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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...
Genetic Screens02:46

Genetic Screens

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
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which result in visible changes...
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Multiple Allele Traits01:49

Multiple Allele Traits

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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...

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Related Experiment Video

Updated: Jun 7, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
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Published on: July 27, 2021

A stochastic expectation and maximization algorithm for detecting quantitative trait-associated genes.

Haimao Zhan1, Xin Chen, Shizhong Xu

  • 1Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, USA.

Bioinformatics (Oxford, England)
|November 2, 2010
PubMed
Summary

We identified genes linked to eight barley agronomy traits using a novel statistical method. This approach maps gene expression variations to specific genomic locations, advancing bioinformatics and plant breeding.

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Published on: November 3, 2010

Related Experiment Videos

Last Updated: Jun 7, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
08:27

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

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An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations
10:17

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations

Published on: November 3, 2010

Area of Science:

  • Genomics
  • Bioinformatics
  • Plant Breeding

Background:

  • Biological traits correlate with gene expression patterns influenced by DNA variation.
  • Understanding gene expression and quantitative trait correlations is key for gene function and regulatory network analysis.

Purpose of the Study:

  • To develop and apply a novel statistical method for analyzing associations between gene expression and quantitative traits.
  • To identify genetic loci controlling gene expression variations and their link to agronomic traits.

Main Methods:

  • Utilized the stochastic expectation and maximization (SEM) algorithm, a novel statistical approach.
  • Clustered gene expression levels based on association strength with quantitative trait phenotypes.
  • Mapped trait-associated genes to genomic loci, identifying expression quantitative trait loci (eQTLs).

Main Results:

  • Successfully applied the SEM algorithm to a barley genetic experiment dataset.
  • Identified genes associated with eight agronomy traits in barley for the first time.
  • Mapped these identified genes to seven chromosomes within the barley genome.

Conclusions:

  • The SEM algorithm provides a powerful tool for dissecting gene-trait associations.
  • This research offers valuable insights for bioinformatics and enhances strategies in plant breeding.
  • The identified gene-trait associations in barley can accelerate crop improvement efforts.