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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...
Genome Annotation and Assembly03:36

Genome Annotation and Assembly

The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
Genomics02:02

Genomics

Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...

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Updated: May 16, 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

Semantically enabling a genome-wide association study database.

Tim Beck1, Robert C Free, Gudmundur A Thorisson

  • 1Department of Genetics, University of Leicester, University Road, Leicester, UK. tb143@leicester.ac.uk.

Journal of Biomedical Semantics
|December 19, 2012
PubMed
Summary
This summary is machine-generated.

We developed a method to annotate genome-wide association study (GWAS) data using ontologies for better data sharing and cross-species comparisons. This approach enhances data integration with the Semantic Web for future research.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Semantic Web technologies

Background:

  • Genome-wide association studies (GWAS) generate vast amounts of data, but challenges remain in data reuse and interchange.
  • Existing resources like GWAS Central need standardized methods for phenotype data organization.
  • The Semantic Web offers potential for integrating GWAS data and contributing to Linked Data.

Purpose of the Study:

  • To develop a methodology for applying phenotype annotations to GWAS data.
  • To enable efficient data retrieval and cross-species phenotype comparisons.
  • To ensure compatibility with Semantic Web standards for enhanced data integration.

Main Methods:

  • Utilized Medical Subject Headings (MeSH) for broad trait description and Human Phenotype Ontology (HPO) for granular phenotypic abnormalities.
  • Mapped diseases in MeSH to HPO to infer associated phenotypic abnormalities.
  • Published a subset of GWAS data as RDF nanopublications to leverage Semantic Web standards.

Main Results:

  • Identified MeSH and HPO as suitable ontologies for describing human phenotypes at various granularities.
  • Successfully mapped diseases to phenotypic abnormalities, enabling richer data annotation.
  • Demonstrated the feasibility of cross-species phenotype comparisons using the developed annotation layer.

Conclusions:

  • A novel methodology for annotating GWAS data with phenotype information has been presented.
  • The approach facilitates cross-species genotype and phenotype comparisons and Semantic Web integration.
  • The publication of GWAS data as nanopublications opens new avenues for data exploration within the Linked Data web.