Random Regression Animal Model Research Articles

Genetics of heat tolerance for milk yield and quality in Holsteins

Tropical and sub-tropical climates are characterized by high temperature and humidity, during at least part of the year. Consequently, heat stress is common in Holstein cattle and productive and reproductive losses are frequent. Our objectives were as follows: (1) to quantify losses in production and quality of milk due to heat stress; (2) to estimate genetic correlations within and between milk yield (MY) and milk quality traits; and (3) to evaluate the trends of genetic components of tolerance to heat stress in multiple lactations of Brazilian Holstein cows. Thus, nine analyses using two-trait random regression animal models were carried out to estimate variance components and genetic parameters over temperature–humidity index (THI) values for MY and milk quality traits (three lactations: MY×fat percentage (F%), MY×protein percentage (P%) and MY×somatic cell score (SCS)) of Brazilian Holstein cattle. It was demonstrated that the effects of heat stress can be harmful for traits related to milk production and milk quality of Holstein cattle even though most herds were maintained in a modified environment, for example, with fans and sprinklers. For MY, the effect of heat stress was more detrimental in advanced lactations (−0.22 to −0.52 kg/day per increase of 1 THI unit). In general, the mean heritability estimates were higher for lower THI values and longer days in milk for all traits. In contrast, the heritability estimates for SCS increased with increasing THI values in the second and third lactation. For each trait studied, lower genetic correlations (different from unity) were observed between opposite extremes of THI (THI 47 v. THI 80) and in advanced lactations. The genetic correlations between MY and milk quality trait varied across the THI scale and lactations. The genotype×environment interaction due to heat stress was more important for MY and SCS, particularly in advanced lactations, and can affect the genetic relationship between MY and milk quality traits. Selection for higher MY, F% or P% may result in a poor response of the animals to heat stress, as a genetic antagonism was observed between the general production level and specific ability to respond to heat stress for these traits. Genetic trends confirm the adverse responses in the genetic components of heat stress over the years for milk production and quality. Consequently, the selection of Holstein cattle raised in modified environments in both tropical and sub-tropical regions should take into consideration the genetic variation in heat stress.

Genotype by environment interaction for activity-based estrus traits in relation to production level for Danish Holstein.

The objective of this study was to investigate whether genotype by environment interaction exists for female fertility traits and production of energy-corrected milk at 70d in milk (ECM70). Fertility traits considered were the activity-based estrus traits interval from calving to first high activity (CFHA), duration of high activity episode (DHA), as an indicator for first estrus duration, and strength of high activity episode (SHA), as an indicator for first estrus strength. The physical activity traits were derived from electronic activity tags for 11,522 first-parity cows housed in 125 commercial dairy herds. Data were analyzed using a univariate random regression animal model (URRM), by regressing the phenotypic performance on the average herd ECM70 as an environmental gradient. Furthermore, the genetic correlations between CFHA and ECM70 as a function of production level were estimated using a bivariate random regression animal model (BRRM). For all traits, heterogeneity of additive genetic variances and heritability estimates was observed. The heritability estimate for CFHA decreased from 0.25 to 0.10 with increasing production level and the heritability estimate for ECM70 decreased from 0.35 to 0.15 with increasing production level using URRM. The genetic correlation of the same trait in low and high production levels was around 0.74 for CFHA and 0.80 for ECM70 using URRM, but when data were analyzed using the multiple-trait analysis (MT), genetic correlation estimates between low and high production levels were not significantly different from unity. Furthermore, the genetic correlation of SHA between low and high production level was 0.22 using URRM, but the corresponding correlation estimate had large standard error when data were analyzed using MT. The genetic correlation between CFHA and ECM70 as a function of production environment was weak but unfavorable and decreased slightly from 0.09 to 0.04 with increasing production level using BRRM. Moreover, the same trend was observed when the data were analyzed using MT where the genetic correlation between CFHA and ECM70 in the low production environment was 0.29 compared with -0.13 in the high production environment, but these estimates had large standard errors. In conclusion, regardless of the trait used, in relation to average herd ECM70 production, the results indicated no clear evidence of strong genotype by environment interaction that would cause significant re-ranking of sires between low and high production environments.

Cluster analysis of breeding values for milk yield and lactation persistency in Guzerá cattle

ABSTRACT: The aim of this study was to explore the pattern of genetic lactation curves of Guzerá cattle using cluster analysis. Test-day milk yields of 5,274 first-lactation Guzerá cows were recorded in a progeny test. A total of 34,193 monthly records were analyzed with a random regression animal model using Legendre polynomials to fit additive genetic and permanent environmental random effects and mean trends. Hierarchical and non-hierarchical cluster analyses were performed based on the EBVs for monthly test-day milk yield, peak yield, lactation persistency, and partial cumulative and 305-day yields. The heritability estimates for test-day milk yields ranged from 0.24 to 0.52. Cluster analysis identified animals in the population that belong to different groups according to milk production level and lactation persistency.

Effects of stage of pregnancy on variance components, daily milk yields and 305-day milk yield in Holstein cows, as estimated by using a test-day model

Pregnancy and calving are elements indispensable for dairy production, but the daily milk yield of cows decline as pregnancy progresses, especially during the late stages. Therefore, the effect of stage of pregnancy on daily milk yield must be clarified to accurately estimate the breeding values and lifetime productivity of cows. To improve the genetic evaluation model for daily milk yield and determine the effect of the timing of pregnancy on productivity, we used a test-day model to assess the effects of stage of pregnancy on variance component estimates, daily milk yields and 305-day milk yield during the first three lactations of Holstein cows. Data were 10 646 333 test-day records for the first lactation; 8 222 661 records for the second; and 5 513 039 records for the third. The data were analyzed within each lactation by using three single-trait random regression animal models: one model that did not account for the stage of pregnancy effect and two models that did. The effect of stage of pregnancy on test-day milk yield was included in the model by applying a regression on days pregnant or fitting a separate lactation curve for each days open (days from calving to pregnancy) class (eight levels). Stage of pregnancy did not affect the heritability estimates of daily milk yield, although the additive genetic and permanent environmental variances in late lactation were decreased by accounting for the stage of pregnancy effect. The effects of days pregnant on daily milk yield during late lactation were larger in the second and third lactations than in the first lactation. The rates of reduction of the 305-day milk yield of cows that conceived fewer than 90 days after the second or third calving were significantly (P<0.05) greater than that after the first calving. Therefore, we conclude that differences between the negative effects of early pregnancy in the first, compared with later, lactations should be included when determining the optimal number of days open to maximize lifetime productivity in dairy cows.

Short communication: Genetic correlation and heritability of milk coagulation traits within and across lactations in Holstein cows using multiple-lactation random regression animal models

Genetic parameters of milk rennet coagulation time (RCT) and curd firmness (a30) among the first 3 lactations in Holstein cows were estimated. The data set included 39,960 test-day records from 5,216 Estonian Holstein cows (the progeny of 306 sires), which were recorded from April 2005 to May 2010 in 98 herds across the country. A multiple-lactation random regression animal model was used. Individual milk samples from each cow were collected during routine milk recording. These samples were analyzed for milk composition and coagulation traits with intervals of 2 to 3 mo in each lactation (7 to 305 DIM) and from first to third lactation. Mean heritabilities were 0.36, 0.32, and 0.28 for log-transformed RCT [ln(RCT)] and 0.47, 0.40, and 0.62 for a30 for parities 1, 2, and 3, respectively. Mean repeatabilities for ln(RCT) were 0.53, 0.55, and 0.56, but 0.59, 0.61, and 0.68 for a30 for parities 1, 2 and 3, respectively. Mean genetic correlations between ln(RCT) and a30 were −0.19, −0.14, and 0.02 for parities 1, 2, and 3, respectively. Mean genetic correlations were 0.91, 0.79, and 0.99 for ln(RCT), and 0.95, 0.94, and 0.94 for a30 between parities 1 and 2, 1 and 3, and 2 and 3, respectively. Due to these high genetic correlations, we concluded that for a proper genetic evaluation of milk coagulation properties it is sufficient to record RCT and a30 only in the first lactation.

Estimation of dominance variance for live body weight in a crossbred population of pigs.

The objective of this study was to estimate the dominance variance for repeated live BW records in a crossbred population of pigs. Data were provided by the Walloon Pig Breeding Association and included 22,197 BW records of 2,999 crossbred Piétrain × Landrace K+ pigs from 50 to 210 d of age. The BW records were standardized and adjusted to 210 d of age for analysis. Three single-trait random regression animal models were used: Model 1 without parental subclass effect, Model 2 with parental subclasses considered unrelated, and Model 3 with the complete parental dominance relationship matrix. Each model included sex, contemporary group, and heterosis as fixed effects as well as additive genetic, permanent environment, and residual as random effects. Variance components and their SE were estimated using a Gibbs sampling algorithm. Heritability tended to increase with age: from 0.50 to 0.64 for Model 1, from 0.19 to 0.42 for Model 2, and from 0.31 to 0.53 for Model 3. Permanent environmental variance tended to decrease with age and accounted for 29 to 44% of total variance for Model 1, 29 to 37% of total variance for Model 2, and 34 to 51% of total variance for Model 3. Residual variance explained <10% of total variance for the 3 models. Dominance variance was computed as 4 times the estimated parental subclass variance. Dominance variance accounted for 22 to 40% of total variance for Model 2 and 5 to 11% of total variance for Model 3, with a decrease with age for both models. Results showed that dominance effects exist for growth traits in pigs and may be reasonably large. The use of the complete dominance relationship matrix may improve the estimation of additive genetic variances and breeding values. Moreover, a dominance effect could be especially useful in selection programs for individual matings through the use of specific combining ability to maximize growth potential of crossbred progeny.

Estimation of genetic parameters for milk yield across lactations in mixed-breed dairy goats

Currently, breeding values for dairy goats in the United Kingdom are not estimated and selection is based only on phenotypes. Several studies from other countries have applied various methodologies to estimate breeding values for milk yield in dairy goats. However, most of the previous analyses were based on relatively small data sets, which might have affected the accuracy of the parameter estimates. The objective of this study was to estimate genetic parameters for milk yield in crossbred dairy goats in lactations 1 to 4. The research was based on data provided by 2 commercial goat farms in the United Kingdom comprising 390,482 milk yield records on 13,591 dairy goats kidding between 1987 and 2012. The population was created by crossing 3 breeds: Alpine, Saanen, and Toggenburg. In each generation, the best-performing animals were selected for breeding and, as a result, a synthetic breed was created. The pedigree file contained 28,184 individuals, of which 2,414 were founders. The data set contained test-day records of milk yield, lactation number, farm, age at kidding, and year and season of kidding. Data on milk composition was unavailable. Covariance components were estimated with the average information REML algorithm in the ASReml package (VSN International Ltd., Hemel Hempstead, UK). A random regression animal model for milk yield with fixed effects of herd test day, year-season, and age at kidding was used. Heritability was the highest at 200 and 250d in milk (DIM), reaching 0.45 in the first lactation and between 0.34 and 0.25 in subsequent lactations. After 300 DIM, the heritability started decreasing to 0.23 and 0.10 at 400 DIM in the first and subsequent lactations, respectively. Genetic correlation between milk yield in the first and subsequent lactations was between 0.16 and 0.88. This study found that milk yields in first and subsequent lactations are highly correlated, both at the genetic and phenotypic level. Estimates of heritability for milk yield were higher than most of the values reported in the literature, although they were in the range reported in this species. This should facilitate genetic improvement for the population studied as part of a broader multi-trait breeding program.

Application of random regression models to infer the genetic background and phenotypic trajectory of binary conception rate by alterations of temperature × humidity indices

Data for conception rate (CR) and somatic cell score (SCS) from 2055, 1344 and 2212 Holstein cows in first parity from the three production systems “indoor”, a continental pasture based system “pasture” and pasture based systems located at the coast “p_coast”, respectively, were used to identify heat stress thresholds, and to study genetic parameters in the course of THI (=temperature × humidity index). The production systems were defined within the federal state of Lower Saxony, Germany, which is characterized by temperate climate conditions. First and second inseminations were considered, resulting in 2959 observations in the production system “indoor”, 2123 observations in the production system “pasture”, and 3249 observations in the production system “p_coast”. For longitudinal data analyses, random regression sire models with threshold methodology for CR were applied. In all production systems, a THI of 65 was identified as a heat stress threshold for CR, because for THI>65, a substantial decline in CR was observed. Decline in CR was most pronounced in high yielding cows kept in the pasture based system. THI measurements from the day of insemination were more relevant compared to more distant THI measurements (recorded either before and after the day of insemination). Heritabilities for the functional traits CR and SCS were as low as expected (h2<0.10) for THI values in the range from THI=35 to THI=65. Heritabilities and additive-genetic variances for both traits increased under heat stress as well as under cold stress conditions. Therefore, results for genetic analyses confirmed a more pronounced genetic differentiation for functional traits in harsher environments. Genetic correlations between CR and SCS were consistently close to zero or negative in the continental pasture-based production system and in the production systems “indoor” and “p_coast” for THI=50 and higher, and indicating the detrimental impact of increasing SCS on CR on the genetic scale. Finally and from a statistical perspective, random regression animal models including threshold methodology can be applied to study genetic effects on binary traits in the course of a temperature x humidity dependent covariate. Nevertheless, some extreme values for genetic parameters at the extreme end of the THI scale might be attributed to a limited number of observations.

Longitudinal analysis of the effects of IGF1-SnaBI genotypes on the growth curve of Angus bull calves

The Insulin Growth Factor 1 (IGF1) has been proposed as a candidate gene for growth related traits as it plays a central role in growth and development of mammals. A relationship between serum IGF1 concentration and different growth traits in cattle has been documented. Furthermore, the IGF1-SnaBI SNP in the gene promoter region influences gene expression and IGF1 blood level. The current research was conducted to perform a genetic analysis of longitudinal data for growth curves through the evaluation of the influence of IGF1-SnaBI genotypes on the growth curve of beef cattle during highly anabolic stages. Data were taken on 275 Angus bull calves on two consecutive years, in two commercial cow–calf operations. Calves were weighed at birth, at weaning, and at 3 to 4 times before the year of age. A random regression animal model was employed for the analysis. Fixed effects were age of dam, herd-year, and age of calf (linear and quadratic terms nested within IGF1-SnaBI genotype). Random effects were age at measure (linear and a quadratic terms nested within animal). The analysis showed significant differences (P<0.01) in the growth curves at ages in between 66 and 291 days for the IGF1-SnaBI genotypes. At 210 days of age, the effect of substituting a T by a C was estimated to be 5.21kg. These results suggest that the effect of IGF1-SnaBI over growth in cattle take place mostly before puberty.

Development of a genetic evaluation for body condition score for Canadian Holsteins

Valacta (Sainte-Anne-de-Bellevue, Québec, Canada) is the Canadian Dairy Herd Improvement organization responsible for milk recording in Québec and Atlantic provinces. Up to 14 first-lactation body condition score (BCS) records were collected per cow (average of 2.5 records per cow), allowing the trait to be described by a random regression animal model so that animals could be ranked by the shape of their BCS curve. However, Valacta’s BCS are available from Québec herds only and the long-term objective of this research is to develop a nationwide genetic evaluation of sires and cows for BCS. Alternatively, Holstein Canada (Brantford, Ontario, Canada) collects type trait records nationwide, primarily for first-lactation cows. Holstein Canada typically collects a single record per trait, so that selection for Holstein Canada BCS would be based on overall BCS level rather than the shape of the BCS curve. Several different methods of genetically evaluating Valacta’s BCS were investigated, including consideration of average BCS level across lactation, the amount of fluctuation in the BCS curve during lactation, and combinations of BCS level and BCS fluctuation. Sires with ≥25 daughters were compared (as opposed to comparing cows) because their BCS estimated breeding values (EBV) are based on more information, and so should be more reliable. Of the different methods of calculating Valacta BCS EBV, ranking bulls based on overall BCS level gave the best results in that their daughter phenotypic BCS curves showed limited loss in early lactation BCS and replenished condition by the end of lactation. Whereas Valacta’s BCS were analyzed using a random regression animal model, Holstein Canada only needs to collect 1 BCS record per cow at classification and the resulting BCS EBV was strongly correlated with Valacta’s BCS EBV. Furthermore, because Holstein Canada’s BCS are collected nationally and Valacta’s BCS are not, a national genetic evaluation for Holstein Canada’s BCS is more convenient. The results of this study do not eliminate the possibility of a genetic evaluation of BCS as a longitudinal trait, but indicate that other methods of calculating Valacta BCS EBV should be explored. Until that time, genetically evaluating Holstein Canada’s BCS is simple, easily implemented, and may be effective in altering the level and shape of the genetic BCS curve.

Genetic correlations between milk production traits and somatic cell scores on test day within and across first and second lactations in Holstein cows

The genetic correlations between milk production traits (daily milk yield, 305-day milk yield, and lactation persistency as the difference between milk yields at days 240 and 60) and daily somatic cell score (SCS) within and across first and second lactations in Holstein cows were estimated by using a multiple trait test day (TD) animal model. The data set consisted of 200,095 TD milk and SCS records from 21,238 cows in their first lactations and 143,051 records from 15,281 cows in their second. Data were analyzed by using a two-trait, two-lactation random regression animal model. Genetic correlations between daily SCS and milk production traits were estimated from additive genetic variance component estimates of random regression coefficients. Genetic correlations between daily milk yield in first lactation and daily SCS in both lactations were positive and peaked in early lactation stage. In contrast, the genetic correlations between persistency and daily SCS in both lactations were negative, except in very early lactation stage. These correlations suggested that focusing selection on increasing milk yield early in the first lactation would likely increase SCS, whereas selecting for increased persistency would decrease SCS during both lactations. The genetic correlations between 305-day milk yield and persistency were positive within and across the first and second lactations. These results suggested that selection for lactation persistency could help to increase lactation yield overall in the first and second lactations without increasing SCS.

Random Regression Animal Model for Genetic Evaluation of Milking Duration in Holstein Friesian Cows

Random Regression Animal Model for Genetic Evaluation of Milking Duration in Holstein Friesian Cows

Genetic variability of the mid-infrared prediction of lactoferrin content in milk for Walloon Holstein first-parity cows

The objective of this study was to assess the genetic variability of the mid-infrared prediction of lactoferrin content in milk (pLF) in Holstein first-parity cows. Variance components were estimated by Average Information Restricted Maximum Likelihood using a single-trait test-day random regression animal model. The dataset included 395,287 test-day records from 67,178 cows in 1190 herds from the Walloon Region of Belgium. Average pLF was 164.89mg/L and the standard deviation was 76.07mg/L. Frequency distribution for pLF was slightly asymmetrical, and pLF seemed to increase almost linearly all along the first lactation after a sharp decrease in early lactation. Genetic variance of pLF increased with days in milk within lactation while the permanent environmental variance was the highest in early lactation, then decreased to become lower than genetic variance at 50 days in milk, and finally increased in the last lactation stages. The pLF was a moderately heritable trait. Daily heritability of pLF was the lowest at 5 days in milk (0.19), then increased to reach a maximum at 260 days in milk (0.44), and finally decreased for the last stages of lactation (0.35 at 365 days in milk). Results from this study indicated that pLF is variable and heritable over the lactation and therefore it could be changed by genetic selection.

Relationship between body condition score and health traits in first-lactation Canadian Holsteins

The objective of this research was to estimate daily genetic correlations between longitudinal body condition score (BCS) and health traits by using a random regression animal model in first-lactation Holsteins. The use of indicator traits may increase the rate of genetic progress for functional traits relative to direct selection for functional traits. Indicator traits of interest are those that are easier to record, can be measured early in life, and are strongly genetically correlated with the functional trait of interest. Several BCS records were available per cow, and only 1 record per health trait (1=affected; 0=not affected) was permitted per cow over the lactation. Two bivariate analyses were performed, the first between BCS and mastitis and the second between BCS and metabolic disease (displaced abomasum, milk fever, and ketosis). For the first analysis, 217 complete herds were analyzed, which included 28,394 BCS records for 10,715 cows and 6,816 mastitis records for 6,816 cows. For the second analysis, 350 complete herds were analyzed, which included 42,167 BCS records for 16,534 cows and 13,455 metabolic disease records for 13,455 cows. Estimation of variance components by a Bayesian approach via Gibbs sampling was performed using 400,000 samples after a burn-in of 150,000 samples. The average daily heritability (posterior standard deviation) of BCS was 0.260 (0.026) and the heritabilities of mastitis and metabolic disease were 0.020 (0.007) and 0.041 (0.012), respectively. Heritability estimates were similar to literature values. The average daily genetic correlation between BCS and mastitis was −0.730 (0.110). Cows with a low BCS during the lactation are more susceptible to mastitis, and mastitic cows are likely to have low BCS. Daily estimates of genetic correlations between BCS and mastitis were moderate to strong throughout the lactation, becoming stronger as the lactation progressed. The average daily genetic correlation between BCS and metabolic disease was −0.438 (0.125), and was consistent throughout the lactation. A lower BCS during the lactation is genetically associated with the occurrence of mastitis and metabolic disease.

Composite β-κ-casein genotypes and their effect on composition and coagulation of milk from Estonian Holstein cows

The objective of this study was to estimate the effect of composite β-κ-CN genotypes on milk coagulation and composition traits, and on the additive genetic variation of these traits in Estonian Holstein dairy cattle. A total of 23,970 milk samples, repeated measurements from the first to third lactation from 2,859 Estonian Holstein cows from 78 herds across the country, were analyzed for milk yield, milk fat and protein percentages, somatic cell count, and milk coagulation properties (milk coagulation time and curd firmness). Each cow had at least 3 measurements per lactation. Two single-trait random regression animal models were fitted for the traits studied. The first model considered fixed effects of year-season of sampling and year-season of calving, calving age (nested within lactation), sample age (only for milk coagulation traits) and days in milk, and random herd, additive genetic, and permanent environmental effects. The animal and permanent environmental effects were modeled over the lactation period by using Legendre polynomials. The second model had the additional fixed β-κ-casein effect in the form of a third-order Legendre polynomial. The 2 most frequent β-κ-casein composite genotypes were A2A2AA and A1A2AA, both with prevalence greater than 20%. Percentages of the remaining 31 genotypes were less than 8%, including 20 genotypes with percentages less than 1%. The β-κ-casein genotype-specific lactation curves were significantly different for milk coagulation traits and milk protein percentage. The B variant of κ-casein showed a favorable effect on both milk coagulation traits, whereas the IB haplotype had an increasing effect on curd firmness and protein percentage. Inclusion of the β-κ-casein genotype effects in the model resulted in decreases in the mean additive genetic variations for milk coagulation time and curd firmness of 12.9 and 51.1%, respectively.

Persistency of lactation using random regression models and different fixed regression modeling approaches

Milk yield test-day records on the first three lactations of 25,500 Holstein cows were used to estimate genetic parameters and predict breeding values for nine measures of persistency and 305-d milk yield in a random regression animal model using two criteria to define the fixed regression. Legendre polynomials of fourth and fifth orders were used to model the fixed and random regressions of lactation curves. The fixed regressions were adjusted for average milk yield on populations (single) or subpopulations (multiple) formed by cows that calved at the same age and in the same season. Akaike Information (AIC) and Bayesian Information (BIC) criteria indicated that models with multiple regression lactation curves had the best fit to test-day milk records of first lactations, while models with a single regression curve had the best fit for the second and third lactations. Heritability and genetic correlation estimates between persistency and milk yield differed significantly depending on the lactation order and the measures of persistency used. These parameters did not differ significantly depending on the criteria used for defining the fixed regressions for lactation curves. In general, the heritability estimates were higher for first (0.07 to 0.43), followed by the second (0.08 to 0.21) and third (0.04 to 0.10) lactation. The rank of sires resulting from the processes of genetic evaluation for milk yield or persistency using random regression models differed according to the criteria used for determining the fixed regression of lactation curve.

Genetic parameters for gaussian and categorical traits in organic and low input dairy cattle herds based on random regression methodology

Organic and low input farming differ substantially from conventional farming, suggesting the need for separate breeding programs. This requires knowledge of (co)variance components of important traits in low input or organic production systems. Test-day data for production and data for reproduction traits from 1283 Brown Swiss cows kept in 54 small, low input farms across Switzerland were available. Production traits milk yield (MY), fat percentage (Fat%), protein percentage (Pro%), lactose percentage (Lac%), somatic cell score (SCS), and milk urea nitrogen (MUN), were analyzed with a multi-trait random regression animal model with days in milk (DIM) as a time covariate. Female fertility traits of number of inseminations (NI), stillbirth (SB), calving ease (CE), calving to first service (CTFS), days open (DO), and gestation length (GL) were analyzed with parity as a time covariate, with threshold methodology applied for the first three traits. A threshold-linear sire model was applied to estimate daily correlations between MY, Fat%, Pro%, SCS, MUN and the binary distributed fertility trait conception rate (CR). In general, daily heritabilities for production traits followed the pattern as found for high input production systems. Expected genetic antagonisms were found between MY and Pro%, and between MY and Fat% for all DIM. An antagonistic relationship between MY and SCS was only found directly after calving in parity 1. In parities 2 to 7, heritabilities for an interval trait describing the cows' ability to recover after calving, e.g. CTFS, were lower than estimates for traits associated with a successful insemination, e.g. NI and DO. Pronounced antagonistic relationships between MY and CR were in the range of −0.40 to −0.80 from DIM 20 to DIM 200. In this study, we showed the variety and flexibility of random regression methodology which can be applied to data from small herds, and for a limited number of repeated measurements of a categorical trait per cow. Estimated genetic parameters for reproduction traits were partly different from those estimated in high input production systems. In particular, these differences underline the necessity to implement an own organic breeding program using estimates from the current study which are based on data obtained only from cows in organic or low input herds.

Genetics of Ascites Resistance and Tolerance in Chicken: A Random Regression Approach

Resistance and tolerance are two complementary mechanisms to reduce the detrimental effects of parasites, pathogens, and production diseases on host performance. Using body weight and ascites data on domesticated chicken Gallus gallus domesticus, we demonstrate the use of random regression animal model and covariance functions to estimate genetic parameters for ascites resistance and tolerance and illustrate the way individual variation in resistance and tolerance induce both genotype re-ranking and changes in variation of host performance along increasing ascites severity. Tolerance to ascites displayed significant genetic variance, with the estimated breeding values of tolerance slope ranging from strongly negative (very sensitive genotype) to weakly negative (less sensitive). Resistance to ascites had heritability of 0.34. Both traits are hence expected to respond to selection. The two complementary defense strategies, tolerance and resistance, were genetically independent. Ascites induced changes to the correlations between ascites resistance and body weight, with the genetic correlations being weak when birds were ascites-free but moderately negative when both healthy and affected birds were present. This likely results because ascites reduces growth, and thus high ascites incidence is genetically related to low adult body weight. Although ascites induced elevated phenotypic and genetic variances in body weight of affected birds, heritability displayed negligible changes across healthy and affected birds. Ascites induced moderate genotype re-ranking in body weight, with the genetic correlation of healthy birds with mildly affected birds being unity but with severely affected birds 0.45. This study demonstrates a novel approach for exploring genetics of defense traits and their impact on genotype-by-environment interactions.

Whole‐genome association study for energy balance and fat/protein ratio in German Holstein bull dams

The metabolic status of cows is important to health and fertility, especially in early lactation, and energy balance (EB) and fat/protein ratio (FPR) are considered as appropriate indicators for metabolic disorders. The aim of this study was to detect SNPs (single nucleotide polymorphisms) associated with EB and FPR in German Holstein bull dams belonging to the research herd Karkendamm. Bull dams were genotyped using the Illumina Bovine SNP 50K Bead chip(®) comprising 54 001 SNPs. A total of 43 593 SNPs and 586 (EB) and 668 (FPR) bull dams passed the quality control criteria. Phenotypes were deregressed breeding values estimated via random regression animal models for lactation days 11, 20, 30, and 42 for EB and FPR. Whole-genome association analyses were carried out fitting principal components as covariates to adjust for genetic substructure. Permutation tests were applied to estimate genome-wise significance. Across all observed lactation days, 19 SNPs located in four different intervals on chromosomes 1, 14, 16, and 27 were detected. For EB, seven markers across four chromosomes were identified. There was no overlap between markers associated with FPR and EB. SNPs associated with FPR were mostly located in QTL regions for milk production traits, especially in the region of DGAT1, whereas SNPs associated with EB mainly showed positional relationships to previously described QTL regions affecting functional traits, especially fertility.

Genetic and environmental relationships between body condition score and milk production traits in Canadian Holsteins

The objective of this research was to estimate genetic parameters of first-lactation body condition score (BCS), milk yield, fat percentage (Fat%), protein percentage (Prot%), somatic cell score (SCS), milk urea nitrogen (MUN), lactose percentage (Lact%), and fat to protein ratio (F:P) using multiple-trait random regression animal models. Changes in covariances between BCS and milk production traits on a daily basis have not been investigated before and could be useful for determining which BCS estimated breeding values (EBV) might be practical for selection in the future. Field staff from Valacta milk recording agency (Sainte-Anne-de-Bellevue, QC, Canada) collected BCS from Québec herds several times per cow throughout the lactation. Average daily heritabilities and genetic correlations among the various traits were similar to literature values. On an average daily basis, BCS was genetically unfavorably correlated with milk yield (i.e., increased milk yield was associated with lower body condition). The unfavorable genetic correlation between BCS and milk yield became stronger as lactation progressed, but was equivalent to zero for the first month of lactation. Favorable genetic correlations were found between BCS with Prot%, SCS, and Lact% (i.e., greater BCS was associated with greater Prot%, lower SCS, and greater Lact%). These correlations were strongest in early lactation. On an average daily basis, BCS was not genetically correlated with Fat% or MUN, but was negatively correlated with F:P. Furthermore, BCS at 5 and 50 d in milk (DIM) had the most favorable genetic correlations with milk production traits over the lactation (at 5, 50, 150, and 250 DIM). Thus, early lactation BCS EBV shows potential for selection. Regardless, this study showed that the level of association BCS has with milk production traits is not constant over the lactation. Simultaneous selection for both BCS and milk production traits should be considered, mainly due to the unfavorable genetic correlation between BCS with milk yield.