Evaluation of genetic variation in the international Brown Swiss population.

The international Brown Swiss cattle population pedigree was studied to measure genetic variations and to identify the most influential animals. Twenty-two countries provided pedigree information on 71 497 Brown Swiss bulls used for artificial insemination (AI). The total number of animals with the pedigree is 181 094. The mean inbreeding coefficient for the pedigree population was 0.77%. There was, in most cases, an increase in the mean inbreeding coefficient, with the highest value at 2.89% during the last 5-year period (2000 to 2004). The mean average relatedness for the pedigree population was 1.1%. The effective population size in 2004 was 204. There was notable variation between average generation intervals for the four parental pathways. The longest average generation interval, at 8.73 years, was observed in the sire-son pathway. The average generation interval for the whole population was 6.53 years. Most genetically influential individuals were sires. The highest contributing founder was a sire with a 3.22% contribution, and the highest contributing founder dam made a contribution of 1.75%. The effective number of founders and the effective number of ancestors were 141 and 88, respectively. The study showed that genetic variation within the pedigree population has been decreasing over recent years. Increasing the number of AI bulls with a low individual coefficient of inbreeding could help to maintain a good level of genetic variation in the Brown Swiss population.

Heritability of shoulder ulcers and genetic correlations with mean piglet weight and sow body condition.

The objective of this paper was to estimate the heritability for shoulder ulcers and the genetic correlations between shoulder ulcers, mean piglet weight and sow body condition. The analyses were based on information on 5549 Norwegian Landrace sows and their 7614 purebred litters. The genetic analysis was performed using the Gibbs sampling method. Shoulder ulcers were analyzed as a threshold trait. Sow body condition and mean piglet weight were analyzed as linear traits. The heritability of shoulder ulcers was estimated at 0.25 (s.d. = 0.03). The heritability for sow body condition was estimated at 0.14 (s.d. = 0.02) and that for mean piglet weight at 0.23 (s.d. = 0.02). The genetic correlation between shoulder ulcers and sow body condition was negative (-0.59, s.d. = 0.09). The genetic correlation between shoulder ulcers and mean piglet weight was positive (0.23, s.d. = 0.10) and the genetic correlation between sow body condition and mean piglet weight was negative (-0.24, s.d. = 0.10).

Estimation of genetic parameters of feed intake and daily gain in Durocs using data from electronic swine feeders.

Genetic parameters for daily feed intake (DFI, g/day) and daily gain (DG, g/day) were estimated using records of 1916 Duroc boars from electronic feeder stations. Management was limited and resulted in varied ranges of age and weight on test. Boars were housed in 102 pens, each equipped with one feeder, and allowed ad libitum feeding. Weekly averages of DFI and DG were used due to large variation in daily records. Six traits were defined as DFI and DG during 85-106 (period 1), 107-128 (period 2) and 129-150 days of age (period 3). A six-trait model included age as a linear and a quadratic covariate for DFI and a linear covariate for DG with a fixed effect of year-week-pen and random effects of litter, additive genetic animal and permanent environmental animal. Variance components were estimated by a Bayesian approach using Gibbs sampling algorithm. Estimates of heritability for respective periods were 18%, 12% and 10% for DFI and 21%, 11% and 10% for DG. Genetic correlations between DFI and DG in the same period were 0.70, 0.73 and 0.32 for the respective periods. DFI and DG obtained from automatic feeders can be analysed to reveal variation across testing periods by using weekly averages when many monthly averages are incomplete.

Use of serial pig body weights for genetic evaluation of daily gain.

This study examined the utility of serial weights from FIRE (Feed Intake Recording Equipment, Osborne Industries, Inc., Osborne, KS, USA) stations for an analysis of daily gain. Data included 884 132 body weight records from 3888 purebred Duroc pigs. Pigs entered the feeder station at age 77-149 days and left at age 95-184 days. A substantial number of records were abnormal, showing body weight close to 0 or up to twice the average weight. Plots of body weights for some animals indicated two parallel growth curves. Initial editing used a robust regression, which was a two-step procedure. In the first step, a quadratic growth curve was estimated assuming small or 0 weights for points far away from the curve; the process is iterative. In the second step, weights more than 1.5 SD from the estimated growth curve were treated as outliers. The retained body weight records (607,597) were averaged to create average daily weight (170,443) and then used to calculate daily gains (152,636). Additional editing steps included retaining only animals with >or=50 body weight records and SD of the daily gain

Genetic determination of the onset of heat stress on daily milk production in the US Holstein cattle.

Existence of individual variation in the onset of heat stress for daily milk yield of dairy cows was assessed. Data included 353,376 test-day records of 38,383 first-parity Holsteins from a random sample of US herds. Three hierarchical models were investigated. Model 1 inferred the value of a temperature-humidity index (THI) at which mean yield began to decline as well as the extent of that decline. Model 2 assumed individual variation in yield decline beyond a common THI threshold. Model 3 additionally assumed individual variation for the onset of heat stress. Deviance information criteria indicated the superiority of model 3 over model 2. For model 2, genetic correlation between milk yield in the absence of heat stress and the THI threshold for heat stress was -0.4 (0.11) [marginal posterior mean (marginal posterior standard deviation)]. For model 3, genetic correlations were -0.53 (0.05) between milk yield and THI threshold and -0.62 (0.08) between milk yield and yield decay beyond the THI threshold. Total standard deviation (sum of additive genetic and permanent environmental standard deviations) for the THI threshold was 3.95 (0.06), and more than half of that variation had an additive genetic origin [56% (5%)]. Because of the high genetic correlation [0.95 (0.03)] between yield decay and THI threshold with model 3, using only one of them as a selection criterion for heat tolerance would modify the other in the desired direction.

Use of a test day model for dairy goat milk yield across lactations in Germany.

Breeding value prediction for dairy goats in Germany is still based on herd mate comparison within breeding society. The objective of this study was to estimate genetic parameters for milk yield based on a test day model. For the analysis 35,308, 30,551 and 23,640 test day records from lactations 1, 2 and 3 from 5079, 4118 and 3132 animals, respectively, were used. The data between 1987 and 2003 were obtained from six German breeding societies. The multiple trait (lactations 1, 2 and 3) repeatability model (RPT) included the fixed effects of breeding society-breed-herd-year, litter size, lambing season, and days in milk of third-order Legendre polynomials nested within herd-year, and the random effects of animal additive and permanent environment. The three-trait random regression model (RR) also included the random regressions based on second-order Legendre polynomials for animal additive and permanent environmental effects. Heritability estimates in RPT were 0.27 +/- 0.02, 0.20 +/- 0.02 and 0.37 +/- 0.02 for the first, second and third lactation, respectively. The genetic correlation between the first and second lactation was 0.69, between the second and third lactation 0.79, and between the first and third lactation 0.45. Heritability estimates from the RR in the first and second lactations decreased from the beginning to the end of the lactation, with average values of 0.28 and 0.27, respectively. Estimates in the third lactation showed a maximum in the middle of lactation, averaging 0.37. Genetic correlations between the first and second lactation averaged 0.64, between the second and third lactation 0.72, and between the first and third lactation 0.46. Despite the small data set and restricted relationship structure the estimates were reasonable with the exception of estimates from the third lactation, which seemed inflated. RR could be used for genetic evaluation of dairy goats in Germany.

Genetic components of heat stress in finishing pigs: Parameter estimation.

The objective of this study was to describe genetic variability of pig carcass weight as a function of heat stress. Data included carcass weights of 23,556 crossbred pigs [Duroc x (Landrace x Large White)] raised on 2 farms in North Carolina and harvested from May 2005 through December 2006. Weather data were obtained from a weather station located about 20 km from the furthest farm. Weekly heat load was calculated as degrees of average temperature-humidity index (THI) in excess of 18 degrees C. The total heat load (H) was the sum of heat loads for 10 wk before harvest. Variance components were estimated with 3 models: univariate (UNI)-not accounting for heat stress, 2-trait (MT2), and random regression (RR). In all of the models, effects included contemporary group, sex, age at harvest, sire, and litter. In MT2, observations in months in which heat stress was observed ("hot") and not observed ("cold") were treated as separate traits. Heat stress was observed in the months of August to November 2005, as well as July to October 2006. No heat stress was observed in the months of May to July 2005, January to June 2006, and November to December 2006. The RR model added a random regression on heat load for the sire effect. Heat load was adjusted to a scale ranging from 0 (no heat stress) to 5 (greatest heat stress). The heritability estimate +/- SE of carcass weight in UNI was 0.17 +/- 0.01. In MT2, the estimates were 0.14 +/- 0.01 for "cold" and 0.28 +/- 0.01 for "hot"; the genetic correlation between carcass weight in "hot" and "cold" months was 0.42 +/- 0.13. The heritability estimates obtained with RR were 0.20 +/- 0.11, 0.19 +/- 0.15, and 0.51 +/- 0.17 for H = 0, 2.5, and 5, respectively. The genetic correlation between the performance in "cold" months (H = 0), and performance under maximum heat load (H = 5) was 0.02, between H = 0 and intermediate heat load (H = 2.5) was 0.52, and between H = 2.5 and H = 5 was 0.86. Rank correlations between EPD derived from the different models ranged from 0.82 to 0.94 between carcass weights under similar H, 0.18 to 0.54 between carcass weights under high and low H, and 0.66 to 0.91 between carcass weights of intermediate and high/low H. Heritability for growth was greater under heat stress. Selection for crossbred performance would be optimal when data for periods both in the absence and presence of heat stress were considered.

Genetic components of heat stress in finishing pigs: development of a heat load function.

The objective of this study was to quantify the effect of heat stress during the life of a pig on its final weight, as a first step toward a genetic evaluation for heat tolerance. Data included carcass weights of 23,556 crossbred pigs [Duroc x (Landrace x Large White)] raised on 2 farms in North Carolina and slaughtered from May 2005 through December 2006. Weather data were available from a nearby weather station. Lifetime of a pig was assumed to be partitioned into 2 periods. During an initial period, the effect of heat stress was assumed to be negligible or compensated for later. During the second period ending in slaughtering, the ADG was assumed to be affected linearly by heat load. Weekly heat load was calculated as degrees of average temperature-humidity index in excess of a threshold (18 degrees C). The total heat load (H) was the sum of weekly heat loads during the second period. During the months of January to May H was 0; H reached a peak in September. The final BW during the peak of heat stress decreased about 6 kg compared with BW during months of non-heat stress. Weekly and monthly averages of carcass weight generally moved similarly to H. However, there were large fluctuations unrelated to H; the fluctuations were different on the 2 farms. The model included the effects of farm-year of slaughter, sex, age at slaughter, and H, where age at slaughter and H were linear regressions. In analyses, the threshold was varied from 16 to 20 degrees C, and the second period was varied from 8 to 16 wk. The greatest R(2) (10.4%) was at the threshold of temperature-humidity index = 18 degrees C for a period of 10 wk. Varying the threshold and the length of time reduced R(2) less than 1%. Least squares means of year-month and year-week of carcass weight were calculated using a model with the fixed effects farm-year-month or farm-year-week of slaughter, sex, and age at slaughter (linear covariate), and the random effect of birth litter. Changes in BW of finisher pigs due to heat stress can be quantified by H during the last 10 wk of the life of the pig.

Genetic correlations between two strains of Durocs and crossbreds from differing production environments for slaughter traits.

The aim of this study was to estimate the genetic correlations between 2 purebred Duroc pig populations (P1 and P2) and their terminal crossbreds [C1 = P1 x (Landrace x Large White) and C2 = P2 x (Landrace x Large White)] raised in different production environments. The traits analyzed were backfat (BF), muscle depth (MD), BW at slaughter (WGT), and weight per day of age (WDA). Data sets from P1, P2, C1, and C2 included 26,674, 8,266, 16,806, and 12,350 animals, respectively. Two-trait models (nucleus and commercial crossbreds) for each group included fixed (contemporary group, sex, weight, and age), random additive (animal for P1 and P2 and sire for C1 and C2), random litter, and random dam (C1 and C2 only) effects. Heritability estimates (+/-SE) for BF were 0.46 +/- 0.04, 0.38 +/- 0.02, 0.32 +/- 0.02, and 0.33 +/- 0.02 for P1, P2, C1, and C2, respectively. Heritability estimates for MD were 0.31 +/- 0.01, 0.23 +/- 0.02, 0.19 +/- 0.01, and 0.12 +/- 0.01 for P1, P2, C1, and C2, respectively. The estimates for WGT and WDA were 0.31 +/- 0.01, 0.21 +/- 0.02, 0.16 +/- 0.01, and 0.18 +/- 0.01 and 0.32 +/- 0.01, 0.22 +/- 0.02, 0.16 +/- 0.01, and 0.19 +/- 0.01, respectively. Genetic correlations between purebreds and crossbreds for BF were 0.83 +/- 0.09 (P1 x C1) and 0.89 +/- 0.05 (P2 x C2), for MD 0.78 +/- 0.05 (P1 x C1) and 0.80 +/- 0.08 (P2 x C2). For WGT and WDA, the correlations were 0.53 +/- 0.08 (P1 x C1), 0.80 +/- 0.10 (P2 x C2), and 0.60 +/- 0.07 (P1 x C1) and 0.79 +/- 0.09 (P2 x C2), respectively. (Co)variances in crossbreds were adjusted to a live BW scale. Compared with purebreds, the genetic variances in crossbreds were lower, and the residual variances were greater. Sire variances in crossbreds were approximately 20 to 30% of the animal variances in purebreds for BF and MD but were 13 to 25% for WGT and WDA. The efficiency of purebred selection on crossbreds, assessed by EBV prediction weights, ranged from 0.43 to 0.91 for line 1 and 0.70 to 0.92 for line 2. When nucleus and commercial environments differ substantially, the efficiency of selection varies by line and traits, and selection strategies that include crossbred data from typical production environments may therefore be desirable.