Location: Genetics and Animal Breeding2021 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.
Excellent progress was made on the Project Plan in Fiscal Year (FY) 2021 despite challenges from pandemic-related limitations and supply shortages. For Objective 1, the new sheep reference genome was finalized and publicly released. This assembly was the basis of completed and ongoing Functional Annotation of Animal Genomes (FAANG) consortium analyses with global partners, that identified the segments of genome underlying gene expression in a wide array of sheep tissues. Data was also generated in support of collaboration with the bovine FAANG consortium to mirror the progress in sheep. Genome assemblies of bison and the Simmental breed of cattle were released as part of the international Bovine Pangenome Consortium effort, that meet or exceed the quality of the current human genome reference, in collaboration with the American Simmental Association and university partners. Objective 2 for cattle proceeded at predicted pace (Subobjective 2c was focused on sheep and moved to a new, separate Project mid-year). The overall goal of Objective 2 is to develop markers and assays for a wide range of production characteristics to guide national cattle evaluation programs in the future. A broad range of phenotypes were collected on the Germplasm Evaluation (GPE) population, for which chip-based and low-pass sequencing-based genotyping data were generated. Based on these measurements, an update to the popular across-breed Expected Progeny Difference (EPD) adjustment factor table was released and heavily referenced by commercial and seedstock beef cattle producers and breed associations. The GPE mating scheme has now resulted in virtually all of the natural service bull contingent for the 18 target breeds being “bred up” to 15/16 purebred, in support of the ultimate objective of estimating breed-specific heterosis. Various factors including animal health and wellbeing have led to a shift in the breeding program to reduce or eliminate fall calving, with the result that the current GPE cow population is approximately 3,200 breeding females, about 400 below target. Analysis of over 40 million genetic markers using low pass sequencing with haplotypic imputation algorithms has begun for over 3,000 animals that are part of the larger 30,000 animal population contributing lower density markers and pedigree information. Comprehensive and contiguous sequence for animals representing multiple breeds (pangenome from Objective 1) contributes significantly to the accuracy of haplotypic imputation underpinning this objective. Statistical methods to incorporate the genotype data into genetic evaluations were prototyped and are being evaluated in both ARS and University of Nebraska herds. The initial phenotypes being analyzed to identify variation contributing to production include female productivity, disease resistance, and lower gastrointestinal tract bacterial population profiles. Other phenotypes being collected include fertility traits and lifetime productivity, that includes 550 females phenotyped as breeding heifers each year with 475 also evaluated for feed efficiency/feed intake at yearling age. Approximately 500 steers were evaluated for feed efficiency, and 700 calves sampled for response to bovine respiratory disease virus vaccination as additional phenotypes for genomic analysis. A novel phenotype of infestation by horn flies was conducted for 2,000 cows using a photographic approach and making use of staff teleworking during the pandemic to quantify the infestation based on these photographs. A neural network to automate analysis of the photos was developed based on these manual annotations and results indicate an approximate 10% heritability of horn fly density. The Beef Grand Challenge is currently evaluating its third calf crop at five different collaborating ARS locations: Clay Center, Nebraska; El Reno, Oklahoma; Miles City, Montana; Nunn, Colorado; Woodward, Oklahoma. This project is designed to test management/environment by genetic interactions and differences in common commercial beef cattle production in each respective location. Approximately 120 spring-born animals will be evaluated each year for 4 years at Clay Center, El Reno, and Miles City and 40 additional fall-born animals each year for Clay Center, Nunn, and Woodward. Each location will apply a different method of growing and backgrounding calves before finishing. The second year of data has been collected from each location although the pandemic did prevent some data collection. Objective 3 met with unexpectedly high success in FY 2021 with the development of novel methods to assemble microbial genomes using DNA extracted from parasite-infested sheep feces. The method is the first to be able to accurately assemble separate microbial genomes representing different members of the same species or genus within a sample. This supports properly identifying the bacteria in the sample 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 bovine respiratory disease (BRD) portion of Objective 3 proceeded at predicted pace, with the set of calves having nasal swabs at preconditioning and weaning time points reaching 7,000. The number of calves with added prebreeding nasal swabs reached 3,500, despite shortages of swabs caused by demand for pandemic testing. The number of calves from the population exhibiting symptoms and sampled reached 700, and analysis of data from three BRD outbreaks that have occurred is nearly complete with mycoplasma bacteria and bovine corona virus identified. The outputs from Objectives 1-3 are feeding into the “grand synthesis” strategy of Objective 4. The selection in the "Selection for Functional Alleles" (SFA) population based on the number of “loss of function” (LOF) alleles of genes has continued, with genotyping fully transitioning from a targeted single nucleotide polymorphism (SNP) chip to imputing sequence to the population based on a completely sequenced panel of beef and dairy cattle. This approach has supported addition of 1,242 new markers that were not present on the genotyping array. The selection has reached the point where replacement heifers differ by an average of 15 LOF alleles between select and control lines. Cattle from these lines have been used to investigate LOF effects on heifer fertility and meat quality by examining 2,790 animals. This data will be used to study the interaction of genetic markers and growth enhancers on meat quality and growth composition.
1. Reduced-cost method for predicting suitability of bulls for improved natural service productivity. Running multiple bulls with cows in a breeding pasture improves fertility over single sire pastures and is lower cost compared to artificial insemination. However, unless the bulls have quite different color patterns and external features it is difficult to know the paternity of the calves in multiple bull breeding pastures. ARS researchers in Clay Center, Nebraska, characterized prolificacy of bulls over multiple breeding seasons and determined that the repeatability of bull prolificacy is high, ~62%. These results indicate that we can select bulls to go into a pasture that will improve the fertility of the cows mated based on past breeding success. However, the cost of individually genotyping calves to determine bull prolificacy is expensive. The researchers also estimated bull prolificacy using pools of calf DNA with similar accuracy to individual genotyping at a reduced cost. Furthermore, genotyping pools of dams in addition to pools of calves achieves higher accuracy with intermediate cost between individual genotyping of calves and genotyping pools of calves. These methods provide a low-cost approach for commercial producers to evaluate bull prolificacy and use it in their future management decisions to improve both the genetic value and the reproductive efficiency of the breeding herd.
2. Selection for calving ease has no effect on cow productivity in later parities. Cattle breeders and producers often choose breeding animals expected to reduce calving difficulty in heifers calving for the first time; however, it was unknown whether these selection decisions could impact the productivity of these heifers in later parities. ARS researchers in Clay Center, Nebraska, examined heifers from 7 genetic lines selected for calving ease and 7 control lines through their fourth calving. Calving difficulty of heifers was reduced in the selection lines as intended, but no differences were observed in calving difficulty or calf survival in parities 2-4. However, heifers from the selected lines were more likely to produce a second calf the following year than the control lines. No impacts from selection were observed for weaning or yearling weights although birth weights and cow mature weights were lower in the selection lines. These results indicate that selection for improved calving ease can positively improve calf survival in heifers without compromising productivity and may result in lower mature weights for selected cows. These results indicate that heifers selected for calving ease may improve lifetime productivity while potentially reducing maintenance costs and improving the production efficiency of the cow herd.
3. Estimating mature cow size among different beef cattle breeds. Mature cow weight is a driver of cost and efficiency in commercial cattle breeding, primarily due to increased feed requirements of larger animals. While some production systems can accommodate larger cows, having genetic tools to manage mature cow weight can be useful for reducing costs for commercial cattle operations. ARS researchers in Clay Center, Nebraska, and university collaborators estimated current breed differences for mature weight from 16 different beef cattle breeds using data from the Clay Center, Nebraska, Germplasm Evaluation Program. Mature weight was predicted for all cows at six years of age and used to determine breed differences for mature weight. Breed differences were adjusted for industry sampling using bull yearling weight expected progeny differences (EPDs). Direct heterosis effects were positive, implying that crossbreed animals had higher mature weights than their respective purebred averages. For example, Angus had the heaviest mature weight effects and Braunvieh the smallest with a difference of 136 kg between them. Commercial producers can use these breed differences to inform breeding programs where limiting cow size could increase efficiency and thus overall profitability.
4. Genome assembly of closely related microbes in metagenomic DNA samples. A longstanding problem in metagenome assembly has been the presence of multiple bacteria that are closely related in genome sequence but representing different strains, species, or subspecies. This has placed limits on the analysis of microbes by this method, for example limiting the accuracy of assigning antibiotic resistance genes to the strain or species in the sample. This can be particularly important when potential pathogenic microbes are present along with closely related non-pathogenic organisms. ARS researchers in Clay Center, Nebraska, and university collaborators developed a method to separately assemble genomes from metagenomic data even when they are of the same species, while simultaneously providing links to antibiotic resistance genes that reside in the bacteria but are not integrated into the bacterial chromosome. This method may be useful in clinical microbiology as well as livestock research by enabling sequence-based tracking of strain-level genomes without the need for laborious culture and isolation techniques and improving the ability to determine if antibiotic resistance genes are associated with known human or animal pathogens. Diagnostic costs and wait times will be reduced for cattle producers and diagnosis accuracy will be improved.
5. Development of a method to genetically evaluate cattle for susceptibility to horn flies. Horn flies are biting flies and feed on blood from their cattle hosts and cost the United States beef industry over $1 billion annually through reduced performance and compromised animal well-being. Cattle vary in the number of horn flies they carry and this appears to be under both genetic and environmental control. However, genetic selection for horn fly resistance is not practiced because of the cost and tedium of counting flies to phenotype individual animals. ARS scientists in Clay Center, Nebraska, and university partners trained a neural network to detect and count horn flies in high-clarity photographic images taken from close to the animal or with a telephoto lens. The method is being used to study the feasibility of phenotypic selection for horn fly resistance and to assess the potential for genetic markers associated with horn fly susceptibility. These markers will lead to selection programs for horn fly resistance that enable producers to reduce production costs while improving the well-being of their cows.
6. Implementation of genomic prediction using functional variant genotypes. Large single-nucleotide polymorphism (SNP) arrays have allowed beef cattle producers to rapidly obtain thousands of genotypes on selection candidates and thus enabled genomically-enhanced selection programs in national cattle evaluation. Recent advancements in next-generation sequencing and genotype imputation algorithms have made genotyping by sequencing a practical approach, resulting in over 50 million genotypes per animal at a similar cost to arrays. This large increase in genotypes is difficult to model as most data sets have far fewer trait observations; thus, ARS scientists in Clay Center, Nebraska, chose to focus on genotypes that alter gene function which are more likely to have causal effects (functional variants) to develop genotypic predictions for growth and meat quality of beef steers as well as beef cow cumulative productivity. These techniques can be adopted by genetic service providers to improve the accuracy of genomically-enhanced predictions of genetic merit that are more likely to be robust across genetic lines and breeds.
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