Genetic risk factors frequently affect multiple common human diseases, providing insight into shared pathophysiological pathways and opportunities for therapeutic development. However, systematic identification of genetic profiles of disease risk is limited by the availability of both comprehensive clinical data on population-scale cohorts and the lack of suitable statistical methodology that can handle the scale of and differential power inherent in multi-phenotype data. Here, we develop a disease-agnostic approach to cluster the genetic risk profiles for 3,025 genome-wide independent loci across 19,155 disease classification codes from 320,644 participants in the UK Biobank, representing a large and heterogeneous population. We identify 339 distinct disease association profiles and use multiple approaches to link clusters to the underlying biological pathways. We show how clusters can decompose the variance and covariance in risk for disease, thereby identifying underlying biological processes and their impact. We demonstrate the use of clusters in defining disease relationships and their potential in informing therapeutic strategies.
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The data shown in this paper are available at https://www.treewas.org/.
The code for the TreeWAS analysis is available at https://github.com/mcveanlab/TreeWASDir.
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This research has been conducted using the UK Biobank Resource (application no. 10625). This work uses data provided by patients and collected by the National Health Service (NHS) as part of their care and support. Computation used the Biomedical Research Computing facility, a joint development between the Wellcome Centre for Human Genetics and the Big Data Institute supported by the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC). The views expressed are those of the author(s) and not necessarily those of the NHS, NIHR or the Department of Health. This research has been conducted with the support of the Wellcome Trust (grant nos. 100956/Z/13/Z and 090532/Z/09/Z to G.M. and grant no. 100308/Z/12/Z to L.F.). L.F. was also supported by the Danish National Research Foundation, Takeda, the Medical Research Council (grant no. MC_UU_12010/3), the Oak Foundation (grant no. OCAY-15-520) and the NIHR Oxford BRC. C.A.D. was supported by the Wellcome Trust/Royal Society (grant no. 204290/Z/16/Z). G.M. was supported by the Li Ka Shing Foundation.
G.M. is a director of and shareholder in GENOMICS plc. He is also a partner in Peptide Groove LLP. The other authors declare no competing interests.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended Data Fig. 1 Comparison of estimated log10(BFtree) in the two implementations of TreeWAS for 25,000 SNPs in the hospital episode statistics data set.
Pearson correlation between the two analysis is noted in the text.
Extended Data Fig. 2 Derivation of an allele frequency-specific log10(BFtree) significance threshold to maintain a false positive rate below 1%.
The threshold for each allele frequency bin was set to be at least log10(BFtree) = 5.
Extended Data Fig. 3 Concordance of TreeWAS analysis results in the two sources of phenotype data from the UK Biobank, self-reported (SR) data-field 20002 and hospitalisation in-patient records (HES) data-fields 41142 and 41078.
We observed high concordance of the observed evidence of association (log10(BFtree)) for 3,025 independent SNPs and 25,640 GWAS catalog SNPs, with Pearson’s correlation of 0.87 and 0.56, respectively.
Extended Data Fig. 4 Hierarchical clustering of 3,025 SNP risk profiles across the ICD-10 classification tree in the UK Biobank HES data set.
Y-axis is the distance between pairs. Blue line is at height value 0 and red line at height value -5.
For all pairwise comparisons we computed the |D’| statistic and the Jaccard index (see Section Disease ontology analyses in the Supplementary Note).
Extended Data Fig. 6 Schematic illustration of the model that is used to motivate the focal phenotype analysis.
We hypothesize that a set of variants, G, that influences risk for a common set of disease phenotypes, Z, can be acting through a single underlying biological process, X. Typically, we are unlikely to have direct measurement of this variable, though of those disease codes that are mediated by this latent variable, some are likely to be closer to it than others, where closer means a larger absolute value for the regression coefficient of the latent variable on the observed outcome (See Supplementary Note).
Extended Data Fig. 7 Principal component analysis of genome-wide genotype data in the UK Biobank cohort.
Each plot corresponds to a projection into two dimensions of the principal component analysis. Individuals in blue were determined to be of recent and genome-wide British Isles ancestry.
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Cortes, A., Albers, P.K., Dendrou, C.A. et al. Identifying cross-disease components of genetic risk across hospital data in the UK Biobank. Nat Genet 52, 126–134 (2020). https://doi.org/10.1038/s41588-019-0550-4
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