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Genetic Similarity Clustering Using the UK Biobank as a Reference Dataset
Ngoc-Quynh Le1,2, Puya Gharahkhani1, Stuart MacGregor1
1Statistical Genetics Lab, QIMR Berghofer Medical Research Institute, Herston, Brisbane, QLD, Australia.
Summary
This study used UK Biobank data to cluster genetically similar individuals, creating a more diverse reference panel. This approach enhances genetic studies for underrepresented populations, improving health equity.
Area of Science:
- Genomics
- Population Genetics
- Bioinformatics
Background:
- Diverse genetic data is vital for disease research and health equity.
- Current reference panels lack population diversity and sufficient sample sizes.
- UK Biobank offers a large dataset for improving population representation.
Purpose of the Study:
- To evaluate the UK Biobank as a reference for clustering genetically similar individuals.
- To enhance population representation and mitigate bias in genetic studies.
- To develop a practical method for inferring population labels using biobank data.
Main Methods:
- Combined UK Biobank country of birth and ethnic background data with genetic information.
- Utilized a random forest model trained on genetic principal components to assign population labels.
- Validated the model using 1000 Genomes and CARTaGENE biobank data.
Main Results:
- Identified 19 diverse reference populations globally, exceeding the diversity of existing datasets like 1000 Genomes.
- Achieved medium to high precision and recall for most identified populations.
- Demonstrated high precision (81.1%) and recall (97.0%) for a Middle Eastern reference sample in CARTaGENE data.
Conclusions:
- Clustering genetically similar individuals using UK Biobank data creates a more representative reference panel.
- This approach can facilitate downstream genetic analyses, including genomewide association studies and polygenic risk scores.
- The method supports research in underrepresented populations, promoting health equity.
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