Survival analysis of piglet pre-weaning mortality

Survival analysis methodology was applied in order to analyse sources of variation of preweaning survival time and to estimate variance components using data from a crossbred piglets population. A frailty sire model was used with the litter effect treated as an additional random source of variation. All the variables considered had a significant effect on survivability: sex, cross-fostering, parity of the nurse-sow and litter size. The variance estimates of sire and litter were closed to 0.08 and 2 respectively and the heritability of pre-weaning survival was 0.03

Genetic correlations between measures of beef quality traits and their predictions by near-infrared spectroscopy in the Piemontese cattle breed.

The aims of this study were to predict beef quality traits (BQ: colour, shear force, drip and cooking losses) of Piemontese cattle using near-infrared spectroscopy (NIRS) and to estimate genetic parameters for measured BQ and their predictions by NIRS. Heritabilities and genetic correlations for measured BQ and their predictions based on NIRS were estimated through bivariate Bayesian analyses. Heritability estimates for measured BQ were of intermediate magnitude (from 0.10 to 0.63) and similar to those for NIRS predictions. The genetic correlations between BQ measures and their predictions by NIRS were very high for colour traits, high for drip loss, and nil for shear force and cooking loss. NIRS predictions can be proposed as indicator traits in breeding programs for enhancement of colour traits and drip loss

Genetic parameters of beef quality traits for Piemontese cattle

The aim of this study was to estimate heritability of beef quality traits in Piemontese cattle. A total of 804 young bulls, progeny of 109 AI sires, were sampled from 124 fattening farms (FF) and slaughtered in different days at the same commercial abattoir. At slaughter, bulls were 523±73 d old and average carcasses weight (CW) was 417±45 kg. Carcasses were scored for fleshiness (EUS) and fatness. An individual beef sample was collected from Longissimus Thoracis 24 h after slaughter and held refrigerated at 4 °C for 8 d. Measured traits were pH at ageing (pH8d), beef colour (L*, a*, b*, Hue and Chroma), shear force (SF), drip (DL) and cooking loss (CL). A REML linear animal model including the fixed effects of FF, slaughter age and CW class and the random effect of the bull was used. The estimated heritability for EUS, SF, DL, Hue, L* and a* was of intermediate magnitude (from 0.22 to 0.49) whereas heritabilities for all other traits were low (from 0.04 to 0.16)

Prediction of protein composition of individual cow milk using mid-infrared spectroscopy.

This study investigated the application of mid-infrared spectroscopy for the prediction of protein composition in individual milk samples (n=1,336) of Simmental cows. Protein fractions were quantified by RP-HPLC and MIR data were recorded over the spectral range from 4,000 to 900 cm-1. Models were developed by partial least squares regression using untreated spectra. The most successful predictions were for protein, casein, αS1-casein, whey protein, and β-lactoglobulin contents. The models could discriminate between high and low values of protein composition (R2=0.50 to 0.58). The root mean square errors of cross-validation were 3.11g/l for protein (range 39.91g/l), 2.76g/l for casein (range 35.16g/l), 1.07 g/l for αS1-casein (range 12.82g/l), 0.51 g/l for whey protein (range 4.97g/l), and 0.43 for β-lactoglobulin (range 4.37). Application of MIR spectroscopy is possible for the routinely assessment of protein, casein, αS1-casein, whey protein, and β-lactoglobulin and its implementation might be in future a tool for improving protein composition of bovine milk through breeding programs

Genetic parameters of coagulation properties, milk yield, quality, and acidity estimated using coagulating and noncoagulating milk information in Brown Swiss and Holstein-Friesian cows

Abstract The aim of this study was to estimate heritabilities of rennet coagulation time (RCT) and curd firmness (a 30 ) and their genetic correlations with test-day milk yield, composition (fat, protein, and casein content), somatic cell score, and acidity (pH and titratable acidity) using coagulating and noncoagulating (NC) milk information. Data were from 1,025 Holstein-Friesian (HF) and 1,234 Brown Swiss (BS) cows, which were progeny of 54 HF and 58 BS artificial insemination sires, respectively. Milk coagulation properties (MCP) of each cow were measured once using a computerized renneting meter and samples not exhibiting coagulation within 31min after rennet addition were classified as NC milk. For NC samples, RCT was unobserved. Multivariate analyses, using Bayesian methodology, were performed to estimate the genetic relationships of RCT or a 30 with the other traits and statistical inference was based on the marginal posterior distributions of parameters of concern. For analyses involving RCT, a right-censored Gaussian linear model was used and records of NC milk samples, being censored records, were included as unknown parameters in the model implementing a data augmentation procedure. Rennet coagulation time was more heritable [heritability (h 2 )=0.240 and h 2 =0.210 for HF and BS, respectively] than a 30 (h 2 =0.148 and h 2 =0.168 for HF and BS, respectively). Milk coagulation properties were more heritable than a single test-day milk yield (h 2 =0.103 and h 2 =0.097 for HF and BS, respectively) and less heritable than milk composition traits whose heritability ranged from 0.275 to 0.275, with the only exception of fat content of BS milk (h 2 =0.108). A negative genetic correlation, lower than −0.85, was estimated between RCT and a 30 for both breeds. Genetic relationships of MCP with yield and composition were low or moderate and favorable. The genetic correlation of somatic cell score with RCT in BS cows was large and positive and even more positive were those of RCT with pH and titratable acidity in both breeds, ranging from 0.80 to 0.94. Including NC milk information in the data affected the estimated correlations and decreased the uncertainty associated with the estimation process. On the basis of the estimated heritabilities and genetic correlations, enhancement of MCP through selective breeding with no detrimental effects on yield and composition seems feasible in both breeds. Milk acidity may play a role as an indicator trait for indirect enhancement of MCP

Relations between different objective milking speed recording systems

This study aimed to analyse the sources of variation of milking speed assessed through automatic computerised devices included in milking machines, to study the relationships between this trait and milking speed assessed through stopwatch and to develop statistical procedures useful for converting automatic device milking time into stopwatch milking time in order to obtain a fast, simple and cheap collection of milking time records for genetic evaluation purposes. A total of 571 records of stopwatch milking time (SMT), device milking time (DMT) and milk yield at milking were collected in 23 herds of the Trentino Alto Adige region in Italy equipped with two types of automatic milking devices. After log-transformation of SMT (lnSMT) and DMT (lnDMT) and a preliminary analysis of sources of variation of lnDMT, dataset was partitioned into two mutually exclusive subsets: a calibration one, used for statistical analysis, and a validation one, used as test set to validate the prediction models. This procedure was replicated 6 times in order to repeat the cross validation accordingly. Three conversion models have been compared, based on different combinations of the effects of lnDMT, milking device and herd within milking device on lnSMT. Solutions of the models have been applied for each replicate to the validation dataset for estimating lnSMT and the soundness of conversion equations have been evaluated considering the correlation between estimated and actual lnSMT and bias and precision of estimates. Milking time assessed through different procedures resulted in differences between methods for both mean and distribution, and these suggested the need of developing statistical procedures aimed to the conversion of DMT into SMT before their use in sire evaulation. The soundness of the models tended to slightly increase with the increase in the number of effects considered. The correlation between estimated and actual SMT was in the range of 0.80 to 0.86, the estimated bias was close to 0 for all models and the precision, i.e. the average standard deviation of the difference between estimated and actual SMT, in the range of 8-9% of the mean of actual SMT. In conclusion, conversion equations proposed for joining the two sources of information performed satisfactorily, giving rise to SMT accurate estimates, which were not distorted and fairly precise. The use of such equations can support the integration of automatically acquired milking time records into breeding schemes, which is advisable for increasing the number of sires progeny tested and the accuracy of breeding values estimated

Use of linear mixed models for genetic evaluation of gestation length and birth weight allowing for heavy-tailed residual effects

<p>Abstract</p> <p>Background</p> <p>The distribution of residual effects in linear mixed models in animal breeding applications is typically assumed normal, which makes inferences vulnerable to outlier observations. In order to mute the impact of outliers, one option is to fit models with residuals having a heavy-tailed distribution. Here, a Student's-<it>t </it>model was considered for the distribution of the residuals with the degrees of freedom treated as unknown. Bayesian inference was used to investigate a bivariate Student's-<it>t </it>(BS<it>t</it>) model using Markov chain Monte Carlo methods in a simulation study and analysing field data for gestation length and birth weight permitted to study the practical implications of fitting heavy-tailed distributions for residuals in linear mixed models.</p> <p>Methods</p> <p>In the simulation study, bivariate residuals were generated using Student's-<it>t </it>distribution with 4 or 12 degrees of freedom, or a normal distribution. Sire models with bivariate Student's-<it>t </it>or normal residuals were fitted to each simulated dataset using a hierarchical Bayesian approach. For the field data, consisting of gestation length and birth weight records on 7,883 Italian Piemontese cattle, a sire-maternal grandsire model including fixed effects of sex-age of dam and uncorrelated random herd-year-season effects were fitted using a hierarchical Bayesian approach. Residuals were defined to follow bivariate normal or Student's-<it>t </it>distributions with unknown degrees of freedom.</p> <p>Results</p> <p>Posterior mean estimates of degrees of freedom parameters seemed to be accurate and unbiased in the simulation study. Estimates of sire and herd variances were similar, if not identical, across fitted models. In the field data, there was strong support based on predictive log-likelihood values for the Student's-<it>t </it>error model. Most of the posterior density for degrees of freedom was below 4. Posterior means of direct and maternal heritabilities for birth weight were smaller in the Student's-<it>t </it>model than those in the normal model. Re-rankings of sires were observed between heavy-tailed and normal models.</p> <p>Conclusions</p> <p>Reliable estimates of degrees of freedom were obtained in all simulated heavy-tailed and normal datasets. The predictive log-likelihood was able to distinguish the correct model among the models fitted to heavy-tailed datasets. There was no disadvantage of fitting a heavy-tailed model when the true model was normal. Predictive log-likelihood values indicated that heavy-tailed models with low degrees of freedom values fitted gestation length and birth weight data better than a model with normally distributed residuals.</p> <p>Heavy-tailed and normal models resulted in different estimates of direct and maternal heritabilities, and different sire rankings. Heavy-tailed models may be more appropriate for reliable estimation of genetic parameters from field data.</p

Analysis of alanine aminotransferase in various organs of soybean (Glycine max) and in dependence of different nitrogen fertilisers during hypoxic stress

Alanine aminotransferase (AlaAT) catalyses the reversible conversion of pyruvate and glutamate into alanine and oxoglutarate. In soybean, two subclasses were identified, each represented by two highly similar members. To investigate the role of AlaAT during hypoxic stress in soybean, changes in transcript level of both subclasses were analysed together with the enzyme activity and alanine content of the tissue. Moreover, the dependency of AlaAT activity and gene expression was investigated in relation to the source of nitrogen supplied to the plants. Using semi-quantitative PCR, GmAlaAT genes were determined to be highest expressed in roots and nodules. Under normal growth conditions, enzyme activity of AlaAT was detected in all organs tested, with lowest activity in the roots. Upon waterlogging-induced hypoxia, AlaAT activity increased strongly. Concomitantly, alanine accumulated. During re-oxygenation, AlaAT activity remained high, but the transcript level and the alanine content decreased. Our results show a role for AlaAT in the catabolism of alanine during the initial period of re-oxygenation following hypoxia. GmAlaAT also responded to nitrogen availability in the solution during waterlogging. Ammonium as nitrogen source induced both gene expression and enzyme activity of AlaAT more than when nitrate was supplied in the nutrient solution. The work presented here indicates that AlaAT might not only be important during hypoxia, but also during the recovery phase after waterlogging, when oxygen is available to the tissue again