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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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Understanding Disease Susceptibility through Population Genomics.

Seonggyun Han1, Junnam Lee, Sangsoo Kim

  • 1School of Systems Biomedical Science, Soongsil University, Seoul 156-743, Korea.

Genomics & Informatics
|January 25, 2013
PubMed
Summary

Genetic variations influence gene expression, a heritable trait. Identifying expression quantitative trait loci (eQTLs) and analyzing gene networks can improve understanding of gene regulation and disease susceptibility.

Keywords:
cis-actingco-expression networkdisease susceptibilityexpression quantitative trait locitrans-acting

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Area of Science:

  • Genetics
  • Systems Biology
  • Bioinformatics

Background:

  • Genetic epidemiology confirms that natural variations in gene expression are heritable and genetically determined.
  • Expression quantitative trait loci (eQTLs), DNA variations linked to expression phenotypes, have been identified in various cell and tissue types.

Purpose of the Study:

  • To explore the integration of eQTL information with transcription module network analysis.
  • To enhance understanding of gene expression regulation mechanisms.
  • To investigate the potential of these findings in predicting disease susceptibility.

Main Methods:

  • Identification of expression quantitative trait loci (eQTLs) in diverse cell and tissue types.
  • Network analysis of transcription modules.
  • Integration of eQTL data with network analysis.

Main Results:

  • Expression quantitative trait loci (eQTLs) provide insights into the genetic basis of gene expression variation.
  • Network analysis of transcription modules reveals regulatory relationships.
  • Integrated analysis links genetic variations to gene expression regulation and biological pathways.

Conclusions:

  • Integrating eQTL data with network analysis offers a powerful approach to understanding gene expression regulation.
  • This integrated approach has potential applications in predicting susceptibility to various diseases by analyzing relevant biological pathways.