The long-term objective of this project is to enhance selection in target ruminant populations by integrating traditional, quantitative-based selection methods with DNA marker-based tools. To successfully meet this objective and better understand the underlying gene networks affecting phenotypic variation, basic research to characterize both genome structure and activity must be done as a complementary effort. Objective 1: Develop biological resources and computational tools to enhance characterization of ruminant genomes. De novo reference genome assemblies will be developed for Zebu cattle (Bos indicus), goat (Capra hircus), and water buffalo (Bos bubalis). In addition, improvements will be made to the existing reference assembly for Bos taurus cattle. These reference genome resources are essential for discovery of single nucleotide polymorphisms (SNP) and copy number variation (CNV) polymorphisms commonly segregating in target populations. Objective 2: Utilize novel genotypic and environmental data to enhance genetic improvement of food animals across a spectrum of ruminant production systems,including the following: SNP markers or haplotype information to identify signatures of natural and artificial selection; novel marker array panels to generate adapted goat genetic lines for extreme environments that improve animal survival, fertility and growth; and "whole herd" molecular pedigree information to further increase the accuracy and speed of genetic improvement for animal populations. Objective 3: Characterize functional genetic variation for improved fertility and environmental sustainability of ruminants.
Completion of the objectives is expected, in the short term, to improve methods of genome-wide selection in the U.S. dairy industry as well as initiate 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 resequencing and comparative genome alignment and annotation, quantitative genetics, and gene expression analyses, all of which are components of this project plan and areas of expertise in the group. Efforts to characterize genome activity and structural conservation/variation are an extension of the current ARS/BA research program in applied genomics. This project plan completely leverages the resources derived from the Bovine and Caprine Genomes and HapMap and ADAPTmap projects and genotypic data derived from both the official USDA genome enhanced genetic evaluations for North American dairy cattle and African Goat Improvement Network under the Feed the Future Initiative.
Under Objective 1, ARS scientists in Beltsville, Maryland continued as global leaders for production of genome sequence information from ruminant species by completing a de novo genome assembly of Bos indicus and the first mammalian (goat) genome assembly based solely on data from a third generation sequence platform (PacBio). Relative to Objective 2, ARS scientists continued to use genomic tools to better understand natural and artificial selection in cattle and goats. ARS scientists developed methods to characterize runs of DNA sequencing homozygosity to detect signatures of artificial selection in contemporary Holsteins compared to an unselected control line. Analysis of sequence signatures from natural selection will continue using more than 1,500 indigenous cattle from Brazil, Venezuela, and Turkey using newly generated data derived from the 700K single nucleotide polymorphism (SNP) beadchip assay (the BovineHD from Illumina) and 600 indigenous goats from Ethiopia, Nigeria, Kenya, Uganda, Turkey, and Egypt using the Illumina Caprine50K assay. To date, regions of the genome under natural selection for thermotolerance (second SLICK mutation), resistance to diseases endemic to Africa, and signatures of selection for carcass traits and fertility have been detected in cattle. This effort also includes a comprehensive screen for candidate regions under positive selection across Holstein, Angus, Charolais, Brahman, Nelore, and N'Dama as a result of geographic adaptation and artificial selection. Other efforts included additional sampling of indigenous goat populations in Mali, Madagascar, Burundi, Tanzania, Egypt, and the Sudan. ARS scientists continued development of a database system for collecting goat phenotypes, and initiated three community breeding programs in Uganda and Malawi. Under Objective 3, ARS scientists performed genome-wide association studies (GWAS) between copy number variations (CNV) and parasite resistance traits in Angus cattle and between CNV and milk traits in Holsteins, and finished an integrated tool/algorithm to merge CNV results derived from various discovery platforms. ARS scientists also generated and analyzed additional whole-genome sequences to identify potential causative mutations affecting fertility haplotype BH2 in Brown Swiss and two different SLICK loci in Senepol and Limonero cattle. Validation genotyping of 900 tropically adapted cattle was completed to confirm the causative variant for thermo-tolerance from the Senepol-derived SLICK allele.
1. Completed a draft reference assembly for Capra hircus (goat) entirely using PacBio sequence data. This is a team effort of ARS scientists in Beltsville, Maryland, working in tandem with members of the National Biodefence and Countermeasures Center (NBACC), USDA MARC, and BioNano Genomics. This reference genome represents a 10-fold improvement in scaffold quality (N50) over the previously available goat reference assembly, which was generated with a sequencing strategy using second-generation short-read sequencing. Additionally, this technique promises to reduce the cost of generating high-quality reference genome assemblies for other animal and plant species by eliminating the need for costly genome finishing experiments to generate chromosome-scale scaffolds. The annotation data and scaffold statistics for the new goat genome now surpass those of the cattle reference genome assembly, which was deemed as the standard for quality among the sequenced agricultural species. It is likely that these methods will be used by computational biologists to generate most future reference genome assemblies and will be used to refine existing resources for agricultural species.
2. Completed one of the first attempts to genotype cattle copy number variation (CNV) within large, diverse populations using sequence data. Taking advantage of the tandem duplication pattern, ARS scientists in Beltsville, Maryland, optimized a new CNV genotyping approach and then performed a population study of cattle CNV. Their findings reveal cattle population structures and uncover lineage-specific or differential CNVs near genes like ASZ1, GAT, GLYAT, and KRTAP9-1. This work provides a new CNV genotyping approach for computational biologists to use for many domesticated animals with genome assemblies currently in the draft status, and where CNV distribution patterns are in tandem. It also provides the foundation for computational biologists to correlate CNV with economically important complex traits.
3. Identified two novel mutations related to thermo-tolerance in cattle. Heat stress is a major problem for efficient production of livestock in tropical and sub-tropical regions. Introduction of adaptive genetic variation for thermo-tolerance is one potential way to increase sustainable production in these regions. Through genome-wide association studies and whole-genome re-sequencing of Criollo breeds of cattle in Venezuela, two novel stop-gain mutations in the prolactin receptor gene were identified that confer the slick hair coat and thermo-tolerance to cattle. These results are convergent adaptive evidence of the importance of heat tolerance in taurine cattle, and provide animal breeders with targets for precision breeding to accelerate genetic improvement of cattle under stress of climate change.
4. Developed tools for precision breeding in dairy production. Traditional selection for dairy production has been focused on polygenic, additive traits of milk production. Due the influence of a few notable historic sires, we hypothesized that signatures of selection encompassing some of the elite Jersey genetics would be evident when comparing animals by birth year. AGIL attempted to identify the genomic locations of traits under selection through analysis of runs of homozygosity using Jersey herdbook genotypes. Our results revealed that genomic inbreeding is not significantly associated with fertility depression; however, many of the recent signatures of selection coincided with genes possibly effecting protein component traits. These results can be used by animal breeders to find variation underlying milk component traits to develop tools for precision breeding.
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