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United States Department of Agriculture

Agricultural Research Service


Location: Animal Genomics and Improvement Laboratory

2013 Annual Report

1a.Objectives (from AD-416):
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 genotypic data to enhance genetic improvement of food animals across a spectrum of ruminant production systems. Objective 3: Characterize functional genetic variation for improved fertility and environmental sustainability of ruminants.

1b.Approach (from AD-416):
Completion of our 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 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 Genome and HapMap projects and genotypic data derived from the official USDA genome enhanced genetic evaluations for NA dairy cattle.

3.Progress Report:
ARS scientists in Beltsville, MD continued as global leaders for production of DNA sequence information from ruminant species by contributing all of the sequence for international efforts to assemble genomes and single nucleotide polymorphism (SNP) discovery for Bos indicus cattle, goat, and water buffalo. ARS scientists generated and analyzed additional whole-genome sequence to identify causative mutations affecting fertility haplotypes HH3 in Holsteins and SLICK in Senepols. ARS scientists continued to develop and use new genomic tools for selection. These efforts included development of: .
1)a new 90K SNP assay for breed diversity determination and genetic prediction in water buffalo (Affymetrix 90K),.
2)a new 80K SNP assay (Neogen’s GGP BovineHD) for genomic prediction in beef and dairy cattle, and.
3)a second new 80K SNP assay (Neogen’s GGP BovineHD for zebu) for genomic prediction in zebu-derived cattle (tropically adapted). ARS scientists continued to characterize tropical adaptation by characterizing the genomes of more than 600 cattle from Africa and SW Asia using genetic data derived from the 700K SNP beadchip assay (Illumina’s BovineHD). This effort included searching for regions of the genome under natural selection for resistance to diseases endemic to Africa. This analysis identified a 1-Mbp region of chromosome 6 containing genes known to be involved in trypanotolerance. Other efforts included sampling indigenous goat populations in Ethiopia, Nigeria, Kenya, and Cameroon. Some of these animals were genotyped along with several North American breeds. The later genotyping results were used to select a San Clemente goat as a genome sequencing animal. Sequence production for a new goat genome assembly model is 95% completed. ARS scientists developed and tested a digital-based system for collecting goat phenotypes in Africa. This project needs further sampling of African goats by collaborative partners to provide the critical information needed to find the best goat to initiate community breeding programs in Sub-Saharan Africa. Other genome characterization, trait mapping, and variant sequencing included completion of projects to determine extent of changes to Holstein and Jersey genomes caused by artificial selection over the past 50 years and imputation of parentage microsatellites from SNP haplotypes for all cattle breeds. Projects also were initiated to elucidate mutations for limber leg and rectovaginal constriction defects in Jersey cattle. ARS scientists also completed transcript sequencing (RNAseq) for improved genome annotation, the first comprehensive prediction of transcription factor binding sites (TFBS) in the cattle genome, and the first comprehensive discovery of copy number variants (CNV) using next-generation sequencing (NGS) data from over 100 cattle individuals. The latter effort continues with an attempt to develop a tool to merge CNV findings across various discovery platforms. Bovine50K SNP data from more than 30,000 dairy cattle samples were collected and processed by PennCNV to find associations with animal production and health traits including fertility, parasite resistance, and feed efficiency.

1. Identified mutations affecting dairy cow fertility. Extensive genotyping of U.S. dairy populations has revealed portions of the genome that appear to contain lethal mutations causing embryonic death during pregnancy. ARS scientists discovered the genetic cause for these recessive lethal mutations known as HH3 in Holsteins using next generation sequencing techniques. Results from this study are being used by producers to guide future mating decisions, thus lowering the rate of infertility caused by embryonic loss or physiological abnormalities, respectively.

2. Developed low-cost genotyping tools for genomic predictions of genetic merit. Better low-cost tools for genotyping were needed encompass parentage and other important DNA tests of economically important traits for the industry. Our continued leadership in DNA tool development for ruminants (three new commercial genotyping tools) provides low cost opportunities for producers to obtain the genetic information needed for genomic selection in the U.S. beef cattle industry and globally for tropically adapted cattle. These tools also should allow DNA service providers to transition from “old technology” to SNP-based technology for parentage determination. The North American dairy industry continues to use products developed in our laboratory at a rate of genotyping more than 15,000 animals per month. Development of a SNP genotyping tool for water buffalo also has the potential to increase the accuracy and efficacy of genomic selection for improved dairy production in the developing world.

3. Developed a computer approach using comparative genomics and identified thousands of gene expression regulatory elements (i.e. transcription factor binding sites - TFBS) in the cattle genome, which serves as a resource for functional genomics studies.

4. Developed a computer approach to detect copy number variation (CNV) based on population scale next-generation sequencing. Generated thousands of new CNV regions, which enable future studies of highly variable regions in the cattle genome.

5. Led “big data” support within the Agricultural Research Service. Next generation sequencing instruments and associated molecular and bioinformatic methods are important experimental tools for genetics and biological discovery in agricultural research. ARS scientists in Beltsville, MD supported genomics research within the Agency and with national and international collaborators in a multitude of species and applications by providing scientific computing, labor, and bioinformatic support for projects at various locations that wanted to use next-generation sequencing methods. These efforts included genomics research in water buffalo, cattle, goat, wood rot fungus, and other agriculturally relevant species.

Review Publications
Hou, Y., Bickhart, D.M., Hvinden, M.L., Li, C., Song, J., Boichard, D.A., Fritz, S., Eggen, A., Denise, S., Wiggans, G.R., Sonstegard, T.S., Van Tassell, C.P., Liu, G. 2012. Fine mapping of copy number variations on two cattle genome assemblies using high density SNP array. Biomed Central (BMC) Genomics. 13:376.

Aslam, M.L., Bastiaansen, J.W., Elferink, M., Megens, H., Crooijmas, R.P., Blomberg, L., Fleischer, R.C., Van Tassell, C.P., Sonstegard, T.S., Schroeder, S.G., Groenen, M.A., Long, J.A. 2012. Whole genome snp discovery and analysis of genetic diversity in turkey (meleagris gallopavo). Biomed Central (BMC) Genomics. 13:391.

Mcclure, M.C., Sonstegard, T.S., Van Tassell, C.P., Wiggans, G.R. 2012. Imputation of Microsatellite Allele from Dense SNP Genotypes for Parentage Verification. Frontiers in Livestock Genomics. 3:140.

Ma, L., Wiggans, G.R., Wang, S., Sonstegard, T.S., Yang, J., Crooker, B.A., Cole, J.B., Van Tassell, C.P., Da, Y. 2012. Effect of sample stratification on dairy GWAS results. Biomed Central (BMC) Genomics. 13:536.

Van Raden, P.M., Null, D.J., Sargolzaei, M., Wiggans, G.R., Tooker, M.E., Cole, J.B., Sonstegard, T.S., Connor, E.E., Winters, M., Van Kaam, J., Van Doormaal, B.J., Faust, M.A., Doak, G.A. 2013. Genomic imputation and evaluation using high density Holstein genotypes. Journal of Dairy Science. 96(1):668-678.

Wiggans, G.R., Cooper, T.A., Van Tassell, C.P., Sonstegard, T.S., Simpson, E.B. 2013. Technical note: Characteristics and use of the Illumina BovineLD and GeneSeek Genomic Profiler low-density bead chips for genomic evaluation. Journal of Dairy Science. 96(2):1258-1263.

Sonstegard, T.S., Cole, J.B., Van Raden, P.M., Van Tassell, C.P., Null, D.J., Schroeder, S.G., Bickhart, D.M., Mcclure, M.C. 2013. Identification of a nonsense mutation in CWC15 associated with decreased reproductive efficiency in Jersey cattle. PLoS One. 8(1):e54872.

Mcclure, M.C., Kim, E., Bickhart, D.M., Null, D.J., Cooper, T.A., Cole, J.B., Wiggans, G.R., Marsan, P.A., Colli, L., Santus, E., Liu, G., Schroeder, S.G., Matukumalli, L., Van Tassell, C.P., Sonstegard, T.S. 2013. Fine mapping for Weaver Syndrome in the Brown Swiss breed with the identification of possible casual mutations across NRCAM, PNPLA8, and CTTNBP2 and Developement of a diagnostic SNP haplotype. PLoS One. 8(3):e59251.

Liu, G., Bickhart, D.M. 2012. Copy Number Variation in the Cattle Genome. Functional and Integrative Genomics. DOI: 10.1007/S10142-012-0289-9.

Zhao, C., Fei, T., Yu, Y., Luo, J., Mitra, A., Zhan, F., Hou, Y., Liu, G., Zan, L., Updike, M., Song, J. 2012. Functional Genomic Analysis of Variation on Beef Tenderness Induced by Acute Stress in Angus Cattle. Comparative and Functional Genomics. DOI: 10.1155/2012/756284.

Bickhart, D.M., Liu, G. 2013. Identification of candidate transcription factor binding sites in the cattle genome. Genomics, Proteomics and Bioinformatics. 11(3):195-198.

Zhao, C., Tian, F., Yu, Y., Liu, G., Zan, L., Updike, M., Song, J. 2012. miRNA-dysregulation associated with tenderness variation induced by acute stress in angus cattle. Animal Science Biotechnol. 3(1):12.

Liu, G., Bickhart, D.M. 2013. The trappin gene famliy: structue, function and evolution. Cattle: Domestication, Disease and Environment. 1st edition. Hauppauge, NY: Nova Science Publishers, Inc. 25-38 p.

Utsunomiya, Y.T., O'Brien, A.P., Sonstegard, T.S., Van Tassell, C.P., Santana Do Carmo, A.D., Meszaros, G., Solkner, J., Garcia, J.F. 2013. Detecting Loci under recent positive selection in dairy and beef cattle by combining different genome-wide scan methods. PLoS One. 8(5):e64280.

Tang, J.D., Perkins, A.D., Sonstegard, T.S., Schroeder, S.G., Nicholas, D.D., Diehl, S.V. 2012. Gene expression analysis of copper tolerance and wood decay in the brown rot fungus Fibroporia radiculosa. Applied and Environmental Microbiology. 79(5):1523-33.

Last Modified: 4/22/2015
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