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A reaction norm sire model to study the effect of metabolic challenge in early lactation on the functional longevity of dairy cows
Abstract
Due to the discrepancy of the high energy demand for rapidly increasing milk production and limited feed intake in the transition period around parturition, dairy cows require considerable metabolic adaptations. We hypothesize that some cows are genetically less suited to cope with these metabolic needs than others, leading to adverse follow-up effects on longevity. To test this, we designed a reaction norm model in which functional lifetime was linked to the metabolic challenge in the beginning of the first lactation. As challenge variables, we used either the sum of milk yield or the accumulated fat-to-protein ratio of the first 3 test-days (<120 d in milk), pre-adjusted for herd-test-day variance. We defined a random regression sire model, in which a random slope was estimated for each sire to assess whether a bull had robust (neutral or positive slopes) or non-robust (negative slopes) daughters. We fitted the model to data of ∼580,000 daughters of ∼5,000 Brown Swiss bulls with suitable observations available (≥10 daughters per bull). To validate our proposed model and assess the reliability of the estimated (co)variance components, we conducted an extensive bootstrap approach. For both challenge variables, we found the sire variance for the slope of the random regression to be significantly different from zero, suggesting a genetic component for metabolic adaptability. The results of the study show that the ability to cope with metabolic stress in the transition period has a genetic component, which can be used to breed metabolically robust dairy cows.
... The development of new selection criteria is a key factor for the advancement of animal breeding programs and it is very important to consider functional traits in dairy animals (Egger-Danner et al. 2015). Many studies have investigated selection criteria related to viral diseases (Lough et al. 2017(Lough et al. , 2018, parasitoses (Bishop 2012;Hayward et al. 2021), and metabolic disorders Ha et al. 2017), applying concepts of resistance, tolerance, and resilience in animal selection programs. ...
... The trend of heritability and the low magnitude of the correlation between extreme levels of weight variation, as well as the genetic variance of the slope, indicate the feasibility of considering the resilience estimated in this study as a possible selection criterion. In a study on Holstein cattle, Ha et al. (2017) also identified criteria that suggest the feasibility of selecting for the effect of the animal's metabolic condition at the beginning of lactation on a variable of economic importance (longevity). However, these authors used milk production and fat-to-protein ratio as metabolic indicators instead of body weight variation. ...
The research article was carried out with the objective of studying the genetic variation on the resilience of buffaloes to negative energy balance—NEB (measured by changes in body weight in early lactation)—as well as investigating genomic regions of interest for this trait. A model of reaction norms was used, considering milk production as the trait to be analyzed and solutions of the contemporary groups to weight changes as environmental gradient. In this methodology, the genetic value of the slope represents the measure of resilience of the animals. After the estimation step, a genome-wide association analysis was performed for the slope of the reaction norms model, to obtain a list of windows and associated genes. The heritability estimates for milk production over the resilience gradient ranged from 0.13 to 0.28, with lower values in the intermediate environmental groups. Regarding the productive resilience of dairy buffalo cows to NEB, the genomic windows with the highest contribution to the genetic variance were detected on chromosomes BBU 1, 2, 3, 4, 9, 12, 19, and 21. A functional analysis of the genes described in the selected windows indicated association with metabolic routes related to growth and immunity of the animals, with an emphasis on the STAT6 gene. The results presented indicate that there is for this trait genetic variation to be used as selection criteria, in addition to genomic regions that can increase the precision of the selection.
... temperature humidity indices) that can indicate heat or cold stress events (e.g. Brügemann, Gernand, von Borstel, & König, 2011;Nguyen, Bowman, Haile-Mariam, Pryce, & Hayes, 2016;Toghiani, Hay, Fragomeni, Rekaya, & Roberts, 2020) or the fat-protein ratio which might be used as proxy for challenging metabolic situations (Ha et al., 2017) or feeding regimes (Rauw & Gomez-Raya, 2015). ...
Genotype x environment (GxE) interaction for production traits in Fleckvieh cattle was assessed by means of various environmental descriptors (EDs). It was also of particular interest to search for EDs useful for studying differing robustness or resilience of individuals which implies reasonable GxE interaction. The set of studied EDs included farm/herd environment (e.g. herd size, housing/feeding regimes, herd production level), geographical location (e.g. height above sea level), temperature humidity index and fat‐to‐protein ratio. Milk, fat and protein yield deviations for the first test day, the first 100 days and the 305‐day‐yield of the first lactation were used as phenotypes. Genetic correlations were estimated with bi‐ and multivariate sire models in case of categorical EDs and reaction norm sire models in case of continuous EDs. Further, rank correlations of bulls' estimated breeding values were calculated for different environmental levels/classes. Almost all estimated genetic correlations were significantly different from 1, but not <0.93. There were some exceptions for extremely different classes/levels of some EDs (e.g. average herd‐year production, organic vs conventional farm systems, way of concentrated feed supply). Rank correlations were rarely below 0.95. In summary, no substantial GxE interactions for milk production traits were found with the studied EDs.
... In reaction norm models, the environment is modeled as a continuously scaled variable such as the temperature-humidity index, herd disease level, or farm input level, as recently reviewed by Hayes et al. (2016) and successfully applied by, for example, Calus et al. (2002), Strandberg et al. (2009), Streit et al. (2012), or Zhang et al. (2019). Several studies found GxE in different dairy cattle breeds using different environmental descriptors (e.g., Hayes et al., 2003;El-Tarabany and Nasr, 2015;Ha et al., 2017). Random regression models result in an estimated intercept and slope effect per individual, whereby the intercept estimate is equivalent to the genotypic value irrespective of the environment and the slope effect shows the environmental sensitivity of the genotype. ...
The production environments of the German–Austrian Brown Swiss population show a wide range due to differences in topography, landscapes, local climates, and different farm management systems. Extensive production systems such as organic farming have become increasingly popular in recent decades because of interest in sustainability and consumer preferences. Compared with conventional farmers, organic farmers put more weight on fitness traits. Besides the official total merit index (TMI), a selection index applying relative economic weights (REWs) suitable for organic production systems is provided for Brown Swiss cattle in Germany. The aim of the study was to investigate genotype-by-environment interactions (GxE) for milk production traits and functional traits (including longevity, fertility traits, and calving traits) in a sample of the German–Austrian Brown Swiss population housed in Baden-Wuerttemberg (southern Germany) by applying bivariate and random regression sire models. For bivariate analyses, the production environment was binary classified by farm management system (organic and conventional) and altitude of farm location (above or below 800 m above sea level (ASL)). Milk energy yields (MEY) obtained from herd effects were used as continuously scaled environmental descriptor in the reaction norm approach. The TMIs for sires were calculated based on breeding values estimated with different models and environment-specific REWs to determine possible GxE at TMI levels and rerankings of sires. In bivariate analyses, genetic correlations at the trait level were high and ranged from rg = 0.99 (calving to first insemination, cystic ovaries, and maternal stillbirth rate) to rg = 0.79 (first insemination to conception for altitude). Except for the latter, no severe GxE were found at the trait level using the bivariate models. Fat yield was the only trait showing minor GxE in the reaction norm model approach. Investigating the environmental sensitivity at the TMI level revealed rank correlations between the different environment-specific TMIs that were close to unity, implying no severe reranking effects. The results show no need to account for different environments in Brown Swiss cattle breeding programs.
... Several studies found GxE in different cattle breeds using different environmental descriptors (e.g. El-Tarabany and Nasr, 2015;Ha et al., 2017) and the suitability of different reaction norm models was investigated (e.g. Lillehammer et al., 2009). ...
Complex breeding programmes are used worldwide to achieve genetic progress in quantitative traits. These programmes are basically similarly structured, independent of breed and species, and are characterised by successive steps. The adjustment and revision of breeding programmes is of ongoing interest due to several reasons, e.g. research findings and various political, economic, social and ethical aspects. For the long-term improvement of livestock populations, multiple traits are included in the breeding goals of such programmes. Unfavourable genetic correlations between economically important production traits and functional traits compromise the genetic progress in animal health and welfare. Novel functional and behavioural traits and the adjustment of relative economic weights for the optimisation of total merit indices are investigated in research projects. In addition, genotype by environment interactions (GxE) can influence the genetic improvement of livestock populations and their consideration is crucial due to the globalisation of breeding structures and associated varying production environments. The main objectives of this thesis were 1) to investigate novel behavioural traits in pig breeding, 2) to derive environmental-specific relative economic weights based on consumers preferences for Brown Swiss cattle, 3) to estimate GxE at a trait and an index level by applying bivariate sire models in Brown Swiss cattle and 4) to analyse GxE for different production and functional traits in Brown Swiss cattle using reaction norm models. In Chapter 1 genetic parameters for mothering ability traits were estimated. Heritabilities (h²) were estimated by applying a linear mixed- and a threshold model, ranging between 0.02 to 0.07 and 0.05 to 0.15, respectively. The slightly higher estimates for the repeatability ranged from 0.05 to 0.09 and from 0.08 to 0.17, indicating the influence of the permanent environment. Highest h² were found for the group- and nursing behaviour of the sows. Highest genetic correlations were found between group-/nursing behaviour and litter balance and piglet weaning weight with estimates between 0.71 and 0.86. Accelerating genetic gain for improved mothering ability in sows is possible. In Chapter 2, the importance of breeding traits of Brown Swiss cattle in terms of selection decisions of farm managers was evaluated by using a discrete choice experiment (DCE). Environment-specific relative economic weights (REW) and the marginal willingness to pay were estimated by using a conditional logit model. Several trait complexes, the semen price, interactions between these traits and specific characteristics of the farms were included. Farmers showed highest preferences for the milk value, conformation/udder and fitness. Interactions indicated a great importance of the fitness trait complex for organically managed farms compared to conventional farmers. The latter weighted the milk value and the trait complex conformation/udder high. The method is suitable to judge farmers preferences for specific traits, especially those which have not yet been monetarily evaluated. Chapter 3 investigated GxE at a trait and an index level for Brown Swiss sires. Bivariate sire models were used to estimate genetic correlations between organic and conventional production systems and two altitude level of the farms for milk production traits and several functional traits. Furthermore, to determine possible GxE and rerankings at an index level, total merit indices for the sires were calculated. The genetic correlations were high between the analysed environments, ranging from 0.79 (first insemination to conception between different altitude levels) to 0.99 (calving to first insemination, cystic ovaries, maternal stillbirth between production systems). The results indicate no severe GxE at a trait level. No putative GxE effects were found for the production system environments at an index level. In Chapter 4, GxE for various production and functional traits in Brown Swiss cattle were analysed using random regression reaction norm models. The continuous environmental descriptor milk energy yield (MEY) was calculated as a linear combination of average herd effects, obtained from the routine breeding value estimation, of milk-, fat- and protein yield. The applied reaction norm model included a random sire effect and a random sire slope effect (environmental sensitivity), i.e. the random regression coefficient of the regression of a specific sire on the environmental descriptor MEY. To investigate putative GxE effects, breeding values for the sires were estimated. Results showed no severe GxE for the functional traits but for the production trait fat yield. In addition, the slope variances as descriptors of the environmental sensitivity and the Spearman rank correlations between the estimated breeding values of the sires at different environmental levels indicate no severe GxE for the investigated traits.
... Cows that exhibited less BW loss produced the same amount of milk during lactation as those that lost more BW, but those with lower BW loss had better fertility. Ha et al. (2017) identified a genetic component to estimate metabolic change during early lactation in Brown Swiss cows. Brown Swiss bulls differed in types of daughters that they sired, and daughters that were more metabolically robust had extended functional lifetimes in herds. ...
The worldwide population in 2067 will reach 10.4 billion with 81% residing in Africa or Asia. Arable land available for food production will decrease to 0.15 ha per person. Temperature will increase in tropical and temperate zones, especially in the Northern Hemisphere, and this will push growing seasons and dairy farming away from arid areas and into more northern latitudes. Dairy consumption will increase because it provides essential nutrients more efficiently than many other agricultural systems. Dairy farming will become modernized in developing countries and milk production per cow will increase, doubling in countries with advanced dairying systems. Profitability of dairy farms will be the key to their sustainability. Genetic improvements will include emphasis on the coding genome and associated noncoding epigenome of cattle, and on microbiomes of dairy cattle and farmsteads. Farm sizes will increase and there will be greater lateral integration of housing and management of dairy cattle of different ages and production stages. Integrated sensors, robotics, and automation will replace much of the manual labor on farms. Managing the epigenome and microbiome will become part of routine herd management. Innovations in dairy facilities will improve the health of cows and permit expression of natural behaviors. Herds will be viewed as superorganisms, and studies of herds as observational units will lead to improvements in productivity, health, and well-being of dairy cattle, and improve the agroecology and sustainability of dairy farms. Dairy farmers in 2067 will meet the world's needs for essential nutrients by adopting technologies and practices that provide improved cow health and longevity, profitable dairy farms, and sustainable agriculture.
... Some cows may be genetically less suited to cope with these metabolic needs than others, leading to adverse follow-up effects on longevity. In a recent study (Ha et al., 2017), functional lifetime of dairy cows was linked to the metabolic challenges in the beginning of the first lactation (e.g., sum of milk yield or the accumulated fat-to-protein ratio of the first 3 test-days (<120 DIM). Significant sire variance indicated a genetic component for metabolic adaptability and suggested that the ability to cope with metabolic stress in the transition period has a genetic component, which may be used to select metabolically more robust dairy cows. ...
Basic knowledge of mechanisms controlling reproductive processes in mammals was limited in the early 20th century. Discoveries of physiologic processes and mechanisms made early in the last century laid the foundation to develop technologies and programs used today to manage and control reproduction in dairy cattle. Beyond advances made in understanding of gonadotropic support and control of ovarian and uterine functions in basic reproductive biology, advancements made in artificial insemination (AI) and genetics facilitated rapid genetic progress of economically important traits in dairy cattle. Technologies associated with management have each contributed to the evolution of reproductive management, including (1) hormones to induce estrus and ovulation to facilitate AI programs; (2) pregnancy diagnosis via ultrasonography or by measuring conceptus-derived pregnancy-associated glycoproteins; (3) estrus-detection aids first devised for monitoring only physical activity but that now also quantitate feeding, resting, and rumination times, and ear temperature; (4) sex-sorted semen; (5) computers and computerized record software packages; (6) handheld devices for tracking cow location and retrieving cow records; and (7) genomics for increasing genetic progress of reproductive and other economically important traits. Because of genetic progress in milk yield and component traits, the dairy population in the United States has been stable since the mid 1990s, with approximately 9 to 9.5 million cows. Therefore, many of these technologies and changes in management have been developed in the face of increasing herd size (4-fold since 1990), and changes from pastoral or dry-lot dairies to increased housing of cows in confinement buildings with freestalls and feed-line lockups. Management of groups of “like” cows has become equally important as management of the one. Management teams, including owner-managers, herdsmen, AI representatives, milkers, and numerous consultants dealing with health, feeding, and facilities, became essential to develop working protocols, monitor training and day-to-day chores, and evaluate current trends and revenues. Good management teams inspect and follow through with what is routinely expected of workers. As herd size will undoubtedly increase in the future, practical reproductive management must evolve to adapt to the new technologies that may find more herds being milked robotically and applying technologies not yet conceived or introduced.
Milk production of dairy cows increased markedly during recent decades and continues to increase further. The evolutionarily conserved direction of nutrients to the mammary gland immediately after calving provided the basis for successful selective breeding toward higher performance. Considerable variation in adaptive responses toward energy and nutrient shortages exists; however, this variation in adaptability recently gained interest for identifying more metabolically robust dairy cows. Metabolic challenges during periods of high milk production considerably affect the immune system, reproductive performance, and product quality as well as animal welfare. Moreover, growing consumer concerns need to be taken into consideration because the public perception of industrialized dairy cow farming, the high dependency on feed sources suitable for human nutrition, and the apparently abundant use of antibiotics may affect the sales of dairy products. Breeding for high yield continues, but the metabolic challenges increasingly come close to the adaptational limits of meeting the mammary gland's requirements. The aim of the present review is to elucidate metabolic challenges and adaptational limitations at different functional stages of the mammary gland in dairy cows. From the challenges and adaptational limitations, we derive perspectives for sustainable milk production. Based on previous research, we highlight the importance of metabolic plasticity in adaptation mechanisms at different functional stages of the mammary gland. Metabolic adaptation and plasticity change among developing, nonlactating, remodeling, and lactational stages of the mammary gland. A higher metabolic plasticity in early-lactating dairy cows could be indicative of resilience, and a high performance level without an extraordinary occurrence of health disorders can be achieved.
Introduction:
Metabolic and health disorders account distinctly for culling in dairy cows. This study investigated if metabolic status obtained once in life during a negative energy balance in early lactation allows to predict age and lifetime performance animals achieved at culling. Metabolically stressed cows entering at least their 3rd lactation (n = 200, parity: 5.0 ± 2.1, mean ± SD) were selected from a field study conducted in Switzerland. Age of cows at culling ranged from 4.7 to 20.2 years with parities from 3 to 17. From cows with known reasons of culling, 28.4% were culled because of fertility, 16.4% due to udder health, 15.5% due to high age and 10.4% because of claw health/lameness. A retrospective classification of the one-time recorded metabolic adaptation in week 4 post partum did not differ between animals of different parities at culling. Furthermore, there was no relationship neither between the metabolic adaptation recorded in a preceding lactation and the number of lactations achieved, nor to the lifetime milk production. Contrary to the wide spread assumptions, an inadequate adaptation due to a high metabolic load in early lactation may not result in an earlier culling of dairy cows, although they are more prone to metabolic disorders.
Metabolic disorders are a key problem in the transition period of dairy cows and often appear before the onset of further health problems. They mainly derive from difficulties the animals have in adapting to changes and disturbances occurring both outside and inside the organisms and due to varying gaps between nutrient supply and demand. Adaptation is a functional and target-oriented process involving the whole organism and thus cannot be narrowed down to single factors. Most problems which challenge the organisms can be solved in a number of different ways. To understand the mechanisms of adaptation, the interconnectedness of variables and the nutrient flow within a metabolic network need to be considered. Metabolic disorders indicate an overstressed ability to balance input, partitioning and output variables. Dairy cows will more easily succeed in adapting and in avoiding dysfunctional processes in the transition period when the gap between nutrient and energy demands and their supply is restricted. Dairy farms vary widely in relation to the living conditions of the animals. The complexity of nutritional and metabolic processes Animals 2015, 5 979 and their large variations on various scales contradict any attempts to predict the outcome of animals' adaptation in a farm specific situation. Any attempts to reduce the prevalence of metabolic disorders and associated production diseases should rely on continuous and comprehensive monitoring with appropriate indicators on the farm level. Furthermore, low levels of disorders and diseases should be seen as a further significant goal which carries weight in addition to productivity goals. In the long run, low disease levels can only be expected when farmers realize that they can gain a competitive advantage over competitors with higher levels of disease.
Nutrient partitioning toward the mammary gland during insufficient energy and nutrient supply is a strategy to ensure survival of the offspring in mammalian species. This homeorhetic priority of the mammary gland is also present in the modern dairy cow, in particular in early lactation. However, despite similar metabolic loads, the adaptive response to a given metabolic load varies considerably among animals. The aim of this study was to investigate if individual cows respond in a consistent manner to a negative energy balance (NEB) in early and mid lactation. Twenty-five dairy cows experienced the usual NEB after parturition and were subjected to a second 3-wk NEB induced by feed restriction in mid lactation. Animals were retrospectively ranked according to their highest plasma nonesterified fatty acid (NEFA) concentration in wk 1 to 4 postpartum. The animals with the 33% highest and 33% lowest values were selected and classified either as the high response (HR) or low response (LR) group. Before parturition, no differences in the studied parameters, dry matter intake, energy balance, concentrations of glucose, NEFA, β-hydroxybutyrate, cholesterol, triglycerides, growth hormone, and insulin-like growth factor-1, were detected between LR and HR. After parturition, milk yield and energy-corrected milk yield was higher for HR compared with LR in wk 2 to 14 and wk 1 to 6, respectively. During feed restriction in wk 15 to 17 postpartum, no differences in energy-corrected milk between LR and HR were found. Energy balance was more negative in HR during the NEB in early lactation, but not different from LR during feed restriction in mid lactation. Although plasma concentrations of glucose, growth hormone, triglycerides, and cholesterol showed group differences in early lactation, but not during feed restriction, the plasma concentrations of NEFA, β-hydroxybutyrate, and insulin-like growth factor-1 in HR changed repeatedly to a greater extent during the NEB at the 2 stages of lactation compared with LR despite the similar extent of the NEB itself in both groups. The repeatedly greater amplitude of adaptive responses in HR compared with LR at different time points might partly indicate an underlying genetic background to enable a sufficient and rapid supply of mobilization-derived nutrients. The individual characteristics of adaptation to an energy and nutrient shortage might be beneficial when implemented in breeding programs.
The metabolic adaptation of dairy cows during the transition period has been studied intensively in the last decades. However, until now, only few studies have paid attention to the genetic aspects of this process. Here, we present the results of a gene-based mapping and pathway analysis with the measurements of three key metabolites, (1) non-esterified fatty acids (NEFA), (2) beta-hydroxybutyrate (BHBA) and (3) glucose, characterizing the metabolic adaptability of dairy cows before and after calving. In contrast to the conventional single-marker approach, we identify 99 significant and biologically sensible genes associated with at least one of the considered phenotypes and thus giving evidence for a genetic basis of the metabolic adaptability. Moreover, our results strongly suggest three pathways involved in the metabolism of steroids and lipids are potential candidates for the adaptive regulation of dairy cows in their early lactation. From our perspective, a closer investigation of our findings will lead to a step forward in understanding the variability in the metabolic adaptability of dairy cows in their early lactation.
Current selection objectives for dairy cattle breeding may be favouring cows that are genetically predisposed to mobilize body tissue. This may have consequences for fertility since cows may resume reproductive activity only once the nadir of negative energy balance (NEB) has passed. In this study, we repeatedly measured food intake, live weight, milk yield and condition score of Holstein cattle in their first lactation. They were given either a high concentrate or low concentrate diet and were either selected or control animals for genetic merit for kg milk fat plus milk protein. Orthogonal polynomials were used to model each trait over time and random regression techniques allowed curves to vary between animals at both the genetic and the permanent environmental levels. Breeding values for bulls were calculated for each trait for each day of lactation. Estimates of genetic merit for energy balance were calculated from combined breeding values for either (1) food intake and milk yield output, or (2) live weight and condition-score changes. When estimated from daily fluxes of energy calculated from food intake and milk output, the average genetic merit of bulls for energy balance was approximately –15 MJ/day in early lactation. It became positive at about day 40 and rose to +18 MJ/day at approximately day 150. When estimated from body energy state changes the NEB in early lactation was also –15 MJ/day. It became positive at about day 80 and then rose to a peak of +10 MJ/day. The difference between the two methods may arise either because of the contribution of food wastage to intake measures or through inadequate predictions of body lipid from equations using live weight and condition score or a combination of both. Body energy mobilized in early lactation was not fully recovered until day 200 of lactation. The results suggest that energy balance may be estimated from changes in body energy state that can be calculated from body weight and condition score. Since body weight can be predicted from linear type measures, it may be possible to calculate breeding values for energy balance from national evaluations for production and type. Energy balance may be more suitable as a breeding objective than persistency.
p38 plays a critical role in the proliferation, survival, migration and metastasis of colorectal cancer (CRC) cells. The present study assessed the correlation between a single nucleotide polymorphism (SNP) in the p38β promoter region (rs2235356, -1628A>G) and the predisposition of individuals to sporadic CRC in a case-control study. A genotyping method was developed to detect this SNP, using polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) analysis. A logistic regression analysis was used to determine the odds ratio (OR) and 95% confidence interval (CI). It was revealed that the -1628G variant allele was correlated with an increased risk of CRC (OR, 1.99; 95% CI, 1.60-2.47; P<0.0001). An in silico analysis revealed several transcription factors that either acquired or lost the ability to bind to -1628AA in the p38β promoter region due to the SNP. Therefore, this allelic variant may be a genetic modifier for CRC susceptibility.
Our objective was to detect and describe genotype by environment interaction on Nordic dairy cattle using a reaction norm model. This model also indicates a possibility to predict breeding values across countries, avoiding the problem with weak genetic ties between coun- tries. Reaction norms were predicted for young sires of the Ayrshire breed group. A linear random regression model was used, regressing phenotypic observations of daughters within sire, on herd environment. The phenotypic measures were 305 days protein production and days open. The environmental value was the herd-year average of protein production or days open. The correlation between the level and the slope of reaction norms was high when the phenotypic observations were regressed on the herd-year average of the same trait. Crossing of reaction norms occurred, showing that genotype by environment interaction exists in the Nordic dairy cattle population. The heritabilities as functions of the environment were higher in herds with high protein yield and long period of days open. The most important result was that reaction norms could be estimated from Nordic field data, resulting in joint predicted breeding values across countries and environments.
During the transition period, the lipid metabolism of dairy cows is markedly affected by energy status. Fatty liver is one of the main health disorders after parturition. The aim of this study was to evaluate the effects of a negative energy balance (NEB) at 2 stages in lactation [NEB at the onset of lactation postpartum (p.p.) and a deliberately induced NEB by feed restriction near 100 d in milk] on liver triglyceride content and parameters of lipid metabolism in plasma and liver based on mRNA abundance of associated genes. Fifty multiparous dairy cows were studied from wk 3 antepartum to approximately wk 17 p.p. in 2 periods. According to their energy balance in period 1 (parturition to wk 12 p.p.), cows were allocated to a control (CON; n = 25) or a restriction group (RES; 70% of energy requirements; n = 25) for 3 wk in mid lactation starting at around 100 d in milk (period 2). Liver triglyceride (TG) content, plasma nonesterified fatty acids (NEFA), and β-hydroxybutyrate were highest in wk 1 p.p. and decreased thereafter. During period 2, feed restriction did not affect liver TG and β-hydroxybutyrate concentration, whereas NEFA concentration was increased in RES cows as compared with CON cows. Hepatic mRNA abundances of tumor necrosis factor α, ATP citrate lyase, mitochondrial glycerol-3-phosphate acyltransferase, and glycerol-3-phosphate dehydrogenase 2 were not altered by lactational and energy status during both experimental periods. The expression of fatty acid synthase was higher in period 2 compared with period 1, but did not differ between RES and CON groups. The mRNA abundance of acetyl-coenzyme A-carboxylase showed a tendency toward higher expression during period 2 compared with period 1. The solute carrier family 27 (fatty acid transporter), member 1 (SLC27A1) was upregulated in wk 1 p.p. and also during feed restriction in RES cows. In conclusion, the present study shows that a NEB has different effects on hepatic lipid metabolism and TG concentration in the liver of dairy cows at early and later lactation. Therefore, the homeorhetic adaptations during the periparturient period trigger excessive responses in metabolism, whereas during the homeostatic control of endocrine and metabolic systems after established lactation, as during the period of feed restriction in the present study, organs are well adapted to metabolic and environmental changes.
A version of the nonparametric bootstrap, which resamples the entire subjects from original data, called the case bootstrap, has been increasingly used for estimating uncertainty of parameters in mixed-effects models. It is usually applied to obtain more robust estimates of the parameters and more realistic confidence intervals (CIs). Alternative bootstrap methods, such as residual bootstrap and parametric bootstrap that resample both random effects and residuals, have been proposed to better take into account the hierarchical structure of multi-level and longitudinal data. However, few studies have been performed to compare these different approaches. In this study, we used simulation to evaluate bootstrap methods proposed for linear mixed-effect models. We also compared the results obtained by maximum likelihood (ML) and restricted maximum likelihood (REML). Our simulation studies evidenced the good performance of the case bootstrap as well as the bootstraps of both random effects and residuals. On the other hand, the bootstrap methods that resample only the residuals and the bootstraps combining case and residuals performed poorly. REML and ML provided similar bootstrap estimates of uncertainty, but there was slightly more bias and poorer coverage rate for variance parameters with ML in the sparse design. We applied the proposed methods to a real dataset from a study investigating the natural evolution of Parkinson's disease and were able to confirm that the methods provide plausible estimates of uncertainty. Given that most real-life datasets tend to exhibit heterogeneity in sampling schedules, the residual bootstraps would be expected to perform better than the case bootstrap. Copyright © 2013 John Wiley & Sons, Ltd.
The Survival Kit is a Fortran 90 Software intended for survival analysis using proportional hazards models and their extension to frailty models with a single response time. The hazard function is described as the product of a baseline hazard function and a positive (exponential) function of possibly time-dependent fixed and random covariates. Stratified Cox, grouped data and Weibull models can be used. Random effects can be either log-gamma or normally distributed and can account for a pedigree structure. Variance parameters are estimated in a Bayesian context. It is possible to account for the correlated nature of two random effects either by specifying a known correlation coefficient or estimating it from the data. An R interface of the Survival Kit provides a user friendly way to run the software.
This paper is a contribution from the PROTEJE project. It presents an approach which makes uses in a sire model of annual information (= average daughter yield deviations per year of performance and lactation number) for each sire. It is shown that with a suitable sire model (including a year x country effect, a lactation x country effect and a within country regression on age of the sire when his daughters are born), the genetic parameters and the sire solutions obtained are robust to over-or under-estimation of genetic trend in the national models. The approach has other benefits: validation of genetic trends is no longer critical (although obviously desirable), deregression becomes obsolete. An extension to categorical or survival traits is briefly described and a general strategy for the computation of equivalent daughter contributions and daughter yield deviations and their validation is proposed.
Homeorhetic and homeostatic controls in dairy cows are essential for adapting to alterations in physiological and environmental conditions. To study the different mechanisms during adaptation processes, effects of a deliberately induced negative energy balance (NEB) by feed restriction near 100 d in milk (DIM) on performance and metabolic measures were compared with lactation energy deficiency after parturition. Fifty multiparous cows were studied in 3 periods (1=early lactation up to 12 wk postpartum; 2=feed restriction for 3 wk beginning at 98±7 DIM with a feed-restricted and control group; and 3=a subsequent realimentation period for the feed-restricted group for 8 wk). In period 1, despite NEB in early lactation [-42 MJ of net energy for lactation (NE(L))/d, wk 1 to 3] up to wk 9, milk yield increased from 27.5±0.7 kg to a maximum of 39.5±0.8 kg (wk 6). For period 2, the NEB was induced by individual limitation of feed quantity and reduction of dietary energy density. Feed-restricted cows experienced a greater NEB (-63 MJ of NEL/d) than did cows in early lactation. Feed-restricted cows in period 2 showed only a small decline in milk yield of -3.1±1.1 kg and milk protein content of -0.2±0.1% compared with control cows (30.5±1.1 kg and 3.8±0.1%, respectively). In feed-restricted cows (period 2), plasma glucose was lower (-0.2±0.0 mmol/L) and nonesterified fatty acids higher (+0.1±0.1 mmol/L) compared with control cows. Compared with the NEB in period 1, the decreases in body weight due to the deliberately induced NEB (period 2) were greater (56±4 vs. 23±3 kg), but decreases in body condition score (0.16±0.03 vs. 0.34±0.04) and muscle diameter (2.0±0.4 vs. 3.5±0.4 mm) were lesser. The changes in metabolic measures in period 2 were marginal compared with the adjustments directly after parturition in period 1. Despite the greater induced energy deficiency at 100 DIM than the early lactation NEB, the metabolic load experienced by the dairy cows was not as high as that observed in early lactation. The different effects of energy deficiency at the 2 stages in lactation show that metabolic problems in early lactating dairy cows are not due only to the NEB, but mainly to the specific metabolic regulation during this period.
An observational study was conducted on 1,498 cows in 3 large Italian dairy farms. The objective of the study was to evaluate the prognostic value of early lactation fat-to-protein ratio in milk. In all 3 herds, an intensive herd health monitoring program was being practiced that included weekly visits and extensive data collection on health, reproduction, production, and culling. A milk sample was collected from all cows at approximately 7 d postpartum and the ratio of fat-to-protein percentage in this milk sample was measured. Animals with a fat-to-protein ratio in early lactation greater than 2.0 showed an increase in postpartum diseases such as retained placenta, left-displaced abomasums, metritis and clinical endometritis. We also observed a decrease in early lactation milk production but this was limited to cows in lactation 2 and higher when the fat-to-protein ratio was greater than 2.0 in the early postpartum milk sample. Finally, an increased risk of being culled from the herd was observed, with the risk of culling increasing with increasing fat-to-protein ratio in the early lactation milk sample. No effect of fat-to-protein ratio was found on the incidence of clinical mastitis in the 3 herds. From this study, we conclude that analyses of milk components in early postpartum (6-9 days in milk), particularly the ratio of fat-to-protein percentage, is a valuable indicator of lipo-mobilization and the negative energy balance status in postpartum cows. Because a single milk sample is sufficient to provide valuable information, we suggest that this is a valuable addition to herd health programs on dairy farms.
Abstract A breeding goal accounting for the effects of genotype by environment interaction (G × E) has to define not only traits but also the environment in which those traits are to be improved. The aim of this study was to predict the selection response in the coefficients of a linear reaction norm, and response in average phenotypic value in any environment, when mass selection is applied to a trait where G × E is modelled as a linear reaction norm. The optimum environment in which to test the selection candidates for a given breeding objective was derived. Optimisation of the selection environment can be used as a means to either maximise genetic progress in a certain response environment, to keep the change in environmental sensitivity at a desired rate, or to reduce the proportion of animals performing below an acceptance level. The results showed that the optimum selection environment is not always equal to the environment in which the response is to be realised, but depends on the degree of G × E (determined by the ratio of variances in slope and level of a linear reaction norm), the correlation between level and slope, and the heritability of the trait.
Variance components and breeding values for protein yield were estimated with REML without and with correction for heterogeneity of variances. Three different sire models were applied, which all accounted for genotype x environment (G x E) interaction. The first model included a sire x herd-year-season subclass (HYS) interaction. The second model divided all records in four different types of management groups, based on estimated HYS subclass effect. The third model, the reaction norm model, performed a random linear regression on the estimated HYS effect. For comparison, a standard model that did not take G x E interaction into account was also applied. Data consisted of 102,899 305-d first-lactation protein records of Holstein Friesians of 1,000 ofthe largest Dutch dairy herds. All animals calved in 1997, 1998, or 1999. Estimated breeding values (EBV) for 2,150 bulls with at least five daughters were calculated. The interaction model detected an interaction variance of 2.5% of the phenotypic variance. The EBV showed a correlation of 1.00 with those of the standard model without interaction. The model with the division in groups showed correlations between groups ranging from 0.73 to 0.86. The EBV showed correlations from 0.84 to 0.91 with the EBV of the standard model. The reaction norm model calculated EBV that had a correlation of 1.00 with the EBV of the standard model. The reaction norm model was not able to detect significant variance of the slope for the protein data corrected for heterogeneity of variances.
Fatty liver (i.e., hepatic lipidosis) is a major metabolic disorder of many dairy cows in early lactation and is associated with decreased health status and reproductive performance. In severe cases, milk production and feed intake are decreased. Therefore, a practical preventative or an efficacious treatment of fatty liver could save millions of dollars yearly in treatment, replacement, and production losses for dairy farmers. Fatty liver develops when the hepatic uptake of lipids exceeds the oxidation and secretion of lipids by the liver, which usually is preceded by high concentrations of plasma NEFA mobilized from adipose tissue. Excess lipids are stored as triacylglycerol in the liver and are associated with decreased metabolic functions of the liver. Liver can be categorized into normal liver or mild, moderate, or severe fatty liver; the latter can be subdivided further into nonencephalopathic severe fatty liver and hepatic encephalopathy. Insufficient or unbalanced dietary intake, obesity, and elevated estrogen concentrations are involved in the etiology of fatty liver, which is associated with greater incidence of dystocia, diseases, infections, and inflammations. Because even mild fatty liver is associated with decreased health status and reproductive performance of dairy cows, prevention of fatty liver by supplying cows with sufficient nutrients and a clean and health-promoting environment in the peripartal period would reduce production losses of cows more than would any treatment of fatty liver. This, however, might not be enough for cows that are obese or do not eat well, had calving difficulties or twins, have metabolic or infectious diseases, or are in severe negative energy balance because of high milk production immediately after calving. Potential and commonly used preventatives, as well as treatments, are discussed in the review. Currently, detection of fatty liver is possible only by minor surgery. Ultrasonic techniques offer a potential tool to noninvasively detect fatty liver. Future gene-array and proteomic studies may provide means to detect early molecular events in the etiology of fatty liver plus their connection with immune function and reproductive performance so that more effective treatments and preventatives of fatty liver can be developed. Such advances hopefully will make fatty liver a problem of the past.
The estimation of genetic correlations between a nonlinear trait such as longevity and linear traits is computationally difficult on large datasets. A two-step approach was proposed and was checked via simulation. First, univariate analyses were performed to get genetic variance estimates and to compute pseudo-records and their associated weights. These pseudo-records were virtual performances free of all environmental effects that can be used in a BLUP animal model, leading to the same breeding values as in the (possibly nonlinear) initial analyses. By combining these pseudo-records in a multiple trait model and fixing the genetic and residual variances to their values computed during the first step, we obtained correlation estimates by AI-REML and approximate MT-BLUP predicted breeding values that blend direct and indirect information on longevity. Mean genetic correlations and reliabilities obtained on simulated data confirmed the suitability of this approach in a wide range of situations. When nonzero residual correlations exist between traits, a sire model gave nearly unbiased estimates of genetic correlations, while the animal model estimates were biased upwards. Finally, when an incorrect genetic trend was simulated to lead to biased pseudo-records, a joint analysis including a time effect could adequately correct for this bias.
The objective of this review was to explore the relationship between lactational performance and health. We focused on lactational incidence rates (LIR) of the production diseases that are of economic importance to the dairy industry. Based on a review of 11 epidemiological and 14 genetic studies we found little evidence that high yielding cows have increased risk of dystocia, retained placenta, metritis and left-displaced abomasum. Results for periparturient paresis were inconsistent. Whilst we found no phenotypical relationship between milk yield and the risk of ketosis and lameness, selection for higher milk yields will probably increase LIR for these diseases. Mastitis was the only disease where there was a clear relationship between milk yield and risk of infection. Continued selection for higher milk yields will worsen this situation. However, our overall conclusion is that reviewing existing literature, even with a structured literature selection, is inadequate to the task of elucidating the relationship between lactational performance and risk of production diseases. There are substantial problems with confounding effects and unaccounted for biological correlations. In the second part of the review we argue towards a common basis for addressing production diseases. We propose abnormal body mobilisation and immune competence as common currencies for metabolic and immune status and argue for the development of indicators of metabolic imbalance and the early development of diseases. Furthermore, we suggest the use of indicators of ‘imbalance’ to guide feeding according to the needs of individual cows with their specific genotype and management history. We believe that this approach has the potential to provide new diagnostic and decision support tools to improve animal health and reproduction, whilst simultaneously maintaining optimal production and efficiency. Further research is needed to identify and validate new indicators and individual feeding strategies.
Genotype by environment interaction (G×E) has been widely reported in dairy cattle. If the environment can be measured on a continuous scale, reaction norms can be applied to study G×E. The average herd milk production level has frequently been used as an environmental descriptor because it is influenced by the level of feeding or the feeding regimen. Another important environmental factor is the level of udder health and hygiene, for which the average herd somatic cell count might be a descriptor. In the present study, we conducted a genome-wide association analysis to identify single nucleotide polymorphisms (SNP) that affect intercept and slope of milk protein yield reaction norms when using the average herd test-day solution for somatic cell score as an environmental descriptor. Sire estimates for intercept and slope of the reaction norms were calculated from around 12 million daughter records, using linear reaction norm models. Sires were genotyped for ∼54,000 SNP. The sire estimates were used as observations in the association analysis, using 1,797 sires. Significant SNP were confirmed in an independent validation set consisting of 500 sires. A known major gene affecting protein yield was included as a covariable in the statistical model. Sixty (21) SNP were confirmed for intercept with P ≤ 0.01 (P ≤ 0.001) in the validation set, and 28 and 11 SNP, respectively, were confirmed for slope. Most but not all SNP affecting slope also affected intercept. Comparison with an earlier study revealed that SNP affecting slope were, in general, also significant for slope when the environment was modeled by the average herd milk production level, although the two environmental descriptors were poorly correlated.
Forty-three pairs of open heifers were purchased as foundation animals to evaluate direct response to selection for milk production and correlated responses of reproduction, digestion, respiration, skin or skeleton, and mammary disorders. One heifer of each pair was selected for high genetic potential, and the other was selected for low potential on pedigree evaluations for milk production. High pedigree cows produced more milk, but they also had 9% more digestive disorders, 5% more foot rot, 14% more skin or skeletal disorders, 11% more cases of udder edema, and 2% more lactations affected by mastitis. High pedigree cows even with the 45.80 more per lactation than did low pedigree cows calving in the same year.
Reaction norm random regression sire models were used to study genotype-by-environment interactions (G × E) in the German Holstein dairy cattle population. Around 2300 sires with a minimum of 50 daughters per sire and seven first-lactation test day observations per daughter were analysed. Corrected test day records for milk yield, protein yield, fat yield and somatic cell score (SCS) were used. Herd test day solutions for milk traits, milk energy yield or SCS were used as environmental descriptors. Second-order orthogonal polynomial regressions were applied to the sire effects. The results revealed significant slope variances of the reaction norms, which caused a non-constant additive genetic variance across the environmental ranges considered. This pointed to the presence of minor G × E effects. The additive genetic variance increased when the environment improved, that is, higher (lower) herd test day solutions for milk traits (SCS). This was also influenced by pure scaling effects, because the non-genetic variance increased in an improved environment and the heritability was less influenced by the environment. The G × E effects caused very little reranking of the sires for the environmental range considered in this study.
Genetic parameters for the relation between milk production and fertility were estimated in the Dutch Black and White dairy cow population, a crossbred population of the Holstein Friesian and Dutch Friesian breeds. In the analyses, 82659 records on first lactation production and fertility were used, containing 13480 progeny from 1011 young bulls and other progeny descending from proven bulls. Restricted Maximum Likelihood was used to estimate variance components for effects of sire of cow and residual. Generalized Least Squares estimates showed a positive breed effect of Holstein Friesians (HF) over Dutch Friesians of about 500 kg for 305 days milk production, and a negative HF-breed effect on both 56 days-nonreturn and calving percentage of eight percent points. Heritability estimates for milk production traits were 0.48, 0.36, and 0.33 for 305 days milk, fat, and protein yields, respectively. Heritability estimates for fertility traits were 0.04, 0.03, 0.04, 0.05 and 0.02 for interval calving to first insemination, calving interval, 56-days nonreturn, calving percentage after first insemination and interval from first insemination to conception, respectively. Phenotypic correlations between milk production traits and fertility traits showed a negative association (-0.05 to -0.18). Genetic correlations showed a stronger negative association (-0.14 to -0.62). Negative genetic associations of fertility traits with protein yield were somewhat stronger than those with milk yield.
A theoretical model of herd life was used to quantify biases in genetic parameter estimates from culling on production and to study effects on response to selection. Herd life was modeled as a linear function of production and ability to survive regardless of production (survival). Genetic improvement of survival is of interest. Results of analytically derived formulas showed that estimates of heritability obtained from analysis of herd life are biased for survival. For moderately negative genetic correlations between production and survival, biases tended to be upward. The genetic correlation between production and survival was severely overestimated when based on production, and herd life. Adjustment of phenotypic herd life for phenotypic production removed some of the biases in genetic parameter estimates unless little direct culling for production occurred.Incorporation of estimated breeding values for herd life that were adjusted for production in a selection index, along with estimated breeding values for production, resulted in more response in a breeding goal consisting of production and survival than did inclusion of estimated breeding values for herd life when the standardized direct effect of production on herd life was larger than .15. For smaller values, adjustment of herd life for production reduced response to selection. Given current levels of culling on production, measures of herd life should be adjusted for production when included in selection strategies.
Ten sets of data from four experiments with individual measurements on 236 cows for one to four weeks in each of second and third months of lactation were used to determine relationships between milk composition and nutritional parameters. Effects of breed and parity of cow as well as experimental rations were removed in analysis. The estimated balance between energy intake and energy requirement for milk and maintenance was calculated from intake of roughage and concentrates together with their chemical composition and in vitro digestibility, milk yield and milk composition. Adjusted mean estimated energy balances ranged from -17.65 to 14.35 MJ NEℓ per day; dry matter intake from 16.1 to 20.1 kg per day and % dietary roughage from 36.6 to 67.5% across data sets. Mean milk fat % was between 3.76 and 4.11% while milk protein % was 2.79-3.15% across data sets. Dry matter intake and % dietary roughage explained relatively small amounts of variation in milk fat %, protein % or the ratio of these two components after effects of ration were removed in data analysis. Estimated energy balance was negatively correlated with milk fat % (-0.07 to -0.65), positively correlated with milk protein % (0.12 to 0.47) and negatively correlated with ratio of milk fat to protein (-0.36 to -0.74) across data sets. Estimated energy balance increased the proportion of explained variation of fat:protein ratio by an average 21% to a total of 43%. In the reverse situation, fat to protein ratio increased coefficients of determination of energy balance by 19 to 52%. The ratio of contents of milk fat and protein was a more sensitive and consistent indicator of changes in nutritional variables and also a better predictor of energy status of the cow than either component by itself.
The question of the existence and uniqueness of an M-matrix which is a square root of an M-matrix is discussed. The results are then used to derive some new necessary and sufficient conditions for a real matrix with nonpositive off diagonal elements to be an M-matrix.
Results from various selection experiments and field data as well as from Monte Carlo studies and model calculations are reviewed to indicate some crucial features concerning longevity in dairy cattle breeding. The following conclusions were drawn: (i) There is some evidence from selection experiments for a significant antagonism between early maturity and longevity. (ii) Estimates of the genetic correlation between first lactation milk yield and longevity traits show a severe upward bias due to voluntary culling on milk yield. Adjustment of herd life for within-herd milk production is recommended but may not prevent estimation biases in general. (iii) The relative economic value of longevity compared to milk yield depends on the level of herd life in the population and the actual quota scenario; a rough average is 1:2. (iv) Type and conformation traits cannot serve as sufficient selection substitutes for longevity traits. (v) Survival analysis is the adequate method for genetic evaluation of lifetime data. (vi) Both selection on determinants of involuntary culling and on measurements of longevity may be used as breeding strategies to improve longevity.
Statistics is a subject of many uses and surprisingly few effective practitioners. The traditional road to statistical knowledge is blocked, for most, by a formidable wall of mathematics. The approach in An Introduction to the Bootstrap avoids that wall. It arms scientists and engineers, as well as statisticians, with the computational techniques they need to analyze and understand complicated data sets.
Postpartum energy status is critically important to health and fertility, and it remains a major task to find suitable indicator traits for energy balance. Therefore, genetic parameters for daily energy balance (EB) and dry matter intake (DMI), weekly milk fat to protein ratio (FPR), and monthly body condition score (BCS) were estimated using random regression on data collected from 682 Holstein-Friesian primiparous cows recorded between lactation d 11 to 180. Average energy-corrected milk (ECM), EB, DMI, BCS, and FPR were 32.0 kg, 9.6 MJ of NE(L), 20.3 kg, 2.95, and 1.12, respectively. Heritability estimates for EB, DMI, BCS, and FPR ranged from 0.03 to 0.13, 0.04 to 0.19, 0.34 to 0.59, and 0.20 to 0.54. Fat to protein ratio was a more valid measure for EB in early lactation than DMI, BCS, or single milk components. Correlations between FPR and EB were highest at the beginning of lactation [genetic correlation (r(g)) = -0.62 at days in milk (DIM) 15] and decreased toward zero. Dry matter intake was the trait most closely correlated with EB in mid lactation (r(g) = 0.73 at DIM 120 and 150). Energy balance in early lactation was negatively correlated to EB in mid lactation. The same applied to DMI. Genetic correlations between FPR across lactation stages were all positive; the lowest genetic correlation (0.55) was estimated between the beginning of lactation and early mid lactation. Hence, to improve EB at the beginning of lactation, EB and indicator traits need to be recorded in early lactation. We concluded that FPR is an adequate indicator for EB during the energy deficit phase. Genetic correlations of FPR with ECM, fat percentage, and protein percentage showed that a reduction of FPR in early lactation would have a slightly negative effect on ECM, whereas milk composition would change in a desirable manner.
Metabolic and endocrine adaptations to support milk production during the transition period vary between individual cows. This variation between cows to adapt to lactation may have a genetic basis. The present field study was carried out to determine hepatic adaptations occurring from late pregnancy through early lactation by measuring mRNA abundance of candidate genes in dairy cows on-farm. Additionally, the objective was to observe the diversity in inter-individual variation for the candidate genes that may give indications where individual adaptations at a molecular level can be found. This study was carried out on-farm including 232 dairy cows (parity >3) from 64 farms in Switzerland. Blood and liver samples were collected on d 20±7 before parturition, on d 24±2, and on d 89±4 after parturition. Blood plasma was assayed for concentrations of glucose, nonesterified fatty acids, β-hydroxybutyrate, cholesterol, triglycerides, urea, albumin, protein, insulin, insulin-like growth factor-1, leptin, 3,5,3'-triiodothyronine, and thyroxine. Liver samples were obtained at the same time points and were measured for mRNA abundance of 26 candidate genes encoding enzymes and nuclear receptors involved in gluconeogenesis, fatty acid β-oxidation, fatty acid and triglyceride synthesis, ketogenesis, citric acid cycle, cholesterol synthesis, and the urea cycle. The cows in the present study experienced a marked metabolic load in early lactation, as presented by changes in plasma metabolites and hormones, and responded accordingly with upregulation and downregulation of almost all candidate genes involved in metabolic processes in the liver. The observed inter-individual variation for the candidate genes, which was highest for acetyl-CoA-carboxylase and glycerol-3-phosphate dehydrogenase 2, should be further investigated to unravel the regulation at molecular level for optimal adaptive performance in dairy cows.
Selection for milk yield increases the metabolic load of dairy cows. The fat:protein ratio of milk (FPR) could serve as a measure of the energy balance status and might be used as a selection criterion to improve metabolic stability. The fit of different fixed and random regression models describing FPR and daily energy balance was tested to establish appropriate models for further genetic analyses. In addition, the relationship between both traits was evaluated for the best fitting model. Data were collected on a dairy research farm running a bull dam performance test. Energy balance was calculated using information on milk yield, feed intake per day, and live weight. Weekly FPR measurements were available. Three data sets were created containing records of 577 primiparous cows with observations from lactation d 11 to 180 as well as records of 613 primiparous cows and 96 multiparous cows with observations from lactation d 11 to 305. Five well-established parametric functions of days in milk (Ali and Schaeffer, Guo and Swalve, Wilmink, Legendre polynomials of third and fourth degree) were chosen for modeling the lactation curves. Evaluation of goodness of fit was based on the corrected Akaike information criterion, the Bayesian information criterion, correlation between the real observation and the estimated value, and on inspection of the residuals plotted against days in milk. The best model was chosen for estimation of correlations between both traits at different lactation stages. Random regression models were superior compared with the fixed regression models. In general, the Ali and Schaeffer function appeared most suitable for modeling both the fixed and the random regression part of the mixed model. The FPR is greatest in the initial lactation period when energy deficit is most pronounced. Energy balance stabilizes at the same point as the decrease in FPR stops. The inverted patterns indicate a causal relationship between the 2 traits. A common pattern was also observed for repeatabilities of both traits, with repeatabilities being largest at the beginning of lactation. Additionally, correlations between cow effects were closest at the beginning of lactation (r(c)=-0.43). The results support the hypothesis that FPR can serve as a suitable indicator for energy status, at least during the most metabolically stressful stage of lactation.
Dairy cows undergo tremendous metabolic and physiological adaptations around parturition to support lactation. The liver is central to many of these processes, including gluconeogenesis and metabolism of fatty acids mobilized from adipose tis- sue. Fat accumulation may impair normal functions of the liver and increase ketogenesis, which in turn may predispose cows to other metabolic abnormalities. Several aspects of dietary management and body condition may alter these adaptations, affect dry matter intake, and increase or decrease susceptibility to periparturient health problems. Overfeeding energy dur- ing the dry period is a prominent risk factor. Considerable progress has been made in recent years in describing the adap- tive changes in the liver and other organs in normal and abnormal states, but this knowledge has not yet identified unequiv- ocally the key steps that might be compromised during development of metabolic disorders. The potential role of signaling compounds, such as the inflammatory cytokines released in response to environmental stressors, infectious challenge, and oxidative stress, in the pathogenesis of periparturient disease is under investigation. New techniques such as functional genomics, using cDNA or oligonucleotide microarrays, as well as proteomics and metabolomics, provide additional high- throughput tools to determine the effects of nutrition, management, or stressors on tissue function in development of dis- ease. Integrative approaches should be fruitful in unraveling the complex interactions of metabolism, immune activation, stress physiology, and endocrinology that likely underlie development of periparturient disease.
Measures of yield and fertility were obtained from breeding receipts of artificial insemination and records of test-day yield. Estimates of heritability were by Henderson Method 3, maximum likelihood, and restricted maximum likelihood. Heritabilities for measures of yield varied, but most were within the range of earlier estimates. Measures of fertility had heritabilities from 0 to .03. Alternative upper bounds were placed on days open, number of services, and service period, and always the measure with the lesser bound had higher heritability for first parity. Measures of yield for early stages of lactation had slight positive phenotypic correlations with fertility whereas those for measures of cumulative yield later in lactation increased in relation to effect of gestation. Genetic correlations of first-parity yield and most measures of fertility were positive and less influenced by stage of lactation than phenotypic correlations. Antagonism moderated for second parity. Most genetic correlations were not significantly different from zero for third parity. Considerable genetic antagonism of yield and fertility may be of limited consequence because estimates of genetic variance of fertility were near zero.
This article discusses the asymptotic behavior of likelihood ratio tests for nonzero variance components in the longitudinal mixed effects linear model described by Laird and Ware (1982, Biometrics 38, 963-974). Our discussion of the large-sample behavior of likelihood ratio tests for nonzero variance components is based on the results for nonstandard testing situations by Self and Liang (1987, Journal of the American Statistical Association 82, 605-610).
The study used field data from a regular herd health service to investigate the relationships between body condition scores or first test day milk data and disease incidence, milk yield, fertility, and culling. Path model analysis with adjustment for time at risk was applied to delineate the time sequence of events. Milk fever occurred more often in fat cows, and endometritis occurred between calving and 20 d of lactation more often in thin cows. Fat cows were less likely to conceive at first service than were cows in normal condition. Fat body condition postpartum, higher first test day milk yield, and a fat to protein ratio of > 1.5 increased body condition loss. Fat or thin condition or condition loss was not related to other lactation diseases, fertility parameters, milk yield, or culling. First test day milk yield was 1.3 kg higher after milk fever and was 7.1 kg lower after displaced abomasum. Higher first test day milk yield directly increased the risk of ovarian cyst and lameness, increased 100-d milk yield, and reduced the risk of culling and indirectly decreased reproductive performance. Cows with a fat to protein ratio of > 1.5 had higher risks for ketosis, displaced abomasum, ovarian cyst, lameness, and mastitis. Those cows produced more milk but showed poor reproductive performance. Given this type of herd health data, we concluded that the first test day milk yield and the fat to protein ratio were more reliable indicators of disease, fertility, and milk yield than was body condition score or loss of body condition score.
The objective of this study was to profile phosphoenolpyruvate carboxykinase (PEPCK) and pyruvate carboxylase (PC) mRNA expression in the liver of dairy cattle during the peripartum transition and determine changes in abundance of these mRNA in response to protein fed during the prepartum period. Thirty-eight multiparous Holstein cows were fed diets containing either 12% crude protein (CP) and 26% rumen undegradable protein (RUP), 16% CP and 26% RUP, 16% CP and 33% RUP, or 16% CP and 40% RUP on a dry-matter basis beginning 28 d before expected calving. After calving, all cows were fed a common diet through 56 d in milk (DIM). Northern analysis of RNA from liver biopsy samples obtained on days -28, -14, +1, +28, and +56 relative to calving indicated that PC and PEPCK mRNA expression were responsive to onset of lactation but not to prepartum protein or RUP concentration. Abundance of PEPCK mRNA was similar at -28, -14, and +1 DIM but was elevated by +28 and +56 DIM relative to precalving levels. Liver PC mRNA abundance was elevated on +1 DIM, remained elevated through 28 DIM, and declined to precalving levels by 56 DIM. The activity of PC enzyme was correlated (r2 = 0.89) with PC mRNA abundance. The data demonstrate increased abundance of PC mRNA during the early transition period followed by increased abundance of PEPCK mRNA during the postpartum period and suggest increased potential metabolism of lactate, pyruvate, and amino acids that contribute to the liver pyruvate pool.
In order to understand the functioning of organisms on the molecular level, we need to know which genes are expressed, when and where in the organism, and to which extent. The regulation of gene expression is achieved through genetic regulatory systems structured by networks of interactions between DNA, RNA, proteins, and small molecules. As most genetic regulatory networks of interest involve many components connected through interlocking positive and negative feedback loops, an intuitive understanding of their dynamics is hard to obtain. As a consequence, formal methods and computer tools for the modeling and simulation of genetic regulatory networks will be indispensable. This paper reviews formalisms that have been employed in mathematical biology and bioinformatics to describe genetic regulatory systems, in particular directed graphs, Bayesian networks, Boolean networks and their generalizations, ordinary and partial differential equations, qualitative differential equations, stochastic equations, and rule-based formalisms. In addition, the paper discusses how these formalisms have been used in the simulation of the behavior of actual regulatory systems.
In Australia, dairy farming is carried out in environments that vary in many ways, including level of feeding and climate variables such as temperature and humidity. The aim of this study was to assess the magnitude of genotype x environment interactions (GxE) on milk production traits (milk yield, protein yield, and fat yield) for a range of environmental descriptors. The environment on individual test days was described by herd size (HS), average herd protein yield (AHTDP), herd test-day coefficient of variation for protein yield (HTDCV), and temperature humidity index (THI). A sire random regression model was used to model the response of a sire's daughters to variation in the environment and to calculate the genetic correlation between the same traits measured in two widely different environments. Using test-day records, rather than average lactation yields, allowed exploitation of within-cow variation as well as between-cow variation at different levels of AHTDP, and led to more accurate estimates of sire breeding values for "response to environment." The greatest GxE observed was due to variation in AHTDP, with a genetic correlation of 0.78 between protein yield when AHTDP = 0.54 kg and protein yield when AHTDP = 1.1 kg (the 5th and 95th percentile of the distribution of AHTDP). The GxE was also observed for THI, with a genetic correlation of 0.90 between protein yield at the 5th and 95th percentile of THI. The use of response to environment estimated breeding values to improve the accuracy of international sire evaluations is discussed.
A breeding goal accounting for the effects of genotype by environment interaction (G x E) has to define not only traits but also the environment in which those traits are to be improved. The aim of this study was to predict the selection response in the coefficients of a linear reaction norm, and response in average phenotypic value in any environment, when mass selection is applied to a trait where G x E is modelled as a linear reaction norm. The optimum environment in which to test the selection candidates for a given breeding objective was derived. Optimisation of the selection environment can be used as a means to either maximise genetic progress in a certain response environment, to keep the change in environmental sensitivity at a desired rate, or to reduce the proportion of animals performing below an acceptance level. The results showed that the optimum selection environment is not always equal to the environment in which the response is to be realised, but depends on the degree of G x E (determined by the ratio of variances in slope and level of a linear reaction norm), the correlation between level and slope, and the heritability of the trait.
Long-term molecular adaptations in liver from high-producing dairy cows are virtually unknown. Liver from five Holstein cows was biopsied at -65, -30, -14, +1, +14, +28, and +49 days relative to parturition for transcript profiling using a microarray consisting of 7,872 annotated cattle cDNA inserts. More than 5,000 cDNA elements represented on the microarray were expressed in liver. From this set we identified 62 differentially expressed genes related to physiological state, with a false discovery rate threshold of P = 0.20. The dominant expression pattern consisted of upregulation from day -30 through day +1, followed by downregulation through day +28. There was a threefold decrease from day -65 through day +14 in expression of IGFBP3, GSTM5, and PDPK1. These genes mediate IGF-I transport, oxidative stress, and glucose homeostasis, respectively. IGFBP3, EIF4B, and GSTM5 mRNA levels were positively correlated with blood serum total protein. Correlation analysis showed positive associations between serum nonesterified fatty acids and mRNA expression for SAA1, CPT1A, ACADVL, and TFAP2A. Transcript levels of ACSL1, PPARA, and TFAP2A were positively correlated with serum beta-hydroxybutyrate. Expression patterns for certain genes (e.g., IGFBP3, HNF4A, GPAM) revealed adaptations commencing well ahead of parturition, suggesting they are regulated by factors other than periparturient hormonal environment. Results provide evidence that hepatic inflammatory responses occurring near parturition initiate or augment adipose catabolism. In this context, cytokines, acute-phase proteins, and serum nonesterified fatty acids are key players in periparturient cow metabolism. We propose a model for integrating gene expression, metabolite, and liver composition data to explain physiological events in placenta, adipose, and liver during the periparturient period.
Blood flow and net nutrient fluxes for portal-drained viscera (PDV) and liver (total splanchnic tissues) were measured at 19 and 9 d prepartum and at 11, 21, 33, and 83 d in milk (DIM) in 5 multiparous Holstein-Friesian cows. Cows were fed a grass silage-based gestation ration initially and a corn silage-based lactation ration peripartum and postpartum. Meals were fed at 8-h intervals and hourly (n = 8) measures of splanchnic metabolism were started before (0730 h and 0830 h) feeding at 0830 h. Dry matter intakes (DMI) at 19 and 9 d prepartum were not different. Metabolism changes measured from 19 to 9 d prepartum were lower arterial insulin and acetate, higher arterial nonesterified fatty acids and increased net liver removal of glycerol. After calving, PDV and liver blood flow and oxygen consumption more than doubled as DMI and milk yield increased, but 85 and 93% of the respective increases in PDV and liver blood flow at 83 DIM had occurred by 11 DIM. Therefore, factors additional to DMI must also contribute to increased blood flow in early lactation. Most postpartum changes in net PDV and liver metabolism could be attributed to increases in DMI and digestion or increased milk yield and tissue energy loss. Glucose release was increasingly greater than calculated requirements as DIM increased, presumably as tissue energy balance increased. Potential contributions of lactate, alanine, and glycerol to liver glucose synthesis were greatest at 11 DIM but decreased by 83 DIM. Excluding alanine, there was no evidence of an increased contribution of amino acids to liver glucose synthesis is required in early lactation. Increased net liver removal of propionate (69%), lactate (20%), alanine (8%), and glycerol (4%) can account for increased liver glucose release in transition cows from 9 d before to 11 d after calving.
Animals can be genotyped for thousands of single nucleotide polymorphisms (SNPs) at one time, where the SNPs are located at roughly 1-cM intervals throughout the genome. For each contiguous pair of SNPs there are four possible haplotypes that could be inherited from the sire. The effects of each interval on a trait can be estimated for all intervals simultaneously in a model where interval effects are random factors. Given the estimated effects of each haplotype for every interval in the genome, and given an animal's genotype, a 'genomic' estimated breeding value is obtained by summing the estimated effects for that genotype. The accuracy of that estimator of breeding values is around 80%. Because the genomic estimated breeding values can be calculated at birth, and because it has a high accuracy, a strategy that utilizes these advantages was compared with a traditional progeny testing strategy under a typical Canadian-like dairy cattle situation. Costs of proving bulls were reduced by 92% and genetic change was increased by a factor of 2. Genome-wide selection may become a popular tool for genetic improvement in livestock.
This study was conducted to investigate individual metabolic and endocrine adaptation to lactation under conditions of identical housing and feeding conditions in high-yielding dairy cows. Forty-five cows were studied on a research farm under standardized but practical conditions. From wk 2 before calving until wk 14 postpartum, blood samples were collected at weekly intervals and assayed for blood chemistry and various metabolites and hormones. Body weight, BCS, and backfat thickness were also recorded weekly. Milk yield, milk composition, and feed intake and energy balance were accordingly measured during the postpartum phase. The animals were retrospectively classified according to their plasma concentration of beta-hydroxybutyrate (BHB): cows in which a BHB threshold of 1 mM was exceeded at least once during the experiment were classified as BHB positive (BHB+); cows with BHB values consistently below this threshold were classified as BHB negative (BHB -). Using this classification, differences for NEFA and glucose concentrations were observed, but the mean calculated energy balance did not differ between the groups during the experimental period (-22.2 MJ of NE(1)/d +/- 4.7 for BHB+ and -18.9 MJ of NE(1)/d +/- 4.9 for BHB-). In BHB+ cows, the peripartum decrease (P < 0.05) of BW, BCS, and backfat thickness was more pronounced than in BHB- cows. Mean milk yields did not differ between groups. However, BHB+ cows had greater milk fat and lesser milk protein contents (P < 0.05), resulting in a greater (P < 0.05) fat:protein ratio than in BHB- cows. Thus, to some extent, cows were able to compensate for the negative energy balance by adjustments in performance. Milk acetone concentrations followed BHB concentrations in blood. Insulin-like growth factor-I and leptin concentrations were greater (P < 0.05) in BHB- cows during the time of observation than in the BHB+ cows. Comparing the reproductive variables recorded (first increase of progesterone, first service conception rate, number of services per conception, interval from calving to first AI, interval from first AI to conception, and days open) between the 2 groups yielded no significant differences. Our findings imply that despite comparable energy balance, there is considerable individual variation of the adaptive ability of cows during early lactation based on a variety of metabolic and endocrine variables.
There are many bootstrap methods that can be used for econometric analysis. In certain circumstances, such as regression models with independent and identically distributed error terms, appropriately chosen bootstrap methods generally work very well. However, there are many other cases, such as regression models with dependent errors, in which bootstrap methods do not always work well. This paper discusses a large number of bootstrap methods that can be useful in econometrics. Applications to hypothesis testing are emphasized, and simulation results are presented for a few illustrative cases. Copyright © 2006 The Economic Society of Australia.
There is on-going concern about the relationship between class size and achievement for children in their first years of schooling. The Institute of Education's class size project was set up to address this issue and began recruiting in the autumn of 1996. However, because of the non-normality of achievement measures, especially in mathematics, the results have hitherto been presented by using transformed achievement measures. This makes the interpretation difficult for non-statisticians. Ideally, the data would be modelled on the original scale and a bootstrap procedure used to ensure that inferences are robust to non-normality. However, the data are multilevel. In the paper we therefore propose a nonparametric residual bootstrap procedure that is suitable for multilevel models, show that it is consistent and present a simulation study which demonstrates its potential to yield substantial reductions in the difference between nominal and actual confidence interval coverage, compared with a parametric bootstrap, when the underlying distribution of the data is non-normal. We then apply our approach to estimate the relationship between class size and achievement for children in their reception year, after adjusting for other possible determinants. Copyright 2003 Royal Statistical Society.