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...

You might also read

Related Articles

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

Sort by
Same author

Disease risk estimates in V30M variant transthyretin amyloidosis (A-ATTRv) from Mallorca.

Orphanet journal of rare diseases·2023
Same author

A POLD3/BLM dependent pathway handles DSBs in transcribed chromatin upon excessive RNA:DNA hybrid accumulation.

Nature communications·2022
Same author

Association between menopausal hormone therapy, mammographic density and breast cancer risk: results from the E3N cohort study.

Breast cancer research : BCR·2021
Same author

A PCSK9 variant and familial combined hyperlipidaemia.

Journal of medical genetics·2008
Same author

A note on allelic tests in case-control association studies.

Annals of human genetics·2008
Same author

Computing power in case-control association studies through the use of quadratic approximations: application to meta-statistics.

Annals of human genetics·2006

Related Experiment Video

Updated: May 23, 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

Alternative methods for H1 simulations in genome-wide association studies.

V Perduca1, C Sinoquet, R Mourad

  • 1MAP5 - UMR CNRS 8145, Université Paris Descartes, Paris, France. vittorio.perduca @ parisdescartes.fr

Human Heredity
|April 5, 2012
PubMed
Summary

We developed a faster method for simulating phenotypes in genome-wide association studies. This approach enhances statistical power assessment without complex genotype modeling, offering a flexible alternative to existing tools.

More Related Videos

Infinium Assay for Large-scale SNP Genotyping Applications
13:33

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

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 23, 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

Infinium Assay for Large-scale SNP Genotyping Applications
13:33

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

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
  • Statistical genetics
  • Computational biology

Background:

  • Statistical power is crucial for genome-wide association (GWA) studies.
  • Empirical power estimation involves simulating phenotypes under a disease model (H1).
  • Current gold standard methods (e.g., Hapgen) simulate genotypes based on phenotypes.

Purpose of the Study:

  • Introduce a novel, faster approach for simulating phenotypes under H1.
  • Avoids the need for generating new genotypes for each simulation.
  • Provides a flexible and efficient alternative for power assessment in GWA studies.

Main Methods:

  • Developed three algorithms: rejection sampling, Markov chain Monte Carlo (MCMC), and backward sampling.
  • Validated algorithms on simulated and realistic datasets, comparing with Hapgen.
  • Applied the method to a 1000 Genomes Project dataset (629 individuals, 8,048 SNPs on chromosome X) with an additive model and epistasis.

Main Results:

  • All three algorithms yielded consistent results, with backward sampling being significantly faster.
  • The proposed method produced results comparable to Hapgen.
  • Epistatic effects were shown to be significant even with simple marker statistics.
  • GWA study performance is highly dependent on disease prevalence, with higher prevalence improving power.

Conclusions:

  • The developed approach is a viable and faster alternative to Hapgen-type methods.
  • Advantages include no need for complex genotype models (haplotypes, recombination rates).
  • Offers unconstrained disease model selection (SNPs, gene-environment interactions, hybrid models).
  • Algorithms are available in the R package 'waffect'.