Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Genome-wide Association Studies-GWAS

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...

Sex Linked Disorders

Sex Linked Disorders

Like autosomes, sex chromosomes contain a variety of genes necessary for normal body function. When a mutation in one of these genes results in biological deficits, the disorder is considered sex-linked.

Dihybrid Crosses

Dihybrid Crosses

Genetic Lingo

Genetic Lingo

Pharmacogenomics: Identification of New Drug Targets

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...

Ligand Binding and Linkage

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...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

The hazards of genotype imputation when mapping disease susceptibility variants.

Genome biology·2024

Same author

The hazards of genotype imputation in chromosomal regions under selection: A case study using the Lactase gene region.

Annals of human genetics·2021

Same author

New Insights Into Mitochondrial Dysfunction at Disease Susceptibility Loci in the Development of Type 2 Diabetes.

Frontiers in endocrinology·2021

Same author

Eugenics history: university geneticists respond.

Nature·2020

Same author

A novel human pain insensitivity disorder caused by a point mutation in ZFHX2.

Brain : a journal of neurology·2017

Same author

World-wide distributions of lactase persistence alleles and the complex effects of recombination and selection.

Human genetics·2017

Search research articles

Related Experiment Video

Updated: Jul 10, 2026

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

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

Published on: July 27, 2021

Linkage disequilibrium maps and disease-association mapping.

Nikolas Maniatis¹

¹Human Genetics Division, Southampton General Hospital, UK.

Methods in Molecular Biology (Clifton, N.J.)

|November 7, 2007

Summary

This study introduces a novel association mapping method for identifying disease genes using linkage disequilibrium (LD) and single nucleotide polymorphisms (SNPs). The approach leverages metric LD maps and composite likelihoods for enhanced genetic analysis.

More Related Videos

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)

Published on: August 21, 2016

Related Experiment Videos

Last Updated: Jul 10, 2026

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

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

Published on: July 27, 2021

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)

Published on: August 21, 2016

Area of Science:

Human Genetics
Statistical Genetics
Genomic Association Studies

Background:

Association mapping is crucial for identifying genes linked to complex diseases.
Linkage disequilibrium (LD) between single nucleotide polymorphisms (SNPs) and disease loci is key for population-based studies.
The discovery of millions of SNPs has accelerated interest in association mapping.

Purpose of the Study:

To present a novel association mapping method utilizing metric LD maps.
To employ a composite likelihood approach for combining SNP information.
To incorporate a parameter for causal polymorphism location within the model.

Main Methods:

Utilizes metric LD maps quantified in LD units.
Employs a composite likelihood approach to aggregate data from single SNP tests.
Applies a statistical model that includes a parameter for the causal variant's position.

Main Results:

Demonstrates a proof-of-principle application on a small genomic region.
Discusses the method's potential applicability to large-scale datasets.
Highlights the utility of combining SNP data through composite likelihoods.

Conclusions:

The presented method offers a robust approach for disease gene association mapping.
Metric LD maps and composite likelihoods enhance the power of SNP-based association studies.
This method holds promise for future large-scale genetic analyses of complex diseases.