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Related Concept Videos

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...
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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...
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Strategies for Assessing and Addressing Confounding

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Functions can be combined to form new mathematical models that describe interactions between variables. These combinations are fundamental in understanding relationships between changing quantities and are commonly encountered in scientific and engineering contexts. The combination methods—addition, subtraction, multiplication, division, and composition—each have unique implications for the resulting function’s domain and behavior.When combining functions through arithmetic operations, such...
Genome-wide Association Studies-GWAS01:11

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Related Experiment Video

Updated: Jun 18, 2026

Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization
13:55

Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization

Published on: February 3, 2013

Combining information from linkage and association methods.

Elizabeth E Marchani1, Andrea Callegaro, E Warwick Daw

  • 1Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, Washington 98195, USA.

Genetic Epidemiology
|November 20, 2009
PubMed
Summary
This summary is machine-generated.

Optimizing genetic analyses with abundant data requires efficient methods. New approaches for identity-by-descent estimation and score statistics improve complex trait gene discovery, despite computational challenges.

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A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
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Last Updated: Jun 18, 2026

Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization
13:55

Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization

Published on: February 3, 2013

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

Area of Science:

  • Genetics and Bioinformatics
  • Statistical Genomics

Background:

  • Traditional linkage and association analyses are foundational in genetic studies.
  • The increasing volume of available genetic data presents opportunities and challenges for analysis.

Framework:

  • Exploration of identity-by-descent (IBD) estimation using marker data subsets.
  • Utilizing score statistics that incorporate covariates or combine linkage and association information.
  • Investigating methods for significance estimation, including simulation and relatedness correction.

Implementation:

  • Optimized IBD estimation through marker subsetting.
  • Inheritance vector estimation provides IBD estimates and uncertainty.
  • Score statistics demonstrated comparable performance to standard methods with reduced computation.
  • Formal combination of linkage and association testing shows promise.

Implications:

  • Computational constraints arise with increased data, necessitating methodological advancements.
  • Improved significance estimation enhances the reliability of genetic findings.
  • Real-data replication confirmed findings, highlighting the need for robust simulated data validation.
  • Advances are crucial for effective identification of complex-trait genes.