Invited review: The future of selection decisions and breeding programs: What are we breeding for, and who decides?

Exploring a Bayesian sparse factor model-based strategy for the genetic analysis of thousands of MIR-spectra traits for animal breeding

With the rapid development of animal phenomics and deep phenotyping, we can get thousands of traditional but also molecular phenotypes per individual. However, there is still a lack of exploration regarding how to handle this huge amount of data in the context of animal breeding, presenting a challenge that we are likely to encounter more and more in the future. This study aimed to (1) explore the use of the Mega-scale linear mixed model (MegaLMM), a factor model-based approach, able to simultaneously estimate (co)variance components and genetic parameters in the context of thousands of milk traits, hereafter called thousand-trait (TT) models; (2) compare the phenotype values and genomic breeding values (u) predictions for focal traits (i.e., traits that are targeted for prediction, compared with secondary traits that are helping to evaluate), from single-trait (ST) and TT models, respectively; (3) propose a new approximate method of estimated genomic breeding values (U) prediction with TT models and MegaLMM. 3,421 milk mid-infrared (MIR) spectra wavepoints (called secondary traits) and 3 focal traits [average fat percent (Fat), average methane (CH4), and average somatic cell score (SCS)] collected on 3,302 first-parity Holstein cows were used. The 3,421 milk MIR wavepoints traits were composed of 311 wavepoints in 11 classes (months in lactation). Genotyping information of 564,439 SNP was available for all animals and was used to calculate the genomic relationship matrix. The MegaLMM was implemented in the framework of the Bayesian sparse factor model and solved through Gibbs sampling (Markov chain Monte Carlo). The heritabilities of the studied 3,421 milk MIR wavepoints gradually increased and then decreased in units of 311 wavepoints throughout the lactation. The genetic and phenotypic correlations between the first 311 wavepoints and the other 3,110 wavepoints were low. The accuracies of phenotype predictions from the ST model were lower than those from the TT model for Fat (0.51 vs. 0.93), CH4 (0.30 vs. 0.86), and SCS (0.14 vs. 0.33). The same trend was observed for the accuracies of u predictions: Fat (0.59 vs. 0.86), CH4 (0.47 vs. 0.78), and SCS (0.39 vs. 0.59). The average correlation between U predicted from the TT model and the new approximate method was 0.90. The new approximate method used for estimating U in MegaLMM will enhance the suitability of MegaLMM for applications in animal breeding. This study conducted an initial investigation into the application of thousands of traits in animal breeding and showed that the TT model is beneficial for the prediction of focal traits (phenotype and breeding values), especially for difficult-to-measure traits (e.g., CH4).

Genetic parameters for udder conformation traits derived from Cartesian coordinates generated by robotic milking systems in North American Holstein cattle

Udder conformation is directly related to milk yield, cow health, workability, and welfare. Automatic milking systems (AMS, also known as milking robots) have become popular worldwide, and the number of dairy farms adopting these systems have increased considerably over the past years. In each milking visit, AMS record the location of the 4 teats as Cartesian coordinates in a xyz plan, which can then be used to derive udder conformation traits. AMS generate a large amount of per milking visit data for individual cows, which contribute to an accurate assessment of important traits such as udder conformation without the addition of human classifier errors (in subjective scoring systems). Therefore, the primary objectives of this study were to estimate genomic-based genetic parameters for udder conformation traits derived from AMS records in North American Holstein cattle and to assess the genetic correlation between the derived traits for evaluating the feasibility of multi-trait genomic selection for breeding cows that are more suitable for milking in AMS. The Cartesian teat coordinates measured during each milking visit were collected by 36 milking robots in 4,480 Holstein cows from 2017 to 2021, resulting in 5,317,488 records. A total of 4,118 of these Holstein cows were also genotyped for 57,600 single nucleotide polymorphisms. Five udder conformation traits were derived: udder balance (UB, mm), udder depth (UD, mm), front teat distance (FTD, mm), rear teat distance (RTD, mm), and distance front-rear (DFR, mm). In addition, 2 traits directly related to cow productivity in the system were added to the study: daily milk yield (DY) and milk electroconductivity (EC; as an indicator of mastitis). Variance components and genetic parameters for UB, UD, FTD, RTD, DFR, DY, and EC were estimated based on repeatability animal models. The estimates of heritability (±standard error, SE) for UB, UD, FTD, RTD, DFR, DY, and EC were 0.41 ± 0.02, 0.79 ± 0.01, 0.53 ± 0.02, 0.40 ± 0.02, 0.65 ± 0.02, 0.20 ± 0.02, and 0.46 ± 0.02, respectively. The repeatability estimates (±SE) for UB, UD, FTD, RTD, and DFR were 0.82 ± 0.01, 0.93 ± 0.01, 0.87 ± 0.01, 0.83 ± 0.01, and 0.88 ± 0.01, respectively. The strongest genetic correlations were observed between the FTD and RTD (0.54 ± 0.03), UD and DFR (-0.47 ± 0.03), DFR and FTD (0.32 ± 0.03), and UD and FTD (-0.31 ± 0.03). These results suggest that udder conformation traits derived from Cartesian coordinates from AMS are moderately to highly heritable. Furthermore, the moderate genetic correlations between these traits should be considered when developing selection sub-indexes. The most relevant genetic correlations between traits related to cow milk productivity and udder conformation traits were between UD and EC (-0.25 ± 0.03) and between DFR and DY (0.30 ± 0.04), in which both genetic correlations are favorable. These findings will contribute to the design of genomic selection schemes for improving udder conformation in North American Holstein cattle, especially in precision dairy farms.

Clustering of countries based on dairy productivity characteristics of Holstein cattle for breeding material selection

Background and Aim

The aim of any breeding process is to create a herd based on certain parameters that reflect an ideal animal vision. Targeted herding involves selecting the source of breeding material to be imported from another country. Therefore, there is a problem in selecting a breeding material importer to rapidly form a uterine canopy with the required properties. The purpose of this study was to evaluate a set of predictive milk productivity traits in Holstein cattle across countries.

Materials and Methods

This research was based on records of 819,358 recorded animals from 28 countries born after January 1, 2018, from open databases. We used the Euclidean metric to construct dendrograms characterizing the similarity of countries according to the complex milk productivity traits of the daughters of bulls. The Ward method was used to minimize intracluster variance when forming clusters and constructing the corresponding diagrams. Principal component analysis was used to reduce dimensionality and eliminate the effect of multicollinearity. The principal components were selected using the Kaiser-Harris criteria.

Results

A ranking of multidimensional complex milk productivity traits in different countries over the past 5 years was performed. A group of leading countries led by the USA was established according to the studied indicators, and the possible reasons for such a division into groups were described.

Conclusion

The pressure of purposeful artificial selection prevails in comparison with the pressure of natural selection concerning milk productivity traits in a certain group of countries, which allows specialists to choose suppliers when buying breeding animals and materials. The findings are based solely on data from recorded animals, which may not represent the entire breed population within each country, especially in regions where record-keeping may be inconsistent. It is expected that further studies will include regional data from large enterprises not part of Interbull, with mandatory verification and validation. An important element of such work is seen as the ability to compare the milk productivity of populations from different countries using a different scale, as well as studying the differentiation of countries by other selection traits of dairy.

Genomic prediction based on a joint reference population for the Xinjiang Brown cattle

Introduction: Xinjiang Brown cattle constitute the largest breed of cattle in Xinjiang. Therefore, it is crucial to establish a genomic evaluation system, especially for those with low levels of breed improvement. Methods: This study aimed to establish a cross breed joint reference population by analyzing the genetic structure of 485 Xinjiang Brown cattle and 2,633 Chinese Holstein cattle (Illumina GeneSeek GGP bovine 150 K chip). The Bayes method single-step genome-wide best linear unbiased prediction was used to conduct a genomic evaluation of the joint reference population for the milk traits of Xinjiang Brown cattle. The reference population of Chinese Holstein cattle was randomly divided into groups to construct the joint reference population. By comparing the prediction accuracy, estimation bias, and inflation coefficient of the validation population, the optimal number of joint reference populations was determined. Results and Discussion: The results indicated a distinct genetic structure difference between the two breeds of adult cows, and both breeds should be considered when constructing multi-breed joint reference and validation populations. The reliability range of genome prediction of milk traits in the joint reference population was 0.142-0.465. Initially, it was determined that the inclusion of 600 and 900 Chinese Holstein cattle in the joint reference population positively impacted the genomic prediction of Xinjiang Brown cattle to certain extent. It was feasible to incorporate the Chinese Holstein into Xinjiang Brown cattle population to form a joint reference population for multi-breed genomic evaluation. However, for different Xinjiang Brown cattle populations, a fixed number of Chinese Holstein cattle cannot be directly added during multi-breed genomic selection. Pre-evaluation analysis based on the genetic structure, kinship, and other factors of the current population is required to ensure the authenticity and reliability of genomic predictions and improve estimation accuracy.

Genetic analysis of lactation consistency in US Holsteins using temporal variation in daily milk weights

The ability of a dairy cow to perform reliably over time is an interesting trait to include in dairy cattle breeding programs aimed at improving dairy cow resilience. Consistency, defined as the quality of performing as expected each day of the lactation, could be highly associated with resilience, defined as animal's ability to maintain health and performance in the presence of environmental challenges, including pathogens, heat waves, and nutritional changes. A total of 51,415,022 daily milk weights collected from 2018 to 2023 were provided for 255,191 multiparous Holstein cows milked 3 times daily in conventional parlor systems on farms in 32 states. The temporal variance (TEMPVAR) of milk yield from 5 to 305 d postpartum was computed as the log-transformed variance of daily deviations between observed and expected individual milk weights. Lower values of TEMPVAR imply smaller day-to-day deviations from expectations, indicating consistent performance, whereas larger values indicate inconsistent performance. Expected daily milk weights were computed using 3 nonparametric and parametric regression models: (1) loceally estimated scatterplot smoothing regression with a 0.75 span; (2) polynomial quantile regression using the median (0.5 quantile), and (3) polynomial quantile regression using a 0.7 quantile. The univariate statistical model included age at first calving and herd-year-season as fixed effects and cow as a random effect. Heritability estimates (standard errors) of TEMPVAR phenotypes calculated over the entire lactation ranged between 0.227 (0.011) and 0.237 (0.011), demonstrating that cows are genetically predisposed to display consistent or inconsistent performance. Estimated genetic correlations calculated using a multiple-trait model between TEMPVAR traits and between lactations were high (>0.95), indicating TEMPVAR is repeatable across lactations and robust to the model used to compute expected daily milk yield. Higher TEMPVAR phenotypes reflect more variation in performance, hence greater inconsistency, which is undesirable. Therefore, correlations between predicted transmitting abilities (PTA) for TEMPVAR and milk yield of 0.57 indicate that high-producing cows exhibit more day-to-day variation in performance. Correlations with productive life and livability were -0.38 and -0.48, respectively. Correlations between PTA for TEMPVAR and those of postpartum health traits were also negative, ranging from -0.41 to -0.08. Given that health traits are derived from disease resistance measurements, and higher health trait PTA are preferred, our results indicate that more consistent cows tend to have fewer health problems and greater longevity. Overall, our findings suggest that temporal variation in daily milk weights can be used to identify consistent animals that maintain expected performance throughout the lactation, which will enable selection for greater resilience to management and environmental perturbations.

Investigating the potential for genetic selection of dairy calf disease traits using management data

Genetic selection could be a tool to help improve the health and welfare of calves; however, to date, there is limited research on the genetics of calfhood diseases. This study aimed to understand the current impact of calf diseases, by investigating incidence rates, estimating genetic parameters, and providing industry recommendations to improve calf disease recording practices on farms. Available calf disease data composed of 69,695 Holstein calf disease records for respiratory problems (RESP) and diarrhea (DIAR), from 62,361 calves collected on 1,617 Canadian dairy herds from 2006 to 2021. Single- and multiple-trait analysis using both a threshold and linear animal model for each trait were evaluated. Furthermore, each trait was analyzed using 2 scenarios with respect to minimum disease incidence threshold criterion (herd-year incidence of at least 1% and 5%) to highlight the effect of different filtering thresholds on selection potential. Observed scale heritability estimates for RESP and DIAR ranged from 0.02 to 0.07 across analyses, while estimated genetic correlations between the traits ranged from 0.50 to 0.62. Sires were compared based on their estimated breeding value and their diseased daughter incidence rates. On average, calves born to the bottom 10% of sires were 1.8 times more likely to develop RESP and 1.9 times to develop DIAR compared with daughters born to the top 10% of sires. Results from the current study are promising for the inclusion of both DIAR and RESP in Canadian genetic evaluations. However, for effective genetic evaluation, standardized approaches on data collection and industry outreach to highlight the importance of collecting and uploading this information to herd management software is required. In particular, it is important that the herd management software is accessible to the national milk recording system to allow for use in national genetic evaluation.

BullVal$: An Integrated Decision-Support Tool for Predicting the Net Present Value of a Dairy Bull Based on Genetic Merit, Semen Production Potential, and Demographic Factors

Deciding when to replace dairy bulls presents a complex challenge for artificial insemination (AI) companies. These decisions encompass multiple factors, including a bull's age, predicted semen production, and estimated genetic merit. This study's purpose was to provide a practical, objective tool to assist in these decisions. We utilized a Markov Chain model to calculate the economic valuation of dairy bulls, incorporating key factors such as housing costs, collection and marketing expenses, and the bull's probable tenure in the herd. Data from a leading AI company were used to establish baseline values. The model further compared a bull's net present value to that of a potential young replacement, establishing a relative valuation (BullVal$). The range of BullVal$ observed spanned from -USD 316,748 to USD 497,710. Interestingly, the model recommended culling for 49% of the bulls based on negative BullVal$. It was found that a bull's net present value was primarily influenced by market allocation and pricing, coupled with the interaction of semen production and genetic merit. This study offers a robust, data-driven model to guide bull replacement decisions in AI companies. Key determinants of a bull's valuation included market dynamics, semen production rates, and genetic merit.

Effects of type traits, inbreeding, and production on survival in US Jersey cattle

Greater longevity is associated with lower replacement costs, higher average milk production, and fewer replacement heifers needed. Longevity data are obtained late in life, and for this reason stayability, defined as the probability of survival from birth until a certain age, can be used as an alternative measure. The objective of this study was to evaluate the effects of different type traits, inbreeding, and production level on the stayability of Jersey cows to various ages, and to assess trends over time. Data consisted of 460,172 to 204,658 stayability records, depending on length of the opportunity period, for survival from birth until 36, 48, 60, 72, or 84 mo of age. Threshold models were used to analyze the stayability traits, including different type traits, inbreeding coefficient, and within-herd production level as explanatory variables. Heritability estimates for stayability traits ranged from 0.05 (36 mo) to 0.22 (84 mo). As expected, the probability of survival decreased as age increased. Highly productive cows were more likely to survive than their poor-producing contemporaries regardless of age and the type trait evaluated. Our data indicate that farmers' selection decisions tend to punish poor production at early ages and reward high production at later stages. Inbreeding negatively affected the probability of survival, especially when inbreeding coefficients exceeded 10%, and this impact was most noticeable at 48 mo of age or later. Some type traits, such as stature and foot angle, had little effect on the probability of survival. Other type traits, such as strength, dairy form, rump width, and rear legs, showed higher probability of survival at intermediate scores, whereas other type traits, such as fore udder attachment, rear udder height, udder depth, and final score, showed higher probability of survival at higher scores. Finally, our results indicate that the probability of survival has decreased in the last decade, probably due to a greater number of heifers available and, therefore, higher culling rates.

Effect of Holstein genotype on ex-vivo interleukin-1β response to lipopolysaccharide (LPS), lipoteichoic acid (LTA) and heat-killed Gram-negative and Gram-positive bacteria

Effects of Holstein genotype on interleukin-1β response were assessed by ex-vivo stimulation of whole blood with lipopolysaccharide (LPS), lipoteichoic acid (LTA), or sonicated, heat-killed Gram-negative or Gram-positive bacteria. Holstein genotypes were unselected Holsteins (UH, n = 14) not subjected to selection pressures since the mid-1960s and contemporary Holsteins (CH, n = 13). Milk yield of UH and CH cows differ by more than 4500 kg/lactation. Whole blood was mixed with 0.01 µg LPS, 10 µg LTA or 2.5 × 10⁶ CFU of sonicated, heat-killed E. coli, K. pneumoniae, S. marcescens, S. aureus, S. dysgalactiae, or S. uberis per mL of blood and incubated (4 h, 37 °C). Plasma IL-1β was quantified by ELISA and log₁₀-transformed concentrations analyzed with a multivariate linear mixed effects model. Responses to bacteria were greater than responses to LPS or LTA. Responses to LPS, LTA and the Gram-negative stimulants were greater in UH than in CH cows while responses to the Gram-positive bacteria did not differ between Holstein genotypes. In both genotypes, strong correlations were detected among IL-1β responses to the Gram-negative stimulants and to LTA. There were strong correlations among IL-1β responses to the Gram-positive bacteria in CH cows but only between S. aureus and S. dysgalactiae in UH cows. The IL-1β response to S. uberis was highly correlated with responses to all of the Gram-negative stimulants in CH cows but only with E. coli in the UH cows. The reduced immune response could make contemporary cows more susceptible to infection by Gram-negative bacteria. Results confirm selection practices since the mid-1960s have altered immune response in the Holstein, at least to Gram-negative bacteria, and validate the need for additional studies to further evaluate the impacts of these selection practices on immune function in contemporary Holsteins.

Recording of calf diseases for potential use in breeding programs: a case study on calf respiratory illness and diarrhea

Calf diseases remain a challenge for dairy producers from both an economic and welfare perspective. Genetically selecting for disease resistance in calves is a promising approach that could contribute to sustainable dairy farming. Genetic evaluations, however, require well-defined and consistently recorded phenotypes to be successful. Therefore, this study aimed to understand the current state of calf disease recording on Ontario farms. Calf disease records of respiratory illness and diarrhea were available from the national milk recording organization (Lactanet Canada, Guelph, Ontario, Canada) from 2009 to 2020. A case study was conducted to describe calf disease diagnoses and recording practices by surveying a subset of 13 Ontario dairy producers. The percentage of milk recorded farms that recorded calf respiratory illness and calf diarrhea increased from 2.6% in 2009 to 11.1% in 2020. Potential sources of data loss were identified along the information chain from farm to genetic evaluation database. Clear definitions and thresholds to diagnose calf disease, standard operating procedures for data recording, as well as a data transfer pipeline, which includes exchange formats, are needed to facilitate the inclusion of calf health traits in genetic evaluations.

Associations among the genome, rumen metabolome, ruminal bacteria, and milk production in early-lactation Holsteins

A multicenter observational study to evaluate genome-wide association was conducted in early-lactation Holstein cows (n = 293) from 36 herds in Canada, the USA, and Australia. Phenotypic observations included rumen metabolome, acidosis risk, ruminal bacterial taxa, and milk composition and yield measures. Diets ranged from pasture supplemented with concentrates to total mixed rations (nonfiber carbohydrates = 17 to 47, and neutral detergent fiber = 27 to 58% of dry matter). Rumen samples were collected <3 h after feeding and analyzed for pH, ammonia, d- and l-lactate, volatile fatty acid (VFA) concentrations, and abundance of bacterial phyla and families. Eigenvectors were produced using cluster and discriminant analyses from a combination of pH and ammonia, d-lactate, and VFA concentrations, and were used to estimate the probability of the risk of ruminal acidosis based on proximity to the centroid of 3 clusters, termed high (24.0% of cows), medium (24.2%), and low risk (51.8%) for acidosis. DNA of sufficient quality was successfully extracted from whole blood (218 cows) or hair (65 cows) collected simultaneously with the rumen samples and sequenced using the Geneseek Genomic Profiler Bovine 150K Illumina SNPchip. Genome-wide association used an additive model and linear regression with principal component analysis (PCA) population stratification and a Bonferroni correction for multiple comparisons. Population structure was visualized using PCA plots. Single genomic markers were associated with milk protein percent and the center logged ratio abundance of the phyla Chloroflexi, SR1, and Spirochaetes, and tended to be associated with milk fat yield, rumen acetate, butyrate, and isovalerate concentrations and with the probability of being in the low-risk acidosis group. More than one genomic marker was associated or tended to be associated with rumen isobutyrate and caproate concentrations, and the center log ratio of the phyla Bacteroidetes and Firmicutes and center log ratio of the families Prevotellaceae, BS11, S24-7, Acidaminococcaceae, Carnobacteriaceae, Lactobacillaceae, Leuconostocaceae, and Streptococcaceae. The provisional NTN4 gene, involved in several functions, had pleiotropy with 10 bacterial families, the phyla Bacteroidetes and Firmicutes, and butyrate. The ATP2CA1 gene, involved in the ATPase secretory pathway for Ca²⁺ transport, overlapped for the families Prevotellaceae, S24-7, and Streptococcaceae, the phylum Bacteroidetes, and isobutyrate. No genomic markers were associated with milk yield, fat percentage, protein yield, total solids, energy-corrected milk, somatic cell count, rumen pH, ammonia, propionate, valerate, total VFA, and d-, l-, or total lactate concentrations, or probability of being in the high- or medium-risk acidosis groups. Genome-wide associations with the rumen metabolome, microbial taxa, and milk composition were present across a wide geographical and management range of herds, suggesting the existence of markers for the rumen environment but not for acidosis susceptibility. The variation in pathogenesis of ruminal acidosis in the small population of cattle in the high risk for acidosis group and the dynamic nature of the rumen as cows cycle through a bout of acidosis may have precluded the identification of markers for acidosis susceptibility. Despite a limited sample size, this study provides evidence of interactions between the mammalian genome, the rumen metabolome, ruminal bacteria, and milk protein percentage.

Genomic evaluation methods to include intermediate correlated features such as high-throughput or omics phenotypes

Gene expression is supposed to be an intermediate between DNA and the phenotype, and it can be measured. Thus, for a trait, we may have intermediate measures, which are in fact a series of genetically controlled traits. Similarly, several traits may be measured or predicted using infrared spectra, accelerometers, and similar high-throughput measures that we will call "omics." Although these measurements have errors, many of them are heritable, and they may be more accurate or easier to record than the trait of interest. It is therefore important to develop methods to use intermediate measurements in selection. Here, we present methods and perspectives for selection based on massively recorded intermediate traits (omics). Recent developments allow a hierarchical integrated framework for prediction, in which a trait is partially controlled by omics. In addition, the omics measures are themselves partly controlled by genetics ("mediated breeding values") and partly by environment or residual factors. Thus, a part of the genetic determinism of a trait is mediated by omics, whereas the remaining part is not mediated, which results in "residual breeding values." In such a framework, genetic evaluations consist of 2 nested genomic BLUP-based models. In the first, the effect of omics on the trait (which can be seen as an improved estimate of the phenotype) and the residual breeding values are estimated. The second model extracts the mediated breeding values from the improved estimate of the phenotype, considering that omics themselves are heritable. The whole procedure is called GOBLUP (genomics omics BLUP) and it allows measures in only some individuals; that is, it is a "single-step"-like method. In this model, heritability is split into "mediated" and "not mediated" parts. This decomposition allows us to predict how accurate the omics measure of the trait would be compared with the direct measure. The ideal omics measure is heritable and explains a large part of the phenotypic variation of the trait. Ideally, this could be the case for some traits with low heritability. However, even if the omics measure explains only a small part of the phenotypic variation, when omics measurement themselves are heritable, the use of such a model would lead to more accurate selection. Expressions for upper bounds of reliability given omics measurements are also presented. More studies are needed to confirm the usefulness of omics or high-throughput prediction. Usefulness of the technology likely needs to be checked on a case-by-case basis.

Effects of Imported Semen Based on Different Selection Indices on Some Production and Reproduction Traits in Iranian Holstein Cattle

The aim of the present study was to evaluate the effects of imported semen of Holstein bulls from different countries on the economic traits of their daughters using the Lifetime Net Income (LNI) index in various climates of Iran. The data included the first lactation records of 274,057 Holstein cows collected during 1993 to 2017 by the Animal Breeding Center of Iran from 10 large dairy farms located in various provinces of Iran. The investigated traits included milk, fat and protein yields, calving age and calving interval. Breeding values of progenies were predicted by the Best Linear Unbiased Prediction (BLUP) method under the multi-trait animal model using DMU software. The genetic-economic merit of the progenies was estimated by the LNI index. There were significant differences between the estimated breeding values (EBVs) of sire groups (based on bull semen origin) for milk, fat and protein yields, calving age and calving interval in each climate (p < 0.01). The obtained results showed that the highest least-square means of LNI index in semi-cold, moderate and warm climates belonged to the daughters of French sires; however, daughters of German sires were estimated to have the highest least-square means in the cold climate.

From a documented past of the Jersey breed in Africa to a profit index linked future

The paper reports on the prevalence and performance of the Jersey cattle breed in Africa, highlighting its geographic distribution and describing the reported performance and other related characteristics from the early 1900s to the present day. The review examines the contribution of Jersey cattle in increasing the volume and efficiency of milk production across the continent. Data relating to the Jersey cattle breed has been reported in more than 30 African countries based on available material published between 1964 and 2020. A key encompassing parameter of any reference was a well-described consideration of the Jersey cattle breed (as pure or crossbred with other exotic and/or indigenous breeds) with reported performance within a variety of production systems and agro-ecologies in Africa. The main focus was on breed and performance parameters, breed types, percentage of different breed types in specific environments, reproduction method and fertility; survival and longevity; disease incidence; and production efficiency metrics such as: feed efficiency (milk unit per dry matter intake, DMI) and milk yield (MY) per unit of body weight (BW). The main performance descriptors identified were based on observations on resilience under both abiotic (heat, nutrition) and biotic (incidences of pests and diseases) stressors, milk production, BW, nutrition and utilisation of feed resources. From the literature consulted, we grouped key dairy cattle performance characteristics reported in each country under the following areas to aid comparisons; a. Milk production (Milk nutrient value, daily MY, lifetime MY and annual MY); b. Fertility traits and AFC; c. Survival and longevity, d. Production efficiency (Feed efficiency, milk per unit BW and milk per unit DMI and e. Disease incidences. Results of the review showed that the smaller stature and lower maintenance nutrient requirements of the Jersey breed means that it is better suited to tolerate the tropical production conditions in the African small-scale dairy farming sector. Detailed analyses on MY and survival showed that Jersey crosses with exotic and African indigenous breeds performed better than purebred cattle with strong evidence to support the suitability of the Jersey breed in crossbreeding with indigenous breeds for use in smallholder production systems.

Effect of Holstein genotype on ex-vivo cytokine response to lipopolysaccharide (LPS) and lipoteichoic acid (LTA) during the periparturient period

Effects of Holstein genotype on innate immune response were assessed with ex-vivo lipopolysaccharide (LPS) and lipoteichoic acid (LTA) stimulation of whole blood from unselected (UH, n = 10) and contemporary (CH, n = 11) Holsteins that differ in production by more than 4,500 kg/lactation. Blood was collected at -14, 7, 28, and 49 days in milk (DIM), mixed with a pathogen-associated molecular pattern (PAMP) molecule (0.01 or 1.0 µg LPS or 10 or 100 µg LTA per mL blood) and incubated (4 h, 37 °C). Plasma cytokines were quantified by ELISA, log₁₀-transformed and analyzed by repeated measures with DIM as the repeated effect. Cytokine responses increased with PAMP dose and decreased as DIM increased. There was a genotype by LPS dose interaction for IL-1β as response to the low dose was greater in UH but did not differ between genotypes for the high dose. The IL-1β response was greater while the IL-6 response to LTA tended to be greater in UH than in CH cows. The more negative energy balance of CH cows did not impact genotype difference in cytokine responses. Results indicate selection since the mid-1960s has decreased ex-vivo, whole blood cytokine response of CH cows to LPS and to LTA.

Applications of Omics Technology for Livestock Selection and Improvement

Conventional animal selection and breeding methods were based on the phenotypic performance of the animals. These methods have limitations, particularly for sex-limited traits and traits expressed later in the life cycle (e.g., carcass traits). Consequently, the genetic gain has been slow with high generation intervals. With the advent of high-throughput omics techniques and the availability of multi-omics technologies and sophisticated analytic packages, several promising tools and methods have been developed to estimate the actual genetic potential of the animals. It has now become possible to collect and access large and complex datasets comprising different genomics, transcriptomics, proteomics, metabolomics, and phonemics data as well as animal-level data (such as longevity, behavior, adaptation, etc.,), which provides new opportunities to better understand the mechanisms regulating animals’ actual performance. The cost of omics technology and expertise of several fields like biology, bioinformatics, statistics, and computational biology make these technology impediments to its use in some cases. The population size and accurate phenotypic data recordings are other significant constraints for appropriate selection and breeding strategies. Nevertheless, omics technologies can estimate more accurate breeding values (BVs) and increase the genetic gain by assisting the section of genetically superior, disease-free animals at an early stage of life for enhancing animal productivity and profitability. This manuscript provides an overview of various omics technologies and their limitations for animal genetic selection and breeding decisions.

Conformation traits of Holstein cows and their association with a Canadian economic selection index

Pro$ is a Canadian economic selection index aimed to maximize profit by increasing production yields, while maintaining conformation and functional traits. Currently, there is an interest in understanding the individual contributions of conformation traits recorded in Canada to the overall economic value of a cow and whether they are equally important. We used multiple polynomial regression and principal component analysis to assess the association of 26 conformation traits with Pro$ using Relative Breeding Values (RBV) from 9,351 proven bulls. The best reduced regression model explained 72.5% of the Pro$ variance, with heel depth and body depth having the highest and lowest effect on Pro$ values, respectively. Four traits classified as intermediate optimum traits, e.g., teat length, showed significant linear association with Pro$ instead of quadratic, whereas bone quality was not significantly associated with the index. Principal component analysis indicated that highly profitable bulls share similar RBV, with a sub-clustering of bulls of daughters with better mammary system versus better dairy strength and feet and legs. These results provide understanding of the individual contributions of conformation traits to Pro$ and give information to guide the Canadian dairy industry on how to best consider these traits in recording and genetic evaluation programs.

Advancing Genetic Selection and Behavioral Genomics of Working Dogs Through Collaborative Science

The ancient partnership between people and dogs is struggling to meet modern day needs, with demand exceeding our capacity to safely breed high-performing and healthy dogs. New statistical genetic approaches and genomic technology have the potential to revolutionize dog breeding, by transitioning from problematic phenotypic selection to methods that can preserve genetic diversity while increasing the proportion of successful dogs. To fully utilize this technology will require ultra large datasets, with hundreds of thousands of dogs. Today, dog breeders struggle to apply even the tools available now, stymied by the need for sophisticated data storage infrastructure and expertise in statistical genetics. Here, we review recent advances in animal breeding, and how a new approach to dog breeding would address the needs of working dog breeders today while also providing them with a path to realizing the next generation of technology. We provide a step-by-step guide for dog breeders to start implementing estimated breeding value selection in their programs now, and we describe how genotyping and DNA sequencing data, as it becomes more widely available, can be integrated into this approach. Finally, we call for data sharing among dog breeding programs as a path to achieving a future that can benefit all dogs, and their human partners too.

Alterations of mRNA and lncRNA profiles associated with the extracellular matrix and spermatogenesis in goats

Objective

Spermatozoa are produced within the seminiferous tubules after sexual maturity. The expression levels of mRNAs and lncRNAs in testicular tissues are different at each stage of testicular development and are closely related to formation of the extracellular matrix (ECM) and spermatogenesis. Therefore, we set out to study the expression of lncRNAs and mRNAs during the different developmental stages of the goat testis.

Methods

We constructed 12 RNA libraries using testicular tissues from goats aged 3, 6, and 12 months, and studied the functions of mRNAs and lncRNAs using the gene ontogeny (GO) and Kyoto encyclopedia of genes and genomes (KEGG) databases. Relationships between differentially expressed genes (DEGs) were analyzed by lncRNA-mRNA co-expression network and protein-protein interaction network (PPI). Finally, the protein expression levels of matrix metalloproteinase 2 (MMP2), insulin-like growth factor 2 (IGF2), and insulin-like growth factor-binding protein 6 (IGFBP6) were detected by western blotting.

Results

We found 23, 8, and 135 differentially expressed lncRNAs and 161, 12, and 665 differentially expressed mRNAs that were identified between 3 vs 6, 6 vs 12, and 3 vs 12 months, respectively. GO, KEGG, and PPI analyses showed that the differential genes were mainly related to the ECM. Moreover, MMP2 was a hub gene and co-expressed with the lncRNA TCONS-0002139 and TCONS-00093342. The results of quantitative reverse-transcription polymerase chain reaction verification were consistent with those of RNA-seq sequencing. The expression trends of MMP2, IGF2, and IGFBP6 protein were the same as that of mRNA, which all decreased with age. IGF2 and MMP2 were significantly different in the 3 vs 6-month-old group (p<0.05).

Conclusion

These results improve our understanding of the molecular mechanisms involved in sexual maturation of the goat testis.