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Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
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In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila
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Exploring the human diseasome: the human disease network.

Kwang-Il Goh1, In-Geol Choi

  • 1Department of Physics, Korea University, Seoul 136-713, Korea. kgoh@korea.ac.kr

Briefings in Functional Genomics
|October 16, 2012
PubMed
Summary

Network medicine revolutionizes disease understanding by viewing human biology through cellular networks. This approach analyzes the human diseasome and disease networks to reveal connections between genetic components and disorders.

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Area of Science:

  • Integrative biology
  • Systems biology
  • Genetics and genomics

Background:

  • Genome-scale molecular biology and genetics have expanded knowledge of human biology and disease components.
  • The significance of cellular networks connecting biological components is increasingly recognized.
  • Emergence of network medicine offers a novel perspective on understanding human diseases.

Purpose of the Study:

  • To review recent advancements in network medicine, focusing on the human diseasome and disease network.
  • To explore the translation of molecular connectivity into disorder relationships on a global scale.
  • To investigate the centrality of disease-causing genetic components within cellular networks.

Main Methods:

  • Survey of current research in network medicine.
  • Analysis of the human diseasome and human disease network concepts.
  • Review of studies integrating diseasome data with interactome networks and other disease factors.

Main Results:

  • Discussion on how molecular connectivity influences disease relationships.
  • Insights into the role of genetic components in cellular network architecture.
  • Demonstration of the utility of the diseasome in conjunction with interactome data.

Conclusions:

  • Network medicine provides a powerful framework for understanding complex human diseases.
  • The human diseasome and disease networks are crucial tools for systems-level disease analysis.
  • Integrating molecular and network data offers deeper insights into disease etiology and progression.