Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

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...
Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
Genetic Variation01:25

Genetic Variation

Genetic variation is the diversity in DNA sequences found among individuals of the same species. This diversity is crucial for a species' survival because it helps organisms adapt to environmental changes. Genetic variation begins with fertilization, where an egg and sperm cell merge. Each of these cells carries 23 chromosomes, up to 46 in the fertilized egg. Chromosomes are long DNA strands that contain genes, the basic units of heredity.
Genes exist in different versions called alleles, which...
Variability: Analysis01:11

Variability: Analysis

Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
Principles of Pharmacogenetics: Types of Genetic Variants01:27

Principles of Pharmacogenetics: Types of Genetic Variants

The human genome is over 99.9% identical between individuals, yet genetic differences exist at millions of bases. The human genome contains approximately 3 million variant positions per individual, many of which are heterozygous, contributing to genetic diversity and individual traits. Genetic variations include single-nucleotide polymorphisms (SNPs), insertions, deletions, and copy number variations (CNVs).SNPs, the most common variation, involve single-base changes in DNA. These can be...

You might also read

Related Articles

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

Sort by
Same author

Modulation of genetic associations with serum urate levels by body-mass-index in humans.

PloS one·2015
Same author

Prevalence of refractive error in Europe: the European Eye Epidemiology (E(3)) Consortium.

European journal of epidemiology·2015
Same author

Genome-wide association analysis on five isolated populations identifies variants of the HLA-DOA gene associated with white wine liking.

European journal of human genetics : EJHG·2015
Same author

Genome of The Netherlands population-specific imputations identify an ABCA6 variant associated with cholesterol levels.

Nature communications·2015
Same author

Rare variants in γ-aminobutyric acid type A receptor genes in rolandic epilepsy and related syndromes.

Annals of neurology·2015
Same author

Age- and sex-specific causal effects of adiposity on cardiovascular risk factors.

Diabetes·2015
Same journal

Mutational scanning reveals substrate-assisted autoregulation of the WNT destruction complex.

Nature genetics·2026
Same journal

Spatial transcriptomic analyses highlight distinct erythroid niches in mice and humans.

Nature genetics·2026
Same journal

Building up pangenome analysis block by block.

Nature genetics·2026
Same journal

Mutations in splicing factor gene U2AF1 rescue defective oncogene splicing in KRAS-mutant cancers.

Nature genetics·2026
Same journal

Assessing the effect of immune surveillance on clonal expansions in the blood.

Nature genetics·2026
Same journal

Improved heritability partitioning and enrichment analyses using summary statistics with graphREML.

Nature genetics·2026
See all related articles

Related Experiment Video

Updated: May 18, 2026

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)
11:35

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

Published on: August 21, 2016

Rapid variance components-based method for whole-genome association analysis.

Gulnara R Svishcheva1, Tatiana I Axenovich, Nadezhda M Belonogova

  • 1Institute of Cytology and Genetics, Siberian Division of the Russian Academy of Sciences, Novosibirsk, Russia.

Nature Genetics
|September 18, 2012
PubMed
Summary
This summary is machine-generated.

GRAMMAR-Gamma offers a fast, computationally efficient method for genome-wide association studies (GWAS). This novel approach provides accurate SNP effect estimates and is suitable for large human cohorts and whole-genome sequencing studies.

More Related Videos

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

Related Experiment Videos

Last Updated: May 18, 2026

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)
11:35

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

Published on: August 21, 2016

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
  • Computational Biology

Background:

  • Genome-wide association studies (GWAS) are computationally intensive with large datasets.
  • Existing variance component tests struggle with hundreds of thousands of genetic markers.

Purpose of the Study:

  • To introduce GRAMMAR-Gamma, a fast, two-step variance components method for GWAS.
  • To provide an analytical approximation within a score test framework for computational efficiency.

Main Methods:

  • Developed GRAMMAR-Gamma, a variance components-based two-step method.
  • Utilized simulated and real human GWAS data for validation.
  • Employed a score test approach for analytical approximation.

Main Results:

  • GRAMMAR-Gamma demonstrates unbiased SNP effect estimates.
  • Achieved statistical power comparable to likelihood ratio test methods.
  • Exhibits near-theoretical minimum computational complexity.
  • Linear dependence of running time on sample size, unlike quadratic dependency in other methods.

Conclusions:

  • GRAMMAR-Gamma significantly enhances computational efficiency in GWAS.
  • The method is suitable for large human cohorts and whole-genome resequencing studies.
  • Offers a powerful and scalable alternative for genetic association testing.