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Evaluating and sharing global genetic ancestry in biomedical datasets.

Olivier Harismendy1,2, Jihoon Kim1, Xiaojun Xu1

  • 1Health Department of Biomedical Informatics, University of California, San Diego, La Jolla, California, USA.

Journal of the American Medical Informatics Association : JAMIA
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Summary

This study introduces a framework to add genetic ancestry data to public human subject datasets. This improves the study of genetic diversity and its role in diseases and traits.

Keywords:
ancestrydiversitygeneticssharing

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Area of Science:

  • Genetics
  • Bioinformatics
  • Population Health

Background:

  • Phenotype-genotype associations are crucial for understanding human diseases.
  • Public molecular datasets often lack genetic ancestry information, hindering research.
  • Self-reported race/ethnicity is insufficient for detailed genetic ancestry analysis.

Purpose of the Study:

  • To develop a framework for enriching public datasets with genetic ancestry information.
  • To create a continental-level genetic diversity score from molecular data.
  • To enhance data discoverability and usability for genetic research.

Main Methods:

  • Developed an analytical framework to infer genetic ancestry from molecular data.
  • Calculated a genetic diversity score at continental resolution.
  • Applied the framework to The Cancer Genome Atlas (TCGA) datasets via DataMed.

Main Results:

  • Successfully enriched TCGA metadata with genetic ancestry information.
  • Demonstrated the framework's utility in assessing genetic diversity within datasets.
  • Provided a method for data repositories to offer genetic diversity metrics.

Conclusions:

  • The proposed framework improves the characterization of genetic diversity in public datasets.
  • This approach facilitates more accurate phenotype-genotype association studies.
  • Standardizing genetic diversity reporting enhances the utility of human genetics research data.