Principled distillation of UK Biobank phenotype data reveals underlying structure in human variation

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 () and Elise B. Robinson
Additional contact information
Caitlin E. Carey: Broad Institute of MIT and Harvard
Rebecca Shafee: Broad Institute of MIT and Harvard
Robbee Wedow: Broad Institute of MIT and Harvard
Amanda Elliott: Broad Institute of MIT and Harvard
Duncan S. Palmer: Broad Institute of MIT and Harvard
John Compitello: Broad Institute of MIT and Harvard
Masahiro Kanai: Broad Institute of MIT and Harvard
Liam Abbott: Broad Institute of MIT and Harvard
Patrick Schultz: Broad Institute of MIT and Harvard
Konrad J. Karczewski: Broad Institute of MIT and Harvard
Samuel C. Bryant: Broad Institute of MIT and Harvard
Caroline M. Cusick: Broad Institute of MIT and Harvard
Claire Churchhouse: Broad Institute of MIT and Harvard
Daniel P. Howrigan: Broad Institute of MIT and Harvard
Daniel King: Broad Institute of MIT and Harvard
George Davey Smith: Broad Institute of MIT and Harvard
Benjamin M. Neale: Broad Institute of MIT and Harvard
Raymond K. Walters: Broad Institute of MIT and Harvard
Elise B. Robinson: Broad Institute of MIT and Harvard

Nature Human Behaviour, 2024, vol. 8, issue 8, 1599-1615

Abstract: 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.

Date: 2024
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DOI: 10.1038/s41562-024-01909-5

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