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Related Concept Videos

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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Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
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Related Experiment Video

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Mass Spectrometry-Guided Genome Mining as a Tool to Uncover Novel Natural Products
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Mass Spectrometry-Guided Genome Mining as a Tool to Uncover Novel Natural Products

Published on: March 12, 2020

Mining gene networks with application to GAW15 Problem 1.

Jing Hua Zhao1, Jian'an Luan, M Fazil Baksh

  • 1MRC Epidemiology Unit, Institute of Metabolic Science, Box 285, Addenbrooke's Hospital, Hills Road, Cambridge CB2 0QQ, UK. jinghua.zhao@mrc-epid.cam.ac.uk

BMC Proceedings
|May 10, 2008
PubMed
Summary

Bayesian network analysis revealed strong gene dependences in immortalized B cells, offering new insights into genetic regulatory relationships. This approach enhances understanding of complex gene interactions within biological pathways.

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Area of Science:

  • Genomics
  • Systems Biology
  • Bioinformatics

Background:

  • The Genetic Analysis Workshop 15 (GAW15) Problem 1 dataset includes gene expression levels for 8793 genes from 194 individuals across 14 Centre d'Etude du Polymorphisme Humain (CEPH) Utah pedigrees.
  • Prior analyses indicated linkage, association, and significant individual variations in gene expression, with specific focus on correlations between 31 genes and 25 target genes linked to two master regulatory regions.

Purpose of the Study:

  • To apply Bayesian network analysis to further investigate gene expression relationships within the GAW15 Problem 1 dataset.
  • To identify strong gene dependences and elucidate underlying relationships between genes involved in expression regulation.

Main Methods:

  • Bayesian network analysis was employed to model the complex interactions between gene expression levels.
  • The analysis focused on identifying significant statistical dependences among genes, particularly those related to previously identified regulatory regions.

Main Results:

  • The study identified strong statistical dependences between various genes within the dataset.
  • These findings provide enhanced insight into the intricate relationships governing gene expression in immortalized B cells.

Conclusions:

  • Bayesian network analysis is a valuable approach for uncovering complex gene interactions and regulatory networks.
  • The methodology is broadly applicable for integrated analysis of genes within biological pathways, advancing systems biology research.