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Related Concept Videos

Pharmacogenomics: Identification of New Drug Targets01:29

Pharmacogenomics: Identification of New Drug Targets

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...
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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Updated: Jun 3, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

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Published on: June 21, 2018

Identifying causal genes and dysregulated pathways in complex diseases.

Yoo-Ah Kim1, Stefan Wuchty, Teresa M Przytycka

  • 1National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland, USA.

Plos Computational Biology
|March 11, 2011
PubMed
Summary

Complex diseases often share molecular pathways despite diverse genetic causes. This study introduces a computational method to identify causal genes and pathways, revealing shared functional pathways in glioblastoma multiforme (GBM).

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In Vivo Modeling of the Morbid Human Genome using Danio rerio
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Last Updated: Jun 3, 2026

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Published on: August 24, 2013

Area of Science:

  • Computational biology
  • Genomics
  • Systems biology

Background:

  • Complex diseases involve multiple genomic changes leading to similar phenotypes.
  • Understanding shared cellular pathways is crucial for disease mechanism insights and drug target identification.

Purpose of the Study:

  • To develop a novel computational method for simultaneously identifying causal genes and dys-regulated pathways in complex diseases.
  • To provide an integrated, pathway-centric perspective on disease mechanisms.

Main Methods:

  • Identified differentially expressed genes in cancer versus control samples.
  • Modeled causal paths from genomic alterations to target genes using molecular interaction networks.
  • Integrated expression Quantitative Trait Loci (eQTL) analysis with pathway information.

Main Results:

  • Applied the method to glioblastoma multiforme (GBM) patient data (158 cases).
  • Uncovered candidate causal genes and causal paths responsible for altered gene expression.
  • Identified intermediate nodes and pathways mediating information flow between causal and target genes.

Conclusions:

  • Different genomic perturbations dys-regulate the same functional pathways, supporting a pathway-centric view of cancer.
  • The developed method is applicable to any disease system where genetic variations are causal.