A standardized framework for representation of ancestry data in genomics studies, with application to the NHGRI-EBI GWAS Catalog

By Joannella Morales, Danielle Welter, Emily Bowler, M. Cerezo, Laura W. Harris, A. McMahon, Peggy Hall, Heather Junkins, Annalisa Milano, Emma Hastings and 8 more. 2018

202 citations recorded by the source corpus.

The accurate description of ancestry is essential to interpret, access, and integrate human genomics data, and to ensure that these benefit individuals from all ancestral backgrounds. However, there are no established guidelines for the representation of ancestry information. Here we describe a framework for the accurate and standardized description of sample ancestry, and validate it by application to the NHGRI-EBI GWAS Catalog. We confirm known biases and gaps in diversity, and find that African and Hispanic or Latin American ancestry populations contribute a disproportionately high number of associations. It is our hope that widespread adoption of this framework will lead to improved analysis, interpretation, and integration of human genomics data.

Subfields: Genetics, Computational Biology & Bioinformatics

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Citations in the corpus

Machine learning and artificial intelligence research for patient benefit: 20 critical questions on transparency, replicability, ethics, and effectiveness (2020, background): “68 The issue of data that are not truly representative of the entire target population is particularly important, 69 because it highlights the importance of fairness considerations at every point in the project cycle.”
The MRC IEU OpenGWAS data infrastructure (2020, background): “Where possible, studies are mapped to existing ontologies, for example the trait name is mapped to an experimental factor ontology term (EFO) 38 and sample ancestry is mapped to the human ancestry ontology 39 .”