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Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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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.
GWAS does not require the identification of the target gene involved in...
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Pharmacogenomics: Identification of New Drug Targets01:29

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Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...
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Related Experiment Video

Updated: Mar 2, 2026

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
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Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

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Large-scale data-driven integrative framework for extracting essential targets and processes from disease-associated

Gaston K Mazandu1, Emile R Chimusa2, Kayleigh Rutherford3

  • 1Institute of Infectious Disease and Molecular Medicine at UCT and a Researcher at AIMS.

Briefings in Bioinformatics
|May 19, 2017
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Summary

This study introduces a computational framework to identify crucial drug targets for diseases like tuberculosis and Ebola. It enhances drug discovery by analyzing complex biological data, potentially aiding rare disease therapy development.

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Area of Science:

  • Computational biology
  • Drug discovery
  • Systems biology

Background:

  • Growing infectious disease prevalence and emergence of new pathogens pose global public health challenges.
  • High costs and failure rates in drug development, particularly for rare diseases, necessitate improved target identification strategies.
  • Current drug discovery processes are slow, expensive, and often fail due to irrelevant target selection.

Purpose of the Study:

  • To develop a data-driven computational framework for identifying essential drug targets and processes.
  • To enhance drug target selection by integrating systems-level drug-target-disease associations.
  • To apply the framework to diseases like tuberculosis and Ebola virus disease for validation.

Main Methods:

  • Developed a large-scale, data-driven integrative computational framework.
  • Combined heterogeneous data sources including protein-protein interactions, functional annotations, and pharmaceutical data.
  • Applied the framework to tuberculosis and Ebola virus disease datasets.

Main Results:

  • Successfully extracted essential drug targets and key biological processes.
  • Demonstrated the framework's effectiveness in enhancing target selection.
  • Identified opportunities for the rational use of existing approved drugs.

Conclusions:

  • The computational framework provides a robust method for optimizing target-based drug discovery strategies.
  • This approach can accelerate the identification of new uses for existing drugs, addressing unmet needs in orphan disease therapy.
  • The model offers a systematic pathway to harness the full therapeutic potential of drugs and bridge gaps in rare disease treatment.