|HARTSOCK, RYAN - Danforth Plant Science Center
Submitted to: Peer J Computer Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/11/2015
Publication Date: 5/27/2015
Publication URL: http://handle.nal.usda.gov/10113/60982
Citation: Ziegler, G.R., Hartsock, R.H., Baxter, I.R. 2015. Zbrowse: An interactive GWAS results browser. Peer J Computer Science. 1:e3. https://dx.doi.org/10.7717/peerj-cs.3.
Interpretive Summary: Advances in sequencing, imaging, chemical analysis and robotics are making it easier to conduct detailed genetic experiments that test thousands or millions of genetic locations across an organisms genome. These experiments are know as genome wide association studies or GWAS. However, sifting through all of this data to make meaningful associations of traits with genes is difficult. Using open source software, we have developed an interactive browser that allows reasearchers to work with GWAS data from multiple traits or experiments in the same window. This browser will allow researchers working in agriculturally important organsims such as soybean, corn and other crops to find candidate genes for the traits that they are working on. The browser can be adpated for use by a wide variety of researchers by loading in their own data or by freely available source code.
Technical Abstract: The growing number of genotyped populations, the advent of high-throughput phenotyping techniques and the development of GWAS analysis software has rapidly accelerated the number of GWAS experimental results. Candidate gene discovery from these results files is often tedious, involving many manual steps search- ing for genes in windows around a significant SNP. This problem rapidly becomes more complex when an analyst wishes to compare multiple GWAS studies for pleiotropic or environment specific effects. To this end, we have developed a fast and intuitive interactive browser for the viewing of GWAS results with a focus on an ability to compare results across multiple traits or experiments. The software can easily be run on a desktop computer with software that bioinformaticians are likely already familiar with. Additionally, the software can be hosted or embedded on a server for easy access by anyone with a modern web browser.