the effect of assay day o in the laboratory, _o The order of polynomials in the reduced model was nf = 3 for the fixed part, and nr = 2 for the random part. The test for fixed effects was done by the MIXED procedure of SAS (Littel et al., 1996) by comparing least squares means for experimental factors fitted at 5 time points along the trajectory (DIM = 21, 42, 84, 168, and 252 d).

Transforming GH Concentrations.
As a means to investigate whether the log-transformation used above was the most appropriate transformation, a family of power transformations was applied (Box and Cox, 1964). The power of transformations is indexed by . If = 1, the data is not transformed, otherwise determines a power transformation; the family includes the reciprocal, = –1, and the logarithmic, = 0. The general form is given by

[2]

where y is the observed value, and zis the transformed GH concentration. The term GM(y) is the geometric mean of y, where GM(y) = (y)^1/n for n observations.

The transformed concentrations were investigated with the repeatability model [1] described above. The model was fitted to GH concentrations for from –1 to 1 in steps of 0.2. The results are presented for concentrations between the special cases (= –1, 0, 1) because of their more immediate interpretation to the observed trait. The likelihood, the coefficient of skewness, and model error were calculated for each transformation.

Application of the Separation Algorithm.
To investigate the effect of excluding peak values, an iterative separation procedure was performed, using the random regression model, equation [1], removing observations with large positive residuals before running the model again, and removing new extreme residuals, and so on. After each iteration, 1 of each 1,000 residuals was identified as extreme and excluded. The remaining data were assumed to belong to a population of basal concentrations. At each round, the Kolmogorov-Smirnov goodness-of-fit criterion for normality was tested. Further iterations were run until the goodness-of-fit criterion did not improve further. To investigate the effect of data lost during the separation process, we also performed all further calculations on the total GH by including all observations.

Estimating Genetic Variation.
Model [1] was extended with random regression coefficients for genetic and permanent environmental variation for the cows:

[3]

where Y_ijlpo is the vector of GH concentrations observed for animal i at time j. Additive genetic and permanent environmental random regression coefficients are indexed as a and pe. The order of polynomials used for the random genetic and permanent environmental components are indicated by na and npe. The other terms are as per model [1]. Initially, the data were fitted with a zero order polynomial for the genetic and permanent environmental components. Then, the order of the covariance function was increased stepwise until a higher order polynomial of the covariance did not significantly improve the fit. The goodness-of-fit was compared using a likelihood ratio test based on the ²-distribution (Kirkpatrick et al., 1990) and a significance level of 0.05, comparing the reduced model with the full model with degrees of freedom equal to the difference in the number of variance components to be estimated. The best fits were second order polynomials for permanent environmental and a zero order polynomial for genetic variance for total and basal GH.

The heritability was calculated for basal and total GH, based on the covariance functions:

where ²_a(j) and ²_pe(j) are the additive genetic and permanent environmental variance for day j, ²_labday is the variance of assay day in the laboratory, and is the residual variance. The corresponding repeatability was calculated as

Standard errors of heritabilities and repeatabilities were calculated from SE of covariance components using a Taylor series expansion. Variance components of the model were estimated using the AI-REML algorithm (Jensen et al., 1997) included in the DMU package (Madsen and Jensen, 2000).

	RESULTS

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Transformations of Data
The family of power transformations compared using the Box-Cox method showed that data on the original scale clearly deviated from the normal distribution (Table 1). Approximate normal distribution was obtained at a value of 0.2, where skewness, model residual, and the likelihood were smallest. The closest simple transformation was at = 0 corresponding to a log-transformation.

View this table: [in this window] [in a new window]   Table 1. The effect of the power transformations () of total GH on the coefficient of skewness, model residual variance, and –2logL

Effects of Genetic Groups and Feeding
Plasma GH concentration was greatest in samples obtained early after calving and decreased thereafter in a similar way across breeds, genetic groups and diet energy density (Figures 1 and 2; P < 0.001). Throughout the first part of lactation, Jerseys and HF on low energy diet had greater GH than RD on the same diet (Figure 1; P < 0.05), whereas GH concentrations in Jerseys and HF were not different. A similar pattern of breed effects was seen on the normal density diet. Within breed, effects of diet energy level were mostly nonsignificant, although when evaluated at 84 d in milk as pooled across breeds, greater GH concentrations were found in cows on low energy diet (P < 0.05), giving only little support for a breed by stage of lactation and diet effect. Breed and genetic group effects showed that Jerseys of both groups and HF-Y had greater plasma GH concentrations than HF-C and both the RD groups (Figure 2; P < 0.01 at 84 d), but differences were lesser in late lactation. Within breed differences between genetic groups were only significant between HF-Y and HF-C (P < 0.01 at 84 d). Breed effects were more pronounced around 84 d, supporting the effect of interaction between physiological status and breed or genetic group.

View larger version (14K): [in this window] [in a new window]   Figure 1. Time course of GH concentrations of Red Dane (RD), Holstein (HF), and Jersey (Jer) cows fed a diet of normal or low energy density. Concentrations of GH are back-transformed from log to the original scale. The symbols indicate energy group for each time point of blood sampling. DIM = days in milk.

View larger version (13K): [in this window] [in a new window]   Figure 2. Time course of GH concentrations of Red Dane (RD) and Holstein (HF), including a group bred for yield (Y-group) and a dual-purpose group bred for a combination of meat and milk yield (C-group), and Jersey (Jer) cows, including a Danish Jersey (D) and an American Jersey (A). Concentrations of GH are back-transformed from log to the original scale. The symbols indicate breed group for each time point of blood sampling. DIM = days in milk.

View larger version (21K): [in this window] [in a new window]   Figure 3. Permanent environmental variance, heritability, and repeatability of GH for cows during lactation. Panel A shows time course of permanent variance during lactation for basal and total GH; panel B shows the basal GH concentrations; and panel C shows the total GH concentrations. The symbols indicate a scheduled blood sampling. Gray lines show the estimate ± SE. DIM = days in milk.

	DISCUSSION

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Transformation and Separation of GH Concentration
Transformation of the entire data set into different distributions can be seen as an attempt to change the weight on the low or the high concentrations. The optimal power transformation as estimated by the Box-Cox method was = 0.2, where likelihood was least and skewness was close to zero. This implies that a log-transformation is close to being optimal, which is in agreement with its frequent use in other studies of GH in cattle (Woolliams et al., 1993; Løvendahl et al., 1994). Hence, log transformation was chosen for further use.

Our hypothesis was that total GH samples could be reduced to a subset of basal GH, where extreme values were excluded, resulting in concentrations belonging to a normal distribution. The basal GH concentrations most closely followed a normal distribution after exclusion of 4.0% of the observations, but a perfectly normal distribution was not reached. However, Nielsen et al. (1995), who used a similar iterative separation method, reached a normal distribution of basal GH concentration in growing pigs. Also, Kazmer et al. (1990) used a method that in an iterative process detected values more than 2 times the SD above the mean as peaks. Because Kazmer et al. sampled frequently (in 15-min intervals) over shorter time frames (3 and 6 h) in young bulls, they were successful in characterizing pulsatile patterns and baseline concentrations. The low number of observations excluded in the current study with extensive sampling procedure agrees with results from a simulation study comparing methods that characterize pulses in time series, where lower sampling rates increased the number of missed pulses (Mauger et al., 1995). A small increase in the repeatability for GH concentration was found when the extreme values were excluded. This shows that even when the GH concentrations are transformed as well as possible, there is still some minor improvement in the repeatability by separating the samples into subsets with improved distribution properties.

Time Course of GH Concentration through Lactation
The GH concentrations were greatest in early lactation followed by a decrease later in lactation. The concentrations found in our study are in accordance with results from earlier studies in lactating dairy cows (Vasilatos and Wangsness, 1981; Schams et al., 1991). Our results support the hypothesis that plasma GH concentrations are associated with high yield or high mobilization, as the high-yield genetic groups had greater GH than the dual-purpose groups. This was also suggested in the lower concentration of GH in the dual-type RD than in Jerseys and HF. This agrees with studies where HF calves released more GH than RD calves after a challenge with GHRH (Løvendahl et al., 1994), and with greater GH concentration in lactating German Black and White cows of dairy type than in with Brown Swiss dual purpose cows (Schams et al., 1991). This pattern may be a reflection of the RD cows having the lowest level of mobilization of body reserves during first lactation in the present trial (Nielsen et al., 2003). Recent studies of the lipolytic potential in the same breeds confirm this pattern because the Jersey and HF had greater lipolytic potential in first lactation than RD (Theilgaard et al., 2002). Furthermore, the negative association between fat reserves and GH was previously shown in steers (Hayden et al., 1993) and in pigs (Lund-Larsen and Bakke, 1975).

Cows fed a low energy density diet had greater plasma concentration of GH, which is consistent with earlier results in steers (Breier et al., 1986; Hayden et al., 1993). The effect of feeding on GH concentration might be related to differences in fat mobilization because the cows fed low energy diet had a more pronounced use of body reserves in their first parity in the present experiment (Nielsen et al., 2003).

Changes in Estimates of Heritability and Repeatability During Lactation
The heritability of basal GH concentration was found to change between 0.08 and 0.18 during lactation and between 0.08 and 0.14 for total GH, indicating a genetic component in the control of GH concentration. This change was a consequence of changing permanent environmental variance component because the genetic variance and residual variance were modeled as being constant. The repeatability of basal GH was between 0.41 and 0.71, and for total GH between 0.24 and 0.61, indicating a high degree of individual variation of plasma GH concentration. The time courses of heritabilities and repeatabilities were similar for total and basal concentration of GH, although the heritability and the repeatability were greater for basal than for total GH. The heritabilities and the absolute concentration of GH have greatest values early in lactation. A plausible biological interpretation is that, at the start of lactation, the cow has a high priority on her current offspring, hence allocating body resources to milk production, whereas the cow later in lactating slowly increases priority on the following lactation. Genetic variation in plasma GH is scarcely reported, but the genetic parameter estimates may be compared with prechallenge concentrations from challenge tests. In 11-mo-old Holsteins fed ad libitum, the heritability was found to be 0.02 for the prechallenge concentration and 0.14 for induced peaks (Grochowska et al., 2001). In a similar experiment on dairy calves between 8 and 10 mo of age, the heritability of prechallenge GH concentration was found to be 0.59, and heritability of induced GH release was 0.63 (Sørensen et al., 2002). In a larger cohort of young bulls (age 9 mo), the heritability of prechallenge GH was 0.10 (Løvendahl and Sørensen, 2001). These calves were on a restricted feeding regimen for 3 d before challenge, and the high heritabilities may be a consequence of the low level feeding. Thus, the range of the reported heritabilities of GH is highly variable and generally low for baseline concentrations. Also, it should be noted that estimates have large SE because they are based on small sample sizes, as in the current study.

	IMPLICATIONS

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Plasma growth hormone concentration was found to be a lowly heritable trait. The approach of supplementing transformation of growth hormone data with a separation method based on extreme residuals proved to be of little extra value compared with transformation alone in studying phenotypic and genetic effects when blood sampling protocols are infrequent and sample size are small.

	Footnotes

 
¹ Appreciation is extended to Inger Marie Jepsen, Janne Adamsen and Connie Ha °rbo Middelhede for technical support; Peter Trier, Gitte Munch, and the farm staff at Skovga °rd for collecting data. This study was a part of research projects funded by the Danish Ministry of Food, Agriculture and Fisheries, the Danish Cattle organization and the Danish Institute of Agricultural Sciences.

² Corresponding author: peth{at}dca.upv.es

Received for publication July 7, 2006. Accepted for publication September 28, 2006.

	LITERATURE CITED

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This Article Abstract Full Text (PDF) All Versions of this Article: jas.2006-447v1 85/2/388    most recent Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Theilgaard, P. Articles by Løvendahl, P. Search for Related Content PubMed PubMed Citation Articles by Theilgaard, P. Articles by Løvendahl, P.