Date of Original Version



Conference Proceeding

Rights Management

© 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Abstract or Description

Association mapping studies promise to link DNA mutations to gene expression data, possibly leading to innovative treatments for diseases. One challenge in large-scale association mapping studies is exploring the results of the computational analysis to find relevant and interesting associations. Although many association mapping studies find associations from a genome-wide collection of genomic data to hundreds or thousands of traits, current visualization software only allow these associations to be explored one trait at a time. The inability to explore the association of a genomic location to multiple traits hides the inherent interaction between traits in the analysis. Additionally, researchers must rely on collections of in-house scripts and multiple tools to perform an analysis, adding time and effort to find interesting associations. In this paper, we present a novel visual analytics system called GenAMap. GenAMap replaces the time-consuming analysis of large-scale association mapping studies with exploratory visualization tools that give geneticists an overview of the data and lead them to relevant information. We present the results of a preliminary evaluation that validated our basic approach.





Published In

Proceedings of IEEE Symposium on Biological Data Visualization (BioVis), 87-94.