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Ligand Binding and Linkage00:49

Ligand Binding and Linkage

Allosteric proteins have more than one ligand binding site; the binding of a ligand to any of these sites influences the binding of ligands to the other sites. When a protein is allosteric, its binding sites are called coupled or linked.  In the case of enzymes, the site that binds to the substrate is known as the active site and the other site is known as the regulatory site. When a ligand binds to the regulatory site, this leads to conformational changes in the protein that can influence the...
Ligand Binding and Linkage00:49

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Epistasis Analysis01:09

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Updated: May 30, 2026

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
05:01

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

Published on: July 1, 2020

Linkage analysis.

Jennifer H Barrett1, M Dawn Teare

  • 1Section of Epidemiology and Biostatistics, Leeds Institute of Molecular Medicine, University of Leeds, Leeds, UK. j.h.barrett@leeds.ac.uk

Methods in Molecular Biology (Clifton, N.J.)
|July 23, 2011
PubMed
Summary
This summary is machine-generated.

Linkage analysis maps genes for diseases by studying relatives. This method uses microsatellite markers or single nucleotide polymorphisms (SNPs) to identify genetic loci influencing traits and diseases.

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

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Area of Science:

  • Genetics
  • Biostatistics
  • Computational Biology

Background:

  • Linkage analysis is a fundamental tool for genetic mapping.
  • It is applicable to both simple Mendelian and complex diseases.
  • The choice of genetic markers, such as microsatellites or SNPs, impacts mapping resolution.

Purpose of the Study:

  • To describe common methods for mapping genetic loci.
  • To illustrate the application of these methods using simulated data and the Merlin program.
  • To provide guidance on selecting appropriate methods and interpreting results.

Main Methods:

  • Parametric linkage analysis for major gene disorders.
  • Non-parametric (model-free) linkage analysis for complex diseases.
  • Utilizing microsatellite markers and single nucleotide polymorphisms (SNPs).

Main Results:

  • Demonstration of linkage analysis techniques on simulated datasets.
  • Practical guidance on applying different methods based on study design.
  • Interpretation of linkage analysis output for genetic locus identification.

Conclusions:

  • Linkage analysis is versatile for mapping disease susceptibility loci and quantitative traits.
  • The Merlin program facilitates the application of these methods.
  • Understanding method selection and output interpretation is crucial for genetic research.