Relationships among mortality, performance, and disorder traits in broiler chickens: a genetic and genomic approach.

Four performance-related traits [growth trait (GROW), feed efficiency trait 1 (FE1) and trait 2 (FE2), and dissection trait (DT)] and 4 categorical traits [mortality (MORT) and 3 disorder traits (DIS1, DIS2, and DIS3)] were analyzed using linear and threshold single- and multi-trait models. Field data included 186,596 records of commercial broilers from Cobb-Vantress, Inc. Average-information restricted maximum likelihood and Gibbs sampling-based methods were used to obtain estimates of the (co)variance components, heritabilities, and genetic correlations in a traditional approach using best linear unbiased prediction (BLUP). The ability to predict future breeding values (measured as realized accuracy) was checked in the last generation when traditional BLUP and single-step genomic BLUP were used. Heritability estimates for GROW, FE1, and FE2 in single- and multi-trait models were similar and moderate (0.22 to 0.26) but high for DT (0.48 to 0.50). For MORT, DIS1, and DIS2, heritabilities were 0.13, 0.24, and 0.34, respectively. Estimates from single- and multi-trait models were also very similar. However, heritability for DIS3 was higher from the single-trait threshold model than for the multi-trait linear-threshold model (0.29 vs. 0.19). Genetic correlations between growth traits and MORT were weak, except for maternal GROW, which had a moderate negative correlation (-0.50) with MORT. The genetic correlation between MORT and DIS1 was strong and positive (0.77). Feed efficiency 1, which was moderately heritable (0.25) and is highly selected for, was not genetically related to MORT of broilers and other disorders. Broiler MORT also had moderate heritability (0.13), which suggests that MORT and FE1 can be improved through selection without negatively impacting other important traits. Selection of heavier maternal GROW also may decrease offspring MORT.

Accuracy of breeding values in small genotyped populations using different sources of external information-A simulation study.

Genetically linked small and large dairy cattle populations were simulated to test the effect of different sources of information from foreign populations on the accuracy of predicting breeding values for young animals in a small population. A large dairy cattle population (PL) with >20 generations was simulated, and a small subpopulation (PS) with 3 generations was formed as a related population, including phenotypes and genomic information. Predicted breeding values for young animals in the small population were calculated using BLUP and single-step genomic BLUP (ssGBLUP) in 4 different scenarios: (S1) 3,166 phenotypes, 22,855 pedigree animals, and 1,000 to 6,000 genotypes for PS; (S2) S1 plus genomic estimated breeding value (GEBV) for 4,475 sires from PL as external information; (S3) S1 plus 221,580 phenotypes, 402,829 pedigree animals, and 53,558 genotypes for PL; and (S4) single nucleotide polymorphism (SNP) effects calculated based on PL data. The ability to predict true breeding value was assessed in the youngest third of the genotyped animals in the small population. When data only from the small population were used and 1,000 animals were genotyped, the accuracy of GEBV was only 1 point greater than the estimated breeding value accuracy (0.32 vs. 0.31). Adding external GEBV for sires from PL did not considerably increase accuracy (0.33 vs. 0.32 in S1). Combining phenotypes, pedigree, and genotypes for PS and PL was beneficial for predicting accuracy of GEBV in the small population, and the prediction accuracy of GEBV in this scenario was 0.38 compared with 0.31 from estimated breeding values. When SNP effects from PL were used to predict GEBV for young genotyped animals from PS, accuracy was greatest (0.56). With 6,000 genotyped animal in PS, accuracy was greatest (0.61) with the combined populations. In a small population with few genotypes, the highest accuracy of evaluation may be obtained by using SNP effects derived from a related large population.

Modeling response to heat stress in pigs from nucleus and commercial farms in different locations in the United States.

The purpose of this study was to analyze the impact of seasonal losses due to heat stress in different environments and genetic group combinations. Data were available for 2 different swine populations: purebred Duroc animals raised in nucleus farms in Texas and North Carolina and crosses of Duroc and F females (Landrace × Large White) raised in commercial farms in Missouri and North Carolina; pedigrees provided links between animals from different states. Traits included BW at harvest age for purebred animals and HCW for crossbred animals. Weather data were collected at airports located close to the farms. Heat stress was quantified by a heat load function, defined by the units of temperature-humidity of temperature-humidity index (THI) greater than a certain threshold for 30 to 70 d before phenotype collection. Heat stress responses were quantified by a linear regression of phenotype on heat load. The greatest coefficient of determination occurred with a length of 30 d before phenotype measurements for all states and genetic groups. In the crossbreed data, THI thresholds were 67 in Missouri and 72 in North Carolina. For pure breeds, heat load had the best fit for THI thresholds greater than 70 in North Carolina, although differences in coefficient of determinations were negligible. On the other hand, no optimal THI threshold existed in Texas. In this study, heat stress had a greater impact in commercial farms than in nucleus farms and the effect of heat stress on weight varied by year and state.

Associations of biochemical changes and maternal traits with mutation 1843 (C>T) in the RYR1 gene as a common cause for porcine stress syndrome.

Stress syndrome is usually caused by a mutation in the ryanodine receptor gene (ryr1) and it is widely studied in humans and swine populations. The protein product of this gene plays a crucial role in the regulation of calcium transport in muscle cells. A G>T mutation in the human ryr1 gene, which results in the replacement of a conserved arginine at position 614 where a leucine occurs at the same position as the previously identified Arg→Cys mutation reported in all cases of porcine stress syndrome (PSS). Porcine stress syndrome affects biochemical pathways in stress-susceptible individuals during a stress episode and some biochemical parameters that were used as markers for diagnostic purposes. Also, PSS has remarkable influence on the maternal characteristics of sows. This study dealt with different genotypes for PSS and its association with possible biochemical changes and maternal traits of sows. Seventy-three reproductive sows genotyped for PSS by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) were included in this survey. Sixty of them were stress-free (NN), 11 were heterozygous carriers (Nn) and two animals were homozygous (nn) for the 1843 (C>T) mutation. Significant differences in non stress induced animals with different PSS genotypes were found in the values of creatine phoshokinase (CPK), lactate dehydrogenase (LDH), alkaline phosphatase (AP) and aspartate aminotransferase (AST). Regarding the maternal traits, our study showed that stress susceptible animals (nn) have an increased number of stillborn piglets and a reduced number of newborn piglets compared with heterozygous and normal animals.

Comparison of random regression and repeatability models to predict breeding values from test-day records of Norwegian goats.

One aim of the research was to challenge a previously selected repeatability model with 2 other repeatability models. The main aim, however, was to evaluate random regression models based on the repeatability model with lowest mean-squared error of prediction, using Legendre polynomials up to third order for both animal additive genetic and permanent environmental effects. The random regression and repeatability models were compared for model fit (using likelihood-ratio testing, Akaike information criterion, and the Bayesian information criterion) and the models' mean-squared errors of prediction, and by cross-validation. Cross-validation was carried out by correlating excluded observations in one data set with the animals' breeding values as predicted from the pedigree only in the remaining data, and vice versa (splitting proportion: 0.492). The data was from primiparous goats in 2 closely tied buck circles (17 flocks) in Norway, with 11,438 records for daily milk yield and 5,686 to 5,896 records for content traits (fat, protein, and lactose percentages). A simple pattern was revealed; for daily milk yield with about 5 records per animal in first lactation, a second-order random regression model should be chosen, whereas for content traits that had only about 3 observations per goat, a first-order polynomial was preferred. The likelihood-ratio test, Akaike information criterion, and mean-squared error of prediction favored more complex models, although the results from the latter and the Bayesian information criterion were in the direction of those obtained with cross-validation. As the correlation from cross-validation was largest with random regression, genetic merit was predicted more accurate with random regression models than with the repeatability model.

Validation of test-day models for genetic evaluation of dairy goats in Norway.

Test-day data for daily milk yield and fat, protein, and lactose content were sampled from the years 1988 to 2003 in 17 flocks belonging to 2 genetically well-tied buck circles. In total, records from 2,111 to 2,215 goats for content traits and 2,371 goats for daily milk yield were included in the analysis, averaging 2.6 and 4.8 observations per goat for the 2 groups of traits, respectively. The data were analyzed by using 4 test-day models with different modeling of fixed effects. Model [0] (the reference model) contained a fixed effect of year-season of kidding with regression on Ali-Schaeffer polynomials nested within the year-season classes, and a random effect of flock test-day. In model [1], the lactation curve effect from model [0] was replaced by a fixed effect of days in milk (in 3-d periods), the same for all year-seasons of kidding. Models [2] and [3] were obtained from model [1] by removing the fixed year-season of kidding effect and considering the flock test-day effect as either fixed or random, respectively. The models were compared by using 2 criteria: mean-squared error of prediction and a test of bias affecting the genetic trend. The first criterion indicated a preference for model [3], whereas the second criterion preferred model [1]. Mean-squared error of prediction is based on model fit, whereas the second criterion tests the ability of the model to produce unbiased genetic evaluation (i.e., its capability of separating environmental and genetic time trends). Thus, a fixed structure with year (year, year-season, or possibly flock-year) was indicated to appropriately separate time trends. Heritability estimates for daily milk yield and milk content were 0.26 and 0.24 to 0.27, respectively.