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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...
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Evolutionary Relationships through Genome Comparisons02:54

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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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Updated: Jan 10, 2026

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
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GWASHub: An Automated Cloud-Based Platform for Genome-Wide Association Study Meta-Analysis.

Nicholas Sunderland, Drew Hite, Patrick Smadbeck

    Medrxiv : the Preprint Server for Health Sciences
    |November 24, 2025
    PubMed
    Summary
    This summary is machine-generated.

    GWASHub is a secure, cloud-based platform that streamlines the complex process of genome-wide association studies (GWAS) meta-analysis. It automates data handling, quality control, and analysis, enabling researchers to accelerate genetic discovery.

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    Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
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    Area of Science:

    • Genetics and Genomics
    • Bioinformatics
    • Computational Biology

    Background:

    • Genome-wide association studies (GWAS) meta-analyses aggregate data from millions of participants across cohorts to increase statistical power for genetic discovery.
    • The growing volume of genomic biobanks and focus on detailed analyses necessitate scalable infrastructures for data handling, quality control (QC), and meta-analysis.
    • Existing workflows often present challenges in managing numerous summary statistic files and associated metadata, increasing the burden on researchers.

    Purpose of the Study:

    • To develop a secure, cloud-based platform, GWASHub, for efficient curation, processing, and meta-analysis of GWAS summary statistics.
    • To provide a user-friendly interface and robust backend infrastructure to manage large-scale GWAS data and workflows.
    • To enhance reproducibility and reduce errors in GWAS meta-analysis, facilitating downstream research and clinical translation.

    Main Methods:

    • Development of GWASHub, a secure cloud-based platform utilizing AWS MySQL database and S3 block storage.
    • Implementation of features including private project spaces, automated file harmonization and data validation, metadata capture, customizable variant QC, and meta-analysis.
    • User interaction via an intuitive web interface built on Nuxt.js, with scalable analysis job distribution to AWS compute resources.

    Main Results:

    • GWASHub provides automated QC dashboards for manual review and a meta-analysis module for processed datasets.
    • Secure project spaces facilitate collaboration among researchers with different roles within a consortium.
    • Individual datasets and meta-analysis results are downloadable by authorized users, ensuring data accessibility.

    Conclusions:

    • GWASHub addresses a critical need for a scalable, secure, and user-friendly platform for large-scale GWAS meta-analyses.
    • The platform's flexible architecture supports ongoing development and incorporation of new QC and meta-analysis procedures.
    • By simplifying complex data management, GWASHub aims to accelerate insights into the genetic architecture of complex traits.