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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...
Epistasis Analysis01:09

Epistasis Analysis

Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
Gene-Environment Interactions01:20

Gene-Environment Interactions

Gene expression is a dynamic process that is significantly influenced by environmental factors. This interaction underlies the complex nature of biological development and the phenotypic differences observed among individuals, even among those with identical genetic makeups. Factors such as radiation, temperature, behavior, nutrition, and stress play pivotal roles in determining how genes are expressed. The concept of the reaction range is central to understanding this interaction. It posits...
Background and Environment Affect Phenotype02:27

Background and Environment Affect Phenotype

Although the genetic makeup of an organism plays a major role in determining the phenotype, there are also several environmental factors, such as temperature, oxygen availability, presence of mutagens, that can alter an organism’s phenotype.
An example of how genetic background affects phenotype can be seen in horses. The Extension gene in horses is responsible for their coat color. A wild-type gene (EE) produces black pigment in the coat, while a mutant gene (ee) produces red pigment. A...
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...

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Related Experiment Video

Updated: Jul 5, 2026

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

Pattern-based mining strategy to detect multi-locus association and gene x environment interaction.

Zhong Li1, Tian Zheng, Andrea Califano

  • 1Department of Computational Genetics, High Throughput Biology Inc, 513 West Mount Pleasant Avenue, Livingston, New Jersey 07039, USA. zli@htbiology.com

BMC Proceedings
|May 10, 2008
PubMed
Summary
This summary is machine-generated.

A new pattern-based data-mining method efficiently identifies genetic factors for complex diseases. This approach detected potential two-locus associations and gene x gender interactions in rheumatoid arthritis data.

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Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
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Last Updated: Jul 5, 2026

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

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

Area of Science:

  • Genetics
  • Bioinformatics
  • Data Mining

Background:

  • Genome-wide association studies (GWAS) are crucial for identifying genetic factors in common and rare diseases.
  • Efficient analytical methods are needed to analyze large genetic datasets and detect complex disease associations.
  • Understanding genotype-phenotype and genotype-environment interactions is key to personalized medicine.

Purpose of the Study:

  • To develop and evaluate a novel pattern-based data-mining approach for genetic analysis.
  • To identify unlinked multilocus genetic effects for complex diseases.
  • To detect genotype-phenotype and genotype-environment interactions.

Main Methods:

  • Developed a pattern-based data-mining algorithm for analyzing genetic variant data.
  • Applied the method to a dense chromosome 18 dataset for rheumatoid arthritis (Genetic Analysis Workshop 15).
  • Focused on discovering multilocus genetic effects and interactions.

Main Results:

  • The method successfully identified two potential two-locus genetic associations.
  • A putative two-locus gene x gender interaction was detected.
  • Demonstrated the utility of the approach in a real-world rheumatoid arthritis dataset.

Conclusions:

  • The developed pattern-based data-mining approach is effective for discovering complex genetic associations.
  • This method can identify multilocus effects and gene x environment interactions, such as gene x gender.
  • The approach offers a valuable tool for genetic research in complex diseases.