bluuebunny/crossref_metadata_2025 huggingface dataset:
Genetic basis of macrolide resistance in porcine Pasteurella multocida isolates from the German national resistance monitoring program GERM-Vet 2008–2021
Authors: Krüger-Haker Henrike , Kostova Valeria , Hanke Dennis , Kaspar Heike , Fiedler Stefan , Schwarz Stefan
Published: September 2024

Objectives

To analyse porcine Pasteurella multocida isolates obtained from the national resistance monitoring program GERM-Vet 2008–2021 in Germany for phenotypic and genotypic macrolide resistance.

Methods

The antimicrobial susceptibility of the isolates was determined by broth microdilution according to CLSI standards. Closed genomes were obtained by WGS via Illumina MiSeq and MinION platforms followed by a hybrid assembly.

Results

Of 1114 porcine P. multocida isolates, only four isolates (0.36%), one each from 2008, 2010, 2019 and 2021, exhibited resistance to at least one macrolide tested. The isolate from 2010 was only resistant to erythromycin and WGS analysis neither revealed a macrolide resistance gene nor a macrolide resistance-mediating mutation. The isolates from 2008 and 2019 were resistant to erythromycin, tilmicosin, tildipirosin, tulathromycin and gamithromycin and showed either only the A2058G mutation in all six 23S rRNA operons or the chromosomally located macrolide resistance genes msr(E) and mph(E), respectively. The isolate from 2021 was resistant to erythromycin, tulathromycin, gamithromycin and tylosin and carried a novel integrative and conjugative element of 64 966 bp, designated Tn7730, in its chromosomal DNA. It harboured the macrolide resistance genes mef(C), mph(G) and estT, the lincosamide resistance gene lnu(H), and the tetracycline resistance gene tet(Y), the last two were detected for the first time in P. multocida.

Conclusion

Macrolide resistance in German porcine P. multocida can be due to resistance-mediating mutations or resistance genes. The presence of the novel Tn7730 carrying three different macrolide resistance genes is alarming and should be monitored.
Development and evaluation of a training curriculum to engage researchers on accessing and analyzing the All of Us data
Authors: Coleman Julie R , Baker Jasmine N , Ketkar Shamika , Butler Ashley M , Williams LaTerrica , Hammonds-Odie Latanya , Atkinson Elizabeth G , Murray Debra D , Lee Brendan , Worley Kim C
Published: September 2024

Objective

The All of Us Evenings with Genetics (EwG) Research Program at Baylor College of Medicine (BCM), funded to engage research scholars to work with the All of Us data, developed a training curriculum for the Researcher Workbench, the platform to access and analyze All of Us data. All of Us EwG developed the curriculum so that it could teach scholars regardless of their skills and background in programming languages and cloud computing. All of Us EwG delivered this curriculum at the first annual All of Us EwG Faculty Summit in May 2022. The curriculum was evaluated both during and after the Faculty Summit so that it could be improved for future training.

Materials and Methods

Surveys were administered to assess scholars’ familiarity with the programming languages and computational tools required to use the Researcher Workbench. The curriculum was developed using backward design and was informed by the survey results, a review of available resources for training users on the Researcher Workbench, and All of Us EwG members’ collective experience training students. The curriculum was evaluated using feedback surveys during the Faculty Summit as well as virtual meetings and emails following the Faculty Summit.

Results

The evaluation results demonstrated the success of the curriculum and identified areas for improvement.

Discussion and Conclusion

The curriculum has been adapted and improved in response to evaluations and in response to changes to the All of Us data and infrastructure to train more researchers through this program and other scholarly programs.
“Goldmine” or “big mess”? An interview study on the challenges of designing, operating, and ensuring the durability of Clinical Data Warehouses in France and Belgium
Authors: Priou Sonia , Kempf Emmanuelle , Jankovic Marija , Lamé Guillaume
Published: September 2024

Objectives

Clinical Data Warehouses (CDW) are the designated infrastructures to enable access and analysis of large quantities of electronic health record data. Building and managing such systems implies extensive “data work” and coordination between multiple stakeholders. Our study focuses on the challenges these stakeholders face when designing, operating, and ensuring the durability of CDWs for research.

Materials and Methods

We conducted semistructured interviews with 21 professionals working with CDWs from France and Belgium. All interviews were recorded, transcribed verbatim, and coded inductively.

Results

Prompted by the AI boom, healthcare institutions launched initiatives to repurpose data they were generating for care without a clear vision of how to generate value. Difficulties in operating CDWs arose quickly, strengthened by the multiplicity and diversity of stakeholders involved and grand discourses on the possibilities of CDWs, disjointed from their actual capabilities. Without proper management of the information flows, stakeholders struggled to build a shared vision. This was evident in our interviewees’ contrasting appreciations of what mattered most to ensure data quality. Participants explained they struggled to manage knowledge inside and across institutions, generating knowledge loss, repeated mistakes, and impeding progress locally and nationally.

Discussion and conclusion

Management issues strongly affect the deployment and operation of CDWs. This may stem from a simplistic linear vision of how this type of infrastructure operates. CDWs remain promising for research, and their design, implementation, and operation require careful management if they are to be successful. Building on innovation management, complex systems, and organizational learning knowledge will help.
In-hospital mortality, readmission, and prolonged length of stay risk prediction leveraging historical electronic patient records
Authors: Bopche Rajeev , Gustad Lise Tuset , Afset Jan Egil , Ehrnström Birgitta , Damås Jan Kristian , Nytrø Øystein
Published: September 2024

Objective

This study aimed to investigate the predictive capabilities of historical patient records to predict patient adverse outcomes such as mortality, readmission, and prolonged length of stay (PLOS).

Methods

Leveraging a de-identified dataset from a tertiary care university hospital, we developed an eXplainable Artificial Intelligence (XAI) framework combining tree-based and traditional machine learning (ML) models with interpretations and statistical analysis of predictors of mortality, readmission, and PLOS.

Results

Our framework demonstrated exceptional predictive performance with a notable area under the receiver operating characteristic (AUROC) of 0.9625 and an area under the precision-recall curve (AUPRC) of 0.8575 for 30-day mortality at discharge and an AUROC of 0.9545 and AUPRC of 0.8419 at admission. For the readmission and PLOS risk, the highest AUROC achieved were 0.8198 and 0.9797, respectively. The tree-based models consistently outperformed the traditional ML models in all 4 prediction tasks. The key predictors were age, derived temporal features, routine laboratory tests, and diagnostic and procedural codes.

Conclusion

The study underscores the potential of leveraging medical history for enhanced hospital predictive analytics. We present an accurate and intuitive framework for early warning models that can be easily implemented in the current and developing digital health platforms to predict adverse outcomes accurately.
Using real-world electronic health record data to predict the development of 12 cancer-related symptoms in the context of multimorbidity
Authors: Bandyopadhyay Anindita , Albashayreh Alaa , Zeinali Nahid , Fan Weiguo , Gilbertson-White Stephanie
Published: September 2024

Objective

This study uses electronic health record (EHR) data to predict 12 common cancer symptoms, assessing the efficacy of machine learning (ML) models in identifying symptom influencers.

Materials and Methods

We analyzed EHR data of 8156 adults diagnosed with cancer who underwent cancer treatment from 2017 to 2020. Structured and unstructured EHR data were sourced from the Enterprise Data Warehouse for Research at the University of Iowa Hospital and Clinics. Several predictive models, including logistic regression, random forest (RF), and XGBoost, were employed to forecast symptom development. The performances of the models were evaluated by F1-score and area under the curve (AUC) on the testing set. The SHapley Additive exPlanations framework was used to interpret these models and identify the predictive risk factors associated with fatigue as an exemplar.

Results

The RF model exhibited superior performance with a macro average AUC of 0.755 and an F1-score of 0.729 in predicting a range of cancer-related symptoms. For instance, the RF model achieved an AUC of 0.954 and an F1-score of 0.914 for pain prediction. Key predictive factors identified included clinical history, cancer characteristics, treatment modalities, and patient demographics depending on the symptom. For example, the odds ratio (OR) for fatigue was significantly influenced by allergy (OR = 2.3, 95% CI: 1.8-2.9) and colitis (OR = 1.9, 95% CI: 1.5-2.4).

Discussion

Our research emphasizes the critical integration of multimorbidity and patient characteristics in modeling cancer symptoms, revealing the considerable influence of chronic conditions beyond cancer itself.

Conclusion

We highlight the potential of ML for predicting cancer symptoms, suggesting a pathway for integrating such models into clinical systems to enhance personalized care and symptom management.
Analysis toolkit for evaluation of drug titration practice in acute lymphoblastic leukemia maintenance
Authors: Mungle Tushar , Mahadevan Ananya , Mukhopadhyay Jayanta , Bhattacharya Sangeeta Das , Saha Vaskar , Krishnan Shekhar
Published: September 2024

Objective

During the 2-year maintenance treatment phase (MT) of acute lymphoblastic leukemia (ALL), personalized patient-specified titration of oral antimetabolite drug doses is required to ensure maximum tolerated systemic drug exposure. Drug titration is difficult to implement in practice and insufficient systemic drug exposure resulting from inadequate dose titration increases risk of ALL relapse.

Materials and Methods

We developed an open-source R-based analytical toolkit, including the allMT R package and an interactive web-based R Shiny VIATAMIN application, to evaluate antimetabolite drug titration during MT.

Results

Evaluation is initiated with basic visualization analysis of drug titration, in both individual patients and patient cohorts. Observations are supplemented with descriptive analyses of hematological toxicity frequency and prescriber compliance rates with protocol drug titration rules. In individual patients, visual and quantitative analyses indicate recurring potentially correctable suboptimal drug titration patterns. In patient cohorts, the toolkit enables evaluation of the impact of interventions to optimize MT drug titration.

Discussion

Incorporation of the toolkit in a forthcoming clinical decision support system for MT would enable real-time examination and course correction of drug titration practice.

Conclusion

Future versions will be enhanced to include other variables that influence drug titration practice, including clinical toxicity data and later, pharmacological markers of antimetabolite, adherence, and drug activity.
325 Longitudinal retrospective study of performance and biomarkers of energy balance and gastrointestinal functionality in unweaned calves subjected to transport or entire commercialization at arrival
Authors: Marti Sonia , Pisoni Lucia , Blanch Marta , Bassols Anna , Saco Yolanda , Pato Raquel , Pujols Joan , Devant Maria
Published: September 2024

Data from 347 unweaned male dairy beef calves enrolled in 3 studies from 2021 to 2023 were used to evaluate the impact of transport or entire commercialization (marketing and transport) on performance recovery [average daily gain (ADG) and concentrate intake (CI)] and biomarkers of energy balance [non-esterified fatty acids (NEFA), beta-hydroxybutyrate (BHB), and glucose] and gastrointestinal functionality [gut permeability (Cr-EDTA) and enterocyte mass (citrulline)] at farm arrival. Calves were distributed in 3 treatments according to the different managements subjected in the previous studies: calves from a sole dairy farm that were not fasted neither transported (CTR), calves from a sole dairy farm that were transported and fasted for 19 h (TRA), and multi-origin calves commercially marketed and transported for 9 h (COM). On arrival at the rearing farm (d 0), calves were allocated individually, and body weight (BW) was recorded. Calves were fed milk replacer twice daily, and they had ad libitum access to concentrate and water. Concentrate (CI) and milk replacer intake were recorded daily, and BW and the biomarkers of energy balance and gut dysfunction were obtained on d 0 (arrival), 1, 2, and 7. Data were analyzed with mixed models. An interaction (P < 0.01) between treatment and time was observed for CI. On d 0 CTR and TRA had similar CI, while COM had the least CI. On d 1, all treatments decreased CI except CTR. While TRA recovered CI to CTR levels, COM had 50% less CI compared with the other calves on d 7. The COM calves had the least (P < 0.01) ADG compared with CTR and TRA calves. On d 0, COM calves had greater (P < 0.01) concentrations of NEFA and BHB compared with TRA and CTR, and TRA greater (P < 0.01) than CTR calves. The COM calves did not recover NEFA and BHB basal concentrations by d 7 as observed in TRA calves. Concentration of Cr-EDTA was greater on d 0 for TRA calves compared with CTR and COM calves, however concentration of Cr-EDTA was greater during the first 7 d in COM calves compared with CTR and TRA calves. Finally, citrulline concentration was less in COM calves compared with CTR and TRA calves independently of the sampling day. The entire commercialization process, marketing and transportation, of unweaned dairy beef calves impairs intake recovery and growth, and these results are aligned with negative energy balance and comprised gastrointestinal functionality at farm arrival.
423 Comparative post-weaning performance characteristics of four breeds of rabbit in hot humid climate of Nigeria
Authors: Abanikannda Olatunji T F , Kareem-Ibrahim Khadeejah O , Olukoya Faidat O , Sanni Aisha , Okulaja Temiloluwa , Otun Islamiyyah O
Published: September 2024

Rabbits are one of the most popular livestock breeds raised for their meat, fur, and companionship. Rabbit meat is fast gaining prominence as a healthier meat compared with beef, mutton, pork and chevon, which are mostly consumed in Nigeria. This study investigated breed differences in mortality, survival rates and post-weaning growth rate of rabbits reared under inimical environmental conditions. The animals were fed commercially compounded feed with 18% crude protein and 2300Kcal metabolizable energy with intermittent feeding of Panicum sp and Tridax sp. The study area, located at latitude 6.5867° N and longitude 3.9700° E, had an average daily temperature of 35°C and relative humidity of 64%, which negatively impacted the adaptation, survival rates and growth performance of the rabbits. A total of 48 weaner rabbits 8 wk of age, comprising 12 each (9 does and 3 bucks) of Checkered, Chestnut, Dutch and Hyla breeds were reared for 10 wk post-weaning. Mortality was recorded across the breeds and the body weights (BW) of the rabbits were also recorded on a weekly basis for the entire 10-wk period. The study evaluated survival rates across breeds and sex and BW gain. Statistical analyses were conducted using the Minitab software for Chi Squared test of independence for counts and descriptive statistics and general linear model analysis of variance for bodyweight gain. The statistical model describing the analysis was Yijk = µ + αi + (α:β)ij + eijk where αi is the ith fixed effect of breed and βj is the jth fixed effect of sex. The model accounted for 67.03% variation in total weight gain. Further mean separation was done using the Tukey’s multiple comparison procedure. Mortality rates across breeds on the average was 56.25%, with Checkered, Chestnut, Dutch and Hyla respectively recording 66.67%, 41.67%, 50.00% and 66.67% mortality, respectively. Mortality rate across sexes was 63.89% for does and 33.33% for bucks; however, mortality rates were independent (P > 0.05) of both breed and sex with a Chi-Squared value of 2.285 and 3.41, respectively, despite the numerical differences in mortality rates across breeds and sexes. Average total BW gain during the study period across the breeds and within breeds is as presented in Table 1. The nesting of sex within breed revealed that the only significant (P > 0.05) difference was noticed between the Hyla buck and Chestnut doe, while other means were not statistically different (P > 0.05). The Chestnut breed with the least BW gain adapted well in the environment with greatest survival rate, while the Hyla breed had the greatest BW gain and least survival rate throughout the study. This study indicated a negative relationship between survival rates and BW gain, that lighter breeds of rabbit tend to have greater survival rates than the heavier breeds.
144 Assessing economic implications of thermal stress of Bos indicus and Bos taurus species during finishing in northern Colorado
Authors: Ritten John , Schilling-Hazlett Ashley K , Stackhouse-Lawson Kimberly R , Place Sara E , Carvalho Pedro H V
Published: September 2024

The objective of this study was to determine the economic implications of thermal stress on climate adaptive capacity, resiliency, and impact of Bos indicus and Bos taurus finishing steers using common finishing systems. The experimental design was a completely randomized design, with a 2 by 2 factorial arrangement of finishing system (‘natural’ or ‘conventional’) and species of cattle (Bos indicus or Bos taurus). Steers [n = 200; body weight (BW) ~450 kg] were housed in 20 feedlot pens (10 steers/pen) for the first 84 d, where cattle were blocked by initial BW. For the remainder of the 180 d (96 d) finishing period, cattle were moved to four Climate Smart Research Pens (50 steers/pen, 1 pen/ treatment, and species combination). Within each Climate Smart Research Pen, five SmartFeed systems were used to evaluate individual animal feed intake, one GreenFeed emission measurement system was used to evaluate individual animal gas emissions flux of enteric CH4, O2, H2, and CO2, and were equipped for nutrient mass balance of carbon, nitrogen, and phosphorus. Animal BW were collected every 28 d for analysis of average daily gain (ADG) and feed efficiency. Thermal stress behaviors were evaluated by one trained personnel on a subset of 7 steers/treatment via the measurement of respiration rate, open mouth breathing, rumen temperature, number of daily water intake events, lying and standing behavior, and pen surface temperature for the last 96 d of the finishing period. At harvest, carcass characteristics, liver scores, and heart scores were collected. Economic analyses were conducted post-hoc to determine the profitability of each species based on economic implications, tradeoffs, carcass quality and quantity, and time to finish. Selection of cattle for climate adaptive features may involve production tradeoffs (e.g., depressed rate of gain, greater yield risk, lower technical efficiency) and adjustment costs (e.g., re-optimizing feed for a new breed), which affect the adoption of less common breeds into the US cattle supply chain. Adding to the complexity of this evaluation, consumers preferences may not adapt quickly, affecting the prospect of Bos indicus cuts of meat in the market. We also, therefore, explore the supply- and demand-side effects of climate change adaptation through breed selection in the beef industry.
82 Linear mixed models for dissecting social and direct genetic effects of automatic feeder visit sequences
Authors: Nunez Andrea N , Steibel Juan P , Chen Ching-Yi , Holl Justin W
Published: September 2024

In this study, we delved into the feeding behaviors of 548 swine, using 114,263 records from FIRE feeders to uncover the direct and social genetic effects of feeding duration. We aimed at dissecting direct and social effects and genetics and environmental components of feeding duration using linear mixed-effects models (LMMs). To accomplish this, we fit and compared several models. The full model included direct and social genetic effects and permanent environmental effects or the social effects or both sets of effects. All models included the effect of the social group. This model facilitates a comprehensive understanding of the determinants of feeding times as it related to social interaction sequences. A novel aspect of our study is the use of sequence of feeder visits as a proxy to social interaction at the feeder, so separate social genetic and social permanent environmental effects of an individual from the ‘Social Group’ effect. The analysis reveals that the full model offers the best fit to the data, as indicated by the smallest Akaike Information Criterion (AIC). However, comparisons with reduced models suggest a partial confounding between additive genetic and permanent environmental effects at both additive and social levels. Notably, the variance explained by the social group remains consistent across models, implying minimal confounding with this factor and underscoring the distinct impact of social dynamics. The observation that social direct genetic effects do not significantly overlap (e.g., M1 vs M2) is promising, suggesting the potential to address the confounding between additive genetic (A) and permanent environmental (PE) effects by incorporating a more detailed pedigree analysis. By presenting variance components and model fit indices (Table 1), our research illuminates the intricate interactions among genetic, environmental, and social factors affecting swine behavior. The use of mixed-effects modeling in this study advances our understanding of the temporal dynamics of animal social behavior, emphasizing the importance of detailed statistical analysis in behavioral research.
84 Low-cost machine learning algorithms to predict growth and carcass traits in pigs
Authors: Billah Masum , Bermann Matias , Chen Ching-Yi , Psota Eric , Holl Justin W , Misztal Ignacy , Lourenco Daniela
Published: September 2024

Genomic prediction involves analyzing pedigree, phenotype, and genomic data, facilitating the early prediction of the genetic merit of an individual. Phenotyping is an essential component of the genomic selection program. However, depending on the trait, traditional phenotyping can be time-consuming and labor-intensive. On the other hand, with the advancement in digital phenotyping, obtaining regular images of animals has become increasingly accessible. Coupled with computer vision and machine learning algorithms, phenotypes for traits of interest can be extracted from the images and used for different purposes, including genomic predictions. Previous attempts to predict genetic merit using machine learning were ineffective and computationally expensive for routine genetic evaluations. This research aimed to present a low-cost algorithm for predicting traits that are commonly used in pig breeding programs such as weight (WT), loin depth (LD), and backfat thickness (BF) from image data. The dataset included two-dimensional side views of 9,283 pigs captured between 2021 and 2023 and manually recorded phenotypes for WT, LD, and BF. We proposed a pipeline consisting of two major tasks to predict phenotypes: body segmentation and feature extraction using a convolutional neural network (CNN). Initially, we used the YOLOv7 model to leverage existing pre-trained layers to extract the individual region of interest from the source image. Then, low-variance images were removed to ensure a wide range of diversity within the individual. The images are then annotated with prerecorded traits and divided into training and test sets (80% and 20%, respectively), with no overlap. Our study benchmarked VGG16, InceptionV3, and ResNet50 model architectures by extracting features from the outputs of the final convolutional blocks or layers to reduce the impact of the fully connected layer on potential feature loss. The results show that the features extracted from the ResNet50 model had the lowest mean absolute error, with values of 5.22 for weight, 5.28 for loin depth, and 1.31 for backfat thickness. The performance could be improved further by fine-tuning the model parameters. As a future step for this study, the predicted phenotypes will be utilized for genomic evaluations using a multiple-trait model, where the subset of predicted animals will have prerecorded traits to account for the prediction error.
86 Awardee Talk: Improving climatic resilience in pigs through genomics: From trait definition and genetic modeling to biological validation
Authors: Brito Luiz F , Johnson Jay S
Published: September 2024

Breeding programs have substantially improved productive and reproductive efficiency in worldwide swine populations. However, increased performance is often accompanied by reduced climatic resilience due to greater metabolic heat production. Genomic selection is an effective tool for alleviating the negative effects of heat stress, but it requires traits that effectively capture the physiological, behavioral, and morphological mechanisms regulating the heat stress response. In this presentation we will first describe the genetic background of heat tolerance based on routinely recorded phenotypes and climatic variables obtained from public-weather-station databases. There are clear genotype-by-environment interactions across environmental conditions and the definition of the critical periods for quantifying the environmental gradient directly impacts the accuracy of genomic prediction of breeding values for heat tolerance. Secondly, we will describe the genetic background of various behavioral, physiological, and morphological indicators of heat stress in maternal line pigs, including automatically recorded vaginal temperature, skin temperature measured in different body locations and times of the day, respiration rate, panting score, body condition score, hair cortisol, hair density, and ear area. We will also present recently developed indicators of climatic resilience based on variability metrics of longitudinal measurements of automatically recorded body temperature in lactating sows. These traits are heritable and can be successfully included in genomic breeding programs. Furthermore, we will discuss the transgenerational effects of heat stress in maternal-line pigs from quantitative and molecular perspectives, including an epigenomic analyses of in utero heat stress that identified over 200 genomic regions that are differentially methylated in in utero heat stressed pigs and provide results from a comprehensive biological validation study based on in-depth phenotyping of genetically divergent animals. We will also discuss key challenges and opportunities for breeding for improved climatic resilience in pigs. In summary, we will: 1) provide a comprehensive description of the environmental-gradient variables and critical periods fitted in genomic evaluations of heat tolerance; 2) describe the genetic background of various indicators of heat tolerance and climatic resilience; 3) present the accuracies of genomic predictions for each indicator trait; 5) describe key results from biological validation studies; and, 6) make recommendations for the implementation of genomic selection for improved heat tolerance in pigs.
85 Genomic regions associated with electronic measures indicative of structural soundness in pigs
Authors: Rohrer Gary A , Ostrand Lexi , Rempel Lea A , Keel Brittney N , Schmidt Ty B , Mote Benny E
Published: September 2024

The objective was to determine the genetic factors influencing mobility traits recorded from 5 mo-old gilts. Mobility was measured using a pressure-sensing mat (GAIT4) and 7 d of video recorded activity (NUtrack). Gilts (n = 3,659) were evaluated for the study, but only 2,172 gilts had data for both systems. GAIT4 system creates a series of measurements for each foot related to pressure, duration and step length of each foot generating a lameness score for each foot. Traits studied summarized values for all 4 feet: average stride length, average stance time, standard deviation of stance time, lameness score and total scaled pressure. NUtrack measurements were rotations, velocity, and distance walked as well as the time spent eating, sitting, standing, lying sternal and lying lateral. All NUtrack traits were standardized to a mean of zero and standard deviation of 1.0 based on each pen-day subgroup. Mixed model analyses were conducted in R fitting animal as a random effect and fixed effects of breed of sire and contemporary group, with day included for NUtrack data. ssGBLUP analyses were conducted in WOMBAT using animal effects from R models as phenotypes. Three generations of pedigree were included and genotypic data from the NeoGen Porcine 50K beadchip on 3,186 animals representing 60% of gilts with NUtrack data, 67% of gilts with GAIT4 data and most parents (>99%). Single trait analyses were conducted for all traits, gEBVs estimated and then SNP effects estimated by back-solving equations. Significance values were determined by 5,000 permutations and a Bonferroni adjustment factor applied. Estimates of heritability ranged from 0.08 to 0.41 and traits from the NUtrack system tended to have greater heritability than GAIT4 traits. There were 343 significant SNP effects in 39 unique chromosomal regions. There were 133 significant SNP associations located on chromosome 14 between 45-47 Mb associated with rotations, distance and times standing and lying. Pathway analysis identified proteoglycan and apelin pathways to be over-represented based on associations with SNP located at 2:45, 2:51, 13:79 and 14:40 (SSC:Mb). Proteoglycans are a subunit of cartilage and apelins affect blood flow and angiogenesis which make them candidates for the development of osteochondrosis, a leading cause of lameness in pigs. These results will facilitate selection of more robust animals capable of withstanding commercial production environments and group sow housing. USDA is an equal opportunity employer.
117 Population structure and genetic diversity of U.S. Polypay Sheep in the National Sheep Improvement Program
Authors: Wilson Carrie S , Petersen Jessica L , Brito Luiz F , Freking Brad A , Nilson Sara M , Lewis Ronald M
Published: September 2024

The development of the Polypay breed focused on improving the lifetime productivity of the Western range ewe with the objective of ewes lambing twice a year starting at 1 yr of age. The Polypay was developed at the U.S. Sheep Experiment Station (USSES) in 1968 as a four-breed composite with 25% each of Dorset, Rambouillet, Targhee, and Finnsheep. The breed is versatile and rugged enough to be used extensively in Western sheep production and throughout the Central and Eastern U.S. in intensive accelerated lambing systems. Our objective was to define the current population structure and genetic diversity in this breed using pedigree- and genomic-based methods as the industry adopts genomic selection. Pedigree records from Polypay breeders participating in the National Sheep Improvement Program representing 193 flocks were combined with pedigree records from USSES (n = 162,997) tracing back to the origins of the breed. A subset of these pedigreed sheep from 32 flocks born from 2011 to 2023 were genotyped with the GGP Ovine 50K BeadChip (n = 1,856). Quality control measures were applied for marker and animal call rates and to remove markers in high linkage disequilibrium. Pedigree completeness and quality was 9.6, 2.3, and 4.6 for mean maximum, mean complete, and mean equivalent generations, respectively. Pedigree-based inbreeding for the full data was 2.19%; for the subset of pedigreed animals also genotyped, it was 2.94%. The rate of inbreeding was consistently 0.07%/yr across years (P = 0.88). Measures of genomic inbreeding for genotyped sheep were 1.26% for Wright’s inbreeding coefficient, measured as heterozygote deficiency, and 2.90% for inbreeding based on runs of homozygosity. Ten rams from 8 flocks contributed up to 20.3% of the current gene pool. Pedigree-based measures of effective population size ranged from 70 to 249 while the genomic estimate was 118. Fixed and rare (≤ 0.01) alleles accounted for 3.2% and 1.4% of the markers, respectively. The majority of runs of homozygosity (72.1%) were less than 6 Mb, indicative of founder effects and ancient inbreeding rather than recent inbreeding. Based on principal component (PC) analysis, PC 1 suggested a separation of USSES sheep from the rest. Model-based population structure showed differentiation among flocks as did Wright’s Fixation Index (FST) values with a mean of 0.07. High levels of genetic diversity exist in the Polypay as demonstrated by low inbreeding, high heterozygosity, and large effective population sizes. Population substructure exists in the population as shown by PC analysis, model-based population structure, and FST values. These parameters indicate that sufficient genetic diversity is present to move forward with genomic selection. Genetic diversity measures should be revisited periodically once genomic selection is implemented in the breed. USDA is an equal opportunity provider and employer.
205 An autosomal recessive mutation in PYGM causes myophosphorylase deficiency in composite cattle
Authors: Batt Mackenzie C , Venzor Leila G , Gardner Keri , Reith Rachel R , Roberts Kelsey A , Herrera Nicolas J , Fuller Anna M , Sullivan Gary A , Mulliniks Travis , Spangler Matthew L , Valberg Stephanie J , Steffen David J , Petersen Jessica L
Published: September 2024

Between 2020 and 2022, eight calves (6 heifers, 2 bulls) in a Nebraska herd (composite Simmental, Red Angus, Gelbvieh) experienced exercise intolerance during extended physical activity. These calves exhibited an inability to keep pace with the rest of the herd, with some eventually succumbing to physical collapse from which they did not recover. Increased creatine kinase, an indication of muscle degeneration, was confirmed in biopsy and necropsy samples. Available sire pedigrees revealed a common paternal ancestor within 2 to 4 generations in all the affected calves. Dam pedigrees were unavailable; however, the herd retained replacement females that occasionally shared common ancestors with breeding bulls. Thus, a de novo autosomal recessive mutation was hypothesized as the cause of exercise intolerance in these calves. Genomic data (100K SNP) from 6 affected calves and 715 herd mates were initially used for a whole genome-wide association study, pinpointing a significant region on chromosome 29. Whole-genome sequencing of 2 affected calves combined with 145 other sequenced cattle led to the identification of a nonsense mutation in the PYGM gene, also located on chromosome 29. The mutation, confirmed to be present in the skeletal muscle transcriptome, was predicted to produce a premature stop codon. The protein product of PYGM, myophosphorylase, has a crucial role in metabolizing glycogen stored within skeletal muscle to generate energy for muscle cells. As a result, the recessive genotype led to greater (P < 0.05) glycogen concentrations in affected calves (n = 2) compared with heterozygous animals (n = 3) and wild-type controls (n = 2). Immunohistochemistry and label-free quantitative proteomics analysis confirmed the absence of the PYGM protein product in skeletal muscle. Myophosphorylase is needed for efficient glycogen utilization, which is crucial for producing high-quality beef. In the 2 affected calves harvested, increased skeletal muscle glycogen persisted postmortem, resulting in increased pH and dark-cutting beef, which is associated with a negative consumer perception and economic losses to the industry. In addition to identifying the cause of exercise intolerance and welfare concerns in these cattle, this study is among the first to identify a specific genetic mutation associated with dark-cutting beef. Carriers of the mutation did not display any discernible differences in meat quality or measures of animal well-being. While cattle heterozygous for the mutation may not immediately impact the beef industry, identifying carriers is paramount for implementing proactive selection and breeding strategies to prevent the production of affected calves.
347 Quantifying phenotypic and genetic variation for cow fertility phenotypes in American Simmentals using total herd reporting data
Authors: Catrett Cassidy , Moorey Sarah , Beever Jonathan E , Rowan Troy N
Published: September 2024

Inefficiencies in reproduction are the largest drivers of loss in United States beef production. Nearly all reproductive traits are at least modestly heritable, making genetic selection and improvement possible over the long term. Cow-centric reproductive traits present unique challenges, as they are not expressed in male animals and will not be observed until later in the productive life of a female, if at all. This makes cow-centric fertility traits ideal candidates for selection with Expected Progeny Differences (EPDs). We used a commercially generated dataset from the American Simmental Association (ASA) to explore the phenotypic and genetic variation in these traits. The dataset comprised Total Herd Enrollment (THE) records, which required unbiased reporting of enrollment, calving, and disposal for 303,158 females (132,403 cows and 170,755 heifers). Using the THE data, we calculated three continuous and two discrete traits that are indicators of heifer and cow fertility and rebreeding ability. These traits were: 1) calving date (calving date of the cow relative to the start of the calving season), 2) calving interval (days between calves), 3) first calving interval (calving interval observation between the first and second calving record for a female), 4) heifer pregnancy (did the animal calve as 2-yr-old?), and 5) discrete early calving (did animal calve in the first 30 d of the calving season?). We observed a significant difference in calving date in mature cows that calved in the first 30 d of the calving season of their contemporary group as heifers compared with those that calved later (Table 1). Early calving heifers averaged a nearly 10 d sooner calving date throughout their productive lives compared with those that calved after 30 d as heifers (29.9 d vs. 40.2 d, P-value < 0.01). We used a combined pedigree and genomic approach, implemented in GIBBSF90+, to estimate genetic, environmental, and permanent environmental variance components. We estimated heritabilities of 0.24 (± 0.037215) for heifer pregnancy, 0.06 (± 0.009265) for calving date, 0.07 (± 0.015451) for discrete early calving, 0.04 (± 0.003954) for calving interval, and 0.04 (± 0.009604) for first calving interval. We observed positive phenotypic and genetic correlations between these traits, ranging from 0.01 to 0.96 (Table 2). This work provides an extensive population survey of phenotypic trends in reproductive performance and management in a large seedstock population. The heritable nature of the inspected traits motivates further work in understanding their genetic architectures and how they are affected by heterosis in crossbreeding programs.
348 Repeat breeding syndrome: Dexamethasone decreases the prevalence of anti-sperm antibodies in dairy cows
Authors: Cockrum Rebecca R , McGehee John , Duncan Jane , Daniels Kristy M
Published: September 2024

Cows that require greater than two inseminations to conceive are diagnosed with repeat breeding syndrome (RBS). Anti-sperm antibodies (ASA) in serum are in greater abundance among individuals exhibiting fertility challenges. In humans, treatment with glucocorticoids has been shown to decrease the prevalence of ASA and improve fertility. Therefore, the purpose of this study was to determine the impact of dexamethasone on ASA and pregnancy outcomes in RBS cattle. Holstein multiparous cows (n = 18) diagnosed as having RBS were randomly allotted to one of three treatment groups: Control (C; n = 6), low dexamethasone (LD; n = 6) and high dexamethasone (HD; n = 6). Intramuscular injections of saline (C), 0.05 mg/kg of BW of dexamethasone (LD), or 0.10 mg/kg of BW of dexamethasone (HD) were administered daily starting 4 d prior to insemination (d 0) through insemination (d 4). All treatment volumes were standardized with saline to achieve the same treatment level. Blood was collected on d 0, 4, 8 and 18, with serum extracted on d 0 through 8. Additionally, milk weights were recorded until d 34. For breeding, cows were synchronized using the Double Ovsynch protocol and only one insemination was conducted at the time of breeding. The MIXED procedure in SAS was used to determine the relationship of treatment with the repeated measures of ASA abundance and milk yield. Additionally, a non-parametric chi-square analysis was conducted in SAS using PROC GENMOD to determine the relationship of treatment with immune cell count and pregnancy outcomes. Day, Treatment and Treatment × Day were designated as fixed effects, and a protected post hoc analysis was conducted to account for multiple comparisons. Anti-sperm antibodies tended to decrease (P ≤ 0.082) as dexamethasone increased compared with C, however, there was no Treatment × Day interaction. Daily milk yields for both LD and HD decreased (P < 0.002) compared with C cows, but there was no interaction with day. Though there was an effect for Treatment × Day (P ≤ 0.002) for white blood cell count, percentage lymphocytes, eosinophils, and neutrophils, there was no effect of treatment (P ≥ 0.181). Therefore, interaction differences were largely driven by day (P < 0.001). Surprisingly, there was a trend (P = 0.069) for an increase in pregnancy rates for C cows compared with LD and HD cows. In fact, one-half of the C cows achieved pregnancy compared with just one cow for LD and HD treatments. Overall, dosage of dexamethasone did not differ for ASA or pregnancy outcomes. However, dexamethasone did decrease ASA but did not increase pregnancy rates. Based on these results, we suggest that alternative treatment measures should be explored to improve pregnancy rates in RBS cows.
343 The relationship between pulmonary arterial pressure phenotypes and oxygen consumption in beef cattle
Authors: Griffin MacKenzie L , Speidel Scott E , Place Sara E , Stackhouse-Lawson Kimberly R , Holt Tim N , Giess Lane K , Zuvich Miranda L , Enns R Mark
Published: September 2024

The objective of this study was to determine if a phenotypic relationship between pulmonary arterial pressure (PAP) and oxygen consumption exists in beef cattle. Pulmonary arterial pressure is used as an indicator of pulmonary hypertension in cattle which is indicative of the susceptibility to both high-altitude disease and feedlot heart failure of an individual. Cattle with greater PAP are typically hypoxic and have initiated expression of compensatory mechanisms their cardio-pulmonary systems employ to combat this alveolar hypoxia. In turn, this causes an increase in PAP and, ultimately, right-sided heart failure. If hypoxic individuals consume more oxygen as a compensation mechanism, oxygen consumption as a trait could be used as an indicator of the prediction of PAP and feedlot heart failure and would be especially useful for cattle in deep phenotyping systems. Data used in the study included cattle with PAP measures on a total of 132 individuals. The data consisted of both steers (n = 99) and heifers (n = 33) from a ranch located in southern Wyoming at an elevation of approximately 2,195 m. After weaning on the ranch, cattle were transported to Colorado State University’s Agriculture Research, Development and Education Center located in Fort Collins, CO (elevation: 1,558 m). Oxygen consumption was measured using a Greenfeed system (C-Lock, Rapid City, South Dakota). To determine if a relationship exists between oxygen consumption and PAP, a simple linear regression model was used to regress PAP on average daily oxygen consumption. Effects included in the model were sex and phenotypic PAP. Average daily oxygen consumption was measured in kilograms per day and averaged 6.6 ± 1.66 kg with a range of 2.3 to 10.45 kg. Pulmonary arterial pressure averaged 42.6 ± 7.96 mm Hg and ranged from 33 to 81 mm Hg. In this data, sex did not account for a significant amount of variability in phenotypic PAP (P > 0.05). Oxygen consumption did account for a significant amount of variability in PAP (P < 0.05), and it was found that with every 1 kg increase of oxygen consumption, PAP is estimated to increase 1.02 mm Hg. These results indicate that a statistical relationship between oxygen consumption and PAP exists. Although these findings suggest that greater oxygen consumption leads to greater PAP, further research is required to fully comprehend the relationship between beef cattle PAP and oxygen consumption.
345 Breed differences within heart scores of fattening feedlot cattle
Authors: Kukor Isabella , Enns R Mark , Holt Tim N , Cleveland Matthew , Sardella Grant , Holland Ben P , Word Alyssa B , Ellis Guy , Theurer Miles , Speidel Scott E
Published: September 2024

Feedlot heart disease (FHD) is a condition in fattening beef cattle that arises from right-side heart remodeling resulting from alveolar hypoxia. This hypoxic environment is hypothesized to result from the large body mass of fattened cattle. Sensitive cattle have increased incidence of pulmonary hypertension resulting from compensation mechanisms of the individual. The severity of this remodeling is quantified at harvest by gross examination of the heart utilizing a visual grading system of 1-5, where a score of 1 is a normal heart and a score of 5 is a severely remodeled heart. Previously, heart score was found to contain sire differences and to be moderately heritable. This led to more questions regarding other potential factors influencing heart scores, one of which was breed differences. Therefore, the objective of this study was to examine the prevalence of heart remodeling in beef and dairy cattle breeds and to investigate whether some breeds had a greater prevalence of heart remodeling. Heart scores were collected on Angus-influenced animals raised in the panhandle of Texas (1,103 m) and a Beef x Dairy group raised in western Kansas (846 m). The total number of animal observations was 2,069. Twenty-four percent of the individuals exhibited moderate to severe remodeling (score 3 and greater). Of this cohort, 1,307 animals had breed composition data (i.e., Angus, Simmental, Jersey, and Holstein) suitable for use in the analysis. Breed proportions ranged from 0% to 100% for Angus, 0 to 93.25% for Simmental, 0% to 69.22% for Jersey, and 0% to 78.06% for Holstein. Utilizing a linear regression model with heart score as the response and breed percentages and heart scorers as fixed effects, the importance of breed composition (covariate) was determined. The percentages of Angus influence tended to be associated with heart score (P = 0.0506), with a regression coefficient of -0.005. The percentage of Simmental had an association with the variation in heart score (P < 0.05) and had a regression coefficient of -0.008. Additionally, the percentages of Jersey and Holstein were found to reduce the incidence of heart score by a factor of -0.014 and -0.018 (P < 0.05), respectively. In summary, an increased percentage of either Jersey or Holstein breed resulted in reduced heart scores, while the incidence of beef breeds was found to be associated with some to little difference in the improvement of heart scores, alluding to potential breed advantages in mitigating right sided heart remodeling.