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 (h2) calculated by the three REP models: REP1 0.355 ±â€¯0.060, REP2 0.344 ±â€¯0.047 and REP3 0.347 ±â€¯0.060. The same applied to the repeatability coefficients, which, for the three REP models, were 0.384 ±â€¯0.065, 0.376 ±â€¯0.051 and 0.378 ±â€¯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 h2 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 h2 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 tRNAleu-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.

Evaluation of Suppressed Mite Reproduction (SMR) Reveals Potential for Varroa Resistance in European Honey Bees (Apis mellifera L.).

In the fight against the Varroa destructor mite, selective breeding of honey bee (Apis mellifera L.) populations that are resistant to the parasitic mite stands as a sustainable solution. Selection initiatives indicate that using the suppressed mite reproduction (SMR) trait as a selection criterion is a suitable tool to breed such resistant bee populations. We conducted a large European experiment to evaluate the SMR trait in different populations of honey bees spread over 13 different countries, and representing different honey bee genotypes with their local mite parasites. The first goal was to standardize and validate the SMR evaluation method, and then to compare the SMR trait between the different populations. Simulation results indicate that it is necessary to examine at least 35 single-infested cells to reliably estimate the SMR score of any given colony. Several colonies from our dataset display high SMR scores indicating that this trait is present within the European honey bee populations. The trait is highly variable between colonies and some countries, but no major differences could be identified between countries for a given genotype, or between genotypes in different countries. This study shows the potential to increase selective breeding efforts of V. destructor resistant populations.

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.