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Related Concept Videos

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Epistasis Analysis

Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
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Related Experiment Video

Updated: May 24, 2026

Phage Phenomics: Physiological Approaches to Characterize Novel Viral Proteins
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Published on: June 11, 2015

Using PhenX measures to identify opportunities for cross-study analysis.

Huaqin Pan1, Kimberly A Tryka, Daniel J Vreeman

  • 1RTI International, Research Triangle Park, NC 27709, USA. hpan@rti.org

Human Mutation
|March 15, 2012
PubMed
Summary
This summary is machine-generated.

The PhenX Toolkit now links human subject research variables to the NIH

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Area of Science:

  • Genomics
  • Biomedical Informatics
  • Human Subject Research

Background:

  • The PhenX Toolkit offers standardized, low-burden measures for human subject research.
  • The National Institutes of Health's database of Genotypes and Phenotypes (dbGaP) archives research data, including genome-wide association studies.
  • dbGaP mandates data dictionaries for study variable submissions.

Purpose of the Study:

  • To demonstrate the feasibility of mapping PhenX measures to variables within dbGaP.
  • To enhance data discoverability and enable cross-study analysis by linking PhenX measures to dbGaP study variables.
  • To improve data exchange through standardization and interoperability.

Main Methods:

  • Variables from 16 dbGaP-deposited studies were mapped to PhenX measures.
  • PhenX measures, protocols, and variables were modeled using Logical Observation Identifiers Names and Codes (LOINC®).
  • PhenX domains and measures were represented in the Cancer Data Standards Registry and Repository (caDSR).

Main Results:

  • A proof-of-concept mapping of 16 dbGaP studies to PhenX measures was successfully completed.
  • Future functionality will allow searching dbGaP using PhenX identifiers to find comparable variables.
  • Integration with LOINC® and caDSR facilitates data standardization.

Conclusions:

  • Mapping PhenX measures to dbGaP variables enhances data utility and facilitates cross-study analysis.
  • Standardization through LOINC® and caDSR improves data exchange and interoperability.
  • This initiative opens new avenues for comparative research using existing datasets.