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Günter Jäger, Florian Battke, Corinna Vehlow, Julian Heinrich, Kay Nieselt (University of Tübingen, Germany; University of Stuttgart, Germany;)The analysis of expression quantitative trait locus (eQTL) data is a challenging scientific endeavor, involving the processing of very large, heterogeneous, and complex data. Typical eQTL analyses involve three types of data: Sequence-based data, gene expression data, and meta-data describing the phenotype. Based on these, the task is to connect certain genotypes with specific phenotypic outcomes to infer causal associations of genetic variation, expression, and disease. We present a new visual analytical approach for the analysis of eQTL data from genome wide association studies. Our strategy involves the generation of a network of associations defined by the influences of SNPs on gene expression. The network is based on association data generated by the whole genome association toolset PLINK. Further analysis of the SNPs included in the association network is performed using iHat. iHAT offers visualization and aggregation strategies to support the user in finding correlations between sequences and metadata. We demonstrate our approach on the eQTL data provided for the data visualization/analysis contest for the BioVis 2011, the 1st IEEE Symposium on Biological Data Visualization.