Should performance at different race lengths be treated as genetically distinct traits in Coldblooded trotters?

Speed, in the form of racing time per kilometre (km), is a performance trait of the Swedish-Norwegian Coldblooded trotter included in the joint Swedish-Norwegian genetic evaluation. A few popular stallions have dominated Coldblooded trotter breeding, which has led to an increasing average relationship between individuals in the population. This study investigated the scope for broadening the breeding goal by selecting for racing time per km over different race lengths (short: 1640 m, medium: 2140 m and long: 2640 m), as this could encourage the use of breeding sires that are less related to the population. Performance data on three- to 12-year-old Coldblooded trotters in all Swedish races run 1995-2021 were obtained from the Swedish Trotting Association. These data consisted of 46,356 observations for 8375 horses in short-distance races, 430,512 observations for 11,193 horses in medium-distance races and 11,006 observations for 3341 horses in long-distance races. Variance components and genetic correlations were calculated using a trivariate animal model with Gibbs sampling from the BLUPF90 suite of programs. Breeding values for the three traits were then estimated using univariate animal models with the same fixed and random effects as in the trivariate model. Heritability estimates of 0.27-0.28 and genetic correlations between racing time per km at the different distances of 0.97-0.99 were obtained. Despite the strong genetic correlation between the traits, there was some re-ranking among the top 10 and top 30 stallions based on distance-specific breeding values. Estimated rank correlation between breeding values for racing time per km in short- and medium-distance races was 0.86, while between short- and long-distance races and between medium- and long-distance races it was 0.61. Mean relationship within the top 10 and top 30 stallions based on breeding values for racing time per km at each distance was 0.31-0.33 and 0.23-0.24 while mean relationship to the rest of the population ranged from 0.17 to 0.18 for all groups, although the 10 and 30 top-ranking stallions differed somewhat in the traits. Estimated average increase in inbreeding was 0.1% per year of birth and 1.2% per generation. The strong genetic correlation between racing time per km at different distances did not support their use as genetically distinct traits. Re-ranking of stallions for racing time per km at different race lengths could favour the use of a larger number of stallions in breeding, but according to our results it would not promote the use of stallions that are less related to the total population. Other traits like longevity or health traits, for example, career length and orthopaedic status, may be more relevant in broadening the breeding goal and preventing a few sires dominating future breeding, and this would be interesting to study further.

Genetic-parameter estimation of milk yield in White Maritza sheep breed using different test day models

The aims of this study were to estimate the genetic parameters of the test day milk yield (TDMY) of the White Maritza sheep breed population and to choose the most appropriate linear models for genetic-parameter estimation of test day milk yield. The White Maritza sheep breed is a multipurpose native sheep breed in Bulgaria. Test day milk yield data were collected from 1992 to 2015 (24 years). Milk yield recordings were made in 18 flocks according to the AC method (official milk recording by ICAR regulations). The database includes 8768 test day milk yield records belonging to 987 ewes. The pedigree file includes 1937 animals. Nine test day models (TDMs) were formulated and tested for the estimation of the genetic parameters of milk yield. The first three models were repeatability models (REP models), the second three were random regression models (RRMs), and the last three models were also random regression models with an added Ali and Schaeffer regression to describe the lactation curve using first-, second- and third-order polynomials. The average TDMY was 764.47 mL. There were no significant differences in the values of heritability (h 2 ) calculated by the three REP models: REP1 0.355 ±  0.060, REP2 0.344 ±  0.047 and REP3 0.347 ±  0.060. The same applied to the repeatability coefficients, which, for the three REP models, were 0.384 ±  0.065, 0.376 ±  0.051 and 0.378 ±  0.065, respectively. Based on REP model 1, three models with random regression RRM1, RRM2 and RRM3 were constructed, which is associated with the use of first-, second- and third-order polynomials (for the random effects of both the animal and the permanent environment). The trajectories of h 2 calculated by the three RRMs were not similar and demonstrated some differences, both at the beginning and in the middle of the milking period. The RRM with third-order polynomials demonstrated more genetic diversity until the 165th day of lactation, but Akaike information criterion (AIC), Bayesian information criterion (BIC) and log-likelihood (LogL) estimates were higher. The regression models with first- and second-degree polynomials were insufficient to reveal genetic diversity to a higher degree than REP model 1. The trend in the trajectories of h 2 calculated by the three random regression models with Ali and Schaeffer regression models (ASRMs) was similar to that of random regression models without the Ali and Schaeffer regression incorporated. Although the noted advantages of the random regression models revealed, to a greater extent, the genetic diversity of test day milk yield, AIC, BIC and LogL estimates indicated that repeatability models achieved a better balance between complexity and fitness and a smaller prediction error compared to random regression models.

Cutting corners: The impact of storage and DNA extraction on quality and quantity of DNA in honeybee (Apis mellifera) spermatheca

The purpose of our study was to investigate methods of short-term storage that allow preservation, transport and retrieval of genetic information contained in honeybee queen's spermatheca. Genotyping of the honeybee colony requires well ahead planned sample collection, depending on the type of data to be acquired. Sampling and genotyping of spermatheca's content instead of individual offspring is timesaving, allowing answers to the questions related to patriline composition immediately after mating. Such procedure is also cheaper and less error prone. For preservation either Allprotect Tissue Reagent (Qiagen) or absolute ethanol were used. Conditions during transportation were simulated by keeping samples 6-8 days at room temperature. Six different storing conditions of spermathecas were tested, complemented with two DNA extraction methods. We have analysed the concentration of DNA, RNA, and proteins in DNA extracts. We also analysed how strongly the DNA is subjected to fragmentation (through amplification of genetic markers ANT2 and tRNA^leu-COX2) and whether the quality of the extracted DNA is suitable for microsatellite (MS) analysis. Then, we tested the usage of spermatheca as a source of patriline composition in an experiment with three instrumentally inseminated virgin queens and performed MS analysis of the extracted DNA from each spermatheca, as well as queens' and drones' tissue. Our results show that median DNA concentration from spermathecas excised prior the storage, regardless of the storing condition and DNA extraction method, were generally lower than median DNA concentration obtained from spermathecas dissected from the whole queens after the storage. Despite the differences in DNA yield from the samples subjected to different storing conditions there was no significant effect of storage method or the DNA extraction method on the amplification success, although fewer samples stored in EtOH amplified successfully in comparison to ATR storing reagent. However, we recommend EtOH as a storing reagent due to its availability, low price, simplicity in usage in the field and in the laboratory, and capability of good preservation of the samples for DNA analysis during transport at room temperature.

Modeling honey yield, defensive and swarming behaviors of Italian honey bees (Apis mellifera ligustica) using linear-threshold approaches

Background

Genetic improvement of honey bees is more difficult compared to other livestock, due to the very different reproductive behavior. Estimation of breeding values requires specific adjustment and the use of sires in the pedigree is only possible when mating of queens and drones is strictly controlled. In the breeding program of the National Registry for Italian Queen Breeders and Bee Producers the paternal contribution is mostly unknown. As stronger modeling may compensate for the lack of pedigree information, we tested two models that differed in the way the direct and maternal effects were considered. The two models were tested using 4003 records for honey yield, defensive and swarming behaviors of Italian honey bee queens produced between 2002 and 2014. The first model accounted for the direct genetic effect of worker bees and the genetic maternal effect of the queen, whereas model 2 considered the direct genetic effect of the queen without maternal effect. The analyses were performed by linear (honey production) and threshold (defensive and swarming behavior) single-trait models; estimated genetic correlations among traits were obtained by a three-trait linear-threshold model.

Results

For all traits, the highest predictability (correlation between breeding values estimated with and without performance records) was obtained with model 2, where direct genetic effect of queens was considered. With this model, heritability estimates were 0.26 for honey yield, 0.36 for defensive behavior, and 0.34 for swarming behavior. Multi-trait estimation resulted in similar or higher heritability estimates for all traits. A low, positive genetic correlation (0.19) was found between honey yield and defensive behavior, whereas the genetic correlation between honey yield and swarming behavior was moderate (0.41). A strong, positive genetic correlation was found between defensive and swarming behaviors (0.62). Predictability for multi-trait evaluations was higher for honey yield (0.46) and defensive behavior (0.30) but almost identical for swarming behavior (0.45) compared to corresponding single-trait predictability.

Conclusions

Multi-trait evaluation using a model that accounts for the direct genetic effect of queen was the best approach for breeding value estimation of Italian honey bees. The results suggest a new direction for selection of linear and categorical traits in breeding programs where drone origin is unknown.

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.

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.

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 (P_L) with >20 generations was simulated, and a small subpopulation (P_S) 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 P_S; (S2) S1 plus genomic estimated breeding value (GEBV) for 4,475 sires from P_L as external information; (S3) S1 plus 221,580 phenotypes, 402,829 pedigree animals, and 53,558 genotypes for P_L; and (S4) single nucleotide polymorphism (SNP) effects calculated based on P_L 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 P_L did not considerably increase accuracy (0.33 vs. 0.32 in S1). Combining phenotypes, pedigree, and genotypes for P_S and P_L 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 P_L were used to predict GEBV for young genotyped animals from P_S, accuracy was greatest (0.56). With 6,000 genotyped animal in P_S, 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.

The Effect of Thermal Environment on Daily Milk Yield of Dairy Cows

The primary objective of this research was to evaluate the impact of environmental air temperatures on the milk production of dairy cows. Therefore, the experiment was carried out at the height of summer (from 15th of June to 15th of September) in three small family dairy farms and one commercial dairy farm. In total, 51 dairy cows from the black-white breed were subjected to the study. The season of research was divided into three periods: the period before the hot season, the period of the hot season and the period after the hot season. In each period there were three test days selected that were used for recording the air temperature inside and outside the barns and cow’s daily milk yield. Unvaried associations between the test day milk yield of cows and independent environmental and cow factors were done using the linear mixed model for repeated measurements. The statistical model showed that the farm management system and the hot season of the year had significant influence on daily milk yield per cow at the level of p<0.001. The test day per periods of the season when milk control was performed and the average test day environmental temperature showed statistically significant influence on daily milk yield per cow at level p<0.01. The present results revealed that environmental air temperatures contributes to considerable loss in the milk production of dairy cows.

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.