Maintenance energy requirements are important to feed intake, growth and feed efficiency in cattle. Current estimates of maintenance energy requirements in cattle are generally expressed as a constant or metabolic rate multiplied by a ‘metabolic body size’ which is derived from measures of body mass (BW). This approach does not allow for determination of individual differences in efficiency between cattle with the same BW, and individual differences in shape and body composition (i.e., density) create inherent errors in estimates of maintenance energy requirements from metabolic body size. The use of computer imaging (CI) and artificial intelligence can facilitate rapid and accurate non-destructive measures of surface area and volume of cattle to determine maintenance energy requirements instead of metabolic body size. Twelve crossbred steers were used to evaluate maintenance energy requirements via fasting heat production (FHP) at 5 different BW (247 ± 19, 302 ± 31, 396 ± 45, 429 ± 44, and 540 ± 50 kg shrunk BW) to estimates of maintenance energy from measures of surface area (SA) collected via CI or metabolic body size. FHP was measured using indirect calorimetry after 5 and 6 d of fasting, respectively. Measures of shrunk BW and SA were collected within 24 h of completing indirect calorimetry. At slaughter, hides were collected to compare SA from CI to measures from hides. Measures of SA from CI (r2 = 0.97) and metabolic body size (r2 = 0.98) were closely related to FHP. However, estimates of metabolic body size yielded estimates of maintenance energy requirements that were 22.4% greater (P < 0.01) than FHP. Correspondingly, measures of FHP per SA of body were less (1,106 ± 23.8 kcal/m2) compared to maintenance energy estimates from metabolic body size per SA (1,354 ± 18.2 kcal/m2). Bias in maintenance estimates from metabolic body size could be attributed to the exponent used to calculate metabolic body size. An assumed metabolic rate of 77 kcals resulted in a calculated exponent of 0.716 ± 0.022, which was less than the 0.75 exponent commonly used to calculate metabolic body size. Limits in the metabolic body size concept were also clearly observed because the calculated exponent increased with increased BW (linear, P < 0.01). At slaughter, measures of SA from CI were closely related to measures from hides when regressed through the origin (r2 = 0.99), but measures of SA were 14.5% greater than measures from hides, which could indicate a more complete measure from CI. These data indicate that CI can be used to estimate maintenance energy requirements of beef cattle and that current weight-based models overestimate maintenance energy requirements when compared to FHP.

The adoption of precision livestock farming is steadily increasing in the United States swine industry due to the availability of innovative sensors, microphones, and cameras for automatic surveillance of pig barns. Wean-to-market mortality in pigs is of utmost economic importance to farmers, especially as mortality rates have increased over time. Machine learning models provide an interesting opportunity to combine data from multiple sources into one model to predict real-time daily mortality outcomes in growing pigs. In this study, we evaluated the ability of three machine learning frameworks [elastic net logistic regression (ELNR), support vector machine (SVM), and gradient-boosted decision trees (i.e., XGBoost; XGB)] to forecast daily sporadic and episodic mortality outcomes. These models were trained and tested on 5,364 daily observations collected from October 2020 to December 2023 at six commercial wean-to-finish farms owned and operated by The Maschhoffs, LLC (Carlyle, Illinois, USA). Features used in the analysis were derived from manually recorded antibiotic treatment data, SoundTalks® cough sensors (SoundTalks NV, Leuven, Belgium), and data from ventilation controller systems that recorded water disappearance and climatic conditions. Across all models, episodic mortality predictions were more accurate than those for sporadic mortality (cross-validation F0.5 scores = 0.30 to 0.51 and 0.16 to 0.24 for episodic and sporadic mortality, respectively). The most complex model, XGBoost, reported the highest F0.5 scores during cross-validation and holdout testing for mortality episode prediction (0.51 and 0.40, respectively), considerably outperforming a naïve baseline classifier that predicted outcomes based on majority class at different pig ages. In addition, XGBoost functioned as a conservative classifier for mortality episodes, reporting false positive rates of 0.08 and 0.14 during cross-validation and holdout testing, respectively. In this model, age, water disappearance, and SoundTalks® respiratory health status were the most important predictors of episodic mortality (0.26, 0.17, and 0.08 relative accuracy gain, respectively). Receiver operating characteristic and precision-recall curves were used to evaluate the overall classification ability of each model based on unseen data from subsequent pig cohorts. Each model framework achieved a moderately high area under the receiver operating characteristic curve (AUC = 0.77 to 0.78), indicating moderate classifier accuracy above baseline expectation for a non-informative classifier (baseline AUC = 0.50). The area under the precision-recall curve for each model framework was also moderately high relative to the naïve baseline classifier (AUCPR = 0.40, 0.34, and 0.46 for ELNR, SVM, and XGB, respectively; baseline AUCPR = 0.25). Findings from this analysis support data-driven decision-making to reduce mortality losses in commercial wean-to-finish pig barns through targeted preventative interventions. However, the development of more robust mortality prediction models will require larger, more diverse datasets, which could further enhance predictive accuracy and model applicability across different production settings.

The incorporation of single nucleotide polymorphisms into genetic evaluations began in 2009. Today, with millions of cattle genotyped and a plethora of functioning evaluations for various breeds of cattle, it is a widely accepted technology among breeders. There are currently three practices available for genomic predictions specifically designed for commercial crossbred cows: Igenity Beef, Inherit Select, and the American Simmental Association’s (ASA) Total Herd Enrollment (THE) commercial option. The ASA’s THE program allows commercial producers to participate in a genetic evaluation similar to that of a registered breeder. Submission of pedigree, phenotypes, and DNA samples provide a genomically enhanced expected progeny difference (EPD). All three approaches are marketed for use on crossbred heifers to genetically rank them for economically relevant traits to help producers select replacements with superior genetic merit. While these products have been available for some time, there has not been any recent third-party validation research conducted to investigate their efficacy. Therefore, the objective of this research is to compare the genomic predictions of commercial crossbred dams to the performance of their progeny to determine their ability to predict genetic merit. DNA samples were collected in triplicate on 888 crossbred cows of varying age and breed composition at the University of Missouri research farms. Phenotypic records on progeny born in 2023 and 2024 were also collected including growth and carcass traits. Pearson and Spearman Correlations were performed first to evaluate the relationships between the genetic predictions and phenotypes. Linear mixed models were used to further investigate the relationship between progeny performance and dam genomic prediction while accounting for variation in calf’s sex, calf’s age, dam’s age, and calf’s contemporary group. There was a significant (P < 0.05) and positive correlation between all three genomic predictions and observed traits of birth weight (r= 0.11-0.29) and weaning weight (r= 0.09-0.17). In addition, linear models evaluating the relationship between a dam’s prediction and her calf’s phenotype showed that Inherit and ASA provide predictions that capture the average performance for birth weight and weaning weight (P < 0.01). In conclusion, all three genomic predictions provide an ability to predict the average genetic merit of progeny from commercial females for both birth and weaning weights. However, this also highlights the need to further investigate these relationships for additional traits of economic importance. Validation of these products will determine accuracy and usefulness of the commercial genomic tools as they have the potential to effectively decrease genetic lag between commercial herds and their seedstock providers.

Often when the beef industry has gotten into problematic situations, it is because we have looked for simple solutions to complex issues. These issues are compounded when decisions are made based on intuition and opinion, rather than sound scientific principles and data. This suboptimal decision making is best exemplified by the wild swings in type from the belt buckle cattle of the 1960s to the frame race of the 1980s. These drastic type changes where strikingly described by Dr. Harlan Ritchie. Efficiency in the beef industry, especially cow efficiency, is a complex system of numerous factors. To improve this system, we do not simply need more data, but more of the right kinds of data. Further, making decisions in a data-driven systems-based approach will be necessary. In regards to beef cattle genetics, one of the easiest ways to make system-based decisions is to select based on economic selection indexes. To improve efficiency, the beef industry will need to take a more proactive approach to systems thinking and technology adoption.

We introduce a high-throughput 3D scanning system designed to accurately measure cattle phenotypes. This scanner employs an array of depth sensors, i.e., time-of-flight (ToF) sensors, each controlled by dedicated embedded devices. The sensors generate high-fidelity 3D point clouds, which are automatically stitched using a point could segmentation approach through deep learning. The deep learner combines raw RGB and depth data to identify correspondences between the multiple 3D point clouds, thus creating a single and accurate mesh that reconstructs the cattle geometry on the fly. In order to evaluate the performance of our system, we implemented a two-fold validation process. Initially, we quantitatively tested the scanner for its ability to determine accurate volume and surface area measurements in a controlled environment featuring known objects. Next, we explored the impact and need for multi-device synchronization when scanning moving targets (cattle). Finally, we performed qualitative and quantitative measurements on cattle. The experimental results demonstrate that the proposed system is capable of producing high-quality meshes of untamed cattle with accurate volume and surface area measurements for livestock studies.

Directional selection alters the genome via hard sweeps, soft sweeps, and polygenic selection. However, mapping polygenic selection is difficult because it does not leave clear signatures on the genome like a selective sweep. In populations with temporally stratified genotypes, the Generation Proxy Selection Mapping (GPSM) method identifies variants associated with generation number (or appropriate proxy) and thus variants undergoing directional allele frequency changes. Here, we use GPSM on two large datasets of beef cattle to detect associations between an animal's generation and 11 million imputed SNPs. Using these datasets with high power and dense mapping resolution, GPSM detected a total of 294 unique loci actively under selection in two cattle breeds. We observed that GPSM has a high power to detect selection in the very recent past (<10 years), even when allele frequency changes are small. Variants identified by GPSM reside in genomic regions associated with known breed-specific selection objectives, such as fertility and maternal ability in Red Angus, and carcass merit and coat color in Simmental. Over 60% of the selected loci reside in or near (<50 kb) annotated genes. Using haplotype-based and composite selective sweep statistics, we identify hundreds of putative selective sweeps that likely occurred earlier in the evolution of these breeds; however, these sweeps have little overlap with recent polygenic selection. This makes GPSM a complementary approach to sweep detection methods when temporal genotype data are available. The selected loci that we identify across methods demonstrate the complex architecture of selection in domesticated cattle.

Abstract Mortality rates in all phases of pork production have increased over the past several years. Decreasing mortality rate is of utmost economic importance to producers, especially in weaning-to-finishing pigs where additional investment in feed, medications, and other inputs are expended. A novel analytical method was used to identify sequences of days of increased mortality, defined as mortality episodes, within cohorts of weaning-to-finishing pigs. Cubic smoothing spline regression equations were estimated between days post placement (DPP) and mortality (total daily number of dead and euthanized pigs) for 42 groups of pigs placed in 12 rooms within 6 barns (2 rooms per barn) in Illinois and Iowa from December 2020 to July 2022 (n = 5,676 observation days). For each group, the instantaneous slope between DPP and mortality from spline regression was calculated and used to identify changepoints [i.e., peaks (negative instantaneous slope at DPPt and positive instantaneous slope at DPPt-1) and valleys (positive instantaneous slope at DPPt and negative instantaneous slope at DPPt-1)]. Within each mortality sequence, the maximum and minimum instantaneous slope were considered the start and end of the mortality episode, respectively. Mortality episodes were only considered high mortality episodes if the following metrics were satisfied: 1) greater than or equal to 1 mortality per day, or 2) greater than or equal to 1/3 of the days in the mortality sequence had at least 1 mortality. Days were classified as “start days” if they were within 1 d from the maximum instantaneous slope; likewise, we classified a day as “end days” if it was 1 d from the minimum instantaneous slope. Any day in between the start and end of a mortality episode were defined as “peak days”, and a “normal day” was all other days that did not fall within the start, peak, or end of a high mortality episode. Least-squares means for predicted mortality from a negative binomial generalized linear model showed that mortality was highest and lowest for peak days and normal days, respectively (3.475 ± 0.1729 and 0.591 ± 0.0156 pigs, respectively; P < 0.05; Table 1). Mortality during start or end days of an episode did not differ from each other (2.185 ± 0.1973 and 2.069 ± 0.1881 pigs, respectively; P > 0.05; Table 1) but were significantly greater and lower than a normal or peak day, respectively (P < 0.05; Table 1). The proposed analytical method provides more precise classification of mortality episodes in pig cohorts than previously proposed methods. Results from this method can be used in models to predict the start of mortality episodes, providing ample time for intervention to reduce the overall number of dead pigs in wean-to-finish pig production.

Seasonal shedding of winter hair at the start of summer is well studied in wild and domesticated populations. However, the genetic influences on this trait and their interactions are poorly understood. We use data from 13,364 cattle with 36,899 repeated phenotypes to investigate the relationship between hair shedding and environmental variables, single nucleotide polymorphisms, and their interactions to understand quantitative differences in seasonal shedding. Using deregressed estimated breeding values from a repeated records model in a genome-wide association analysis (GWAA) and meta-analysis of year-specific GWAA gave remarkably similar results. These GWAA identified hundreds of variants associated with seasonal hair shedding. There were especially strong associations between chromosomes 5 and 23. Genotype-by-environment interaction GWAA identified 1,040 day length-by-genotype interaction associations and 17 apparent temperature-by-genotype interaction associations with hair shedding, highlighting the importance of day length on hair shedding. Accurate genomic predictions of hair shedding were created for the entire dataset, Angus, Hereford, Brangus, and multibreed datasets. Loci related to metabolism and light-sensing have a large influence on seasonal hair shedding. This is one of the largest genetic analyses of a phenological trait and provides insight into both agriculture production and basic science.

The GeneMax (GMX) Advantage test, developed by Zoetis, uses approximately 50,000 single nucleotide poly-morphisms (SNP) to predict the genomic potential of a commercial Angus heifer. Genetic predictions are pro-vided for Calving Ease Maternal, Weaning Weight, Heifer Pregnancy, Milk, Mature Weight, Dry Matter Intake, Carcass Weight, Marbling, Yield, and three economic selection indices. Test results can inform selection and culling decisions made by commercial beef cattle producers. To measure the accuracy of the trait predictions, data from commercial Angus females and their progeny at the University of Missouri Thompson Research Center were utilized to analyze weaning weight, milk, marbling, fat, ribeye area, and carcass weight. Progeny pheno-typic data were matched to the respective dam, then the cow's genomic predictions were compared to the calf's age-adjusted phenotypes using correlation and linear model effect sizes. All tested GeneMax scores of the dam were significantly correlated with and predicted calf performance. Our predicted effect sizes, except for fat thickness, were similar to those reported by Zoetis. In conclusion, the GeneMax Advantage test accurately ranks animals based on their genetic merit and is an effective selection tool in commercial cowherds.

BackgroundArtificial selection on quantitative traits using breeding values and selection indices in commercial livestock breeding populations causes changes in allele frequency over time at hundreds or thousands of causal loci and the surrounding genomic regions. In population genetics, this type of selection is called polygenic selection. Researchers and managers of pig breeding programs are motivated to understand the genetic basis of phenotypic diversity across genetic lines, breeds, and populations using selection mapping analyses. Here, we applied generation proxy selection mapping (GPSM), a genome-wide association analysis of single nucleotide polymorphism (SNP) genotypes (38,294-46,458 markers) of birth date, in four pig populations (15,457, 15,772, 16,595 and 8447 pigs per population) to identify loci responding to artificial selection over a period of five to ten years. Gene-drop simulation analyses were conducted to provide context for the GPSM results. Selected loci within and across each population of pigs were compared in the context of swine breeding objectives.ResultsThe GPSM identified 49 to 854 loci as under selection (Q-values less than 0.10) across 15 subsets of pigs based on combinations of populations. The number of significant associations increased when data were pooled across populations. In addition, several significant associations were identified in more than one population. These results indicate concurrent selection objectives, similar genetic architectures, and shared causal variants responding to selection across these pig populations. Negligible error rates (less than or equal to 0.02%) of false-positive associations were found when testing GPSM on gene-drop simulated genotypes, suggesting that GPSM distinguishes selection from random genetic drift in actual pig populations.ConclusionsThis work confirms the efficacy and the negligible error rates of the GPSM method in detecting selected loci in commercial pig populations. Our results suggest shared selection objectives and genetic architectures across swine populations. The identified polygenic selection highlights loci that are important to swine production.

AbstractSeasonal shedding of winter hair at the start of summer is well studied in wild and domesticated populations. However, the genetic influences on this trait and their interactions are poorly understood. We use data from 13,364 cattle with 36,899 repeated phenotypes to investigate the relationship between hair shedding and environmental variables, single nucleotide polymorphisms, and their interactions to understand quantitative differences in seasonal shedding. Using deregressed estimated breeding values from a repeated records model in a genome-wide association analysis (GWAA) and meta-analysis of year-specific GWAA gave remarkably similar results.These GWAA identified hundreds of variants associated with seasonal hair shedding. There were especially strong associations on chromosomes 5 and 23. Genotype-by- environment interaction GWAA identified 1,040 day length-by-genotype interaction associations and 17 apparent temperature-by-genotype interaction associations with hair shedding, highlighting the importance of day length on hair shedding. Accurate genomic predictions of hair shedding were created for the entire dataset, Angus, Hereford, Brangus, and multi-breed datasets. Loci related to metabolism and light- sensing have a large influence on seasonal hair shedding. This is one of the largest genetic analyses of a phenological trait and provides insight for both agriculture production and basic science.

Reductions in beef genetics Extension specialists and outreach funding in the US has led a group of beef genetics Extension faculty to coordinate their efforts and develop a national Extension program to meet beef producers and Extension educators' needs in beef genetics programming. This effort, collectively called eBEEF, has utilized four platforms to provide these outreach efforts: Beef Improvement Federation, National Beef Cattle Evaluation Consortium, eBEEF.org website and the National Cattlemen's Beef Association education program. Materials provided through eBEEF include the publication of a Sire Selection Manual, major contributions to the Beef Improvement Federation Guidelines for Uniform Beef Improvement Programs, 32 factsheets, 165 videos, over 18 train-the-trainer webinar series, 5 direct producer education webinars, and numerous in person educational programs. This collaboration has proven to be an effective model to provide beef genetics outreach programming to a national audience.

Seasonal shedding of winter hair at the start of summer is well studied in wild and domesticated populations. However, the genetic influences on this trait and their interactions are poorly understood. We use data from 13,364 cattle with 36,899 repeated phenotypes to investigate the relationship between hair shedding and environmental variables, single nucleotide polymorphisms, and their interactions to understand quantitative differences in seasonal shedding. Using deregressed estimated breeding values from a repeated records model in a genome-wide association analysis (GWAA) and meta-analysis of year-specific GWAA gave remarkably similar results. These GWAA identified hundreds of variants associated with seasonal hair shedding. There were especially strong associations on chromosomes 5 and 23. Genotype-by- environment interaction GWAA identified 1,040 day length-by-genotype interaction associations and 17 apparent temperature-by-genotype interaction associations with hair shedding, highlighting the importance of day length on hair shedding. Accurate genomic predictions of hair shedding were created for the entire dataset, Angus, Hereford, Brangus, and multi-breed datasets. Loci related to metabolism and light- sensing have a large influence on seasonal hair shedding. This is one of the largest genetic analyses of a phenological trait and provides insight for both agriculture production and basic science.

Understanding the underlying effects of ongoing selection is of interest to livestock breeding programs. Commercial livestock genotyping has generated datasets well-suited to explore how strong artificial selection has impacted the genome over very short time periods. We have developed a method for mapping ongoing polygenic selection in populations with temporally stratified genomic data called Generation Proxy Selection Mapping (GPSM). GPSM utilizes a genome-wide linear mixed model to identify allelic associations with an animal's generation number or some proxy (i.e., birth date). We applied GPSM to two large contemporary beef cattle datasets from Red Angus (n = 46,454) and Simmental (n = 90,580) breeds, with 11,759,568 high-quality imputed variants per animal. This analysis identified 294 distinct loci actively under selection. Due to the high power of these datasets, we found that GPSM could detect very small and recent(< 10 years) allele frequency changes consistent with polygenic selection. These analyses identified variants within genomic regions associated with known breed characteristics, like fertility and maternal ability in Red Angus and carcass merit and coat color in Simmental. Over 60% of GPSM loci resided in or near (< 50kb) annotated genes. We leveraged the resolution of our imputed sequence variants to overlay known epigenetic marks and possibly functional regions. 36% of GPSM loci overlapped these putatively functional regions genomic intervals. In addition to GPSM, we used two other more traditional selective sweep methods, the number of segregating loci (nSL) and Raised Accuracy in Sweep Detecting (RAiSD), to detect selection in this data. We observed minimal overlap (< 10% of loci) between traditional selective sweep mapping and GPSM. This suggests that selection mapping with GPSM can complement traditional sweep mapping methods when temporal genomic data exists. In addition to better understanding the biological processes and features that underlie artificial selection, GPSM signatures might serve as important genomic annotations.

Cat domestication likely initiated as a symbiotic relationship between wildcats (Felis silvestris subspecies) and the peoples of developing agrarian societies in the Fertile Crescent. As humans transitioned from hunter-gatherers to farmers ~12,000 years ago, bold wildcats likely capitalized on increased prey density (i.e., rodents). Humans benefited from the cats' predation on these vermin. To refine the site(s) of cat domestication, over 1000 random-bred cats of primarily Eurasian descent were genotyped for single-nucleotide variants and short tandem repeats. The overall cat population structure suggested a single worldwide population with significant isolation by the distance of peripheral subpopulations. The cat population heterozygosity decreased as genetic distance from the proposed cat progenitor's (F.s. lybica) natural habitat increased. Domestic cat origins are focused in the eastern Mediterranean Basin, spreading to nearby islands, and southernly via the Levantine coast into the Nile Valley. Cat population diversity supports the migration patterns of humans and other symbiotic species.

The Neosho madtom (Noturus placidus) is a small catfish, generally less than 3 inches in length, unique to the Neosho-Spring River system within the Arkansas River Basin. It was federally listed as threatened in 1990, largely due to habitat loss. For conservation efforts, we generated whole-genome sequence data from 10 Neosho madtom individuals originating from 3 geographically separated populations to evaluate genetic diversity and population structure. A Neosho madtom genome was de novo assembled, and genome size and content were assessed. Single nucleotide polymorphisms were assessed from de Bruijn graphs, and via reference alignment with both the channel catfish (Ictalurus punctatus) reference genome and Neosho madtom reference genome. Principal component analysis and structure analysis indicated weak population structure, suggesting fish from the 3 locations represent a single population. Using a novel method, genome-wide conservation and divergence between the Neosho madtom, channel catfish, and zebrafish (Danio rerio) was assessed by pairwise contig alignment, which demonstrated that genes important to embryonic development frequently had conserved sequences. This research in a threatened species with no previously published genomic resources provides novel genetic information to guide current and future conservation efforts and demonstrates that using whole-genome sequencing provides detailed information of population structure and demography using only a limited number of rare and valuable samples.

Selection on complex traits can rapidly drive evolution, especially in stressful environments. This polygenic selection does not leave intense sweep signatures on the genome, rather many loci experience small allele frequency shifts, resulting in large cumulative phenotypic changes. Directional selection and local adaptation are changing populations; but, identifying loci underlying polygenic or environmental selection has been difficult. We use genomic data on tens of thousands of cattle from three populations, distributed over time and landscapes, in linear mixed models with novel dependent variables to map signatures of selection on complex traits and local adaptation. We identify 207 genomic loci associated with an animal's birth date, representing ongoing selection for monogenic and polygenic traits. Additionally, hundreds of additional loci are associated with continuous and discrete environments, providing evidence for historical local adaptation. These candidate loci highlight the nervous system's possible role in local adaptation. While advanced technologies have increased the rate of directional selection in cattle, it has likely been at the expense of historically generated local adaptation, which is especially problematic in changing climates. When applied to large, diverse cattle datasets, these selection mapping methods provide an insight into how selection on complex traits continually shapes the genome. Further, understanding the genomic loci implicated in adaptation may help us breed more adapted and efficient cattle, and begin to understand the basis for mammalian adaptation, especially in changing climates. These selection mapping approaches help clarify selective forces and loci in evolutionary, model, and agricultural contexts.

Feral populations, those which successfully persist outside of cultivation or husbandry, provide unique opportunities to study the genomic impacts of domestication and local adaptation. We argue that by leveraging genomic resources designed for domestic counterparts, powerful phylogenetic and population genomic data collection and analyses can be designed to disentangle complex demographic processes.