| Title: | Principled distillation of UK Biobank phenotype data reveals underlying structure in human variation. |
| Journal: | Nature Human Behaviour |
| Published: | 4 Jul 2024 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/38965376/ |
| DOI: | https://doi.org/10.1038/s41562-024-01909-5 |
Publication 11710
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Abstract
Data within biobanks capture broad yet detailed indices of human variation, but biobank-wide insights can be difficult to extract due to complexity and scale. Here, using large-scale factor analysis, we distill hundreds of variables (diagnoses, assessments and survey items) into 35 latent constructs, using data from unrelated individuals with predominantly estimated European genetic ancestry in UK Biobank. These factors recapitulate known disease classifications, disentangle elements of socioeconomic status, highlight the relevance of psychiatric constructs to health and improve measurement of pro-health behaviours. We go on to demonstrate the power of this approach to clarify genetic signal, enhance discovery and identify associations between underlying phenotypic structure and health outcomes. In building a deeper understanding of ways in which constructs such as socioeconomic status, trauma, or physical activity are structured in the dataset, we emphasize the importance of considering the interwoven nature of the human phenome when evaluating public health patterns.</p>
19 Authors
- Caitlin E Carey
- Rebecca Shafee
- Robbee Wedow
- Amanda Elliott
- Duncan S Palmer
- John Compitello
- Masahiro Kanai
- Liam Abbott
- Patrick Schultz
- Konrad J Karczewski
- Samuel C Bryant
- Caroline M Cusick
- Claire Churchhouse
- Daniel P Howrigan
- Daniel King
- George Davey Smith
- Benjamin M Neale
- Raymond K Walters
- Elise B Robinson
1 Application
| Application ID | Title |
|---|---|
| 31063 | Methodological extensions to estimate genetic heritability and shared risk factors for phenotypes of the UK Biobank |
Enabling scientific discoveries that improve human health