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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.
GWAS does not require the identification of the target gene involved in...
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Human Genetics

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Other than maintaining genome stability via DNA repair, homologous recombination plays an important role in diversifying the genome. In fact, the recombination of sequences forms the molecular basis of genomic evolution. Random and non-random permutations of genomic sequences create a library of new amalgamated sequences. These newly formed genomes can determine the fitness and survival of cells. In bacteria, homologous and non-homologous types of recombination lead to the evolution of new...
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Gregor Mendel's work (1822 - 1884) was primarily focused on pea plants. Through his initial experiments, he determined that every gene in a diploid cell has two variants called alleles inherited from each parent. He suggested that amongst these two alleles, one allele is dominant in character and the other recessive. The combination of alleles determines the phenotype of a gene in an organism.

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Related Experiment Video

Updated: Jun 8, 2026

In Vivo Modeling of the Morbid Human Genome using Danio rerio
12:31

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Published on: August 24, 2013

Broker genes in human disease.

James J Cai1, Elhanan Borenstein, Dmitri A Petrov

  • 1Department of Veterinary Integrative Biosciences, Texas A&M University, TX, USA. jcai@tamu.edu

Genome Biology and Evolution
|October 13, 2010
PubMed
Summary
This summary is machine-generated.

Human disease genes exhibit unique protein-protein interaction (PPI) network properties. Brokers, with high connectivity and low clustering, characterize complex disease genes, unlike those from genome-wide association studies (GWAS).

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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Published on: June 21, 2018

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Last Updated: Jun 8, 2026

In Vivo Modeling of the Morbid Human Genome using Danio rerio
12:31

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Published on: August 24, 2013

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

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

Published on: June 21, 2018

Area of Science:

  • Systems biology
  • Network biology
  • Genetics

Background:

  • Understanding the biological basis of human diseases is crucial for developing effective treatments.
  • Genes associated with Mendelian and complex diseases are key targets in systems biology research.
  • Protein-protein interaction (PPI) networks offer insights into gene function and disease mechanisms.

Purpose of the Study:

  • To investigate distinct protein-protein interaction (PPI) network properties of Mendelian and complex disease genes.
  • To identify key network metrics that differentiate disease-associated genes.
  • To compare network patterns of genes identified through genome-wide association studies (GWAS) versus other methods.

Main Methods:

  • Analysis of the human PPI network using network properties such as degree and clustering coefficient.
  • Reduction of five network properties to two independent metrics.
  • Robustness testing against gene age and inspection bias.

Main Results:

  • Disease genes possess distinct PPI properties, characterized by high degree and low clustering coefficients.
  • These genes act as 'brokers,' connecting otherwise disparate proteins.
  • Genes identified via GWAS show network patterns similar to non-disease genes, differing significantly from complex disease genes found by other means.

Conclusions:

  • Complex disease genes exhibit unique 'broker' network characteristics.
  • GWAS may be biased towards specific disease types or encounter challenges in accurately mapping SNPs to causal genes.