Abstract
Analysis of expression quantitative trait loci (eQTL) helps elucidate the connection between genotype, gene expression levels, and phenotype. However, standard statistical genetics can only attribute the changes in expression levels to loci on the genome, not specific genes. Each locus can contain many genes, making it very difficult to discover which gene is controlling the expression levels of other genes. Furthermore, it is even more difficult to find a pathway of molecular interactions responsible for controlling the expression levels. Here we describe a series of techniques for finding explanatory pathways by exploring the graphs of molecular interactions. We show several simple methods can find complete pathways that explain the mechanism of differential expression in eQTL data.
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Acknowledgments
This work was supported by NSF Graduate Research Fellowship DGE0203031.
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Rashid, I., McDermott, J., Samudrala, R. (2009). Inferring Molecular Interactions Pathways from eQTL Data. In: Ireton, R., Montgomery, K., Bumgarner, R., Samudrala, R., McDermott, J. (eds) Computational Systems Biology. Methods in Molecular Biology, vol 541. Humana Press. https://doi.org/10.1007/978-1-59745-243-4_10
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DOI: https://doi.org/10.1007/978-1-59745-243-4_10
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