Location: Genetics and Animal Breeding2022 Annual Report
Objective 1: Improve genomic tools for beef cattle and sheep. Sub-objective 1A: Complete improved reference assemblies for beef cattle and sheep using genome-wide and locus-targeted approaches, in addition to comparative approaches, to improve accuracy and contiguity. Sub-objective 1B: Improve annotation of the reference assemblies by conducting specific assays as outlined in the FAANG consortium guidelines, enhanced with parent-of-origin allele expression pattern data. Sub-objective 1C: Develop comprehensive databases of existing variation with predicted impact of those variations on gene expression and protein sequence. Objective 2: Develop systems to improve performance through combined genetic and genomic approaches. Sub-objective 2A: Improve breeding and management decisions by characterizing current genetic and phenotypic variation within and between predominant beef breeds and crosses. Sub-objective 2B: Identification of genomic variation associated with industry-relevant phenotypes in beef cattle. Sub-objective 2C: Development of low-input production lines of sheep, including genetic and genomic resource development to support characterization of these lines. Objective 3: Identify and characterize microbes, microbial populations, and parasites associated with normal and diseased populations. Sub-objective 3A: Profile microbial populations in the respiratory tract (RT) of cattle throughout the production life-cycle in the context of BRDC. Sub-objective 3B: Characterize genomic variation among sheep parasites, for correlation with anthelmintic resistance and animal genotype. Objective 4: Combine products from Objectives 1, 2, and 3 to synthesize a broader knowledge base. Sub-objective 4A: Synthesize genome annotation from Objective 1 and genetics by selection and assessment of impact of predicted non-functional alleles. Sub-objective 4B: Synthesize parasite and metagenomics from Objective 3 with genetics and genomics from Objective 2. Sub-objective 4C: Synthesize variant genotypes and annotation from Objective 1, animal phenotypes from Objective 2, and microbial profiles from Objective 3, by partitioning microbial variation into host genetic and enviromental influences on phenotypic expression.
Challenges to sustainability of beef and lamb production include aspects of animal health and wellbeing, societal expectations of reduced antibiotic use and/or development of alternatives, and pressure to reduce environmental impact of production. Advances in genomic and related technologies have opened new avenues to better understand the relationships between variants of animal genomes, production traits, and the microbes that are associated with animal production. The technologies support and depend on development of research populations with pertinent phenotypes that broadly sample industry genetics, continuing improvement in annotation of animal genomes, identification and characterization of microbial species relevant to animal production, and continued assessment of the interaction of genome variation and production phenotypes. This project plan will merge previous genetics and genomics projects into a broader systems approach, that will encompass (1) genome annotation and identification of functional variation among genomes, (2) development of phenotyped populations in which the effects of variation can be estimated, (3) characterization of the overall microbial diversity associated with the animals and dependencies of this diversity on animal genome variation, and (4) molecular-level characterization of microbial or parasitic organisms that impact on animal health, productivity, and reproduction. The systems approach will be combined with population management strategies, application of advancements in statistical methodology, and partnering with commercial producers. This combination will enable broader understanding of the components contributing to production efficiency, environmental impact, and animal welfare, while developing specific technologies for release to beef cattle producers and improved strains for the sheep industry.
This is the final report for project 3040-31000-100-000D “Developing a Systems Biology Approach to Enhance Efficiency and Sustainability of Beef and Lamb Production”. This project will be replaced with project 3040-31000-104-000D in fiscal year (FY) 2023. The research team achieved all major goals set for the 5-year cycle, despite challenges from pandemic-related limitations and supply shortages. Objective 1 greatly exceeded expectations. New reference assemblies for beef cattle (Hereford breed), sheep (Rambouillet breed), and swine (Duroc breed) were prepared, along with development of new assembly technologies that supported genomes for the Angus and Brahman breeds with accuracy and contiguity increased by one to two orders of magnitude beyond the original references, as planned. These assemblies were made public and are used globally as standards for genome research to permit cross-study comparisons and are the basis of completed and ongoing Functional Annotation of Animal Genomes (FAANG) consortium analyses with global partners, which identified the segments of genomes involved with gene expression. We created reference-quality assemblies of multiple additional cattle and sheep breeds, including Simmental, Piedmontese, Holstein, Jersey, Nelore, Braunvieh, and Scottish Highland cattle breeds, and White Dorper, Romanov, Native Churro, and East Friesian sheep breeds. The concept of species specific “pangenomes”, where reference assemblies of single animals are replaced by a graph of genome segments representing genetic material from a broad array of animals, became recognized as the most fruitful path forward for genome research, and we are leading an international consortium of researchers for cattle and sheep pangenomes. These studies contextualize variants with respect to control elements in the genome, to support identification of causative variants that make the best genetic markers for selection to improve production traits. Variants identified in this way are being imputed in the Germplasm Evaluation (GPE) and Selection for Functional Alleles (SFA) beef cattle populations for determination of causative genetic variants underlying phenotypic traits, using techniques developed in this project including pangenome-aware imputation. The overall goal of Objective 2 is to develop markers and genetic parameters for a wide range of production characteristics to guide national cattle evaluation programs in the future. This is a long-term, ongoing objective that requires continued resampling of current industry germplasm to remain relevant and provide producers tools to improve genetically influenced production traits. The project maintained and improved this resource by collecting a broad range of phenotypes on the GPE population, moving to a low-pass sequencing-based genotyping approach over this project cycle with over 6,000 animals genotyped for over 40 million polymorphisms using haplotypic imputation algorithms. The project utilizes both artificial insemination (over 1,000 calves/year) combined with natural service based on >100 fertile bulls developed in the herd from sampling top industry sires. Updates to the popular cross-breed Expected Progeny Difference (EPD) adjustment factor table were released during the project cycle and heavily referenced by commercial and seedstock beef cattle producers and breed associations. We are on track to achieve the ultimate objective of estimating breed-specific heterosis, with minor delays to improve animal welfare by eliminating the fall breeding season. Comprehensive and contiguous sequencing for animals representing multiple breeds (pangenome from Objective 1) contributes significantly to the accuracy of haplotypic imputation underpinning this objective. The project became part of the Beef Grand Challenge initiative to test management and environment by genetic interactions, based on providing approximately 120 animals per year from the GPE herd to management facilities in Oklahoma, Colorado, and Montana. This provided a common genetic base to allow comparisons of genotype/phenotype associations in different production environments. Phenotypes being analyzed include female productivity, disease resistance, feed efficiency, lower gastrointestinal tract bacterial population profiles, fertility traits and lifetime productivity. A novel phenotype of infestation by horn flies was conducted for 2,000 cows using a photographic approach, to develop a neural network for automated quantification of horn fly infestation. Objective 3 met with unexpectedly high success including the development of novel methods to accurately assemble microbial genomes from closely-related species using DNA extracted from parasite-infested sheep feces. This allows for properly identifying the bacteria and accurately assigning antibiotic resistance genes to them, which can be important for determining if the resistance is associated with pathogenic strains or species in the sample. The technique is being expanded to examine the microbes in the cattle rumen to identify associations with feed efficiency, methane production, and other traits. The bovine respiratory disease (BRD) portion of Objective 3 successfully profiled the microbial communities of the upper respiratory tract in animals before and after being diagnosed with the disease. This provided information for studying the association of particular microbes on the likelihood of developing the disease and generating a profile of “healthy” microbes in preweaning calves. Over 10,000 nasal swabs at preconditioning, weaning, and prebreeding time points were profiled, despite shortages of swabs caused by demand for pandemic testing. The study identified mycoplasma bacteria and bovine corona virus as important agents of current BRD infections in Nebraska. The outputs from Objectives 1-3 fed into the “grand synthesis” strategy of Objective 4. The long-term selection in the SFA population has surpassed an average of 15 more “loss of function” (LOF) genes in replacement heifers in the selected animals compared to control lines, allowing evaluation of LOF effects on heifer fertility and meat quality. Approaches for integrating microbial profiling into genetic predictions have showed promise but are still in development. The SFA project has continued selection each year for control and select lines that differ in frequencies of LOF alleles. Approximately 1,000 calves are part of the project each year and are genotyped for LOF alleles. The SFA lines (control and select) were initially selected using LOF genotypes obtained from a functional single nucleotide polymorphism (SNP) assay. In 2020, genotyping transitioned to imputation from low coverage sequence, supported by a reference panel representing 946 deeply sequenced beef and dairy cattle. The more comprehensive sequence-level genotypes introduced 1,242 previously unselected LOF variants that are segregating in the SFA population. Replacement heifers selected from the 2020 calf crop differed by an average of 15 LOF alleles (control – select line means). Changes in the commercially available low coverage sequencing product have necessitated development of an in-house sequencing and imputation strategy for 2022.
1. Estimation of mitochondrial abundance through low-coverage sequencing. The abundance of mitochondria (MT) in cells has been hypothesized to impact production traits in beef cattle. ARS scientists at Clay Center, Nebraska, collaborated with researchers at the University of Nebraska to evaluate the heritability of MT abundance and potential association with a range of production traits. A method for using low coverage sequencing to estimate MT abundance from blood cell-derived DNA was developed. The group demonstrated that MT abundance is moderately heritable, but that genetic correlations with production traits were not strong enough to include it as an indicator in genetic evaluations. Further research is warranted to see if mitochondrial copy number from other tissue sources (e.g., muscle, adipose) would show stronger relationships with traits like lean growth rate and marbling, as the DNA for this study was derived from blood.
2. Evaluation of parameters affecting cow lifetime productivity. Overall productivity of cows is a function of multiple variables, including the years of successful production of weaned calves and the maintenance requirements of the cow to achieve reproductive age and maintain reproduction. ARS scientists at Clay Center, Nebraska, examined parameters in beef cows including cumulative productivity over reproductive lifespan, cow weight, and inbreeding coefficients. Cow weight was found to be highly heritable, suggesting genetic selection for lighter cows might reduce feed requirements. However, cow productivity was less heritable and influenced more by non-additive effects. Inbreeding measured by areas of homozygosity in the genome was found to be inversely related to productivity. Since cumulative productivity is measured late in life, direct selection would be slow albeit accelerated by crossbreeding, but selection against genomic areas of homozygosity might be a new efficient means to impact this trait. This work provides a path to improving overall productivity of cows while reducing maintenance requirements.
3. Evaluation of the impact of assembly quality on pangenome construction. Pangenome graphs are representations of a species' DNA that attempt to include the entirety of the genome that exist around the world for all breeds and genetic lines. For example, individual cattle breeds contain in the range of 3 to 14 million bases of DNA not found in the Hereford breed reference genome assembly of cattle, which represent genome segments present in the common ancestor that were lost during breed separation. However, it has not been previously demonstrated whether lower-quality (less expensive) genome assemblies of breeds can be used in pangenome graphs, or if assemblies created by non-uniform methods can produce a useful pangenome graph. ARS scientists at Clay Center, Nebraska, in collaboration with researchers in Switzerland, generated both high-and low-quality assemblies with a variety of methods and evaluated the pangenome graphs that could be produced with them. The results demonstrated pangenome graphs are resilient to inclusion of assemblies of lower quality and there is no need to enforce a single pipeline for generating them. This will allow a much broader representation of the >800 cattle breeds found around the world, as the cost of high-quality genome assemblies for all of them would be prohibitive. The pangenome will be useful for improved genome annotation, understanding cattle genome function, and for identifying unique genes found in only certain cattle populations that might be exploited for improving production traits in popular breeds.
4. Improved methods of DNA pooling for leveraging trait data collected in the beef cattle industry. A large number of beef cattle are processed each year with many trait measures (e.g. disease incidence, growth) that represent an untapped resource for connecting genetic diversity with phenotype. The cost of individually genotyping every animal remains prohibitive, but methods for pooling animals by phenotype for bulk genotype measurement have shown promise. However, errors in estimating allele frequency in pools and consequent association between alleles and phenotype must be understood. ARS scientists at Clay Center, Nebraska, designed an experiment using DNA of individuals that were pooled at various levels of contribution to estimate the error inherent in current pooling methods. The results indicated that increasing the number of animals per pool and increasing the number of genetic markers in the pooled breeding value estimation reduces the error. Thus, pooling larger numbers of samples decreases both total genotyping cost and estimation error enabling a broader sampling of commercial animals to refine the connections between genotype and phenotype and improve genome-enabled expected progeny differences.
5. Contributions of viral and bacterial pathogens during cattle respiratory disease outbreaks. Respiratory disease incidence is intimately associated with an animal’s commensal bacteria populations (microbiome), as microbes that are involved with morbidity and mortality are commonly found in animals with no sign of disease. In addition, viral pathogens affect the immune system and appear to play an integral role in the overall incidence of bovine respiratory disease (BRD). Therefore, an understanding of the interaction of the bacterial and viral pathogens in the upper respiratory tract (URT) may help us to understand the impact of these pathogens on development of BRD. For this research, ARS scientists at Clay Center, Nebraska, characterized bacterial and viral populations in the URT associated with a BRD outbreak in nursing beef calves. Overall, calves were nasally shedding bovine coronavirus and a large percentage had a coinfection with Mycoplasma sp, with Mycoplasma bovirhinis being the predominant species. Analysis of these specific respiratory pathogens in the URT during a BRD outbreak will present a clearer picture of the distribution of bacterial and viral populations in nursing beef calves enabling producers and veterinarians to better prevent and treat BRD in beef cattle.
Safonova, Y., Shin, S.B., Kramer, L., Reecy, J., Watson, C.T., Smith, T.P.L., Pevzner, P.A. 2022. Variations in antibody repertoires correlate with vaccine responses. Genome Research. 32:791-804. https://doi.org/10.1101/gr.276027.121.
Ren, Y., MacPhillamy, C., To, T., Smith, T.P.L., Williams, J.L., Low, W.Y. 2021. Adaptive selection signatures in river buffalo with emphasis on immune and major histocompatibility complex genes. Genomics. 113(6):3599-3609. https://doi.org/10.1016/j.ygeno.2021.08.021.
Harhay, G.P., Harhay, D.M., Brader, K.D., Smith, T.P.L. 2021. A conserved Histophilus somni 23S intervening sequence yields functional, fragmented 23S rRNA. Microbiology Spectrum. 9(3). Article e0143121. https://doi.org/10.1128/Spectrum.01431-21.
Keele, J., McDaneld, T., Lawrence, T., Jennings, J., Kuehn, L. 2021. Estimation of pool construction and technical error. Agriculture. 11(11). Article 1091. https://doi.org/10.3390/agriculture11111091.
Ribeiro, A.F., Sanglard, L.P., Snelling, W.M., Thallman, R.M., Kuehn, L.A., Spangler, M.L. 2022. Genetic parameters, heterosis, and breed effects for body condition score and mature cow weight in beef cattle. Journal of Animal Science. 100(2). Article skac017. https://doi.org/10.1093/jas/skac017.
Sanglard, L.P., Kuehn, L.A., Snelling, W.M., Spangler, M.L. 2022. Influence of environmental factors and genetic variation on mitochondrial DNA copy number. Journal of Animal Science. 100(5). Article skac059. https://doi.org/10.1093/jas/skac059.
Cushman, R.A., Bennett, G.L., Tait, R.G., McNeel, A.K., Casas, E., Smith, T.P.L., Freetly, H.C. 2021. Relationship of molecular breeding value for beef tenderness with heifer traits through weaning of their first calf. Theriogenology. 173:128-132. https://doi.org/10.1016/j.theriogenology.2021.07.020.
Freetly, H.C., Cushman, R.A., Bennett, G.L. 2021. Production performance of cows raised with different postweaning growth patterns. Translational Animal Science. 5(3):1-7. https://doi.org/10.1093/tas/txab031.
Hille, M.M., Clawson, M.L., Dickey, A.M., Lowery, J.H., Loy, J.D. 2021. MALDI-TOF MS biomarker detection models to distinguish RTX toxin phenotypes of Moraxella bovoculi strains are enhanced using calcium chloride supplemented agar. Frontiers in Cellular and Infection Microbiology. 11. Article 632647. https://doi.org/10.3389/fcimb.2021.632647.
Schmidt, J.W., Murray, S.A., Dickey, A.M., Wheeler, T.L., Harhay, D.M., Arthur, T.M. 2022. Twenty-four-month longitudinal study suggests little to no horizontal gene transfer in situ between third-generation cephalosporin-resistant Salmonella and third-generation cephalosporin-resistant Escherichia coli in a beef cattle feedyard. Journal of Food Protection. 85(2):323-335. https://doi.org/10.4315/JFP-21-371.
Heaton, M.P., Harhay, G.P., Bassett, A.S., Clark, H.J., Carlson, J.M., Jobman, E.E., Sadd, H.R., Pelster, M.C., Workman, A.M., Kuehn, L.A., Kalbfleisch, T.S., Piscatelli, H., Carrie, M., Krafsur, G.M., Grotelueschen, D.M., Vander Ley, B.L. 2022. Association of ARRDC3 and NFIA variants with bovine congestive heart failure in feedlot cattle. F1000Research. 11. Article 385. https://doi.org/10.12688/f1000research.109488.1 .
Workman, A.M., Heaton, M.P., Webster, D.A., Harhay, G.P., Kalbfleisch, T.S., Smith, T.P.L., Falkenberg, S.M., Carlson, D.F., Sonstegard, T.S. 2021. Evaluating large spontaneous deletions in a bovine cell line selected for bovine viral diarrhea virus resistance. Viruses. 13(11). Article 2147. https://doi.org/10.3390/v13112147.
Zimmermann, M.J., Kuehn, L.A., Spangler, M.L., Thallman, R.M., Snelling, W.M., Lewis, R.M. 2021. Breed and heterotic effects for mature weight in beef cattle. Journal of Animal Science. 99(8). Article skab209. https://doi.org/10.1093/jas/skab209.
Lindholm-Perry, A.K., Kuehn, L.A., Wells, J., Rempel, L.A., Chitko-McKown, C.G., Keel, B.N., Oliver, W.T. 2021. Hematology parameters as potential indicators of feed efficiency in pigs. Translational Animal Science. 5(4). Article txab219. https://doi.org/10.1093/tas/txab219.
Bickhart, D.M., Koch, L.M., Smith, T.P., Riday, H., Sullivan, M.L. 2022. Chromosome-scale assembly of the highly heterozygous genome of red clover (Trifolium pratense L.), an allogamous forage crop species. GigaByte. 42:1-13. https://doi.org/10.46471/gigabyte.42.
Gillespi, A., Yirsaw, A., Gunasekaran, K.P., Smith, T.P., Bickhart, D.M., Turley, M., Connelley, T., Telfer, J.C., Baldwin, C.L. 2021. Characterization of the domestic goat yd T cell receptor gene loci and gene usage. Immunogenetics. 73:187-201. https://doi.org/10.1007/s00251-021-01203-y.
Herrera-Uribe, J., Wiarda, J., Sivasankaran, S.K., Daharsh, L., Liu, H., Byrne, K.A., Smith, T.P., Lunney, J.K., Loving, C.L., Tuggle, C.K. 2021. Reference transcriptomes of porcine peripheral immune cells created through bulk and single-cell RNA sequencing. Frontiers in Genetics. 12. Article 689406. https://doi.org/10.3389/fgene.2021.689406.
Childers, A.K., Geib, S.M., Sim, S.B., Poelchau, M.F., Coates, B.S., Simmonds, T.J., Scully, E.D., Smith, T.P.L., Childers, C., Corpuz, R.L., Hackett, K.J., Scheffler, B.E. 2021. The USDA-ARS Ag100Pest Initiative: High-quality genome assemblies for agricultural pest insect research. Insects. 12(7):626. https://doi.org/10.3390/insects12070626.
Davenport, K.M., Massa, A.T., Bhattarai, S., McKay, S.D., Mousel, M.R., Herndon, M.K., White, S.N., Cockett, N.E., Smith, T.P., Murdoch, B.M. 2021. Characterizing genetic regulatory elements in ovine tissues. Frontiers in Genetics. 12. Article 628849. https://doi.org/10.3389/fgene.2021.628849.
Oppert, B.S., Muszewska, A., Steczkiewicz, K., Šatovic-Vukšic, E., Plohl, M., Fabrick, J.A., Vinokurov, K.S., Koloniuk, I., Johnston, J., Smith, T.P., Guedes, R.C., Terra, W.R., Ferreira, C., Dias, R.O., Chaply, K.A., Elpidina, E.N., Tereshchenkova, V., Mitchell, M.F., Jenson, A.J., Mckay, R., Shan, T., Cao, X., Xiong, C., Jiang, H., Morrison III, W.R., Koren, S., Schlipalius, D., Lorenzen, M.D., Bansal, R., Wang, Y., Perkin, L.C., Poelcheau, M., Friesen, K.S., Olmstead, M.L., Scully, E.D., Campbell, J.F., et al. 2022. The genome of Rhyzopertha dominica (Fab.) (Coleoptera: Bostrichidae): Adaptation for success. Genes. 13(3). Article 446. https://doi.org/10.3390/genes13030446.
Bickhart, D.M., Kolmogorov, M., Tseng, E., Portik, D., Korobeynikov, A., Tolstoganov, I., Uritskiy, G., Liachko, I., Sullivan, S.T., Shin, S.B., Zorea, A., Andreu, V., Panke-Buisse, K., Medema, M., Mizrahi, I., Pevzner, P., Smith, T.P. 2022. Generating lineage-resolved, complete metagenome-assembled genomes from complex microbial communities. Nature Biotechnology. 40:711-719. https://doi.org/10.1038/s41587-021-01130-z.
Trigo, B.B., Utsunomiya, A.T., Fortunato, A., Milanesi, M., Torrecilha, R.B., Lamb, H., Hayes, B., Nguyen, L., Ross, E., Padula, R.C., Sussai, T.S., Zavarez, L.B., Cipriano, R.S., Caminhas, M.M., Lopes, F.L., Lung, L.H., Pelle, C., Leeb, T., Bannasch, D., Bickhart, D.M., Smith, T.P.L, Garcia, J.F., Utsunomiya, Y.T. 2021. Variants at the ASIP locus contribute to coat color darkening in Nellore cattle. Genetics Selection Evolution. 53. Article 40. https://doi.org/10.1186/s12711-021-00633-2.
Baller, J.L., Kachman, S.D., Kuehn, L.A., Spangler, M.L. 2022. Using pooled data for genomic prediction in a bivariate framework with missing data. Journal of Animal Breeding and Genetics. Article 12727. https://doi.org/10.1111/jbg.12727.
Leonard, A.S., Crysnanto, D., Fang, Z., Heaton, M.P., Vander Ley, B.L., Herrera, C., Bollwein, H., Bickhart, D.M., Kuhn, K.L., Smith, T.P.L., Rosen, B.D., Pausch, H. 2022. Structural variant-based pangenome construction has low sensitivity to variability of haplotype-resolved bovine assemblies. Nature Communications. 13. Article 3012. https://doi.org/10.1038/s41467-022-30680-2.
Henniger, M.T., Wells, J.E., Hales, K.E., Lindholm-Perry, A.K., Freetly, H.C., Kuehn, L.A., Schneider, L.G., McLean, K.J., Campagna, S.R., Christopher, C.J., Myer, P.R. 2022. Effects of a moderate or aggressive implant strategy on the rumen microbiome and metabolome in steers. Frontiers in Animal Science. 3. Article 889817. https://doi.org/10.3389/fanim.2022.889817.
Ault-Seay, T.B., Brandt, K.J., Henniger, M.T., Payton, R.R., Mathew, D.J., Moorey, S.E., Schrick, F.N., Pohler, K.G., Smith, T.P.L., Rhinehart, J.D., Schneider, L.G., McLean, K.J., Myer, P.R. 2022. Bacterial communities of the uterus and rumen during heifer development with protein supplementation. Frontiers in Animal Science. 3. Article 903909. https://doi.org/10.3389/fanim.2022.903909.
Snelling, W.M., Thallman, R.M., Spangler, M.L., Kuehn, L.A. 2022. Breeding sustainable beef cows: Reducing weight and increasing productivity. Animals. 12(14). Article 1745. https://doi.org/10.3390/ani12141745.