Breeding Values Research Articles

Genetic Variation in Yield and Fruit Weight Among Strawberry (Fragaria ×ananassa) Cultivars and the Interaction With Year and Location Effect

ABSTRACTStrawberry (Fragaria ×ananassa) is an important crop in the United States. In this study, we analysed strawberry trials established across 7 years and two locations. Our objective was to estimate genetic parameters and predict breeding values for yield. Linear mixed models were used for unbalanced data across years and locations and study the genotype by year and location interactions. The results suggested significant differences between cultivars for fruit weight but not for total and marketable yield. Fruit weight was controlled by genetic factors, shown by the high heritability of cultivar means (0.93), whereas marketable yield and total yield showed lower heritability estimates (0.48 and 0.63, respectively), indicating a substantial environmental influence. For fruit weight, cultivars exhibited stability across years and locations, supported by the high additive genetic correlations observed for cultivar‐by‐year (r = 0.90) and cultivar‐by‐location interactions (r = 0.98). Fruit weight and total yield were genetically independent as suggested near zero additive genetic correlation (−0.06 ± 0.416) between two traits, and they should be considered for an index selection in the strawberry breeding strategy.

Association of pedigree indexes and genomic breeding values with the performance of Polish Holstein-Friesian cows.

Pedigree indices (PI) and genomically enhanced breeding values (GBV) of Polish Holstein-Friesian heifers were compared with their future performance. Phenotypes of 15,794 cows from 294 herds were analyzed. The traits evaluated included milk, fat and protein yield, somatic cell score (SCC), stature, overall udder and feet and legs score, heifer conception rate, and longevity. PI and GBV were from official evaluation systems, and performance records were adjusted for non-genetic effects. Correlations between breeding values and adjusted phenotypes were analyzed. Additionally, cows within each herd were divided into quartiles based on their breeding values, and the performance of cows from the top and bottom quartiles was compared. For production traits, similar analyses were conducted separately for high and low milk-producing herds to check for possible genotype by environment interaction. The analyses confirmed the significant correlation between breeding values and cow performance. Incorporating genomic information into PI significantly improved the predictive accuracy for milk, fat, and protein yields, as well as for SCC and stature, with increases ranging from 34 to 64%. Comparisons of cows' performance from the top and bottom quartiles supported these observations for all these traits except protein yield, with differences in performance being 43-92% greater when cows were ranked by GBV instead of PI. In a more productive environment, greater differences in performance were found between the top and bottom quartiles. These findings suggest that Polish breeders can enhance the outcomes of their breeding decisions by using GBV instead of PI. This change offers particular benefits in improving the predictability of future performance for the most economically important traits such as milk yield, fat yield, protein yield, somatic cell score, and stature.

Genomic Prediction Using Imputed Whole-Genome Sequence Data in Australian Angus Cattle.

Whole-genome sequence (WGS) data was used to estimate genomic breeding values for growth and carcass traits in Australian Angus cattle. The study aimed to compare the accuracy and bias of genomic predictions with three marker densities, including 50K, high-density (HD) and WGS. The dataset used in this study consisted of animals born between 2013 and 2022. Body weight traits included birthweight, weight at 400 days and weight at 600 days of age. The carcass traits were carcass weight, carcass intramuscular fat and carcass marbling score. The accuracy and bias of prediction were assessed using the cross-validation. Further, for the growth traits, animals in the validation group were subdivided into two subgroups, which were moderately or highly related to the reference. Genomic best linear unbiased prediction (GBLUP) was used to compare genomic predictions with the three marker densities. The prediction accuracies were generally similar across the marker densities, ranging between 0.61 and 0.68 for the body weight traits and between 0.40 and 0.52 for the carcass traits. However, the accuracies marginally decreased as the marker density increased for all the traits studied. A similar lack of difference was found when considering the accuracy by the relatedness subgroups. The results indicated that no meaningful difference in prediction accuracy was estimated when comparing the three marker densities due to the population structure. In conclusion, there was no substantial improvement in genomic prediction when using the WGS in this study.

Morpho-biological study of the grain sorghum collection of the fsbsi «ARC «Donskoy»

The current paper has presented the estimation results of the grain sorghum varieties and samples in a collection nursery. The purpose of the study was to analyze the collection material of grain sorghum and select the best variety samples for breeding work. The study of the samples was carried out in the laboratory for grain sorghum breeding and seed production of the FSBSI “ARC “Donskoy” in 2020–2023. The objects of the study were 40 grain sorghum samples and varieties. Morphometric analysis was carried out according to the methods presented by B.A. Dospekhov (2014). The variety ‘Zernogradskoe 88’ was used as a standard one. During the analysis, there have been identified the samples that exceeded the standard variety in 1000-grain weight, number of grains per panicle, and productivity per plot. According to productivity there have been identified such samples as ‘Milo 168/Combeine 7078’, ‘No. 93-23-13’, ‘Hegari’, ‘Kitayskoe 3’, ‘DN – 33f’, ‘D.D. Yellow Sooner Milo-2501’, ‘No. 50-13’, which produced over 600 g/m2. According to 1000-grain weight, the sample ‘No. 61-13’ was of the greatest breeding value with a 1000-grain weight of 39.3 g. According to number of grains per panicle the variety ‘Kitayskoe 3’ was the best with 2324 pcs. The identified samples have been recommended for use in breeding programs as grain sorghum sources.

A genome-wide association study of the racing performance traits in Yili horses based on Blink and FarmCPU models.

Racing performance traits are the main indicators for evaluating the performance and value of sport horses. The aim of this study was to identify the key genes for racing performance traits in Yili horses by performing a genome-wide association study (GWAS). Breeding values for racing performance traits were calculated for Yili horses (n = 827) using an animal model. Genome-wide association analysis of racing performance traits in horses (n = 236) was carried out using the Blink, and FarmCPU models in GAPIT software, and genes within the significant regions were functionally annotated. The results of GWAS showed that a total of 24 significant SNP markers (P < 6.05 × 10- 9) and 22 suggestive SNP markers (P < 1.21 × 10- 7) were identified. Among them, the Blink associated 16 significant SNP loci and FarmCPU associated 12 significant SNP loci. A total of 127 candidate genes (50 significant) were annotated. Among these, CNTN6 (motor coordination), NIPA1 (neuronal development), and DCC (dopamine pathway maturation) may be the main candidate genes affecting speed traits. SHANK2 (neuronal synaptic regulation), ISCA1 (mitochondrial protein assembly), and KCNIP4 (neuronal excitability) may be the main candidate genes affecting ranking score traits. A common locus (ECA1: 22698579) was significantly associated with racing performance traits, and the function of the genes at this locus needs to be studied in depth. These findings will provide new insights into the detection and selection of genetic variants for racing performance and will help to accelerate the genetic improvement of Yili horses.

Correlation between productivity traits and protein percentage in pea seeds in the conditions of Donbass

The solution to the most urgent problem of stable and smooth production of vegetable protein in the agro industrial complex of Donbass is practically impossible without introducing new pea varieties into production, the main legume of the region. The main tasks set by the breeders are to improve productivity stability over the years and the overall productivity potential by developing and introducing new, more environmentally flexible and adapted to specific growing conditions genotypes into production. In this regard, there has been given a special role to improving a number of quantitative traits that directly or indirectly affect the overall productivity of plants. The purpose of the current research was to identify the closeness and direction of the correlation of traits that determine productivity and protein percentage in pea seeds, their stability and variability depending on weather changes during a vegetation period. The weather conditions of 2022–2024 were very contrasting, which allowed evaluating the significance of each productivity element in more detail. There has been found that close correlations between seed weight per plant and a number of seeds per plant (r = 0.79***…0.83***), as well as a number of beans per plant (r = 0.69**…0.80***), between a number of beans and seeds per plant (r = 0.79***…0.85***) were studied in relatively favorable years (2022 and 2023). Identification of valuable breeding material by indirect traits in extreme years (2024), when the HTC &lt; 0.2, was significantly complicated. All the obtained correlation coefficients under these conditions were low and unreliable. The results of the correlation of protein percentage in pea seeds with yield structure elements did not reveal a clear correlation between these traits. The correlation between protein percentage in pea seeds and yield structure elements has not been identified due to low and unreliable correlation coefficients, which allows directing pea breeding towards increasing protein percentage in seeds without taking into account the effect of seed productivity.

Genomic evaluation of residual feed intake in US Holstein cows: insights into lifetime feed efficiency

Residual feed intake (RFI) is an important trait of feed efficiency that has been increasingly considered in the breeding objectives for dairy cattle. The objectives of this study were to estimate the genetic parameters of RFI and its component traits, namely, dry-matter intake (DMI), body weight (BW), and energy-corrected milk (ECM), in lactating Holstein cows; we thus developed a system for genomic evaluation of RFI in lactating Holstein cows and explored the associations of the RFI of heifers and cows. The RFI values were calculated from 2,538 first (n = 2,118) and second (n = 420) lactation Holsteins cows between 2020 and 2024 as part of the STgenetics EcoFeed® program. Of the animals, 1,516 were heifers from the same research station with previously established RFI values . After quality control, 61,283 single-nucleotide polymorphisms were used for the analyses. Univariate analyses were performed to estimate the heritabilities of RFI and its components in lactating cows; bivariate analyses were then performed to estimate the genetic correlations between the RFI of heifers and lactating cows using the genomic best unbiased linear prediction method. Animals with phenotypes and genotypes were used as the training population, and animals with only genotypes were considered the prediction population. The reliability of breeding values was obtained by approximation based on partitioning a function of the accuracy of the training population’s genomic estimated breeding values (GEBVs) and magnitudes of genomic relationships between the individuals in the training and prediction populations. The heritability estimates (mean ± SE) of the RFI, DMI, ECM, and BW were 0.43 ± 0.07, 0.44 ± 0.04, 0.40 ± 0.05, and 0.46 ± 0.04, respectively. The average reliability of the GEBVs for RFI from the training and prediction populations were 44% and 30%, respectively. The genetic correlations for the RFI were 0.42 ± 0.08 between heifers and first lactation cows and 0.34 ± 0.06 between heifers and first and second lactation cows. Our results show that the genetic components of RFI are not fully carried over from heifers to cows and that there is re-ranking of the individuals at different life stages. Selection of animals for feed efficiency on a lifetime basis thus requires accounting for the efficiencies during animal growth and milk production as a lactating cow.

Refining penalized ridge regression: a novel method for optimizing the regularization parameter in genomic prediction.

The popularity of genomic selection as an efficient and cost-effective approach to estimate breeding values continues to increase, due in part to the significant saving in phenotyping. Ridge regression is one of the most popular methods used for genomic prediction; however, its efficiency (in terms of prediction performance) depends on the appropriate tunning of the penalization parameter. In this paper we propose a novel, more efficient method to select the optimal penalization parameter for Ridge regression. We compared the proposed method with the conventional method to select the penalization parameter in 14 real data sets and we found that in 13 of these, the proposed method outperformed the conventional method and across data sets the gains in prediction accuracy in terms of Pearson's correlation was of 56.15%, with not-gains observed in terms of normalized mean square error. Finally, our results show evidence of the potential of the proposed method, and we encourage its adoption to improve the selection of candidate lines in the context of plant breeding.

The impact of bull breeding management on reproductive function (review)

Providing the population with quality food products is currently relevant. High-tech production of meat and dairy products requires breeding animals with high genetic potential. The main role is given to bull sires, since they are responsible for the constant reproduction of the breeding stock, which depends on the non-stop renovation of the herd with high breeding value’s calves. Management of growing and breeding of young bulls can directly affect the reproductive function of sires and the physiological health. The article provides analytical data based on literary sources containing research by various authors on this topic, and reviews methods and technologies for keeping breeding bulls on various farms in the Russia and abroad. Currently, there is a many problems of getting high-quality breeding young bulls to an artificial insemination center. Currently, there are problems with obtaining high-quality breeding bulls of the enterprise. There are animals with abnormal development of the musculoskeletal system, joints, hooves, with signs of osteomalacia, etc. Such animals require additional costs for veterinary and breeding services. The quality of the sperm products they produce often does not comply with state normative. The reason is related either to the choice of maintenance technology or feeding technology. It is necessary to competently organize management, breeding and veterinary work in farms that are purposefully engaged in breeding young animals.

Estimation of dominance genetic effects on pre-weaning efficiency-related traits in Baluchi sheep

Abstract This study was conducted to estimate the relative contribution of dominance genetic effects to efficiency-related traits including Kleiber ratio (KR), efficiency of growth (EF) and relative growth rate (RGR) in Baluchi sheep. To this end, each trait was analysed with a series of 12 animal models which were identical for fixed and additive genetic effects but differed for combinations of dominance genetic and maternal effects. The Akaike's information criterion (AIC) was used to rank models. (Co)variances between traits were estimated using bivariate analyses. For all traits studied, according to AIC values, models containing the dominance genetic effects provided a better data fit than otherwise identical models. By including dominance genetic effects in the model, additive genetic variance did not change, but a significant decrease was observed in the residual variance (24, 19 and 25% for KR, EF and RGR, respectively). Estimates of dominance heritability $( {\boldsymbol h}_{\boldsymbol d}^ 2 )$ were 0.20 ± 0.05, 0.17 ± 0.05 and 0.19 ± 0.07 for KR, EF and RGR, respectively, more than corresponding estimates of additive heritability ${\bf ( }{\boldsymbol h}_{\boldsymbol a}^{\bf 2} {\bf ) }$ as 0.14 ± 0.02, 0.09 ± 0.03 and 0.13 ± 0.02, respectively. Dominance genetic correlations between traits were 0.89 ± 0.17 (KR-EF), 0.86 ± 0.20 (KR-RGR) and 0.93 ± 0.21 (EF-RGR). Additive genetic correlations between traits were 0.84 ± 0.05 (KR-EF), 0.78 ± 0.04 (KR-RGR) and 0.83 ± 0.04 (EF-RGR). The Spearman correlation between additive breeding values including and excluding dominance genetic effects were close to unity either for all animals or top ranked animals. Since presence of dominance genetic effects increased the model power to fit the data, inclusion of these effects in the genetic evaluation models for Baluchi sheep was recommended.

Productivity and adaptive properties of different origin potato varieties in the Russia’s Far East south

The paper presents the results of a study on the productivity of potato genotypes of different origin depending on the water availability in soil, including the hydrothermal coefficient of Selyaninov (HTC). The group of mid-season varieties was noted to have the highest productivity (710 g/plant). Waterlogging occurred during the growing period of plants (HTC ≥ 2.5), especially at the stage of tuber formation, negatively affecting productivity. High productivity was observed in the years with relatively favorable weather conditions (2019, 2021, and 2022) when the index of environmental conditions (I) was positive and ranged within +201.63 – +221.35. The average productivity of all the specimens was 950 g/plant. Very low productivity (420–670 g/plant) was observed in 2015–2018, 2020, and 2023 due to waterlogging (I = -99.52 – -269.81). Among the studied specimens, we identified the following varieties that were characterized by not only high productivity (above 900 g/plant) but also high responsiveness to changes in the environmental conditions and stability (average bi = 1.49; S2d·103 = 0.42): early-maturing varieties – Antonina, Bastion, Kolymskii, Krepysh, Matushka, Meteor, Pamyati Kulakova, Udacha, Vitesse, Red Lady, and Red Scarlett; medium-early varieties – Arktika, Briz, Zoya, Kamchatka, Lileya, Charodei, and Gala; mid-season varieties – Ocharovanie, Utro, and Favorit; medium-late and late varieties – Kazachok and Pobeda. The research allowed us to identify potato specimens with high productivity (1040-1480 g/plant), marketability (83.2–92.8%), plasticity (bi = 1.20–1.85), stability (S2d = 0,15–5,77), homeostasis (Hom = 9.51–40.62) and breeding value (Sc = 532.79–852.14) under the conditions of the south of the Russian Far East: early maturing varieties – Bastion, Kolymskii, Krepysh, and Pamyati Kulakova; medium-early varieties – Arktika and Zoya; mid-season variety Alyaska; medium-late and late varieties – Vetraz’ and Pobeda.

Methods of Calculating Prediction Error Variance and Prediction Accuracy for Restricted Best Linear Unbiased Prediction of Breeding Values.

Prediction error variance (PEV) and prediction accuracy (PA) of breeding values (BVs) are essential for formulating breeding plans and predicting response to selection. However, restricted best linear unbiased prediction method (RBLUP method) carries many unknowns: in particular, the formulas for calculating PEV and PA are not clear. New findings were obtained using the RBLUP method. The uniqueness of RBLUP of BVs was proven. The formulas of PEV and PA for the RBLUP of BVs were derived from restricted mixed model equations. A method was also devised for easily calculating the PEV and PA for the RBLUP of BVs. Finally, the relationship between the RBLUP and ordinary BLUP of BVs was derived. It has become easier to calculate the PEV and PA for the RBLUP of BVs. This method is particularly effective for calculating the PEV and PA when applying the RBLUP method to achieve relative desired changes in all traits. This has also made it possible to predict the response to selection using the RBLUP method.

Genetic improvement of indigenous cattle breeds in Ethiopia: A systematic review of the Fogera cattle open nucleus breeding scheme

Fogera cattle are one of the valuable indigenous milk-type local breeds of Ethiopia, widely adapted to the area around Lake Tana in the Amhara region. The objective of this systematic review was to evaluate the performance of the Fogera cattle breed under an open nucleus breeding scheme. The review was done systematically by collecting published and unpublished data sources on the breed. The overall milk yield of the nucleus Fogera cattle herd was 2.26±0.794L/day. From the total herd, the top 10% and 25% of them produced daily milk yields of 3.31 and 2.87L, respectively, and some elite cows gave an average of 5.45±0.73L/day with a maximum yield of 8L/day. The predicted 305-day milk yield for the top 10% and 25% of the total herd was 883.64 and 772.83L, respectively. The average lactation milk yield and lactation length were reported to be 489±184L and 243±72.79 days, respectively. The respective heritability estimates for the aforementioned traits were 0.20±0.23 and 0.27±0.001. The birth and weaning weights (at 8 months of age) of village Fogera cattle born from community-based breeding programmes (CBBP) were 23.77±.21 and 85.89±1.07kg, respectively. The average weaning age for the CBBP herds was reduced to 8 months. The overall calf mortality in the nucleus herd was 3%. The CBBP demonstrated that it could act as a significant entry point for ensuring the conservation and restocking efforts of this breed as a country asset.

WC2, a Novel Rosa roxburghii Genotype with Superior Nutritional and Breeding Value

WC2, a Novel Rosa roxburghii Genotype with Superior Nutritional and Breeding Value

Genetic aspects of major blood metabolites in the Italian Simmental cattle population

The Simmental breed is widely known for its resilience, robustness, and resistance to disease and therefore the incidence of ketosis in this breed is generally lower compared with Holsteins. Blood concentrations of nonesterified fatty acids (NEFA), BHB, and urea provide valuable information about the metabolic, health, and nutritional status of lactating animals. In the present study we estimated h2 of BHB, NEFA, and urea in blood predicted from milk mid-infrared spectra and assessed their genetic correlation with milk yield and composition traits in the Italian Simmental cattle breed using phenotypes of 3,549 cows in 207 herds. Two sets were considered: early (1,920 records - 1 per cow - between 5 and 35 DIM) and whole (14,378 records - at least 3 per cow - between 5 and 305 d in milk) lactation. In early lactation, h2 were 0.06 for blood BHB as is, 0.06 for log-transformed BHB, 0.18 for NEFA, 0.14 for log-transformed NEFA, and 0.05 for blood urea. In whole lactation, the h2 were 0.09 for blood BHB as is, 0.16 for log-transformed BHB, 0.03 for NEFA, 0.04 for log-transformed NEFA, and 0.04 for blood urea. As far as the genetic correlations were concerned, blood BHB was positively correlated with NEFA and blood urea. Blood BHB and NEFA were generally positively correlated with milk fat-to-protein ratio and milk BHB; blood BHB was negatively correlated with lactose content and positively with SCS. Sires' estimated breeding values for BHB (accuracy ≥0.60) were extrapolated for a-posteriori evaluation of the daughters' observed performance. The progeny of the top 5% of sires exhibited, on average, lower blood BHB, NEFA, and urea compared with the daughters of the bottom 5%. Overall, it is highly recommended to monitor the genetic variability of the metabolic traits in the dual-purpose Italian Simmental breed to monitor the metabolic diseases incidence in future generations.

African Horned Melon (Cucumis metuliferus): Climate‐Resilient Crop and Gene Donor in the Breeding of Major Cucurbits

ABSTRACTAfrican horned melon (Cucumis metuliferus E. Meyer ex Naudin, 2n = 2x = 24) is an under‐researched and under‐utilised cucurbit crop primarily grown for its nutritious fruit. In its centre of diversity, the crop is valued for its relatively high tolerance to insect pests, diseases, drought and heat stress. It is a potential opportunity crop and a valuable source of genes to major Cucumis species, including cucumber (Cucumis sativas L.) and melon (C. melo L.). Its climate resilience and nutrient‐rich fruit provide niche market opportunities. Hence, production and value‐adding will make African horned melon a crop of choice globally. There is a need for an in‐depth investigation into the genetic diversity, breeding and food composition of the crop. Therefore, the objective of this review was to provide perspectives on the production and breeding status of African horned melon to appraise its genetic value for human welfare, strategic production, genetic conservation and breeding of market‐preferred varieties, including closely related Cucumis species. The first section described the botany, production status, germplasm resources and characterisation based on phenotypic and genetic markers. This is followed by breeding progress for biotic and abiotic stress tolerance, utilities and challenges of gene transfer and potential rootstock to Cucumis species, especially cucumber and melon. The review summarised the main breeding goals and approaches, including mutation breeding to fast‐track the development of new varieties. Information presented in the review will guide cultivar design in African horned melon or related cucurbits, aiming for superior agronomic and nutritional quality traits and tolerance to biotic and abiotic stresses.

Genotype Performance Estimation in Targeted Production Environments by Using Sparse Genomic Prediction.

In plant breeding, Multi-Environment Trials (METs) evaluate candidate genotypes across various conditions, which is financially costly due to extensive field testing. Sparse testing addresses this challenge by evaluating some genotypes in selected environments, allowing for a broader range of environments without significantly increasing costs. This approach integrates genomic information to adjust phenotypic data, leading to more accurate genetic effect estimations. Various sparse testing methods have been explored to optimize resource use. This study employed Incomplete Block Design (IBD) to allocate lines to environments, ensuring not all lines were tested in every environment. We compared IBD to Random line allocation, maintaining a consistent number of environments per line across both methods. The primary objective was to estimate grain yield performance of lines using Genomic Estimated Breeding Values (GEBVs) computed through six Genomic Best Linear Unbiased Predictor (GBLUP) methods. In the first five methods, missing values were predicted before cross-environment adjustment; in the sixth, adjustment was performed directly. Using the Bayesian GBLUP model, we analyzed genotype performance under both IBD and random allocation. Results indicate that computing GEBVs for a target population of environments (TPE) using available phenotype and marker data is effective for selection. The IBD method showed superior performance with less variability compared to random allocation. These findings suggest that using IBD designs can enhance selection accuracy and efficiency, and that pre-adjustment prediction of missing lines may not necessarily improve selection outcomes.

Genomic selection in forest trees comes to life: unraveling its potential in an advanced four-generation Eucalyptus grandis population.

Genomic Selection (GS) in tree breeding optimizes genetic gains by leveraging genomic data to enable early selection of seedlings without phenotypic data reducing breeding cycle and increasing selection intensity. Traditional assessments of the potential of GS in forest trees have typically focused on model performance using cross-validation within the same generation but evaluating effectively realized predictive ability (RPA) across generations is crucial. This study estimated RPAs for volume growth (VOL), wood density (WD), and pulp yield (PY) across four generations breeding of Eucalyptus grandis. The training set spanned three generations, including 34,461 trees with three-year growth data, 6,014 trees with wood quality trait data, and 1,918 trees with 12,695 SNPs (single nucleotide polymorphisms) data. Employing single-step genomic BLUP, we compared the genomic predictions of breeding values (GEBVs) for 1,153 fourth-generation full-sib seedlings in the greenhouse with their later-collected phenotypic estimated breeding values (EBVs) at age three years. RPAs were estimated using three GS targets (individual trees, trees within families, and families), two selection criteria (single- and multiple-trait), and training populations of either all 1,918 genotyped trees or the 67 direct ancestors of the selection candidates. RPAs were higher for wood quality traits (0.33 to 0.59) compared to VOL (0.14 to 0.19) and improved for wood traits (0.42 to 0.75) but not for VOL when trained only with direct ancestors, highlighting the challenges in accurately predicting growth traits. GS was more effective at excluding bottom-ranked candidates than selecting top-ranked ones. The between-family GS approach outperformed individual-tree selection for VOL (0.11 to 0.16) and PY (0.72 to 0.75), but not for WD (0.43 vs. 0.42). Furthermore, higher levels of relatedness and lower genotype by environment (G × E) interaction between training and testing populations enhanced RPAs for VOL (0.39). In summary, despite limited effectiveness in ranking top VOL individuals, GS effectively identified low-performing individuals and families. These multi-generational findings underscore GS's potential in tree breeding, stressing the importance of considering relatedness and G × E interaction for optimal performance.

Comparison of blood parameters in two genetically different groups of horses for functional longevity in show jumping.

In order to find early selection criteria to improve the longevity of show jumping horses, a specific protocol was designed. Before entering competition, young horses selected from extreme stallions for longevity were measured for many traits, including blood parameters. Blood samples were taken from 952 horses aged 2-4years old, sired by two groups of stallions: one with unfavorable (U) and the other with favorable (F) extreme estimated breeding values for functional longevity. These breeding values were previously calculated from data on 202,320 horses that participated in show jumping competitions between 1985 and 2022. Functional longevity was defined as time spent in competition, adjusted for the level of performance. The 59 measured parameters included hematology, proteins, cytokines, liver and kidney function, bone and joint health, oxidative stress and endocrinology. Heritability was estimated using a mixed model that accounted for the effect of age, sex, estimated weight, visit (place and date of collection), and animal random additive value with 10,280 horses in pedigree. A Partial Least Square logistic regression was performed to predict the sire group. Age, sex and estimated weight significantly affected 36, 19 and 16 variables, respectively. The visit had a significant effect on all variables. Heritability estimates were high, with 75% higher than 0.20% and 30% higher than 0.50. The most heritable traits included mean corpuscular volume (0.92, se 0.11), mean corpuscular hemoglobin (0.90, se 0.11), white blood cells (0.55, se 0.13), total alkaline phosphatase (0.68, se 0.12) and percentage of γ-globulin (0.57, se 0.12). The logistic regression that predicted the group of sires favorable for longevity identified 16 significant variables. Key findings included: lower mean corpuscular hemoglobin (p-value < 0.001), lower mean corpuscular volume (p-value < 0.001), lower number of white blood cells (p-value < 0.01), higher percentage of intestinal and bone alkaline phosphatase (p-value < 0.01) for a lower total alkaline phosphatase (p-value < 0.01), higher percentage of α2-globulin (p-value < 0.001) and lower percentage of β1-globulin (p-value < 0.01). Blood parameters measured at rest in young horses may be predictive of their genetic value for functional longevity in show jumping.

Genomic Prediction and Genome-Wide Association Studies for Productivity, Conformation and Heat Tolerance Traits in Tropical Smallholder Dairy Cows.

Genomic selection (GS) and genome-wide association studies (GWAS) have not been investigated in Vietnamese dairy cattle, even for basic milk production traits, largely due to the scarcity of individual phenotype recording in smallholder dairy farms (SDFs). This study aimed to estimate heritability (h2) and test the applicability of GS and GWAS for milk production, body conformation and novel heat tolerance traits using single test day phenotypic data. Thirty-two SDFs located in either the north (a lowland vs. a highland) or the south (a lowland vs. a highland) of Vietnam were each visited for an afternoon and the next morning to collect phenotype data of all lactating cows (n = 345). Tail hair from each cow was sampled for subsequent genotyping with a 50K SNP chip at that same visit. Milk production traits (single-test day) were milk yield (MILK, kg/cow/day), energy corrected milk yield adjusted for body weight (ECMbw, kg/100 kg BW/day), fat (mFA, %), protein (mPR, %) and dry matter (mDM, %). Conformation traits were body weight (BW, kg) and body condition score (BCS, 1 = thin to 5 = obese). Heat tolerance traits were panting score (PS, 0 = normal to 4.5 = extremely heat-stressed) and infrared temperatures (IRTs, °C) at 11 areas on the external body surface of the cow (inner vulval lip, outer vulval surface, inner tail base surface, ocular area, muzzle, armpit area, paralumbar fossa area, fore udder, rear udder, forehoof and hind hoof), assessed by an Infrared Camera. Univariate linear mixed models and a 10-fold cross-validation approach were applied for GS. Univariate single SNP mixed linear models were applied for the GWAS. Estimated h2 (using the genotype information to build relationships among animals) were moderate (0.20-0.37) for ECMbw, mFA, mPR, mRE, BW, BCS and IRT at rear udder; low (0.08-0.19) for PS and other IRTs; and very low (≤ 0.07) for MILK, ECM and mDM. Accuracy of genomic estimated breeding values (GEBVs) was low (≤ 0.12) for MILK, ECM, mDM and IRT at hind hoof; and moderate to high (0.32-0.46) for all other traits. The most significant regions on chromosomes (BTA) associated with milk production traits were 0.47-1.18 Mb on BTA14. Moderate to high h2 and moderate accuracies of GEBVs for mFA, mPR, ECMbw, BCS, BW, PS and IRTs at rear udder and outer vulval surface suggested that GS using single test day phenotypic data could be applied for these traits. However, a greater sample size is required to decrease the bias of GEBVs by GS and increase the power of detecting significant quantitative trait loci (QTLs) by GWAS.