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

15.2K
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...
15.2K

You might also read

Related Articles

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

Sort by
Same author

PRED-TMSdeep: Prediction of Transmembrane Topology and Signal Peptides Using Deep Learning.

Biology·2026
Same author

Genomic characterization and preclinical evaluation of the candidate probiotic strain <i>Lactococcus cremoris</i> FBMS_5810.

Frontiers in microbiology·2026
Same author

Transcriptomic Landscape and Regulatory Pathways of Drought Response in Rice (<i>Oryza sativa</i> L.): A Meta-Analysis of Microarray and RNA-Seq Data.

International journal of molecular sciences·2026
Same author

De-novo assembly of 82 bacterial genomes using Nanopore sequencing and prediction of biosynthetic capacity.

Scientific data·2026
Same author

PYRAMA: an open-source tool for advanced meta-analysis of genome wide association studies.

Bioinformatics (Oxford, England)·2026
Same author

<i>Humulus lupulus</i> (Hop)-Derived Chemical Compounds Present Antiproliferative Activity on Various Cancer Cell Types: A Meta-Regression Based Panoramic Meta-Analysis.

Pharmaceuticals (Basel, Switzerland)·2025

Related Experiment Video

Updated: Jan 7, 2026

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

4.7K

fastMETA: a fast and efficient tool for multivariate meta-analysis of GWAS.

Georgios A Manios1, Dionysios Kandylas1, Athanasios Kylonis1

  • 1Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece.

Frontiers in Genetics
|January 5, 2026
PubMed
Summary

fastMETA offers a computationally efficient framework for multivariate meta-analysis of Genome-Wide Association Studies (GWAS) summary statistics. This novel approach enables researchers to efficiently explore pleiotropy and complex trait relationships in large-scale genetic studies.

Keywords:
GWASmeta-analysismultiple traitsmultivariatepleiotropy

More Related Videos

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

3.7K
Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images SDM-PSI
06:26

Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images SDM-PSI

Published on: November 27, 2019

76.8K

Related Experiment Videos

Last Updated: Jan 7, 2026

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

4.7K
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

3.7K
Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images SDM-PSI
06:26

Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images SDM-PSI

Published on: November 27, 2019

76.8K

Area of Science:

  • Genetics and Genomics
  • Statistical Bioinformatics
  • Computational Biology

Background:

  • Genome-Wide Association Studies (GWAS) identify genetic loci for complex traits but often lack statistical power.
  • Traditional univariate meta-analysis methods analyze traits individually, risking the discovery of pleiotropy and trait correlations.
  • Existing multivariate methods can be computationally intensive, limiting their application in large-scale genetic analyses.

Purpose of the Study:

  • To introduce fastMETA, a novel, computationally efficient framework for multivariate meta-analysis of GWAS summary statistics.
  • To address the limitations of univariate analyses by enabling the exploration of pleiotropy and complex trait relationships.
  • To provide a scalable and robust tool for next-generation genomic meta-analyses.

Main Methods:

  • fastMETA implements an adaptation of the marginal method of moments (MmoM) for computational efficiency.
  • It offers three estimation strategies: classical MmoM, Pearson correlation-based, and an SNP-aggregated correlation matrix approach.
  • The framework was benchmarked against existing multivariate meta-analysis packages using real and synthetic datasets.

Main Results:

  • fastMETA demonstrated 15-20x faster runtimes compared to existing methods while maintaining high concordance.
  • Applications showed successful replication of pleiotropic effects and near-identical results to published findings in complex diseases.
  • The method proved robust, even when within-study correlations were unavailable, highlighting its flexibility for large-scale GWAS.

Conclusions:

  • fastMETA provides a practical, scalable, and computationally efficient solution for multivariate meta-analysis of GWAS.
  • The framework facilitates the efficient exploration of pleiotropy and complex trait genetic architectures.
  • Its open-source Python implementation and web service lower adoption barriers, supporting deeper insights into multifactorial diseases.