Expanding the Human Ancestry Ontology to include under-represented populations and ethnicities for broader utility in annotations
Abstract
Successful discovery, integration and reuse of data relies on the availability of rich, well-structured and machine-readable metadata to describe every aspect of the data, from sample sources to collection processes to experimental protocols. The use of standardised terminologies to express concepts in a harmonised fashion lies at the core of high-quality data annotation, increasing the FAIRness of the data, facilitating data integration and promoting reproducibility. Here, we describe the Human Ancestry Ontology (HANCESTRO), originally developed to improve standardised reporting of genetic ancestry genomic resources such as the NHGRI-EBI GWAS Catalog and the Human Cell Atlas through high-level population descriptors, and more recently expanded to include diverse and previously under-represented populations in genomics and genetics research. HANCESTRO provides a framework for population descriptors that includes both ancestry based on the analysis of genetic information and self-reported ethnicity, which is based on social and cultural factors that don't necessarily align with genetic populations. By enabling the accurate and interoperable representation of population-related data, it promotes inclusive, representative and reproducible science.
Related
- Native American Ancestry Is Associated with NAT2 Haplotype and Inferred Metabolizer Phenotype Variation in Latin American Populations
- A New Family of Electron-Bifurcating Enzymes Represented by Ferredoxin- and NAD-Dependent Glycerol-3-Phosphate Oxidoreductase.
- DR-GEM enables self-supervised machine learning for single-cell embeddings and annotations
- DBCLS BioHackathon 2026 report: RDF publication of Marchantia polymorpha gene annotations, and agent-assisted preparation of DDBJ submissions
- An Annotation Consistency Checker for Evidence-Based Evaluation of Untargeted LC-MS/MS Metabolite Annotations
BibTeX
@misc{caron2026expanding,
title = {{Expanding the Human Ancestry Ontology to include under-represented populations and ethnicities for broader utility in annotations}},
author = {Anita Caron and Melek Chaouch and Zoë May Pendlington and Helen Parkinson and Danielle Welter},
howpublished = {arXiv},
year = {2026},
month = aug,
doi = {10.48550/arxiv.2608.28030},
eprint = {2608.28030},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2608.28030},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Expanding the Human Ancestry Ontology to include under-represented populations and ethnicities for broader utility in annotations
AU - Anita Caron
AU - Melek Chaouch
AU - Zoë May Pendlington
AU - Helen Parkinson
AU - Danielle Welter
PY - 2026
DA - 2026/08/28
PB - arXiv
DO - 10.48550/arxiv.2608.28030
UR - https://arxiv.org/abs/2608.28030
AB - Successful discovery, integration and reuse of data relies on the availability of rich, well-structured and machine-readable metadata to describe every aspect of the data, from sample sources to collection processes to experimental protocols. The use of standardised terminologies to express concepts in a harmonised fashion lies at the core of high-quality data annotation, increasing the FAIRness of the data, facilitating data integration and promoting reproducibility. Here, we describe the Human Ancestry Ontology (HANCESTRO), originally developed to improve standardised reporting of genetic ancestry genomic resources such as the NHGRI-EBI GWAS Catalog and the Human Cell Atlas through high-level population descriptors, and more recently expanded to include diverse and previously under-represented populations in genomics and genetics research. HANCESTRO provides a framework for population descriptors that includes both ancestry based on the analysis of genetic information and self-reported ethnicity, which is based on social and cultural factors that don't necessarily align with genetic populations. By enabling the accurate and interoperable representation of population-related data, it promotes inclusive, representative and reproducible science.
ER -