SVS for Agrigenomic Research - Raise Your Expectations
As the world's population grows, improving the value of crops and livestock is essential. To do so requires an in-depth understanding of genetic variation and how it relates to traits of interest. Such understanding starts with high quality data, but it's made possible through powerful analytics.
Our SNP & Variation Suite™ (SVS) software delivers a world-class analytic tool and powerful visualizations in a user-friendly interface. No more struggling to coerce command-line software -- designed exclusively for human genetics -- to work for the plant or animal species you're studying. SVS supports a wide variety of analysis methods for a wide variety of species enabling you to quickly and easily identify variants related to pest and disease resistance, increased feed efficiency, milk production, and more.
Our software tools manage, analyze, visualize and filter data all through a friendly user interface. We take great care in researching and implementing best practices as well as the latest methods and algorithms and serve them up in an intuitive way. We eliminate the need to learn how to script, unless you want to. While SVS provides a flexible, user-friendly interface on the front-end, it also provides full programmatic access on the back-end. So whether you're one who shudders at the thought of writing another line of code, or one who lives for it, SVS empowers you to do more than you ever thought possible.
SVS supports the broadest array of data formats for both import and export, eliminating the hassles of working with large data. You'll also find a host of tools that make it easy to format and recode data, merge data sets, create subsets, and more.
No more coercing software designed for human genetics to work with the plant or animal species you're studying. SVS supports a wide variety of species, including different genomic builds for some. If your particular species is not included by default, you can easily add it along with corresponding annotations from a point-and-click interface. And switching between species and genomes is easy too. Just select the species you're studying from the project options tab or from a drop down menu in the genome browser and you're ready to go. See the full list of animal and plant genomes available in Golden Helix® software here.
GWAS is growing rapidly in agricultural applications as a very effective method to identify genes of interest such as those affecting production and resistance traits. SVS provides you with a number of intuitive workflows from basic to advanced SNP analyses to lead you beyond single marker associations. With support for case-control and quantitative traits, whole genome and candidate gene data, you can run a breadth of statistical tests under several genetic models. Advanced regression can further help elucidate even the most complex gene-gene and gene-environment interactions. See the full description of our GWAS capabilities here.
Genomic Prediction is quickly becoming one of the most used tools for researchers interested in milk production, weight gain, and marbling or increased yield. SVS allows researchers to determine which animals or plants to continue breeding for desired traits including various means of defining the relationship between samples, the ability to validate models and visualize the results. See our full Genomic Prediction capabilities here.
All visualizations in SVS are dynamic, interactive and integrative so you can quickly identify hot spots and navigate to areas of interest. Compare variant maps, LD structure or copy number patterns among breeds. Produce Manhattan plots for any number of dependent variables. Assess identity by descent or population stratification to locate problematic samples. The best part: any genomic-based plot can be visualized in an interactive genome browser alongside relevant annotations with proper genomic coordinates for the species you're studying.
Whether you are using candidate genes or whole genomes, microarrays or next-generation sequencing, SVS delivers unparalleled performance on any size data. You can seamlessly navigate spreadsheets with billions of data points and easily plot variant maps, heat maps and genomic annotations across the entire genome for thousands of samples.
SVS provides the most complete set of statistical and visual analytic tools to help you quickly identify genetic variants or haplotypes and their association with traits of interest. We support SNP, CNV, or sequence data allowing you to perform a wide range of analyses. Not to mention all the time you'll save by not having to move large, complex datasets between multiple packages.
High quality data is critical to high quality results. To ensure your data is of the highest quality, SVS provides the most comprehensive set of quality assurance tools helping you to not only assess the quality of your data, but remedy any problems as well.
by Dr. Andreas Scherer
Webcast - Genomic Analyses for Palatability of Beef
Genomic Prediction eBook
Genomic Prediction in Agriculture
by Dr. Andreas Scherer
View a sample project in SVS
Explore a Genomic Prediction Project in SVS - FREE!
Dr. Matthew McClure is a lead geneticist at the Irish Cattle Breeding Federation, and was previously at the Bovine Functional Genomics Lab at the USDA-ARS. In the case study, Dr. McClure talks about using SVS at the ICBF and the results he's been able to achieve. In the webcast, Dr. McClure describes his research at the USDA-ARS to identify causal mutations for Mendelian and complex traits.
Gonzalo Rincon, DVM is a Project Scientist in the Medrano Lab, part of the Department of Animal Science, at the University of California, Davis. While the lab works on several diff erent species including canine and ovine, the main focus is bovine research. Rincon is in charge of the analysis, and his goal is to find SNP and CNV variations that affect phenotypes such as the quality of milk and beef. Through the use of tools from Golden Helix, Rincon and his colleagues have been able to publish 14 papers and obtain 2 patents, three-to-four times faster than they would have been able to.
The International Maize and Wheat Improvement Center's (CIMMYT) mission is "To sustainably increase the productivity of maize and wheat systems to ensure global food security and reduce poverty." To accomplish this task, one area of focus for the CIMMYT is genetic research and molecular marker technology to improve certain traits of maize and wheat, such as drought tolerance, resistance to disease, and amino acid balance. In this case study, Dr. Raman Babu describes his experience using SVS to analyze model populations in order to predict certain traits at CIMMYT.
Selected Agrigenomic Publications
- Wickramasinghe, S et al. (2011) Variants in the pregnancy-associated plasma protein-A2 gene on Bos taurus autosome 16 are associated with daughter calving ease and productive life in Holstein cattle. Journal of Dairy Science, 94(3):1552-1558, doi:10.3168/jds.2010-3237. Abstract
- Rincon, G et al. (2009) Fine mapping and association analysis of a quantitative trait locus for milk production traits on Bos taurus autosome 4. Journal of Dairy Science, 92:758-764. doi:10.3168/jds.2008-1395. Abstract
- Rincon, G et al. (2011) Comparison of buccal and blood-derived canine DNA, either native or whole genome amplified, for array-based genome-wide association studies. BMC Research Notes, 4:226, doi:10.1186/1756-0500-4-226. Abstract
- Go, Y et al. (2011) Genome-Wide Association Study Among Four Horse Breeds Identifies a Common Haplotype Associated with the In Vitro CD3+ T Cell Susceptibility/Resistance to Equine Arteritis Virus Infection. Journal of Virology, doi:10.1128/JVI.06068-11. Abstract
- Dillon, S et al. (2013) Signatures of adaptation and genetic structure among the mainland populations of Pinus radiata (D. Don) inferred from SNP loci. Tree Genetics & Genomes, doi:10.1007/s11295-013-0650-8. Abstract
- Emanuelli, F et al. (2010) A candidate gene association study on muscat flavor in grapevine (Vitis vinifera L.). BMC Plant Biology, 10:241, doi:10.1186/1471-2229-10-241. Abstract