Breeding Values Research Articles

65 Effect of post-weaning fecal egg count EBV on grazing ewe behavior after weaning

Abstract Post weaning fecal egg count (PFEC) estimated breeding value (EBV) has been recently linked with improved immune competence and lamb survival to weaning, thus improving health though selection. Animal movement is also linked to animal health, however little is known about behavior differences in sheep with divergent PFEC EBV. Thirty Katahdin ewes were selected after weaning, from the Southwest Virginia Agricultural Research and Extension Center. Where high PFEC ewes (EBV > 172) and 15 low PFEC (EBV < -84) were selected. Upon placement into pastures, ewes were fitted with Digitanimal GPS collars that measure GPS location every 15 min and store data on a micro SD card. Additional data collection included bi-weekly body weights, FAMACHA scores, body condition scores and fecal egg count. There was no difference in any physiological measurement in these ewes for the duration of this 4-week study. However, significant differences were observed between groups for daily distance traveled. On average, High-PFEC ewes traveled 4.52 km, where low ewes traveled only 4.16 km (P < 0.0001). The average distance traveled between GPS signals (every 15 min) was 48.6 m for high ewes and 47.0 m for Low-PFEC ewes (P < 0.0001). Interestingly, the time of day where maximum activity occurred (1506 h vs 1448 h; High and Low-PFEC respectively) was not different, nor was the distance those ewes traveled in that hour (522.6 vs 519.2 m). These data provide a preliminary snapshot of movement differences that occur between PFEC groups and may inform future studies to evaluate linkage between health and movement in grazing livestock.

66 Effect of copper oxide wire particles on parasitism and growth in Katahdin lambs divergently selected for fecal egg count estimated breeding value

Abstract Internal parasites are one of the greatest threats to small ruminant production across the United States and parasitic resistance to anthelmintics continues to increase. Genetic selection and copper oxide wire particles (COWP) are two tools that could help reduce effects of parasites in small ruminants. The objective of this project was to determine the effects and interactions of COWP and genetic selection based on fecal egg count (FEC) estimated breeding value (EBV) on gastrointestinal nematode (GIN) infection and performance in naturally infected Katahdin lambs. Spring-born Katahdin ram lambs (n = 52) selected for extremely high FEC EBV (HighFEC, PFEC EBV = +100%) or low FEC EBV (LowFEC, PFEC EBV = -81%) were managed on a fescue-based pasture at the Southwest Virginia Agricultural Research and Extension Center (Glade Spring, VA). At weaning (June 29), lambs were dewormed with a three-combination treatment and rested for 21 d. At the start of the project (July 18), a random subset of lambs (HighFEC, n = 11; LowFEC, n = 15) were given a 2 g copper oxide wire particle bolus, while an equal number of remaining lambs were managed as controls without a copper bolus. All lambs were managed as one contemporary group. Fecal egg counts, FAMACHA scores, body weights, and packed cell volume (PCV) were measured bi-weekly for 10 weeks. Statistical analysis was performed using PROC Mixed Procedure of SAS (SAS Institute, Cary, NC) and FEC data were log transformed for normality. There were no effects of COWP treatment on FEC (P = 0.18). However, LowFEC-selected lambs had less FEC than HighFEC-selected lambs (230 vs. 565 eggs/g, respectively; P < 0.01). There was no interaction of COWP treatment and FEC genotype (P = 0.37). Copper oxide wire particle treatment had no effect on lamb body weight (P = 0.21), PCV (P = 0.69), FAMACHA score (P = 0.41), or overall ADG (P = 0.12). Lamb FEC genotype did not affect FAMACHA score or overall ADG (P = 0.10 and P = 0.22, respectively). LowFEC-selected lambs had lighter body weights (27.7 vs. 30.6 kg, respectively) and PCV (29.2% vs. 30.4%, respectively) than the HighFEC-selected lambs (P < 0.05). None of the 52 lambs met the requirement for deworming (FAMACHA ≥ 3) during the study. These results indicate that lamb FEC genotype may have a greater effect on parasitism than COWP treatment when given once during the post-weaning period. Even so, multiple tools should be considered, in addition to selection, for an integrated parasite management plan.

55 Evaluation of feed efficiency and feeding behavior in Katahdin lambs housed in a Vytelle Sense system

Abstract Genetic selection focused on feed efficiency may prove to be valuable in reducing feed consumption costs and animal maintenance requirements without compromising growth. The Vytelle Sense system allows measurement of individual lamb DMI and varying feeding behaviors to identify feed efficient lambs. The aim of this study was to evaluate the impact of sire selection based on fecal egg count estimated breeding value (FEC EBV) on residual feed intake (RFI), gain:feed (G:F), and daily DMI. Katahdin ewes at the Virginia Tech Southwest Virginia Agricultural and Research Center (SWAREC) were mated to Katahdin rams (LowFEC: n = 4; HighFEC: n = 4) to divergently select for FEC EBV in their offspring (LowFEC: n = 36; HighFEC: n = 32). Post-weaning, lambs were fed a complete pellet (14% CP, 76% TDN) ad libitum in the Vytelle Sense system for 58 days. RFI was calculated as the difference between actual and expected DMI. DMI was predicted based on ADG and metabolic body size. Feeding duration per meal and DMI per meal were analyzed to determine feeding behavior. Statistical analyses were performed using PROC Mixed SAS (SAS Institute, Cary, NC). Sire FEC EBV type tended to have an effect on RFI (P = 0.06). Individual sire had a significant effect on RFI, G:F, and daily DMI (P < 0.05). Sex did not impact RFI (P > 0.10). Ram lambs had greater G:F values compared with ewes (0.32 vs. 0.26 kg; P < 0.05). Rams had greater daily DMI compared with ewes (2.37 vs. 1.89 kg/d; P < 0.05). Significance was detected between sires for meal duration (P < 0.05). Ewe lambs exhibited greater meal duration compared with rams (237.3 vs. 203.4 s/meal; P < 0.05). However, ewes had reduced DMI per meal compared with rams (0.29 vs. 0.34 kg/meal; P < 0.05). Individual sire did not have an effect on meal DMI (P > 0.10). To evaluate sire impact on RFI across multiple locations, LowFEC Katahdin rams (n = 2), mentioned previously, were bred to Katahdin ewes at the NC State University Upper Piedmont Research Station (UPRS) and the SWAREC. Genetic connectedness between locations allowed for comparisons of progeny performance. Post-weaning, UPRS lambs were fed a complete pellet (16% CP, 75% TDN) ad libitum in the Vytelle sense system for 45 d. Location did not impact RFI prediction for either sire (P > 0.10). Variation between sires indicates potential sire impact on progeny feed efficiency and feeding behavior. Further studies are needed to evaluate sire RFI trends across multiple locations.

74 Effect of grazing native warm season grasses on parasitism and growth in weaned Katahdin lambs

Abstract Parasitism in lambs during the post-weaning period has effects on growth, survival, and sheep enterprise profitability. Opportunities to minimize exposure to infective larvae during this period may reduce clinical effects and improve lamb performance. Native warm season grasses (NWSG) offer increased growth during summer months and taller plants that allow grazing at greater heights. The objective here was to evaluate NWSG as a forage for post-weaning development of lambs in spring lambing, forage-based systems. Over 2 yr, Katahdin lambs (Yr 1, n = 30; Yr 2, n = 31) were managed at a producer farm in the piedmont region of North Carolina. Beginning 2 wk post-weaning, lambs were randomly assigned to graze on either NWSG (Big Bluestem and Indiangrass) or cool season grass (CSG, Kentucky 31 Tall Fescue base) pasture. Lambs were continuously grazed for 70 d in Yr 1 and 55 d in Yr 2. Fecal egg count (FEC), FAMACHA score, and body weight (BW) were measured every two weeks. Lambs were dewormed at FAMACHA ≥ 3. Forage analysis was conducted at the start and end of the grazing period. Statistical analysis were performance using the PROC Mixed procedure of SAS. Lambs in each group were raised in similar litter sizes and sire and sex were evenly distributed between groups. Fecal egg count and growth estimated breeding values (EBV) were similar between NWSG and CSG groups. In Yr 1 and 2, no differences were observed in FEC between grazing groups (P > 0.05). However, FEC did increase in both groups throughout the grazing period (P < 0.05). In Yr 1, no differences were observed in total BW gain (P = 0.74). However, in Yr 2, lambs grazing CSG had greater total BW gain than the NWSG group (P < 0.05). In both years, there were no differences in the percentage of lambs requiring deworming (Yr 1, P = 0.36; Yr 2, P = 0.47) but lambs requiring treatment had greater FEC EBVs (P < 0.05). Nutrient quality of NWSG pasture was reduced compared with CSG pastures. Total digestible nutrients and crude protein were less in NWSG compared with CSG pastures (Yr 2: 61.5% vs. 63.9% and 8.9% vs. 13.6%, respectively) and neutral detergent fiber was greater (Yr 2: 74.1% vs. 65.2%, respectively) resulting in decreased nutrient availability for NWSG group. While NWSG may provide additional biomass for summer grazing, rapid maturity rates may limit nutrient availability for lambs resulting in decreased growth performance despite similar levels of parasitism. Lamb FEC EBV may have a role in parasite resistance and deworming requirements regardless of forage type. Thus, genetics must be considered along with grazing management as part of an integrated parasite management plan.

Morphological and molecular analysis of rose cultivars from the Grandiflora and Kordesii garden groups.

The breeding of remontant rose cultivars that are resistant to diseases and adverse conditions, with high decorative value and continuous flowering is the most important task during work with the gene pool of garden roses. Currently, intercultivar hybridization within a single garden group has largely outlived its usefulness. It is necessary to breed for highly decorative forms or cultivars that have outstanding resistance, morphological characters and patterns of seasonal rhythms, and use these plants as parental forms in further breeding. This study represents a comparative analysis of rose cultivars from two garden groups, Grandiflora (Gurzuf, Lezginka, Korallovy Syurpriz, Queen Elizabeth, Komsomolsky Ogonyok, Love) and Rosa Kordesii (Letniye Zvyozdy, Dortmund, Gutsulochka). These cultivars proved themselves during many years of testing in harsh climatic conditions. The objectives of the study were to determine the genetic relationship within the groups and to assign phenotypically different cultivars to one or another garden group. The analysis was carried out by morphological, phenological and ISSR markers. According to the phenological observations on the Grandiflora cultivars, Komsomolsky Ogonyok had later budding and flowering stages. Polymorphic data generated from the ISSR markers showed that this cultivar was the most distant from the others and formed a separate cluster on the dendrogram. A comparison of the morphological characters (flower diameter, number of petals, peduncle length, bush height) showed a significant difference ( p < 0.05) between Komsomolsky Ogonyok and the other Grandiflora cultivars. A dendrogram based on a molecular analysis showed a lack of close relationships between Komsomolsky Ogonyok and the Kordesii group, which formed a separate cluster. A pairwise comparison of the morphological characters in Komsomolsky Ogonyok with the Kordesii group revealed a significant ( p <0.05) difference in three of the four characters studied. The exceptions were flower diameter when comparing with Dortmund and Letniye Zvyozdy and peduncle length when comparing with Gutsulochka. Although Komsomolsky Ogonyok has a pattern of seasonal development similar to Dortmund in the Kordesii group, the molecular analysis did not assign the former to this group of roses. The cultivars that have valuable characters that no average rose does and that are phenotypically different from such roses represent the most valuable breeding material.

Measures to Revitalize Seed Processing to Improve Added Value in Crop Breeding

Measures to Revitalize Seed Processing to Improve Added Value in Crop Breeding

Connectedness rating among commercial pig breeding herds in Korea.

This study aims to estimate the connectedness rating (CR) of Korean swine breeding herds. Using 104,380 performance and 83,200 reproduction records from three swine breeds (Yorkshire, Landrace and Duroc), the CR was estimated for two traits: average daily gain (ADG) and number born alive (NBA) in eight breeding herds in the Republic of Korea (hereafter, Korea). The average CR for ADG in the Yorkshire breed ranges from 1.32% to 28.5% depending on the farm. The average CR for NBA in the Yorkshire herd ranges from 0% to 12.79%. A total of 60% of Yorkshire and Duroc herds satisfied the preconditions suggested for genetic evaluation among the herds. The precondition for the genetic evaluation of CR for ADG, as a productive trait, was higher than 3% and that of NBA, as a reproductive trait, was higher than 1.5%. The ADG in the Yorkshire herds showed the highest average CR. However, the average CR of ADG in the Landrace herds was lower than the criterion of the precondition. The prediction error variance of the difference (PEVD) was employed to assess the validation of the CR, as PEVDs exhibit fluctuations that are coupled with the CR across the herds. A certain degree of connectedness is essential to estimate breeding value comparisons between pig herds. This study suggests that it is possible to evaluate the genetic performance together for ADG and NBA in the Yorkshire herds since the preconditions were satisfied for these four herds. It is also possible to perform a joint genetic analysis of the ADG records of all Duroc herds since the preconditions were also satisfied. This study provides new insight into understanding the genetic connectedness of Korean pig breeding herds. CR could be utilized to accelerate the genetic progress of Korean pig breeding herds.

Using mixed kernel support vector machine to improve the predictive accuracy of genome selection1

The advantages of genome selection (GS) in animal and plant breeding are self-evident. Traditional parametric models have disadvantage in better fit the increasingly large sequencing data and capture complex effects accurately. Machine learning models have demonstrated remarkable potential in addressing these challenges. In this study, we introduced the concept of mixed kernel functions to explore the performance of support vector machine regression (SVR) in GS. Six single kernel functions (SVR_L, SVR_C, SVR_G, SVR_P, SVR_S, SVR_L) and four mixed kernel functions (SVR_GS, SVR_GP, SVR_LS, SVR_LP) were used to predict genome breeding values. The prediction accuracy, mean squared error (MSE) and mean absolute error (MAE) were used as evaluation indicators to compare with two traditional parametric models (GBLUP, BayesB) and two popular machine learning models (RF, KcRR). The results indicate that in most cases, the performance of the mixed kernel function model significantly outperforms that of GBLUP, BayesB and single kernel function. For instance, for T1 in the pig dataset, the predictive accuracy of SVR_GS is improved by 10% compared to GBLUP, and by approximately 4.4 and 18.6% compared to SVR_G and SVR_S respectively. For E1 in the wheat dataset, SVR_GS achieves 13.3% higher prediction accuracy than GBLUP. Among single kernel functions, the Laplacian and Gaussian kernel functions yield similar results, with the Gaussian kernel function performing better. The mixed kernel function notably reduces the MSE and MAE when compared to all single kernel functions. Furthermore, regarding runtime, SVR_GS and SVR_GP mixed kernel functions run approximately three times faster than GBLUP in the pig dataset, with only a slight increase in runtime compared to the single kernel function model. In summary, the mixed kernel function model of SVR demonstrates speed and accuracy competitiveness, and the model such as SVR_GS has important application potential for GS.

SYMPOSIUM REVIEW: EXPLORING SUSTAINABILITY IN DAIRY CATTLE BREEDING FOCUSING ON FEED EFFICINCY AND METHANE EMISSIONS

Reducing emissions is vital to improve sustainability and industry leaders have set emission goals to either reduce gross emissions, lower emissions intensity, or reach net-zero. However, additional traits should also be measured and compared in terms of their impact on the broader definition of sustainability. In addition to environmental impact, a sustainable breeding objective must consider profit, animal welfare, farmer well-being, and social responsibility. Traits to be considered include direct emissions (e.g., nitrogen and methane), production efficiency (e.g., feed efficiency), health (e.g., calf and transition cow health) and welfare traits (e.g., polled). Many of these novel traits require labor- and/or cost-intensive phenotyping procedures. Consequently, this results in relatively modest data sets and estimated breeding values with limited reliability. Opportunities exist to overcome this limitation by developing cost-effective and easily quantifiable proxy traits and utilizing international collaboration to expand novel phenotype reference populations. Furthermore, non-economic values can be estimated to quantify the impact a trait has on societal perspective (e.g., farmer preference) or environmental impact (methane emissions), and combined with economic weights to calculate aggregate weights for each trait. While validation techniques are still uncertain, the United Nations Sustainable Development Goals may be applied to determine the improvement in sustainability due to genetic selection. This approach provides the flexibility to accommodate diverse sustainability perspectives, encompassing the disparities between developed and developing countries. Currently, the number and quality of relevant phenotypes are the main limiting factor. However, as confidence grows in the opportunity to improve sustainability through genetic selection, substantial new investment will be required both in refining phenotyping methodologies and conceptualizing novel breeding designs.

Expected values for the accuracy of predicted breeding values accounting for genetic differences between reference and target populations

BackgroundGenetic merit, or breeding values as referred to in livestock and crop breeding programs, is one of the keys to the successful selection of animals in commercial farming systems. The developments in statistical methods during the twentieth century and single nucleotide polymorphism (SNP) chip technologies in the twenty-first century have revolutionized agricultural production, by allowing highly accurate predictions of breeding values for selection candidates at a very early age. Nonetheless, for many breeding populations, realized accuracies of predicted breeding values (PBV) remain below the theoretical maximum, even when the reference population is sufficiently large, and SNPs included in the model are in sufficient linkage disequilibrium (LD) with the quantitative trait locus (QTL). This is particularly noticeable over generations, as we observe the so-called erosion of the effects of SNPs due to recombinations, accompanied by the erosion of the accuracy of prediction. While accurately quantifying the erosion at the individual SNP level is a difficult and unresolved task, quantifying the erosion of the accuracy of prediction is a more tractable problem. In this paper, we describe a method that uses the relationship between reference and target populations to calculate expected values for the accuracies of predicted breeding values for non-phenotyped individuals accounting for erosion. The accuracy of the expected values was evaluated through simulations, and a further evaluation was performed on real data.ResultsUsing simulations, we empirically confirmed that our expected values for the accuracy of PBV accounting for erosion were able to correctly determine the prediction accuracy of breeding values for non-phenotyped individuals. When comparing the expected to the realized accuracies of PBV with real data, only one out of the four traits evaluated presented accuracies that were significantly higher than the expected, approaching h2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sqrt{{{\ ext{h}}}^{2}}$$\\end{document}.ConclusionsWe defined an index of genetic correlation between reference and target populations, which summarizes the expected overall erosion due to differences in allele frequencies and LD patterns between populations. We used this correlation along with a trait’s heritability to derive expected values for the accuracy (R\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{R}}$$\\end{document}) of PBV accounting for the erosion, and demonstrated that our derived ER|erosion\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{E}}\\left[{\ ext{R}}|{\ ext{erosion}}\\right]$$\\end{document} is a reliable metric.

Genetic parameters and trends for Johne's disease in US Holsteins: An updated study

Johne's disease (JD) is an infectious enteric disease in ruminants, causing substantial economic loss annually worldwide. This work aimed to estimate JD's genetic parameters and the phenotypic and genetic trends by incorporating recent data. It also explores the feasibility of a national genetic evaluation for JD susceptibility in Holstein cattle in the United States (US). The data were extracted from a Johne's disease data repository, maintained at the Council on Dairy Cattle Breeding (CDCB), and initially supplied by 2 dairy records processing centers. The data comprised 365,980 Holstein cows from 1,048 herds participating in a voluntary control program for JD. Two protocol kits, Idexx Paratuberculosis Screening Ab Test (IDX) and Parachek® 2 (PCK), were used to analyze milk samples with the Enzyme-Linked Immunosorbent Assay (ELISA) technique. Test results from the first 5 parities were considered. An animal was considered infected if it had at least one positive outcome. The overall average of JD incidence was 4.72% in these US Holstein cattle. Genotypes of 78,964 SNP markers were used for 25,000 animals randomly selected from the phenotyped population. Variance components and genetic parameters were estimated based on 3 models, namely, a pedigree-only threshold model (THR), a single-step threshold model (ssTHR), and a single-step linear model (ssLR). The posterior heritability estimates of JD susceptibility were low to moderate: 0.11 to 0.16 based on the 2 threshold models and 0.05 to 0.09 based on the linear model. The average reliability of estimated breeding values of JD susceptibility using single-step analysis for animals with or without phenotypes varied from 0.18 (THR) to 0.22 (ssLR) for IDX and from 0.14 (THR) to 0.18 (ssTHR and ssLR) for PCK. Despite no prior direct genetic selection against JD, the estimated genetic trends of JD susceptibility were negative and highly significant (P-value <0.01). The correlations of bulls' predicted transmitting abilities with economically important traits such as milk yield, milk protein, milk fat, somatic cell score, and mastitis were low, indicating a non-overlapping genetic selection process with traits in current genetic evaluations. Our results suggest the feasibility of reducing the JD incidence rate by incorporating it into the national genetic evaluation programs.

Enhancing honeybee breeding for sustainable agriculture through temperature and relative humidity monitoring

Through their vital role in pollination, honeybee colonies play a crucial part in sustaining biodiversity and ensuring global food security. This paper aims to: 1) assess temperature and relative humidity variations within the hive and at the brood level; 2) determine genetic parameters for these traits; and 3) predict Breeding Values (PBVs) for honeybee colonies. Temperature and relative humidity data, during the period 2020-2023, were collected in Northern Tunisia using sensors placed inside hives and at the brood level. A dataset comprising 214,128 records for temperature and relative humidity within hives, sourced from 317 devices, was used in this study. Additionally, 20,740 records for temperature and relative humidity obtained from 78 brood-level devices were incorporated into the analysis. Phenotypic and genetic parameters were computed for the four examined traits, and using a BLUP Animal model, colony breeding values (PBVs) were predicted. Main results indicated a highly significant influence (p&lt;0.01) of the month effect on the four temperature and relative humidity traits. Heritability estimates for in-hive temperature, in-hive relative humidity, brood relative humidity, and brood temperature were 0.14, 0.12, 0.16, and 0.28, respectively. Positive correlations were observed between relative humidity inside hives and at the brood level, as well as between temperature within beehives and at the brood level. Colony breeding values were predicted to select the best adapted bee queens to enhance honeybee’s sustainable use under Southern Mediterranean climatic conditions.

Relationship between evaluations of sires in pale-motley cattle population at different levels of management

Reproductive technologies have led to a wide and global distribution of genetic material from animals with high productivity. However, the distribution of semen from the same bulls to different regions (countries) may not always meet the expectations of livestock breeders. This aspect may be due to the fact that the genetic structure of the breeding stock, breeding goals and environmental conditions vary from region to region. The article presents the results of evaluating the breeding value of 286 sires used on the breeding stock of the pale-motley dairy cattle population in five regions of the Russian Federation (Belgorod, Voronezh, Kursk, Oryol regions and Altai Territory). The evaluation was carried out according to individual traits of daughters' milk productivity, and on multiple traits (selection index). Based on the results of the study, differences were identified in estimates of the breeding value of the same sires at the level of total information (population level) and in the herds of single regions (regional level). The accuracy of evaluating genotypes at the population management level was significantly higher (by 7–15 percent) than it was at regional levels. The correlations between the breeding value of the same sires at different levels of management (population-region) for single traits of daughter’s milk productivity were 0.522–0.960, for the complex of traits (selection index) – from 0.157 to 0.937. This indicates that when selecting the best sires at the level of an individual region, mistakes can reach 4–48 % for single milk productivity traits and from 6.3 to 84 % for their complex.

A comparison of genetic and genomic breeding values in Saanen and Alpine goats

Nowadays, several countries are developing or adopting genomic selection in the dairy goat sector. The most used method to estimate breeding values is Single-Step Genomic Best Linear Unbiased Prediction (ssGBLUP) which offers several advantages in terms of computational process and accuracy of the estimated breeding values (EBVs). Saanen and Alpine are the predominant dairy goat breeds in Italy, and both have similar breeding programs where EBVs for productive traits are currently calculated using BLUP. This work describes the implementation of genomic selection for these two breeds in Italy, aligning with the selection practices already carried out in the international landscape. The available dataset included 3 611 genotyped animals, 11 470 lactation records, five traits (milk, protein and fat yields, and fat and protein percentages), and three-generation pedigrees. EBVs were estimated using BLUP, GBLUP, and ssGBLUP both with single and multiple trait approaches. The methods were compared in terms of correlation between EBVs and genetic trends. Results were also validated with the linear regression method excluding part of the phenotypic data. In both breeds, EBVs and GEBVs were strongly correlated and the trend of each trait was similar comparing the three methods. The average increase in accuracy across traits and methods amounted to +13 and +10% from BLUP to ssGBLUP for Alpine and Saanen breeds, respectively. Results indicated higher prediction accuracy and correlation for GBLUP and ssGBLUP compared to BLUP, implying that the use of genotypes increases the accuracy of EBVs, particularly in the absence of phenotypic data. Therefore, ssGBLUP is likely to be the most effective method to enhance genetic gain in Italian Saanen and Alpine goats.

Predicting bovine daily milk yield by leveraging genomic breeding values

The main goal of this work, conducted on a herd of 502 Holstein cows situated in Italy, is to propose a machine learning-based approach to forecast the individual bovine daily milk production by explicitly leveraging genotypic information. As part of our study, we also evaluated the importance in the prediction of genotypic and phenotypic variables usually available within herd. The methodology we propose is based on two consecutive models: a genomic prediction one to calculate the animal’s genomic breeding value from marker data, followed by a feed forward neural network combining such additive genetic effect and the environmental features (parity, days in milk, age at calving in months, month of calving) for milk yield forecasting. In particular, we both assess the inclusion of genomic breeding values calculated within herd or provided by the breeders’ association, discovering that the latter ones allow for better final predictions. The results of our model outperform the ones by a linear mixed model with the same inputs on average, day by day and at the individual level. Moreover, we propose a problem formulation that also leverages additional factors partially controllable by breeders: in this case, features such as the number of milkings and the concentrate consumption inside the automatic milking system prove to highly impact on the final prediction, and hence on milk production. To the best of our knowledge, the proposed problem formulation based on genomic breeding values is a novelty in the individual bovine milk yield machine learning forecasting literature. Given the low genotyping costs and the availability of a larger number of environmental features in farms equipped with a wide range of sensors, as automatic milking systems, our solution can support breeders’ herd management and animal monitoring, thanks to the possibility to forecast the full lactation curve in advance even for primiparous bovines and newborn calves. With this work, we successfully achieve our objectives of including genomic information in bovine milk yield machine learning-based forecasting, thus improving the performance on this task, and of evaluating the impact on prediction of common genotypic and phenotypic information available to breeders.

An overview of the red-fleshed apple: History and its importance for horticulturists, gardeners, nurserymen, and consumers

The present review summarizes the information on the botany, breeding, genetic features, cultivation, and nutraceutical values of red-fleshed apples. Malus sieversii var. niedzwetzkyana or Niedzwetzky’s apple became widely known after the publication of Georg Dieck in the last XIX century. It is characterized by anthocyanin coloration of all organs. Another source of red flesh is the ancient variety ‘Surprise’, which differs from the Niedzwetzky’s apple by a mutation in another gene. These two mutated genes, MYB10 and MYB110a, are located on chromosomes 9 and 17, respectively. Their expression correlated with the anthocyanin accumulation. In type 1 red-fleshed apples, which belong to the Niedzwetzky’s apple progeny, the transcription factor MYB10 confers extensive anthocyanin accumulation in fruit flesh and skin, flowers, leaves, and stems. In type 2 red-fleshed apples, the ‘Surprise’ progeny, only the flesh is red, determined by the transcription factor MYB110а. A new combination Malus domestica f. niedzwetzkyana comb. nov. has been proposed for orchard apples, originated from Niedzwetzky’s apple. The apple cultivars which are progeny of Niedzwetzky’s apple and ‘Surprise’ apple can be placed to Malus Niedzwetzkyana Group and Malus Surprise Group, respectively. All red-fleshed cultivars of orchard apple can be grouped together into Malus domestica Red Flesh Group. Many dozens of new ornamental apple trees have been developed for of European and North American conditions, including the most famous red-flowered centennial apple varieties such ‘Aldenhamensis’, ‘Lemoinei’, ‘Makamik’, and ‘Oekonomierat Echtermeyer’. Rootstocks ‘MB’, ‘PB9’, ‘54-118’, ‘62-396’ and others have become popular in nurseries for the propagation of dwarf and semi-dwarf apple trees thanks to their red leaves, which are useful when removing scion shoots from budded plants. In the F3-F4 and next generation, the new high-quality red-fleshed cultivars have been developed by crossing both Niedzwetzky’s apple and ‘Surprise’ apple with the best orchard apple varieties. Marker-assisted breeding shows great promise for accelerating the development of new red-fleshed cultivars with outstanding traits. The use of the Redlove®, Red Moon®, Kissabel®, and Lucy™ apples brands will ensure the red-fleshed apples’ best path to the consumer as well as the fruit quality and production volume of these novelties. Increasing the consumption of red-fleshed apples is essential for a healthy diet.

Multi-tissue gene expression profiling of cows with a genetic predisposition for low and high milk urea levels

Milk urea (MU) concentration is proposed as an indicator trait for breeding toward reduced nitrogen (N) emissions and leaching in dairy. We selected 20 German Holstein cows based on MU breeding values, with 10 cows each having low (LMUg) and high (HMUg) MU genetic predisposition. Using RNA-seq, we characterized these cows to unravel molecular pathways governing post-absorptive body N pools focusing on renal filtration and reabsorption of nitrogenous compounds, hepatic urea formation and mammary gland N excretion. While we observed minor adjustments in cellular energy metabolism in different tissues associated with different MU levels, no transcriptional differences in liver ammonia detoxification were detected, despite significant differences in MU between the groups. Differential expression of AQP3 and SLC38A2 in the kidney provides evidence for higher urea concentration in the collecting duct of LMU cows than HMU cows. The mammary gland exhibited the most significant differences, particularly in tricarboxylic acid (TCA) cycle genes, amino acid transport, tRNA binding, and casein synthesis. These findings suggest that selecting for lower MU could lead to altered urinary urea (UU) handling and changes in milk protein synthesis. However, given the genetic variability in N metabolism components, the long-term effectiveness of MU-based selection in reducing N emissions remains uncertain.

The genetic basis of dispersal in a vertebrate metapopulation.

Dispersal affects evolutionary processes by changing population size and genetic composition, influencing the viability and persistence of populations. Investigating which mechanisms underlie variation in dispersal phenotypes and whether populations harbour adaptive potential for dispersal is crucial to understanding the eco-evolutionary dynamics of this important trait. Here, we investigate the genetic architecture of dispersal among successfully recruited individuals in an insular metapopulation of house sparrows. We use an extensive long-term individual-based ecological data set and high-density single-nucleotide polymorphism (SNP) genotypes for over 2500 individuals. We conducted a genome-wide association study (GWAS), and found a relationship between dispersal probability and a SNP located near genes known to regulate circadian rhythm, glycogenesis and exercise performance, among other functions. However, this SNP only explained 3.8% of variance, suggesting that dispersal is a polygenic trait. We then used an animal model to estimate heritable genetic variation (σA 2 ), which composes 10% of the total variation in dispersal probability. Finally, we investigated differences in σA 2 across populations occupying ecologically relevant habitat types (farm vs. non-farm) using a genetic groups animal model. We found different adaptive potentials across habitats, with higher mean breeding value, σA 2 , and heritability for the habitat presenting lower dispersal rates, suggesting also different roles of environmental variation. Our results suggest a complex genetic architecture of dispersal and demonstrate that adaptive potential may be environment dependent in key eco-evolutionary traits. The eco-evolutionary implications of such environment dependence and consequent spatial variation are likely to become ever more important with the increased fragmentation and loss of suitable habitats for many natural populations.

Genetic Basis of Potato Tuber Defects and Identification of Heat-Tolerant Clones.

Heat stress during the potato growing season reduces tuber marketable yield and quality. Tuber quality deterioration includes external (heat sprouts, chained tubers, knobs) and internal (vascular discoloration, hollow heart, internal heat necrosis) tuber defects, as well as a reduction in their specific gravity and increases in reducing sugars that result in suboptimal (darker) processed products (french fries and chips). Successfully cultivating potatoes under heat-stress conditions requires planting heat-tolerant varieties that can produce high yields of marketable tubers, few external and internal tuber defects, high specific gravity, and low reducing sugars (in the case of processing potatoes). Heat tolerance is a complex trait, and understanding its genetic basis will aid in developing heat-tolerant potato varieties. A panel of 217 diverse potato clones was evaluated for yield and quality attributes in Dalhart (2019 and 2020) and Springlake (2020 and 2021), Texas, and genotyped with the Infinium 22 K V3 Potato Array. A genome-wide association study was performed to identify genomic regions associated with heat-tolerance traits using the GWASpoly package. Quantitative trait loci were identified on chromosomes 1, 3, 4, 6, 8, and 11 for external defects and on chromosomes 1, 2, 3, 10, and 11 for internal defects. Yield-related quantitative trait loci were detected on chromosomes 1, 6, and 10 pertaining to the average tuber weight and tuber number per plant. Genomic-estimated breeding values were calculated using the StageWise package. Clones with low genomic-estimated breeding values for tuber defects were identified as donors of good traits to improve heat tolerance. The identified genomic regions associated with heat-tolerance attributes and the genomic-estimated breeding values will be helpful to develop new potato cultivars with enhanced heat tolerance in potatoes.

Analysis of Yield Stability in Diverse Rice Genotypes

Aims/ objectives: This study aims to improve stable and sustainable rice varieties adaptable to changing climatic conditions. It involves assessing genetic variability and Genotype X Environment interaction (G x E) among 186 diverse rice genotypes. The goal is to select genotypes with high breeding value, contributing to the development of rice varieties well-suited to varying climatic conditions. Study Design: The study employed an augmented design in a Randomized Complete Block Design (RCBD). Swarna, Madhuraj-55, Safri-17, Improved Samba Mahsuri, Thavalkannan, and IR64 checks were replicated across environments. Place and Duration of Study: The study took place in College of Agriculture, Raipur, IGKV over two years (wet season-2020 and 2021). Methodology: Analysis of genetic variability and G X E interaction among 186 rice genotypes. Execution of the experiment in an augmented design with a randomized complete block design. Replication of standard rice checks across different environments. Assessment of yield-attributing traits such as plant height, number of effective tillers, panicle length, number of grains per panicle, biological yield per plot, and grain yield. Evaluation of stability was done using univariate (Shukla stability variance, Wricke’s ecovalence, Kang stability statistic) and multivariate (AMMI yield stability index and GGE biplot) stability parameters. Selection of stable genotypes with high yield based on stability analyses. Results: Significant phenotypic variation was observed in yield-attributing traits across seasons. Genetic variability and G x E interaction effect demonstrated by variable genotype performance across environments. Univariate and multivariate stability parameters (S2i, W2i, KSi, AMMI stability value, GGE biplot) were used for stability analyses. Identification of stable genotypes with high yield across environments, including IR13f167, ARC13156, IR93354, F50, Ngalongyi, Giza 178, Arc 10159, Sadajira 19-317, Arith, IR 57920-Ac 25-2-B, Pesagro 102, Mekenzie small, Nasaenge, Kula Karuppan, Vary Gony, MR 69, Kanu Dam, IRRI 123, Sativa IRGC17083-1, Kalia, and Swarna. Conclusion: The study concludes that stability in genotype performance across diverse environments is crucial for the development of sustainable rice varieties. Genotypes with high stability and yield, as identified through stability analyses, hold potential breeding value for developing rice varieties adaptive to climate change. The stable genotypes listed, including IR13f167, ARC13156, IR93354, and others, are recommended for further breeding and development efforts to enhance rice productivity and adaptability.