托佩克(Topigs)种猪日增重与背标厚选择效果分析

文章对托佩克(Topigs)3个品系5年的日增重和背膘厚性状测定数据作选择效果分析.在对两个性状作遗传参数计算的基础上,明确了这两个性状的遗传力为中等以上,3个品系稍有差异(A系日增重和背膘厚遗传力分别为0.68和0.61;B系分别为0.67和0.48,E系分别为0.48和0.38),同一性状存在阶段性的差别.以此为基...     

甘肃黑猪血浆蛋白多态性与初生重、早期日增重关系的研究

采用聚丙烯酰胺凝胶电泳 (PAGE)对 37头甘肃黑猪合成系猪的转铁蛋白 (Tf)、前白蛋白 (Pa)、脂酶 (Es)、淀粉酶 1 (Am1 )、淀粉酶 2(Am2 ) 5个蛋白质基因座的多态性进行了检测。并运用通用线性模型分析了这 5个蛋白质基因座的共 1 7种基因型对初生重、 45日龄内平均日增重的相关效应。结果表明Am1AA型具有显著提高初生重的效应 ,PaAA型具有显著提高 45日龄内平均日增重的效应。因此这 2个位点可望用于标记辅助选择。     

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.     

Estimates of genetic parameters for milkability from automatic milking

Genetic parameters were estimated by restricted maximum likelihood with a multi-trait animal model for three milkability traits with serial data from an automatic milking system from a research farm (401 dairy cows) collected between September 2000 and June 2003 (320834 milkings). Furthermore, daily values for milk yield and milkability were formed from all single milkings resulting in 104 132 records and, subsequently, an estimation of genetic parameters was carried out based on these daily values.The resulting estimated heritability coefficients (based on daily values) are h² = 0.55, h² = 0.55 and h² = 0.39 for average milk flow, maximum milk flow and milking time, respectively. The heritabilities are at a high level and thus breeding for good milkability makes sense. The genetic correlations between the three milkability traits are near unity with r_g = 0.98 between average and maximum milk flow, r_g = − 0.89 between average milk flow and milking time and r_g = − 0.86 between maximum milk flow and milking time. Thus it may be sufficient to record only one of these traits in performance tests. The genetic correlations between milk yield and average milk flow, maximum milk flow and milking time are r_g = 0.51, r_g = 0.44 and r_g = − 0.23, respectively.In future, serial data on milkability, already existing on many farms with automatic milk yield recording, should be used to greater extent for selective breeding with the aim of achieving good milkability.     

Optimal definition of contemporary groups for crossbred pigs in a joint purebred and crossbred genetic evaluation

In the pig industry, purebred animals are raised in nucleus herds and selected to produce crossbred progeny to perform in commercial environments. Crossbred and purebred performances are different, correlated traits. All purebreds in a pen have their performance assessed together at the end of a performance test. However, only selected crossbreds are removed (based on visual inspection) and measured at different times creating many small contemporary groups (CGs). This may reduce estimated breeding value (EBV) prediction accuracies. Considering this sequential recording of crossbreds, the objective was to investigate the impact of different CG definitions on genetic parameters and EBV prediction accuracy for crossbred traits. Growth rate (G_P) and ultrasound backfat (BF_P) records were available for purebreds. Lifetime growth (G_X) and backfat (BF_X) were recorded on crossbreds. Different CGs were tested: CG_all included farm, sex, birth year, and birth week; CG_week added slaughter week; and CG_day used slaughter day instead of week. Data of 124,709 crossbreds were used. The purebred phenotypes (62,274 animals) included three generations of purebred ancestors of these crossbreds and their CG mates. Variance components for four-trait models with different CG definitions were estimated with average information restricted maximum likelihood. Purebred traits’ variance components remained stable across CG definitions and varied slightly for BF_X. Additive genetic variances (and heritabilities) for G_X fluctuated more: 812 ± 36 (0.28 ± 0.01), 257 ± 15 (0.17 ± 0.01), and 204 ± 13 (0.15 ± 0.01) for CG_all, CG_week, and CG_day, respectively. Age at slaughter (AAS) and hot carcass weight (HCW) adjusted for age were investigated as alternatives for G_X. Both have potential for selection but lower heritabilities compared with G_X: 0.21 ± 0.01 (0.18 ± 0.01), 0.16 ± 0.02 (0.16 + 0.01), and 0.10 ± 0.01 (0.14 ± 0.01) for AAS (HCW) using CG_all, CG_week, and CG_day, respectively. The predictive ability, linear regression (LR) accuracy, bias, and dispersion of crossbred traits in crossbreds favored CG_day, but correlations with unadjusted phenotypes favored CG_all. In purebreds, CG_all showed the best LR accuracy, while showing small relative differences in bias and dispersion. Different CG scenarios showed no relevant impact on BF_X EBV. This study shows that different CG definitions may affect evaluation stability and animal ranking. Results suggest that ignoring slaughter dates in CG is more appropriate for estimating crossbred trait EBV for purebred animals.     

Use of robust multivariate linear mixed models for estimation of genetic parameters for carcass traits in beef cattle

Assumptions of normality of residuals for carcass evaluation may make inferences vulnerable to the presence of outliers, but heavy‐tail densities are viable alternatives to normal distributions and provide robustness against unusual or outlying observations when used to model the densities of residual effects. We compare estimates of genetic parameters by fitting multivariate Normal (MN) or heavy‐tail distributions (multivariate Student's t and multivariate Slash, MSt and MS) for residuals in data of hot carcass weight (HCW), longissimus muscle area (REA) and 12th to 13th rib fat (FAT) traits in beef cattle using 2475 records from 2007 to 2008 from a large commercial operation in Nebraska. Model comparisons using deviance information criteria (DIC) favoured MSt over MS and MN models, respectively. The posterior means (and 95% posterior probability intervals, PPI) of v for the MSt and MS models were 5.89 ± 0.90 (4.35, 7.86) and 2.04 ± 0.18 (1.70, 2.41), respectively. Smaller values of posterior densities of v for MSt and MS models confirm that the assumption of normally distributed residuals is not adequate for the analysis of the data set. Posterior mean (PM) and posterior median (PD) estimates of direct genetic variances were variable with MSt having the highest mean value followed by MS and MN, respectively. Posterior inferences on genetic variance were, however, comparable among the models for FAT. Posterior inference on additive heritabilities for HCW, REA and FAT using MN, MSt and MS models indicated similar and moderate heritability comparable with the literature. Posterior means of genetic correlations for carcass traits were variable but positive except for between REA and FAT, which showed an antagonistic relationship. We have demonstrated that genetic evaluation and selection strategies will be sensitive to the assumed model for residuals.     

Estimation of phenotypic and genetic parameters for weight gain and weight at fixed ages in the double-muscled Belgian Blue Beef breed using field records

In the double‐muscled Belgian Blue beef (DM‐BBB) breed, selection focuses on muscular conformation and not on weight gain and higher weight. There are very few studies on growth in the DM‐BBB using field records. Therefore, farms have no available useful figures on weight at fixed ages and weight gain for the DM‐BBB. This study describes and evaluates live weights of DM‐BBB animals. All the data were gathered on farms in Belgium. It was found that a male DM‐BBB weighs an average of 51 kg at birth, 98 kg at 3 months, 242 kg at 7 months, 430 kg at 13 months and 627 kg at 20 months. Between the age of 7 and 20 months, weight gain is more than 1200 g a day. Females weigh 47 kg at birth, 96 kg at 3 months, 189 kg at 7 months and 332 kg at 13 months. For males, estimates of heritability for weights at 7, 13 and 20 months were between 0.21 and 0.36. The heritability for weight gain between 13 and 20 months was 0.13. This demonstrates that it is possible to select for higher weights and for increased growth between 13 and 20 months. Animals having high weights at a young age (7 and 13 months) tend to have also high weight at slaughtering age (20 months; r_g between 0.81 and 0.98), but no additional growth between 13 and 20 months (r_g between −0.09 and 0.00). High weight at 20 months is partially due to growth between 13 and 20 months (r_g = 0.49).     

Simultaneous Bayesian estimation of genetic parameters for curves of weight,feed intake,and residual feed intake in beef cattle

Rates of gain and feed efficiency are important traits in most breeding programs for growing farm animals. The rate of gain (GAIN) is usually expressed over a certain age period and feed efficiency is often expressed as residual feed intake (RFI), defined as observed feed intake (FI) minus expected feed intake based on live weight (WGT) and GAIN. However, the basic traits recorded are always WGT and FI and other traits are derived from these basic records. The aim of this study was to develop a procedure for simultaneous analysis of the basic records and then derive linear traits related to feed efficiency without retorting to any approximation. A bivariate longitudinal random regression model was employed on 13,791 individual longitudinal records of WGT and FI from 2,827 bulls of six different beef breeds tested for their own performance in the period from 7 to 13 mo of age. Genetic and permanent environmental covariance functions for curves of WGT and FI were estimated using Gibbs sampling. Genetic and permanent covariance functions for curves of GAIN were estimated from the first derivative of the function for WGT and finally the covariance functions were extended to curves for RFI, based on the conditional distribution of FI given WGT and GAIN. Furthermore, the covariance functions were extended to include GAIN and RFI defined over different periods of the performance test. These periods included the whole test period as normally used when predicting breeding values for GAIN and RFI for beef bulls. Based on the presented method, breeding values and genetic parameters for derived traits such as GAIN and RFI defined longitudinally or integrated over (parts of) of the test period can be obtained from a joint analysis of the basic records. The resulting covariance functions for WGT, FI, GAIN, and RFI are usually singular but the method presented here does not suffer from the estimation problems associated with defining these traits individually before the genetic analysis. All the results are thus estimated simultaneously, and the set of parameters is consistent.     

Autoregressive and random regression test-day models for multiple lactations in genetic evaluation of Brazilian Holstein cattle

Autoregressive (AR) and random regression (RR) models were fitted to test-day records from the first three lactations of Brazilian Holstein cattle with the objective of comparing their efficiency for national genetic evaluations. The data comprised 4,142,740 records of milk yield (MY) and somatic cell score (SCS) from 274,335 cows belonging to 2,322 herds. Although heritabilities were similar between models and traits, additive genetic variance estimates using AR were 7.0 (MY) and 22.2% (SCS) higher than those obtained from RR model. On the other hand, residual variances were lower in both traits when estimated through AR model. The rank correlation between EBV obtained from AR and RR models was 0.96 and 0.94 (MY) and 0.97 and 0.95 (SCS), respectively, for bulls (with 10 or more daughters) and cows. Estimated annual genetic gains for bulls (cows) obtained using AR were 46.11 (49.50) kg for MY and −0.019 (−0.025) score for SCS; whereas using RR these values were 47.70 (55.56) kg and −0.022 (−0.028) score. Akaike information criterion was lower for AR in both traits. Although AR model is more parsimonious, RR model assumes genetic correlations different from the unity within and across lactations. Thus, when these correlations are relatively high, these models tend to yield to similar predictions; otherwise, they will differ more and RR model would be theoretically sounder.     

Estimates of (co)variance components and genetic parameters for body weights and first greasy fleece weight in Malpura sheep

Estimates of (co)variance components and genetic parameters were calculated for birth weight (BWT), weaning weight (WWT), 6 month weight (6WT), 9 month weight (9WT), 12 month weight (12WT) and greasy fleece weight at first clip (GFW) for Malpura sheep. Data were collected over a period of 23 years (1985–2007) for economic traits of Malpura sheep maintained at the Central Sheep & Wool Research Institute, Avikanagar, Rajasthan, India. Analyses were carried out by restricted maximum likelihood procedures (REML), fitting six animal models with various combinations of direct and maternal effects. Direct heritability estimates for BWT, WWT, 6WT, 9WT, 12WT and GFW from the best model (maternal permanent environmental effect in addition to direct additive effect) were 0.19 ± 0.04, 0.18 ± 0.04, 0.27, 0.15 ± 0.04, 0.11 ± 0.04 and 0.30 ± 0.00, respectively. Maternal effects declined as the age of the animal increased. Maternal permanent environmental effects contributed 20% of the total phenotypic variation for BWT, 5% for WWT and 4% for GFW. A moderate rate of genetic progress seems possible in Malpura sheep flock for body weight traits and fleece weight by mass selection. Direct genetic correlations between body weight traits were positive and ranged from 0.40 between BWT and 6WT to 0.96 between 9WT and 12WT. Genetic correlations of GFW with body weights were 0.06, 0.49, 0.41, 0.19 and 0.15 from birth to 12WT. The moderately positive genetic correlation between 6WT and GFW suggests that genetic gain in the first greasy fleece weight will occur if selection is carried out for higher 6WT.     

Effects of correcting missing daily feed intake values on the genetic parameters and estimated breeding values for feeding traits in pigs

Daily feed intake (DFI) is an important consideration for improving feed efficiency, but measurements using electronic feeder systems contain many missing and incorrect values. Therefore, we evaluated three methods for correcting missing DFI data (quadratic, orthogonal polynomial, and locally weighted (Loess) regression equations) and assessed the effects of these missing values on the genetic parameters and the estimated breeding values (EBV) for feeding traits. DFI records were obtained from 1622 Duroc pigs, comprising 902 individuals without missing DFI and 720 individuals with missing DFI. The Loess equation was the most suitable method for correcting the missing DFI values in 5–50% randomly deleted datasets among the three equations. Both variance components and heritability for the average DFI (ADFI) did not change because of the missing DFI proportion and Loess correction. In terms of rank correlation and information criteria, Loess correction improved the accuracy of EBV for ADFI compared to randomly deleted cases. These findings indicate that the Loess equation is useful for correcting missing DFI values for individual pigs and that the correction of missing DFI values could be effective for the estimation of breeding values and genetic improvement using EBV for feeding traits.     

Epidemiological studies of pig diseases: 1. Use of French protocols for risk factor assessment to predict the health status of Australian herds

Objective To determine in Australian pig herds the accuracy of French protocols for risk factor assessment.
Procedure Data on health indicators and risk factors were collected for three syndromes, 'pre-weaning diarrhoea', 'post-weaning diarrhoea' and 'respiratory problems', using the French protocols. The protocols were used on 118 occasions in 32 Western Australian pig herds during 3 years (1988 to 1991).
Results There was a wide variation in pre-weaning performance, for example growth rate was 107 to 273 g/day (< 200 g/day in 33% of herds). Respiratory lesions at weaning were associated with poor pre-weaning performance. Post-weaning (21 days after weaning) growth rate was 114 to 408 g/day (< 250 g/day in 54% of herds). In the grower herds, 91% of herds had pneumonia, and growth rate was 439 to 625 g/day (< 550 g/day in 54% of herds). Pleurisy as well as pneumonia was associated with reduced growth rate. The risk factor most closely associated with respiratory health status was air volume per pig.
Conclusion Risk factors were most accurate at predicting the health status in post-weaning problems. A weaning weight of at least 7.9 kg and weaning age of 30 days optimised weaner performance. Stocking densities and shed designs providing at least 3 m³ air volume and 0.6 m² floor space per pig throughout the growing phase should be considered for an improved respiratory health status. Australian pig sheds often do not provide a satisfactory environment for optimum health. The technique of risk factor assessment as an aid to the maintenance of health in pig herds is applicable in Australia, but further research is necessary to determine the most important Australian risk factors.     

Implementation of genetic evaluation and mating designs for the endangered local pig breed ‘Bunte Bentheimer’

A pedigree including 1538 individuals of the endangered pig breed ‘Bunte Bentheimer’ and 3008 records of the fertility traits ‘number of piglets born alive’ (NBA) and ‘number of piglets weaned’ (NW) were used to i) characterize the population structure, ii) to estimate genetic (co)variance components and estimated breeding values (EBVs) and iii) to use EBVs for the application of the concept of optimal genetic contributions. The average coefficient of inbreeding increased from F = 0.103 to F = 0.121 within the two recent cohorts. Average rate of inbreeding amounted to 1.66%, which resulted in an effective population size of N_e = 30 animals in the recent cohort. Average generation interval was 3.07 years considering the whole pedigree, and in total, only 612 sows and boars generated offspring. Estimated heritabilities for both traits NBA and NW were 0.12, and the estimated genetic correlation between both traits was 0.96. The variance component due to the service sire was higher than in commercial pig breeds, presumably due to the widespread use of natural service boars. The EBVs for NBA from 333 selection candidates (63 boars and 270 sows) were used to determine optimal genetic contributions. Based on selected animals and their optimal genetic contributions, specific mating designs were evaluated to minimize inbreeding in the next generation. Best results were achieved when using a simulated annealing algorithm and allowing artificial insemination.     

Evaluation of estimated genetic merit for carcass weight in beef cattle: Live weights, feed intake, body measurements, skeletal and muscular scores, and carcass characteristics

Genetic merit for growth rate, expressed as expected progeny difference for carcass weight (EPD_CWT), is available for dairy and beef sires used in Ireland. The once predominantly Friesian (FR) dairy herd has experienced significant introgression of Holstein (HO) genes over the past two decades, and cross-breeding of dairy cows, not required to produce herd replacements, with beef bulls is common. The objective of this study was to compare growth rate, feed intake, live animal measurements and slaughter traits of progeny of Holstein–Friesian dairy cows and bulls of two contrasting maturity beef breeds namely Aberdeen Angus (AA) and Belgian Blue (BB), each selected for either high (H) or low (L) estimated genetic merit for carcass weight. Two dairy strains (FR and HO) were also included giving six genetic groups in total. A total of 170 male progeny from spring-calving cows were artificially reared indoors and subsequently managed together at pasture until the end of their second grazing season when they were assigned to one of two mean slaughter weights (i) 560 kg (Light) or (ii) 620 kg (Heavy). Daily feed intake was recorded during the first winter and during finishing. Body measurements were recorded four times during the animals' life, and linear scoring was carried out at 9 months of age and again at slaughter. Carcasses were graded for conformation and fatness (15 point scale). Slaughter and carcass weights per day of age for AAH, AAL, BBH, BBL, FR and HO were 782, 719, 795, 793, 804 and 783 (SE 12.9) g, and 415, 372, 438, 436, 413 and 401 (SE 5.8) g, respectively. Corresponding values for carcass weight, kill-out proportion, carcass conformation class (15 point scale) and carcass fat class (15 point scale) were 314, 283, 334, 333, 317 and 305 (SE 4.7) kg, 526, 518, 553, 550, 519 and 511 (SE 2.9) g/kg, 6.2, 5.4, 8.0, 7.9, 5.3 and 3.7 (SE 0.26), and 9.8, 9.3, 7.4, 7.2, 9.3 and 8.2 (SE 0.26). There were significant interactions between estimated genetic merit for carcass weight and beef breed with the differences between H and L mainly expressed for AA only. Feed intake differences between H and L animals were negligible and largely attributable to the differences in live weight. Following scaling for live weight, beef breeds of high estimated genetic merit for carcass weight had lower skeletal measurements, indicating greater compactness, with the effect more pronounced in AA. It is concluded that using beef sires of estimated high genetic merit for carcass weight on dairy cows increases growth rate and carcass weight of the progeny but the effect may not be similar for all breeds.