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 searching 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. Subjects Bioinformatics, Computational Biology, Visual Analytics
Highlights
The recent development of high-throughput phenotyping techniques coupled with the ability to genotype large populations of diverse individuals has revolutionized the way that forward genetics research is performed
Tools have rapidly become available to perform genome-wide association studies (GWAS) in a variety of species (Kang et al, 2010; Segura et al, 2012; Lipka et al, 2012) that can map traits to the genome with high enough resolution to quickly provide a tractable list of potential causal genes
No No No No Not on the same track Yes resources do not allow for easy viewing and comparison of GWAS results across phenotypes and studies, a situation that frequently arises with structured populations
Summary
The recent development of high-throughput phenotyping techniques coupled with the ability to genotype large populations of diverse individuals has revolutionized the way that forward genetics research is performed. The extra steps involved in exploring the data in this way makes it more likely that interesting associations may be missed either due to (1) mistakes made in attempting to mine the large results files or (2) the dataset not being mined deeply enough due to the difficulty of looking for genes under less significant peaks. This method quickly becomes tedious when analyzing multiple phenotypes or relatively complex traits.
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