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Related Concept Videos

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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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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: Jul 30, 2025

Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information
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Mapping Exposome Derived Phenotypes into SNOMED Codes.

Christopher Hawthorne1, Luis Marco Ruiz2, Guillermo Lopez Campos1

  • 1Wellcome-Wolfson Institute for Experimental Medicine, Queen's University Belfast, Belfast, UK.

Studies in Health Technology and Informatics
|May 19, 2023
PubMed
Summary
This summary is machine-generated.

This study links human exposures (the exposome) to health outcomes using text mining. It successfully mapped most Human Phenotype Ontology terms to clinical codes, aiding exposomic and clinical data integration.

Keywords:
BioinformaticsExposomeHuman Phenotype OntologySNOMED-CTprecision medicine

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Area of Science:

  • Environmental Health
  • Bioinformatics
  • Clinical Informatics

Background:

  • Human phenotypes reflect health status, influenced by genetics and environmental exposures (the human exposome).
  • The human exposome encompasses diverse physical and socioeconomic factors.
  • Integrating exposomic data with clinical information is crucial for understanding health and disease.

Purpose of the Study:

  • To develop a method for integrating exposomic data with clinical information.
  • To map Human Phenotype Ontology (HPO) terms associated with exposome factors to standardized clinical codes.
  • To create a proof-of-concept for enhanced clinical data analysis.

Main Methods:

  • Utilized text mining techniques to identify Human Phenotype Ontology terms linked to exposome factors.
  • Mapped retrieved HPO terms to SNOMED clinical codes.
  • Developed a proof-of-concept approach for data integration.

Main Results:

  • Retrieved 1295 and 1903 HPO terms associated with exposome factors.
  • Successfully mapped 83% and 90% of these HPO terms to SNOMED codes.
  • Demonstrated a feasible approach for integrating exposomic and clinical data.

Conclusions:

  • Text mining effectively links exposome factors to human phenotypes.
  • High percentages of HPO terms can be mapped to SNOMED, enabling clinical actionability.
  • The developed approach facilitates the integration of exposomic and clinical data for research and practice.