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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.
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Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...
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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,...
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Related Experiment Video

Updated: May 5, 2026

Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information
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Phenotype Information Retrieval for Existing GWAS Studies.

Neda Alipanah1, Ko-Wei Lin, Vinay Venkatesh

  • 1Division of Biomedical Informatics, School of Medicine, University of California San Diego La Jolla, CA, USA.

AMIA Joint Summits on Translational Science Proceedings. AMIA Joint Summits on Translational Science
|December 5, 2013
PubMed
Summary

This study standardizes phenotype variables from Genome Wide Association Studies (GWAS) by creating a semantic ontology. This improves data retrieval and analysis across diverse genetic studies.

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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:

  • Bioinformatics
  • Genomics
  • Data Science

Background:

  • The database of Genotypes and Phenotypes (dbGaP) contains valuable Genome Wide Association Study (GWAS) data.
  • Phenotype variables within dbGaP are not harmonized, hindering cross-study analysis.
  • Standardization of phenotype data is crucial for advancing genetic research.

Purpose of the Study:

  • To develop a method for standardizing phenotype variables across different GWAS datasets.
  • To create a semantically-driven ontology for improved data retrieval and analysis.
  • To demonstrate the utility of the proposed ontology in enhancing information discovery.

Main Methods:

  • Extracted and enriched variable descriptions using domain knowledge.
  • Computed semantic distances between variables and applied clustering techniques.
  • Utilized domain experts for cluster auditing and annotation.
  • Constructed a semantically-driven Genotypes and Phenotypes (sdGaP) ontology using UMLS.
  • Implemented a density measure (DM) for semantic similarity-based information retrieval.

Main Results:

  • Successfully classified similar phenotype variables based on semantic distances.
  • Developed the sdGaP ontology, integrating domain knowledge and semantic relationships.
  • Demonstrated improved information retrieval capabilities using the sdGaP ontology in a case study.
  • Showcased the potential of semantic metrics like DM for data exploration.

Conclusions:

  • The proposed method effectively standardizes phenotype variables for GWAS data.
  • The sdGaP ontology enhances data discoverability and semantic querying.
  • This approach facilitates more comprehensive and accurate analysis of genetic and phenotypic information.