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A Strategy for Sensitive, Large Scale Quantitative Metabolomics
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Systems genetics applications in metabolism research.

Marcus Seldin1,2,3,4, Xia Yang5, Aldons J Lusis6,7,8

  • 1Department of Medicine, Division of Cardiology, University of California, Los Angeles, Los Angeles, CA, USA.

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|April 8, 2020
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Systems genetics offers a comprehensive approach to understanding complex metabolic diseases by analyzing multiple factors. This method integrates

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Area of Science:

  • * Metabolic and cardiovascular disease research.
  • * Genetics and systems biology.

Background:

  • * Metabolic diseases involve complex interactions of genes, environment, lifestyle, age, sex, and gut microbiome.
  • * Traditional reductionist approaches examine single elements, limiting understanding of multifactorial diseases.
  • * Systems genetics provides a population-based framework to address this complexity.

Purpose of the Study:

  • * To introduce systems genetics applications in metabolic and cardiovascular diseases.
  • * To explain how large-scale clinical and omics data can be leveraged.
  • * To highlight the integration of reductionist and systems genetics data.

Main Methods:

  • * Utilizes high-throughput 'omics' technologies to assess molecular differences within populations.
  • * Relates molecular variations to physiological functions and disease states.
  • * Employs large clinical and omics databases from human and animal populations.

Main Results:

  • * Systems genetics moves beyond identifying single disease genes to understanding higher-order molecular interactions.
  • * Facilitates placing genes within pathways and networks for hypothesis generation.
  • * Enables experimental examination of complex genetic and molecular interactions.

Conclusions:

  • * Systems genetics is crucial for unraveling the complexity of metabolic and cardiovascular diseases.
  • * Integration of diverse data sources aids in understanding disease mechanisms.
  • * Emerging applications include developing improved nutritional and pharmacological strategies for metabolic diseases.