Abstract
The goal of systems genetics is to understand the impact of genetic variation across all levels of biological organization, from mRNAs, proteins, and metabolites, to higher-order physiological and behavioral traits. This approach requires the accumulation and integration of many types of data, and also requires the use of many types of statistical tools to extract relevant patterns of covariation and causal relations as a function of genetics, environment, stage, and treatment. In this protocol we explain how to use the GeneNetwork web service, a powerful and free online resource for systems genetics. We provide workflows and methods to navigate massive multiscalar data sets and we explain how to use an extensive systems genetics toolkit for analysis and synthesis. Finally, we provide two detailed case studies that take advantage of human and mouse cohorts to evaluate linkage between gene variants, addiction, and aging.
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Acknowledgment
We thank Lei Yan, Arthur Centeno, and Zachary Sloan, for their many contributions to building and maintaining GN over the past decade. GN code has benefited greatly from contributions by Jintao Wang, Sam Ockman, Xiaodong Zhou, Ning Liu, and Alex G. Williams, and Drs. Rudi Alberts, Arends, Elissa J. Chesler, Kenneth Manly, Danny and Evan G. Williams. We also thank M. Trevor Houseal and Austin Kimes for their help in editing this chapter. Support for GeneNetwork has been provided by NIH grants U01AA013499, U01AA16662, U01AA014425, P20DA21131, U01CA105417, and U24 RR021760. GN is also generously supported by the UT Center for Integrative and Translational Genomics, and funds from the UT-ORNL Governor’s Chair.
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Mulligan, M.K., Mozhui, K., Prins, P., Williams, R.W. (2017). GeneNetwork: A Toolbox for Systems Genetics. In: Schughart, K., Williams, R. (eds) Systems Genetics. Methods in Molecular Biology, vol 1488. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-6427-7_4
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DOI: https://doi.org/10.1007/978-1-4939-6427-7_4
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