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Genomic selection is widely implemented in livestock breeding programmes across species. Its potential is also evident for sheep breeding; however, it has several limitations, particularly because of the high genetic diversity across and within sheep breeds. In Germany, the predominant sheep breed is the Merino sheep. Until now, there has been no use of genomic selection in the German Merino sheep breeding programme. In this simulation study, different genomic selection strategies were compared with a reference scenario with a breeding value estimation based on pedigree BLUP. A simplified version of the German Merino sheep breeding programme, including a health and a production trait in the breeding goal, was simulated via the R package Modular Breeding Program Simulator (MoBPS). Real genotype data were used to create a population specific simulation. The reference scenario was compared with several alternative scenarios in which selection was based on single-step GBLUP (ssGBLUP) breeding value estimation with varying genotyping strategies. In addition to scenarios in which all male and all male plus all female lambs were genotyped, scenarios with a preselection of lambs, that is only a certain proportion (top 25%, top 50%) genotyped, were simulated. The results revealed that genetic gain increased with increasing numbers of available genotypes. However, marginal gains decreased with increasing numbers of genotypes. Compared with the reference scenario, genotyping the top 25% of male lambs increased the genetic gain for the breeding ram population by 13% for both traits whereas genotyping the top 50% of male lambs or all male lambs led to increases of 18% (17%) or 26% (21%) for the health (production) trait, respectively. The potential of genotyping females in addition to male lambs was less evident on the male side with no significant differences between the scenarios with different proportions of genotyped females. The results have shown that genomic selection can be a valuable tool to increase genetic gain in the German Merino sheep population and that the genotyping of a certain proportion of animals might lead to substantial improvement over pedigree-based breeding value estimation. Nevertheless, further studies, especially economic evaluations, are needed before practical implementation.

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The major drawback to the implementation of genomic selection in a breeding program lies in long-term decrease in additive genetic variance, which is a trade-off for rapid genetic improvement in short term. Balancing increase in genetic gain with retention of additive genetic variance necessitates careful optimization of this trade-off. In this study, we proposed an integrated index selection approach within the genomic inferred cross-selection (GCS) framework to maximize genetic gain across multiple traits. With this method, we identified optimal crosses that simultaneously maximize progeny performance and maintain genetic variance for multiple traits. Using a stochastic simulated recurrent breeding program over a 40-years period, we evaluated different GCS methods along with other factors, such as the number of parents, crosses, and progeny per cross, that influence genetic gain in a pulse crop breeding program. Across all breeding scenarios, the posterior mean variance consistently enhances genetic gain when compared to other methods, such as the usefulness criterion, optimal haploid value, mean genomic estimated breeding value, and mean index selection value of the superior parents. In addition, we provide a detailed strategy to optimize the number of parents, crosses, and progeny per cross that can potentially maximize short- and long-term genetic gain in a public breeding program.

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The continuous increasing demand for egg quality and quantity, and the expanding market share have enabled the egg industry to achieve significant benefits through genetic improvement. This study aims to estimate the genetic parameters and explore selectable breeding traits in the purebred Rhode Island Red (RIR) and White Leghorn (WL), which are 2 high-yielding layer breeds, and better understand their underlying genetic basis and accelerate genetic progress. The DMU software was utilized to analyze 12 egg quality traits, including egg length (EL), egg width (EW), egg shape index (ESI), egg weight (EWT), albumen height (AH), yolk color (YC), Haugh unit (HU), yolk weight (YW), albumen weight (AW), albumen-to-egg weight ratio (AWR), yolk-to-albumen ratio (YAR), and yolk-to-egg weight ratio (YWR). In RIR, the heritability of egg quality traits ranged from 0.196 to 0.427, while the repeatability ranged from 0.395 to 0.668. In WL, the heritability of egg quality traits ranged from 0.203 to 0.347, and the repeatability ranged from 0.424 to 0.656. In both RIR and WL, highly strong genetic correlations were observed between AW and EW, as well as between AW and EWT. The genetic correlations for AW and EW were 0.902 in RIR and 0.864 in WL, while the genetic correlations for AW and EWT were 0.981 in RIR and 0.960 in WL. The egg quality traits in both breeds showed moderate heritability, indicating great genetic potential for improvement through selective breeding. This can help breeders meet the increasingly diverse egg preferences of consumers through genetic selection. Additionally, there is a highly strong correlation between egg width/egg weight, and albumen weight in both breeds. In practical production, it is feasible to estimate albumen weight by measuring egg width and egg weight, which can simplify the method for measuring albumen weight. In conclusions, our finding provided valuable insights into the genetic architecture of egg quality traits in RIR and WL chickens. They help our understanding of the potential for genetic improvement of these traits through selective breeding programs.

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Genetic evaluations in beef cattle have evolved over the past 50 years relative to the hardware or software used, the statistical methodology underpinning them, and the traits evaluated. However, the underlying premise has remained the same; to generate predictions of genetic merit such that selection decisions can be made that materialize as phenotypic changes in commercial animals. The wide-spread availability and adoption of genomic technology has enabled more accurate genetic predictions of young animals albeit with the requirement of continual collection and reporting of phenotypic data.

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Morphological scoring is a common evaluation method for domestic animals. The National Association of Maremmano Breeders (ANAM) has provided a dataset containing the records of 600 horses, four metric measurements (cm) and 24 traits with a continuous evaluation scale, each one with 15 classes. Moreover, a body condition score (BCS) with five classes is included. In this study, factor analysis was conducted to create a small number of informative factors (3) obtained from these traits, and a new BLUP-AM-MT index was established. The New Estimated Breeding Value (NEBV1) of each horse was computed by adding the genetic indexes of the three factors, with each one multiplied using a coefficient indicated by ANAM. The practical feasibility of the NEBV1 was evaluated through Spearman correlations between the rankings of the NEBV1 and the rankings of the BLUP-AM-MT, estimated through the four biometric measures and the morphological score (MS) assigned to each horse by the ANAM judges. The factorial analysis was used to estimate three factors: the "Trunk Dimension", "Legs" and "Length". As the explained variance was only 32%, the model was rotated, and the heritability of the three factors were 0.51, 0.05 and 0.41, respectively. After rotation, the estimated correlations between the new NEBV1 and the biometric measures were improved. These results should encourage breeders to adopt a breeding value index that takes into consideration the factors derived from all the variables observed in the morphological evaluation of the Maremmano. In this way, breeders can use it to select the best animals for breeding.

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The uncertainty resulting from missing genotypes in low-coverage whole-genome sequencing (LCWGS) data complicates genotype imputation. The aim of this study is to find out an optimal strategy for accurately imputing LCWGS data and assess its effectiveness for genomic prediction (GP) and genome-wide association study (GWAS) on economically important traits of Large White pigs. The LCWGS data of 1 423 Large White pigs were imputed using three different strategies: (1) using the high-coverage whole-genome sequencing (HCWGS) of 30 key progenitors as the reference panel (Ref_LG); (2) mixing HCWGS of key progenitors with LCWGS (Mix_HLG) and (3) self-imputation in LCWGS (Within_LG). Additionally, to compare the imputation effects of LCWGS, we also imputed SNP chip data of 1 423 Large White pigs to the whole-genome sequencing level using the reference panel consisting of key progenitors (Ref_SNP). To evaluate effects of the imputed sequencing data, we compared the accuracies of GP and statistical power of GWAS for four reproductive traits based on the chip data, sequencing data imputed from chip data and LCWGS data using an optimal strategy. The average imputation accuracies of the Within_LG, Ref_LG and Mix_HLG were 0.9893, 0.9899 and 0.9875, respectively, which were higher than that of the Ref_SNP (0.8522). Using the imputed sequencing data from LCWGS with the Ref_LG imputation strategy, the accuracies of GP for four traits improved by approximately 0.31-1.04% compared to the chip data, and by 0.7-1.05% compared to the imputed sequencing data from chip data. Furthermore, by using the sequence data imputed from LCWGS with the Ref_LG, 18 candidate genes were identified to be associated with the four reproductive traits of interest in Large White pigs: total number of piglets born - EPC2, MBD5, ORC4 and ACVR2A; number of piglets born healthy - IKBKE; total litter weight of piglets born alive - HSPA13 and CPA1; gestation length - GTF2H5, ITGAV, NFE2L2, CALCRL, ITGA4, STAT1, HOXD10, MSTN, COL5A2 and STAT4. With the exception of EPC2, ORC4, ACVR2A and MSTN, others represent novel candidates. Our findings can provide a reference for the application of LCWGS data in livestock and poultry.

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Mating in animal communities must be managed in a way that assures the performance increase in the progenies without increasing the rate of inbreeding. It has currently become possible to identify millions of single nucleotide polymorphisms (SNPs), and it is feasible to select animals based on genome-wide marker profiles. This study aimed to evaluate the impact of five mating designs among individuals (random, positive and negative assortative, minimized and maximized inbreeding) on genomic prediction accuracy. The choice of these five particular mating designs provides a thorough analysis of the way genetic diversity, relatedness, inbreeding, and biological conditions influence the accuracy of genomic predictions. Utilizing a stochastic simulation technique, various marker and quantitative trait loci (QTL) densities were taken into account. The heritabilities of a simulated trait were 0.05, 0.30, and 0.60. A validation population that only had genotypic records was taken into consideration, and a reference population that had both genotypic and phenotypic records was considered for every simulation scenario. By measuring the correlation between estimated and true breeding values, the prediction accuracy was calculated. Computing the regression of true genomic breeding value on estimated genomic breeding value allowed for the examination of prediction bias. The scenario with a positive assortative mating design had the highest accuracy of genomic prediction (0.733 ± 0.003 to 0.966 ± 0.001). In a case of negative assortative mating, the genomic evaluation's accuracy was lowest (0.680 ± 0.011 to 0.899 ± 0.003). Applying the positive assortative mating design resulted in the unbiased regression coefficients of true genomic breeding value on estimated genomic breeding value. Based on the current results, it is suggested to implement positive assortative mating in genomic evaluation programs to obtain unbiased genomic predictions with greater accuracy. This study implies that animal breeding programs can improve offspring performance without compromising genetic health by carefully managing mating strategies based on genetic diversity, relatedness, and inbreeding levels. To maximize breeding results and ensure long-term genetic improvement in animal populations, this study highlights the importance of considering different mating designs when evaluating genomic information. When incorporating positive assortative mating or other mating schemes into genomic evaluation programs, it is critical to consider the complex relationship between gene interactions, environmental influences, and genetic drift to ensure the stability and effectiveness of breeding efforts. Further research and comprehensive analyzes are needed to fully understand the impact of these factors and their possible complex interactions on the accuracy of genomic prediction and to develop strategies that optimize breeding outcomes in animal populations.

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The aim of this study was to analyze suitable genetic models and selection indices to estimate the genetic parameters and breeding values of native Thai roosters. A total of 3475 records of seven semen traits (mass movement, semen pH, semen color, volume, sperm viability, sperm abnormalities, and sperm concentration) from 242 Thai native grandparent roosters were analyzed. Multiple-trait random regression test-day models with five covariance functions were used to analyze the variance components, genetic parameters, and breeding values. The selection index (SI) was calculated to determine the optimal genetic value for different selection percentages. The results showed that a multiple-trait random regression test-day model with a second-order Legendre polynomial function was the most appropriate genetic model for this population. The estimated heritability values were low to moderate, ranging from 0.110 to 0.112 (mass movement), 0.040 to 0.051 (semen pH), 0.092 to 0.097 (semen color), 0.220 to 0.225 (semen volume), 0.067 to 0.083 (sperm viability), 0.086 to 0.099 (sperm abnormalities), and 0.134 to 0.138 (sperm concentration). The repeatability values exceeded the heritability values and were within the range of 0.133 to 0.688. The genetic correlations among semen traits ranged from -0.332 to 0.677, and phenotypic correlations ranged from -0.260 to 0.460. When considering heritability and genetic correlation values, semen volume, sperm concentration, and mass movement were the top three priority semen traits calculated as selection indices. Finally, the top 10% of the selection index was recommended for creating the next generation. Our findings provide useful information on genetic parameters and an appropriate selection index of semen traits for selecting the genetics of individual Thai native grandparent roosters. The heritability estimates for semen traits reported here suggest an adequate response to selection through a genetic evaluation approach. Our results indicate that it is possible to select grandparent roosters with better reproductive performance.

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The welfare of calves is important to both farmers and consumers. Practices that increase the proportion of calves born alive and enable them to thrive through to weaning contribute to improved sustainability. Stillbirths (SB) are calvings where the calf dies at birth or within 24 h after birth. Preweaning mortality (PWM) refers to calves that die after the first day of life but before weaning based on termination data. Both SB and PWM are binary traits characterized by low heritability. Data collection for these traits is incomplete, compared with traits such as milk yield in cows. Despite these challenges, genetic variation can be measured and used to produce breeding tools, such as EBVs, to reduce calf mortality over time. The aim of this study was to compare the performance of various linear models to predict SB and PWM traits in Holstein and Jersey cattle and evaluate their applicability for industry-wide use in the Australian dairy industry. Calving records from around 2.25 million Holstein and Jersey dams were obtained from DataGene's Central Data Repository from the year 2000 onward, to calculate genetic parameters. About 7% of calves were recorded as SB in the period from 2000 to 2021 (n = 1.48 million calvings). The prevalence of PWM was much lower than SB during the same period at 2% (n = 0.89 million calves). Genetic parameters were estimated for SB direct, SB maternal, and PWM using bivariate linear models with calving ease (CE) as the second trait in the model. The heritability of these calf traits was low and varied between 1% and 5% depending on the breed, trait, and model. In Holstein cattle, heritabilities were 2% for PWM and SB direct and 1% for SB maternal, whereas in Jersey cattle heritabilities were 5% for PWM, 2% for SB direct, and 1% for SB maternal. The genetic trends for both SB direct and SB maternal in Holstein cattle indicate improvement in both traits, whereas there was no apparent increase or decrease in PWM in the past 2 decades. The coefficient of genetic variation for SB direct and PWM was between 11.7% and 23.0% in Holstein and Jersey cattle, demonstrating considerable genetic variation in calf survival traits as a first step to using genetic selection to increase the proportion of calves born alive and calves weaned. A focus on improved calf and calving recording practices is expected to increase the reliability of genetic predictions.

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Inner Mongolia cashmere goat is an excellent livestock breed formed through long-term natural selection and artificial breeding, and is currently a world-class dual-purpose breed producing cashmere and meat. Multi trait animal model is considered to significantly improve the accuracy of genetic evaluation in livestock and poultry, enabling indirect selection between traits. In this study, the pedigree, genotype, environment, and phenotypic records of early growth traits of Inner Mongolia cashmere goats were used to build multi trait animal model., Then three methods including ABLUP, GBLUP, and ssGBLUP wereused to estimate the genetic parameters and genomic breeding values of early growth traits (birth weight, weaning weight, average daily weight gain before weaning, and yearling weight). The accuracy and reliability of genomic estimated breeding value are further evaluated using the five fold cross validation method. The results showed that the heritability of birth weight estimated by three methods was 0.13-0.15, the heritability of weaning weight was 0.13-0.20, heritability of daily weight gain before weaning was 0.11-0.14, and the heritability of yearling weight was 0.09-0.14, all of which belonged to moderate to low heritability. There is a strong positive genetic correlation between weaning weight and daily weight gain before weaning, daily weight gain before weaning and yearling weight, with correlation coefficients of 0.77-0.79 and 0.56-0.67, respectively. The same pattern was found in phenotype correlation among traits. The accuracy of the estimated breeding values by ABLUP, GBLUP, and ssGBLUP methods for birth weight is 0.5047, 0.6694, and 0.7156, respectively; the weaning weight is 0.6207, 0.6456, and 0.7254, respectively; the daily weight gain before weaning was 0.6110, 0.6855, and 0.7357 respectively; and the yearling weight was 0.6209, 0.7155, and 0.7756, respectively. In summary, the early growth traits of Inner Mongolia cashmere goats belong to moderate to low heritability, and the speed of genetic improvement is relatively slow. The genetic improvement of other growth traits can be achieved through the selection of weaning weight. The ssGBLUP method has the highest accuracy and reliability in estimating genomic breeding value of early growth traits in Inner Mongolia cashmere goats, and is significantly higher than that from ABLUP method, indicating that it is the best method for genomic breeding of early growth weight in Inner Mongolia cashmere goats.

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The traditional genetic evaluation methods generally consider additive genetic effects only and often ignore non-additive (dominance and epistasis) effects that may have contributed to genetic variation of complex traits of livestock species. The available dense single nucleotide polymorphisms (SNPs) panels offer to investigate the potential benefits of including non-additive genetic effects in the genomic evaluation models. Data from 16 971 genotyped (Illumina Bovine 50 K SNP chip) Korean Hanwoo cattle were used to estimate genetic variance components and prediction accuracy of genomic breeding values (GEBVs) for four carcass and meat quality traits: carcass weight (CWT), eye muscle area (EMA), back fat thickness (BFT) and marbling score (MS). Five different genetic models were evaluated through including additive, dominance and epistatic interactions (additive by additive, A × A; additive by dominance, A × D and dominance by dominance, D × D) successively in the models. The estimates of additive genetic variances and narrow sense heritabilities (ha2) were found similar across the evaluated models and traits except when additive interaction (A × A) was included. The dominance variance estimates relative to phenotypic variance ranged from 1.7-3.4% for CWT and MS traits, whereas, they were close to zero for EMA and BFT traits. The magnitude of A × A epistatic heritability (haa2) ranged between 14.8 and 27.7% in all traits. However, heritability estimates for A × D and D × D epistatic interactions (had2 and hdd2) were quite low compared to haa2 and were contributed only 0.0-9.7% of the total phenotypic variation. In general, broad sense heritability (hG2) estimates were almost twice (ranging between 0.54 and 0.68) the ha2 for all of the investigated traits. The inclusion of dominance effects did not improve the prediction accuracy of GEBV but improved 2.0-3.0% when epistatic effects were included in the model. More importantly, rank correlation revealed that partitioning of variance components considering dominance and epistatic effects in the model would enable to re-rank of top animals with better prediction of GEBV. The present result suggests that dominance and epistatic effects could be included in the genomic evaluation model for better estimates of variance components and more accurate prediction of GEBV for carcass and meat quality traits in Korean Hanwoo cattle.

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We performed a plateau-linear reaction norm model (RNM) analysis of number born alive (NBA) in purebred Landrace pigs, where breeding value changes according to maximum temperature at mating day, using public meteorological observation data in Japan. We analysed 52,668 NBA records obtained from 10,320 Landrace sows. Pedigree data contained 99,201 animals. Off-farm daily temperature data at the nearest weather station from each of the farms were downloaded from the Japan Meteorological Agency website. A plateau-linear RNM analysis based on daily maximum temperature on mating day (threshold temperature of 16.6°C) was performed. The percentage of the records with daily maximum temperatures at mating days of ≤16.6, ≥25.0, ≥30.0 and ≥35.0°C were 34.3%, 33.6%, 14.0% and 0.8%, respectively. The value of Akaike's information criterion for the plateau-linear RNM was lower than that for a simple repeatability model (RM). With the plateau-linear RNM, estimated value of heritability ranged from 0.14 to 0.15, while that from the RM analysis was 0.15. Additive genetic correlation between intercept and slope terms was estimated to be -0.52 from the plateau-linear RNM analysis. Estimated additive genetic correlations were >0.9 between NBA at different temperatures ranging from 16.6 to 37.6°C. For the 10,320 sows, average values of prediction reliability of the intercept and slope terms for breeding values in the plateau-linear RNM were 0.47 and 0.16, respectively. Increasing weight for slope term in linear selection index could bring positive genetic gain in the slope part, but prediction accuracy would decrease. Our results imply that genetically improving heat tolerance in sows reared in Japan focusing on NBA using RNM is possible, while RNM is more complex to implement and interpret. Therefore, further study should be encouraged to make genetic improvement for heat tolerance in sows more efficient.

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The Holstein breed is the mainstay of dairy production in Korea. In this study, we evaluated the genomic prediction accuracy for body conformation traits in Korean Holstein cattle, using a range of π levels (0.75, 0.90, 0.99, and 0.995) in Bayesian methods (BayesB and BayesC). Focusing on 24 traits, we analyzed the impact of different π levels on prediction accuracy. We observed a general increase in accuracy at higher levels for specific traits, with variations depending on the Bayesian method applied. Notably, the highest accuracy was achieved for rear teat angle when using deregressed estimated breeding values including parent average as a response variable. We further demonstrated that incorporating parent average into deregressed estimated breeding values enhances genomic prediction accuracy, showcasing the effectiveness of the model in integrating both offspring and parental genetic information. Additionally, we identified 18 significant window regions through genome-wide association studies, which are crucial for future fine mapping and discovery of causal mutations. These findings provide valuable insights into the efficiency of genomic selection for body conformation traits in Korean Holstein cattle and highlight the potential for advancements in the prediction accuracy using larger datasets and more sophisticated genomic models.

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A comprehensive study on maize flowering traits, focusing on the regulation of flowering time and the elucidation of molecular mechanisms underlying the genes controlling flowering, holds the potential to significantly enhance our understanding of the associated regulatory gene network. In this study, three tropical maize inbreds, CML384, CML171, and CML444, were used, along with a temperate maize variety, Shen137, as parental lines to cross with Ye107. The resulting F1s underwent seven consecutive generations of self-pollination through the single-seed descent (SSD) method to develop a multiparent population. To investigate the regulation of maize flowering time-related traits and to identify loci and candidate genes, a genome-wide association study (GWAS) was conducted. GWAS analysis identified 556 SNPs and 12 candidate genes that were significantly associated with flowering time-related traits. Additionally, an analysis of the effect of the estimated breeding values of the subpopulations on flowering time was conducted to further validate the findings of the present study. Collectively, this study offers valuable insights into novel candidate genes, contributing to an improved understanding of maize flowering time-related traits. This information holds practical significance for future maize breeding programs aimed at developing high-yielding hybrids.

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Infection by gastrointestinal nematodes (GIN), particularly Haemonchus contortus, can be detrimental to sheep health and performance. Genetic susceptibility to GIN varies between breeds, with those lacking high levels of natural resistance often requiring frequent anthelmintic treatment when facing parasitic challenge. Genetic technology can serve as a tool to decrease GIN susceptibility via selection for sheep with reduced fecal egg count (FEC) estimated breeding values (EBVs). However, the physiological changes that result from implementation of this strategy are not well described. Additionally, there is a need for comparison of animals from recent selective breeding against breeds with inherent GIN resistance. In this study we administered a challenge of H. contortus to Dorper x White Dorper (DWD; n = 92) lambs that have been genetically selected for either low (DWD-) or high (DWD+) FEC EBVs and Barbados Blackbelly x Mouflon (BBM; n = 19) lambs from a genetically resistant breed backgrounds. Lamb FEC, packed-cell volume (PCV) and serum IgG were measured at intermittent levels over 5 weeks. At day 21 and day 35, the selectively bred DWD- had a lower mean FEC compared to DWD+, but were higher than BBM. Reductions in both PCV and serum IgG from initial day 0 levels were observed in DWD lambs, but not in BBM. Furthermore, from a subset of lambs (n = 24) harvested at day 21, DWD- only tended (p = 0.056) to have lower mean worm counts than DWD+, with BBM having the lowest mean worm count. Differentially expressed genes (DEGs) identified via RNA-sequencing of abomasal tissue at day 21 indicate a more pronounced Th2 immune response and more rapid worm expulsion occurred in iBBM than iDWD- and iDWD+ lambs. However, gene expression in DWD- suggests an association between reduced FEC EBV and gastric acid secretion and the ability to limit worm fecundity. Ultimately, selection of Dorper sheep for low FEC EBV can reduce susceptibility to GIN, but it will likely require multiple generations with this trait as a breeding priority before presenting a similar resistance level to Caribbean breeds.

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The objectives of this study were to evaluate the influence of inbreeding on growth traits and body measurements, as well as on the estimation of genetic parameters and genetic trends in Guzerá cattle. Phenotypic records of 4,212 animals selected for postweaning weight from Guzerá Breeding Program of Advanced Beef Cattle Research Center were utilized. The pedigree file contained records from 7,213 animals born from 1928 to 2019. The traits analyzed were: birth weight (BW), weights adjusted to 210, 378 and 550 days of age (W210, W378 and W550, respectively), chest girth at 378 and 550 days of age (CG378 and CG550), scrotal circumference (SC), and hip height at 378 and 550 days of age (HH378 and H550). Linear regression was used to evaluate the effects of inbreeding on traits. Genetic parameters were obtained using models including or not the effect of inbreeding as a covariate. Inbreeding had negative effects (P ≤ 0.01) on BW (-0.09 kg), W378 (-2.86 kg), W550 (-2.95 kg), HH378 (-0.10 cm), and H550 (-0.29 cm). The lowest and highest heritability estimates were obtained for W210 (0.21 ± 0.07) and HH550 (0.57 ± 0.06), respectively. The genetic correlations were strong and positive between all traits, ranging from 0.44 ± 0.08 (SC x HH) to 0.99 ± 0.01 (W378 x W550). Spearman correlations between EBVs obtained with or without inbreeding effect ranged from 0.968 to 0.995 (P < 0.01). The results indicate loss of productive performance in inbred animals. However, the inclusion of inbreeding coefficient in genetic evaluation models did not alter the magnitude of genetic parameters or genetic trends for the traits studied.

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For many years, pig production has focused on maximizing performance by selecting for maximal muscle growth and feeding diets that allow the animals to express their genetic potential. However, it is unclear whether this selection for muscle deposition has affected the capacity of pigs to cope with by-product-based diets, which rely on fat as the primary energy source instead of starches and sugars. Therefore, an experiment was set up to investigate if different types of boars affect how their progeny cope with alternative ingredients in the diet, with a possible need for adapted breeding schemes. Two types of boars within the Piétrain sire line were used based on either a high or low estimated breeding value for daily feed intake (HFI: high feed intake, low feed intake). When their progeny reached 14 weeks of age, two dietary strategies were compared: a control (CON) vs a by-product-based diet high in fat and fiber (HFF). The CON diet was mainly based on cereals (corn, wheat, barley) and soybean meal. The HFF diet was formulated to contain the same net energy, CP and digestible amino acid levels without any cereals or soybean meal. In total 192 animals were included in the experiment (48 animals/type of boar/diet) and performance, digestibility, carcass and meat quality were compared. None of the parameters showed a significant interaction (P < 0.05) between the type of boar and diet, suggesting that shifting to diets that are less prone to feed-food competition is equally feasible in different types of pigs. Type of boar did affect performance, carcass quality and intramuscular fat content. HFI pigs showed higher daily feed intake (DFI) and daily gain (P < 0.001), with no significant difference in feed conversion ratio (P = 0.205), lower carcass quality (P < 0.001) and higher intramuscular fat content (P = 0.030). For both boar types, pigs fed the CON diet performed better, with a higher daily gain (P = 0.028), DFI (P = 0.011) and dressing yield (P = 0.009) and better digestibility (P < 0.001), but without differences in feed conversion ratio or meat quality. In conclusion, there was no indication that pigs differing in feed intake capacity cope differently with a high-fat, high-fiber diet based on by-products. Different types of pigs may cope well with diets that are less prone to feed-food competition.

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Sire has an important role because they could have more offspring than dam does. The research aims to determine the production performance of Friesian Holstein, estimation of heritability values, estimation of breeding values, and ranking of dairy cattle sires. The objects of the study were the complete record of milk production, lactation length, lactation peak, and dry period length from first to fourth lactation from 2017-2021. This study used the descriptive method. The results of the research showed that productivity performance is great, 1st lactation milk production was 8,029.28±1,112 kg, the lactation length was 321.26±38.48 days, the condition of average peak production was obtained on day 85.35±29.25 with milk production of 32.55±4.16 kg, and dry period length was 51.37±9.33 days. 2nd Lactation milk production was 7,761.66±1,145, the lactation length was 323.66±43.06 days, the condition of average peak production was on day 58.43±21.11 with milk production of 40.79±5.30 kg and the dry period length was 65.10±22.69 days. 3rd lactation milk production 3 was 7,788.92±1,148 kg, the lactation length was 326.64±46.74 days, peak production was on day 61.88 ±22.72 with milk production of 43.62±5.11 kg; the dry period length was 65.00±20.49 days. 4th Lactation milk production was 7,484.18±1,133 kg, lactation length was 323.04±42.23 days, peak production was on day 66.39±24.26 with the milk production of 43.82±5.68, the dry period length was 65.78±21.60 days. The estimated heritability value for milk production, 0.03 ± 0.02, is included in the low category. The ranking of 10 sires that have the potential to increase the genetic based on their estimated breeding value is 595.91 kg (O.S.Elmer-XA), 264.16 kg (M.Z.Merlin.-XA), 252.38 kg (L.Muscadet-XA), 247 .12 kg (C.Toyjet), 239.01 kg (S. Gypsy B), 214.82 kg (WestCoastPldge), 188.14 kg (M.Z.Merlin-ET), 178.56 kg (Brasilia), 166.43 kg (L.JetBowser-XA), 162.06 kg (MRMUDD-XA).

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Traditionally, selective breeding has been used to improve tree growth. However, traditional selection methods are time-consuming and limit annual genetic gain. Genomic selection (GS) offers an alternative to progeny testing by estimating the genotype-based breeding values of individuals based on genomic information using molecular markers. In the present study, we introduced GS to an open-pollinated breeding population of Korean red pine (Pinus densiflora), which is in high demand in South Korea, to shorten the breeding cycle. We compared the prediction accuracies of GS for growth characteristics (diameter at breast height [DBH], height, straightness, and volume) in Korean red pines under various conditions (marker set, model, and training set) and evaluated the selection efficiency of GS compared to traditional selection methods. Training the GS model to include individuals from various environments using genomic best linear unbiased prediction (GBLUP) and markers with a minor allele frequency larger than 0.05 was effective. The optimized model had an accuracy of 0.164-0.498 and a predictive ability of 0.018-0.441. The predictive ability of GBLUP against that of additive best linear unbiased prediction (ABLUP) was 0.86-5.10, and against the square root of heritability was 0.19-0.76, indicating that GS for Korean red pine was as efficient as in previous studies on forest trees. Moreover, the response to GS was higher than that to traditional selection regarding the annual genetic gain. Therefore, we conclude that the trained GS model is more effective than the traditional breeding methods for Korean red pines. We anticipate that the next generation of trees selected by GS will lay the foundation for the accelerated breeding of Korean red pine.

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BACKGROUND: The accurate prediction of genomic breeding values is central to genomic selection in both plant and animal breeding studies. Genomic prediction involves the use of thousands of molecular markers spanning the entire genome and therefore requires methods able to efficiently handle high dimensional data. Not surprisingly, machine learning methods are becoming widely advocated for and used in genomic prediction studies. These methods encompass different groups of supervised and unsupervised learning methods. Although several studies have compared the predictive performances of individual methods, studies comparing the predictive performance of different groups of methods are rare. However, such studies are crucial for identifying (i) groups of methods with superior genomic predictive performance and assessing (ii) the merits and demerits of such groups of methods relative to each other and to the established classical methods. Here, we comparatively evaluate the genomic predictive performance and informally assess the computational cost of several groups of supervised machine learning methods, specifically, regularized regression methods, deep, ensemble and instance-based learning algorithms, using one simulated animal breeding dataset and three empirical maize breeding datasets obtained from a commercial breeding program. RESULTS: Our results show that the relative predictive performance and computational expense of the groups of machine learning methods depend upon both the data and target traits and that for classical regularized methods, increasing model complexity can incur huge computational costs but does not necessarily always improve predictive accuracy. Thus, despite their greater complexity and computational burden, neither the adaptive nor the group regularized methods clearly improved upon the results of their simple regularized counterparts. This rules out selection of one procedure among machine learning methods for routine use in genomic prediction. The results also show that, because of their competitive predictive performance, computational efficiency, simplicity and therefore relatively few tuning parameters, the classical linear mixed model and regularized regression methods are likely to remain strong contenders for genomic prediction. CONCLUSIONS: The dependence of predictive performance and computational burden on target datasets and traits call for increasing investments in enhancing the computational efficiency of machine learning algorithms and computing resources.

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