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.
For Objective 1, ARS scientists in Beltsville, Maryland continued as global leaders for production of DNA sequence information by completing the first mammalian genome assembly based solely on caprine sequence data from a third generation sequence platform (PacBio) and contributing sequence for international efforts to assemble genomes and single nucleotide polymorphism (SNP) discovery for catfish, extinct Bos primigenius cattle, and other species. For Objective 2, ARS scientists continued to develop new genomic tools for selection. A turkey genotyping array development was led by Beltsville Agricultural Research Center scientists under a public-private partnership with Hendrix Genetics, Aviagen Ltd and Affymetrix to provide a better understanding of Turkey genetics. The commercial release of that chip occurred in November, 2015. For Objective 3, ARS scientists continued to use genomic tools to better understand natural and artificial selection in cattle and goats. ARS published a paper about identifying causative mutations affecting fertility haplotypes HH1 in Holsteins. ARS scientists developed methods in discovering microsatellite or short tandem repeats based on high throughput sequencing. This analysis identified more than 60,000 microsatellites and made it possible to study selection using microsatellites in Holsteins. ARS contributed to the collaboration to characterize tropical adaptation by characterizing the Nellore cattle genome using genetic data derived from genome sequencing and the BovineHD SNP array. To assist genetic selection, this effort included searching for genetic variations (SNPs, short insertions and deletions), recessive lethal alleles, runs of homozygosity, regions of the genome under natural selection for beef production, tropical adaptation, health and fertility. ARS scientists along with collaborators at INIFAP (National Institute of Forestry, Agricultural, and Livestock Research) in Mexico characterized the profound impact of genomic selection on population dynamics in dairy cattle. ARS scientists developed two novel photo methods and a database system for collecting goat phenotypes, and implemented three community breeding programs in Uganda and Malawi. In collaborating with IGCC and ADAPTmap, analysis of signatures from natural selection continued using more than 2000 goat data derived from the Illumina Caprine50K assay. Additionally, for Objective 2, ARS scientists performed the first genomic predictions combining copy number variation (CNV) and SNP markers in Nellore cattle. This effort demonstrates that combining CNV and SNP marker information can be beneficial for several traits. ARS scientists performed genome-wide association studies (GWAS) between copy number variations (CNV) and mastitis resistance traits in Holstein cattle and host responses to porcine reproductive and respiratory syndrome virus infection in pigs. ARS scientists performed and published an two CNV-based population genetics studies using BovineHD SNP array data and high-throughput sequencing data.
1. Completed a reference genome assembly for Capra hircus (goat) 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 is a team effort of ARS scientists in Beltsville, Maryland working in tandem with members of the National Human Genome Research Institute (NGHRI), USDA MARC, BioNano Genomics, Phase Genomics and the PirBright Institute (based in the UK). This reference genome represents a 250-fold improvement in continuity over the previously available goat reference assembly, which was generated with a sequencing strategy using second-generation short-read sequencing. 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. This technique – described in a submitted publication -- promises to reduce the cost of generating high-quality reference genome assemblies for other animal and plant species.
2. Phenotype and genotype collection for African goats in Community Based Breeding Programs (CBBP). ARS scientists in Beltsville, Maryland collected and characterized a broad representation of African goats to develop SNP panels for parentage and to identify selection signatures for optimized selection. Two digital phenotype software methods were developed as an alternative to manual body measurement weight prediction, with electronic phenotype recording. Preliminary results from over 4000 goats have been sampled from over 20 countries, representing over 50 breeds or populations and over 60 locations, show many current breed definitions are inaccurate. A low-density genotyping panel for parentage identification to enable selective breeding from performance history is under development. We plan to genotype a subset of foundation animals from all CBBP sites, with a single site per country for deeper genotyping across a population, to enable association of genetic markers with community selected phenotypes which may enable them to apply genomics to selection.
3. Completed the first genome-wide discovery of cattle microsatellites using high-throughput sequencing. Microsatellites or STRs have become an essential marker for mapping quantitative trait loci (QTL) due to their high variability and easy amplification by PCR, however, there was no existing genome-wide microsatellite data in cattle. ARS scientists in Beltsville, Maryland optimized a human microsatellite detection approach and then performed the first discovery study of cattle microsatellites. This work identified a total of more than 60,000 microsatellites, generated the first high-resolution microsatellite map and found hundreds of candidate microsatellite loci under selection. This study provided the foundation for future microsatellite-based studies of cattle genome evolution and selection.
4. Performed the first genomic predictions combining copy number variation and SNP markers in cattle. Traditional genomic selection fails in varying degrees for some complex traits, because SNP markers are not sufficient to predict these traits due to the missing heritability caused by additional genetic variations, including copy number variation (CNV). ARS scientists in Beltsville, along with their Brazilian collaborators in St. Paulo State University (UNESP), performed the first CNV-enhanced genetic prediction in cattle. CNVs derived from high density SNP microarray data were included in a SNP-based genomic selection framework and modeled through currently best available models. This result revealed that combining CNV and SNP marker information was beneficial for genomic prediction of some traits in Nellore cattle.
Sun, J., Aswath, K., Schroeder, S.G., Lippolis, J.D., Reinhardt, T.A., Sonstegard, T.S. 2015. MicroRNA expression profiles of bovine milk exosomes in response to Staphylococcus aureus infection. Biomed Central (BMC) Genomics. 16:806.
Bickhart, D.M., Xu, L., Hutchison, J.L., Cole, J.B., Schroeder, S.G., Song, J., Garcia, J., Van Tassell, C.P., Sonstegard, T.S., Schnabel, R.D., Taylor, J.F., Lewin, H.A., Liu, G. 2016. Whole-genome sequencing reveals the diversity of cattle copy number variations and multicopy genes. DNA Research. 23(3):253-62.
Li, Y., Carrillo, J.A., Ding, Y., He, Y., Zhao, C., Lui, J., Liu, G., Zan, L., Song, J. 2015. Transcriptomic profiling of spleen in grass-fed and grain-fed Angus cattle. PLoS One. 10(9):e0135670.
Wang, L., Xu, L., Liu, X., Zhang, T., Li, N., Zhang, Y., Yan, H., Zhao, K., Liu, G., Zhang, L., Wang, L. 2015. CNV-based genome wide association study reveals additional variants contributing to meat quality in swine. Scientific Reports. 5:12535.
Xu, L., Hou, Y., Bickhart, D.M., Zhou, Y., Hay, E.A., Song, J., Sonstegard, T., Van Tassell, C.P., Liu, G. 2016. Population-genetic properties of differentiated copy number variations in cattle. Scientific Reports. 6:23161.
Zhou, Y., Utsunomiya, Y.T., Xu, L., Hay, E.A., Bickhart, D.M., Carvalheiro, R., Neves, H.E., Sonstegard, T., Van Tassell, C.P., Garcia, J., Liu, G. 2016. Genome-wide CNV analysis reveals variants associated with growth traits in Bos indicus. Biomed Central (BMC) Genomics. 17(1):419.2016.
Park, S.D., Magee, D.A., Mcgettigan, P.A., Teasdale, M.D., Edwards, C.J., Lohan, A.J., Murphy, A., Braud, M., Donoghue, M.T., Liu, Y., Chamberlain, A.T., Rue-Albrecht, K., Schroeder, S.G., Spillane, C., Tai, S., Bradley, D.G., Sonstegard, T.S., Loftus, B.J., Machugh, D.E. 2015. Genome sequencing of the extinct Eurasian wild aurochs illuminates the phylogeography and evolution of cattle. Genome Biology. 16(1):1-15.
Whitacre, L.K., Tizioto, P.C., Kim, J., Sonstegard, T., Schroeder, S.G., Alexander, L.J., Medrano, J.F., Schnabel, R.D., Taylor, J.F., Decker, J.E. 2015. What's in your next-generation sequence data? An exploration of unmapped DNA and RNA sequence reads from the bovine reference individual. BMC Genomics. 16:1114.
Benavides, M., Sonstegard, T.S., Kemp, S., Mugambi, J.M., Gibson, J., Baker, R., Hanottte, O., Marshall, K., Van Tassell, C.P. 2015. Genome-wide scan of gastrointestinal nematode resistance in closed Angus population selected for minimized influence of MHC. PLoS One. doi: 10.1371/journal.pone.0122797.eCollection2015.
Song, Q., Jia, G., Hyten, D.L., Jenkins, J., Hwang, E., Schroeder, S.G., Schmutz, J., Jackson, S.A., Mcclean, P., Cregan, P. 2015. SNP marker development for linkage map construction, anchoring of the common bean whole genome sequence and genetic research. G3, Genes/Genomes/Genetics. doi: 10.1534/g3.115.020594.
Wiggans, G.R., Cooper, T.A., Van Raden, P.M., Van Tassell, C.P., Bickhart, D.M., Sonstegard, T.S. 2016. Increasing the number of single nucleotide polymorphisms used in genomic evaluation of dairy cattle. Journal of Dairy Science. 99(6):4504-4511.
Utsunomiya, Y.T., Ribeiro, E.S., Quintal, A.P., Sangalli, J.R., Gazola, V.R., Paula, H.B., Trinconi, C.M., Lima, V.M., Perri, S.H., Taylor, J.F., Schnabel, R.D., Sonstegard, T.S., Garcia, J.F., Nunes, C.M. 2015. Genome-wide scan for visceral leishmaniasis in mixed-breed dogs identifies candidate genes involved in T helper cells and macrophage signaling. PLoS One. 10(9):e0136749.