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Processing Large-Scale, High-Dimension Genetic and Gene Expression Data

  • Cliona MolonyEmail author
  • Solveig K. Sieberts
  • Eric E. Schadt
Chapter
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Abstract

The now routine generation of large-scale, high-throughput data in multiple dimensions (genotype, gene expression, and so on) provides a significant challenge to researchers who desire to integrate data across these dimensions in hopes of painting a more comprehensive picture of complex system behavior. This type of integration promises to elucidate networks that drive disease traits associated with common human diseases like obesity, diabetes, and atherosclerosis. However, to effectively carry out this type of research not only requires the generation of large-scale genotype and molecular profiling data but also requires the development and application of methods and software in addition to a computing infrastructure capable of processing the large-scale data sets. Mastery of the methods and tools and having access to an appropriate computing environment capable of processing large-scale data will be critical to maintaining a competitive advantage, given future successes in biomedical research will likely demand a more comprehensive view of the complex array of interactions in biological systems and how such interactions are influenced by genetic background, infection, environmental states, life-style choices, and social structures more generally. In this chapter, we detail the methodological and computing issues associated with carrying out large-scale genome-wide association studies on tens of thousands of phenotypes, where the aim is to identify those phenotypes that are intermediate to DNA variations and disease phenotypes. This type of analysis can provide insights into the molecular networks that are perturbed by DNA and environmental variations, and as a result, induce changes in disease associated traits, providing a path to interpret genome-wide association study data as well as uncover networks that drive disease processes.

Keywords

Message Passing Interface Expression Trait Parallel Virtual Machine Common Human Disease Surrogate Variable Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Cliona Molony
    • 1
    Email author
  • Solveig K. Sieberts
    • 1
  • Eric E. Schadt
    • 1
  1. 1.Rosetta Inpharmatics, LLCa wholly owned subsidiary of Merck & Co., Inc.SeattleUSA

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