Location: Genetics and Animal Breeding
2025 Annual Report
Objectives
Objective 1. Identify proteins, genes, and quantitative trait loci associated with important phenotypes to improve efficiency of swine production.
Sub-objective 1.A: Improving methodologies to enhance the analysis of gene expression
data.
Sub-objective 1.B: Improve nutrient utilization and conversion of feed to muscle.
Sub-objective 1.C: Improve lifetime reproductive performance.
Objective 2. Develop machine learning methods and artificial intelligence models that incorporate production data, biological assays, and data acquired via electronic monitoring.
Sub-objective 2.A: Enable prediction of animal performance to inform management decisions in real-time.
Sub-objective 2.B: Enhance selection decisions to improve performance of future generations.
Approach
Demand for pork products continues to grow, while producers are being asked to reduce their environmental impact; therefore, commercial swine production needs to reduce inefficiencies while continuing to improve product quality and quantity. Inefficiencies are magnified by a pig’s response to stressors encountered, whether these stressors are environmental (temperature), health (pathogens) or social interactions with pen-mates. The greatest opportunities for increased efficiency are to improve nutrient utilization and increase sow lifetime reproductive performance, by reducing reproductive failure and culling associated with lameness. These challenges are complex and require novel approaches to rapidly identify solutions. The proposed research will contribute to the development of more accurate computational tools for incorporating production data, biological assays, and data acquired via electronic monitoring into selection decisions. These tools will be crucial to create a catalogue of genetic variants that alter gene function (either in amount or function of protein) and trait expression. Putative functional genetic variants identified in our research population will be used to inform selection decisions in commercial populations once their effects have been validated. Our research will focus on identification of genetic markers responsible for variation in conversion of feed to pork product and lifetime reproductive performance in commercial sows. In addition to genetic variant discovery, tools developed in the proposed research will allow early identification of sick or stressed animals via real-time monitoring through electronic systems. This will improve animal welfare and reduce associated mitigation costs. The unique swine resources at USMARC will be coupled with our genomic capabilities to permit more accurate prediction of phenotypes or breeding values, contributing to the goals of National Program 101’s Action Plan. These predictions will inform herd management decisions, resulting in improved performance in current pigs as well as enhance selection decisions to create more productive pigs in future generations.
Progress Report
Our lab focuses on the improvement of methodologies for analyzing gene expression data across several projects (Objective 1A). Most studies that employ RNA-sequencing (RNA-seq) methods select animals with the most extreme phenotypic measurements and utilize a categorical value (i.e. high or low feed efficiency) in the analysis. This type of experimental design likely fails to capture all the variation in gene expression that contributes to the phenotype. Therefore, we developed a protocol for analyzing RNA-seq data using quantitative phenotypes from all animals in a study. Initial testing of the approach was conducted using data from a cattle feed efficiency study that was readily available.
Experiments in Objectives 1 and 2 of the project plan involve genome-wide association studies (GWAS). The aim of GWAS is to map quantitative trait loci (QTL) to understand the genetic architecture and find DNA variants associated with a trait of interest. One of the key factors influencing the accuracy of GWAS is the number of single nucleotide polymorphisms (SNP) included in the analysis. Inclusion of whole-genome sequence (WGS) genotypes should increase the accuracy of QTL mapping compared to lower-density commercial SNP arrays. We currently have commercial SNP array genotypes for 20,248 animals in our population, spread across five different commercial arrays. We imputed those animals to WGS genotypes using the Swine Imputation (SWIM) server. Starting this year, we will be genotyping animals in-house, using low-coverage WGS (lcWGS), rather than commercial SNP arrays. To facilitate this change, we have developed a computational pipeline for obtaining WGS genotypes from the lcWGS data. We have submitted approximately 6,000 samples to the USMARC core lab for sequencing.
We continue to utilize automated feeding systems to study both feeding behavior and feed efficiency in pigs (Objective 1B). We currently possess feeding records for 10,648 grow-finish pigs, collected on the Osborne FIRE feeder system, and we are continuing to add records for approximately 1,200 pigs each year. A GWAS was performed on 9,937 pigs for 16 feed efficiency traits, utilizing imputed sequence variants in a single-step genomic best linear unbiased prediction (GBLUP) analysis. Results from this analysis are currently being interpreted. In 2024, we published a mathematical model for detecting anomalous patterns in the total time spent at the feeder for individual pigs. We have begun to expand this model to incorporate additional parameters recorded from the automated feeders, such as number of meals, length of meals, time between meals, and timing of meals. A similar approach is being utilized for measures of lifetime productivity in sows (Objective 1C) where phenotypes evaluated include mammary gland development and factors associated with lifetime number of piglets produced.
In 2024, we initiated a new experiment to study the effects of social interactions among pen mates and chronic stress (Objective 1B). As part of that experiment, well-being assessments, which include lesion scoring and overall assessment of the animal, are being conducted biweekly and at each mixing event from weaning up through the gilts’ entrance into the breeding herd. In 2025, we completed well-being assessments on 5,290 animals.
A new experiment to utilize computer vision for the development of novel phenotypes was initiated. This project addresses Objectives 1 and 2 of the project plan. Cameras have been placed above pens in our feed efficiency barn and will be capturing continuous video footage of the animals for the 6-week period that they are in the barn. Machine learning models are being developed to track the animals in real-time and identify behaviors of interest. We also are continuing to use cameras and geophones to monitor sow and piglet behavior in farrowing stalls as well as beginning to collect data to see if depth cameras can be used to assess body condition of sows during gestation.
Accomplishments
1. Genetic factors associated with failures to express estrus in swine. During estrus, young females (gilts) are receptive to boars and ovulation occurs. Therefore, proper expression of estrus by gilts is critical for successful breeding. Failure to express estrus when ovulating occurs in about 5% of breeding-age gilts and gilts are referred to as behavioral anestrus. ARS scientists at Clay Center, Nebraska, identified numerous significant genomic regions using a case-control genome-wide association study with 655 behavioral anestrus and 2,712 normal estrus control gilts. Sequence-level information for these regions were analyzed and identified genotypes for eight candidate genes. These candidate genes and regions highlighted a linkage between olfactory receptors (odor receptors in the nasal cavity) and estrus expression. The results provide a better physiological understanding of estrus initiation in pigs including a linkage between fertility and olfaction. Marker associations can now be used to enhance selection decisions and provide more rapid improvement in reproductive efficiency of pigs, reducing economic losses for pork producers.
2. Genes associated with fatty acid composition in pork loins identified. Fatty acids are a key component of the nutritional value of pork and a critical determinant of pork quality and flavor. Fatty acid composition can be influenced by genetics, sex, environment, and diet in pigs. A genome-wide association study was conducted by ARS scientists at Clay Center, Nebraska, and significant associations were found in or near seven genes for sixteen important fatty acid traits. Dozens of variants in these genes and two additional gene candidates were identified through further analyses. For the eighty most significant genetic variants, over half were found to reside in predicted regulatory sites, suggesting these are functional variants that modify expression of important genes and do play a significant role in fatty acid composition in pork. Identification of these functional variants in genes regulating fatty acid composition will allow selection of pigs for higher nutritional value and more desirable pork quality, improving consumer demand and healthfulness of pork, and improving the economic competitiveness for producers.
3. Created system to monitor sow posture during lactation. Preweaning mortality in commercial pig production is approximately 15%. Many of the piglets are lost due to the sow accidentally laying on them. A greater understanding of sow postural preferences and rates in which a sow changes postures would enable development of a sow mothering index. To obtain the basic information on sow posture behavior, a computer model was developed that uses images from an overhead mounted depth camera to determine the posture of the sow (Standing, Sitting, Kneeling, Lying Sternal, Lying Lateral Right and Lying Lateral Left). The computer model was installed and monitored by ARS scientists in Clay Center, Nebraska, and was shown to achieve 98% accuracy in the tested environment and 96% accuracy in a novel environment. Continuously monitoring sow behavior enables determination of behaviors associated with greater piglet survival and will lead to development of a Sow Mothering Ability Index to select animals that better care for their piglets. Successful development of this index will decrease preweaning mortality, improve animal well-being, and improve the efficiency of pork production for producers.
Review Publications
Corrigan, J., Mares, J.A., Hutzler, J.D., Nonneman, D.J., Burmeister, D.M. 2025. The incidence of volatile anesthesia porcine stress syndrome in pigs (Sus scrofa domesticus) gives implications for physiology during anesthesia. Journal of the American Association for Laboratory Animal Science. 64(1):179-188. https://doi.org/10.30802/aalas-jaalas-24-077.
Ostrand, L.M., Rempel, L.A., Keel, B.N., Snelling, W.M., Schmidt, T.B., Psota, E.T., Mote, B.E., Rohrer, G.A. 2025. Genomic analysis of mobility measures on 5-month-old gilts associated with structural soundness. Journal of Animal Science. 103. Article skaf001. https://doi.org/10.1093/jas/skaf001.
Paudel, S., Brown-Brandl, T.M., Rohrer, G., Sharma, S.R. 2025. Deep learning algorithms to identify individual finishing pigs using 3D data. Biosystems Engineering. 255. Article 104143. https://doi.org/10.1016/j.biosystemseng.2025.104143.
Funk, T.H., Rohrer, G.A., Brown-Brandl, T.M., Keel, B.N. 2024. Online feeding behavior monitoring of individual group-housed grow-finish pigs using a low-frequency RFID electronic feeding system. Translational Animal Science. 8. Article txae051. https://doi.org/10.1093/tas/txae051.
Kaps, M., Quail, L.K., Rosasco, S.L., Snider, A.P., Zoca, S.M., Epperson, K.M., Rich, J.J.J., Miles, J.R., Crouse, M.S., Keel, B.N., Summers, A.F., Perry, G.A., Lents, C.A., Cushman, R.A. 2025. Delayed endometrial preparation for the induction of luteolysis as a potential factor for improved reproductive performance in Angus beef heifers with high antral follicle counts. Biology of Reproduction. 112(1):130-139. https://doi.org/10.1093/biolre/ioae146.
Lindholm-Perry, A.K., Bradford, H.L., Foote, A.P., Freetly, H.C., Chitko-McKown, C.G., Kuehn, L.A., Keele, J.W., Neville, B.W., Oliver, W.T., Keel, B.N. 2025. The association between hematological profiles and whole-blood transcriptome genes identified using quantitative analysis with average daily gain and feed efficiency in forage-fed beef heifers. International Journal of Molecular Sciences. 26(10). Article 4633. https://doi.org/10.3390/ijms26104633.
King, D.A., Shackelford, S.D., Nonneman, D.J., Katz, T.S., Wheeler, T.L. 2025. Categorization of beef longissimus lumborum and gluteus medius muscles based on metabolic attributes is more informative than muscle pH. Meat and Muscle Biology. 9(1). Article 18388. https://doi.org/10.22175/mmb.18388.
Pacheco, V.M., Brown-Brandl, T., Rohrer, G.A., Vieira De Sousa, R., Silva Martello, L. 2024. Impacts of farrowing pen design, season, and sow parity on litter performance and piglet mortality. Animals. 14(2). Article 325. https://doi.org/10.3390/ani14020325.
Bery, S., Brown-Brandl, T.M., Rohrer, G.A., Sharma, S., Leonard, S.M. 2024. Impact of crate design, number of heat lamps, and lying posture on the occurrence of shoulder lesions in sows. Biosystems Engineering. 247:249-256. https://doi.org/10.1016/j.biosystemseng.2024.09.017.
Pacheco, V.M., Brown-Brandl, T.M., Vieira de Sousa, R., Rohrer, G.A., Sharma, S.R., Martello, L.S. 2024. Deep learning-based sow posture classifier using colour and depth images. Smart Agricultural Technology. 9. Article 100563. https://doi.org/10.1016/j.atech.2024.100563.
Dong, Y., Song, Z., Codling, J.R., Rohrer, G.A., Miles, J.R., Sharma, S., Brown-Brandl, T.M., Zhang, P., Noh, H. 2025. Robust piglet nursing behavior monitoring through multi-modal fusion of computer vision and ambient floor vibration. Computers and Electronics in Agriculture. 238. Article 110804. https://doi.org/10.1016/j.compag.2025.110804.