Objective 1: Develop biological resources and computational tools to enhance characterization of breed-specific bovine and other genomes. De novo reference genome assemblies will be developed for dairy cattle breeds (Holstein and Jersey). In addition, improvements will be made to the existing, but suboptimal, reference assemblies for Bos taurus cattle and Zebu cattle (Bos indicus). These reference genome resources are essential for discovery of single nucleotide polymorphisms (SNP) and copy number variation (CNV) polymorphisms segregating in target populations. Genome characterization will be done by state-of-the-art platforms using short- and long-read sequencing of selected animals. Candidate animals will be derived from those populations targeted for genome-based genetic improvement to enable development of novel tools for proper parent and breed composition identification. To complement these studies, epigenomic and metagenomic surveys will be explored to better define DNA methylation and ruminant microbiome, which in turn will improve overall annotation of genes, genetic variation, epigenetic variation and other sequence motifs affecting phenotype expression. Objective 2: Utilize genotypic data to enhance genetic improvement in ruminant production systems. This objective has two components. The first component identifies signatures of selection and evaluates the potential to develop community-based breeding programs based on population structure and management system limitations in goats. The second component requires the optimization and application of statistical methodologies to develop cheap low-density SNP panels that can be used to guide genetic improvement of production traits while maintaining variants enriched by natural selection during adaptation of local breeds to marginal production environments. Objective 3: Characterize functional genetic variation for improved fertility, growth, and environmental sustainability of ruminants. The third objective involves detection of genetic variation affecting fertility, growth and environmental sustainability during early embryonic development or adaptation to climate or disease using whole genome or exome resequencing. The resultant sequence information will be integrated with other database resources that provide basic information about gene expression activity and motif patterns to guide selection of positional candidate genes for further study and validation of functional annotation in ruminants. Sub-objectives for objectives 1,2 and 3 are listed in post plan under related documents.
Completion of our objectives is expected, in the short term, to improve genome-wide selection in the U.S. dairy industry as well as facilitate new genome-enhanced breeding strategies to bring economic and genetic stability to various ruminant value chains in developing nations. Ultimately, longer term objectives to identify and understand how causative genetic variation affects livestock biology will require a combination of genome sequencing and comparative genomics, quantitative genetics, epigenomics and metagenomics, all of which are components of this project plan and areas of expertise in our group. Efforts to characterize genome activity and structural conservation/variation are an extension of our current research program in applied genomics. This project plan completely leverages the resources derived from the Bovine Genomes, HapMap, 1000 Bull Genomes and FAANG projects, and genotypic data derived from the Council on Dairy Cattle Breeding (CDCB) genome-enhanced genetic evaluations for North American dairy cattle.
For Objective 1, ARS scientists in Beltsville, Maryland, continued as global leaders for production of DNA sequence information by improving the cattle genome assembly based on sequence data from a third-generation sequencing and mapping platforms (PacBio, optical mapping, and Hi-C) and leading international efforts to assemble breed-specific genomes for Holstein, Angus, Brahman, Jersey and other species. ARS scientists also completed transcript sequencing (RNA-Seq/Iso-Seq) for improved genome annotation, the first whole genome bisulphite sequencing (WGBS) study for sperm DNA methylation. Based on SNP array data and high-throughput sequencing data, ARS scientists performed copy number variation (CNV) discovery and CNV-based population genetics studies in water buffalo, sheep and goats. ARS scientists computed the first genomic predictions that combined CNV and SNP markers. For Objective 2, ARS scientists in Beltsville, Maryland, developed novel genomic tools for selection. These efforts included development of multiple specialized SNP assays for genomic prediction in beef and dairy cattle breeds, Bos indicus cattle, water buffalo, goat, and other species. ARS scientists used genetic data derived from genome sequencing and SNP chips to better understand natural and artificial selection in cattle and goats. In collaborating with IGGC and ADAPTmap, analysis of signatures from selection was completed using SNP data derived from the Illumina Caprine50K assay for more than 3,000 goats identifying several signatures of selection in different chromosomic regions. These regions contain genes that are involved in important biological processes, such as milk, meat or fiber related production, coat color, glucose pathway, oxidative stress response, size, and circadian clock differences, confirming previous findings in other species about adaptation to extreme environments and human purposes and provide new genes that could explain goat differentiation to geographical distributions and adaptation to different environments. For Objective 3, ARS scientists generated sequencing data to better understand functional genetic variations for improved fertility, growth, and environmental sustainability of ruminants. Using 172 sequenced Holstein bulls and newly assembled immune gene haplotypes, we discovered 155 candidate single nucleotide polymorphisms that could distinguish between alleles of cattle immune genes that provide innate resistance to diseases. Of these candidate markers, 67 have been used in custom genotype panels to determine their frequency in a cohort of 1,800 cows. We plan to perform association studies between bovine tuberculosis phenotypes and these new genetic markers to see if any of these newly discovered sites is predictive of tuberculosis resistance or susceptibility. If successful, these marker sites could be used in future association studies to determine their effects on resistance to other common cattle diseases.
1. Improved the reference genome assembly for Hereford L1 Dominette 01449 using PacBio sequence data and advanced genome scaffolding technologies. Genome assemblies have been produced for numerous species as a result of advances in sequencing technologies; however, many of the assemblies are fragmented, with many gaps, ambiguities, and errors. This was a team effort of ARS scientists in Beltsville, Maryland, working in tandem with members of ARS, Clay Center, Nebraska, University of California Davis, Computomics, Dovetail Genomics, University of Maryland, and University of Missouri Columbia. This assembly includes an over 250 times increase of fragment size and near 200 times decreases of gaps representing many fold improvements over the existing cattle assembly. The associated annotation data and statistics for the new cattle reference are now publicly available on the NCBI website. This technique described in an article published in Nature Genetics, promises to reduce the cost of generating high-quality reference genome assemblies for other animal and plant species.
2. Whole genome sequencing was conducted to study a diverse group of African goats. A total of over 300 goats collected by the African Goat Improvement Network (AGIN) through a collaboration with several groups. The project is part of the AdaptMap consortium that is combining efforts to characterize a global resource population. Sequencing has been done by ARS scientists at Beltsville, Maryland, as well as through two collaborations. A project called VarGoat has been coordinated by scientists at Institut national de la recherche agronomique (INRA) as part of the AdaptMap efforts as well as a collaboration with Roslin Institute, Scottish Agricultural College. These collaborations have resulted in far more animals being sequenced than AGIL staff would have been able to achieve.
3. Completed first genomic prediction study by integrating copy number variation (CNV) and single nucleotide polymorphism (SNP) in livestock. Combining CNV and SNP marker information proved to be beneficial for genomic prediction of some production traits in cattle. CNV was shown to be associated with disease and complex traits in humans and livestock. However, its impact on genomic selection has never been evaluated. In this study, CNV were included in a SNP based genomic selection framework. A small increase in prediction accuracy for some traits was detected when including CNVs in the models.
4. Completed the first high resolution maps of DNA methylation in bovine sperm using sequencing. DNA methylation plays important functions in individual development and reproduction. ARS scientists at Beltsville, Maryland, profiled the DNA methylation of cattle sperm through comparison with three somatic tissues (mammary gland, brain, and blood). Large differences between cattle sperm and somatic cells were observed in the methylation patterns. This study provides a comprehensive resource for bovine sperm epigenomic research and enables new discoveries about DNA methylation and its role in male fertility.
5. Development of a new method of DNA extraction that enables long-read sequencing of microbial samples. By using a robust, yet gentle, breakup protocol, longer DNA molecules are able to be extracted from complex, reluctant microbial samples. This protocol will enable the use of long-read sequence data to be used in metagenome sequencing and assembly, which is likely to improve the rumen microbial reference genomes. Additionally, this method has applications in the preparation of other metagenomics communities for long-read sequencing, particularly soil-based communities that may be important for assessing agriculturally relevant runoff dynamics.
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