| Title: | The high-dimensional space of human diseases built from diagnosis records and mapped to genetic loci |
| Journal: | Nature Computational Science |
| Published: | 22 May 2023 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/38177845/ |
| DOI: | https://doi.org/10.1038/s43588-023-00453-y |
| URL: | https://www.nature.com/articles/s43588-023-00453-y.pdf |
| Citations: | 1 (1 in last 2 years) as of 8 Aug 2024 |
Publication 7847
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Abstract
Human diseases are traditionally studied as singular, independent entities, limiting researchers' capacity to view human illnesses as dependent states in a complex, homeostatic system. Here, using time-stamped clinical records of over 151 million unique Americans, we construct a disease representation as points in a continuous, high-dimensional space, where diseases with similar etiology and manifestations lie near one another. We use the UK Biobank cohort, with half a million participants, to perform a genome-wide association study of newly defined human quantitative traits reflecting individuals' health states, corresponding to patient positions in our disease space. We discover 116 genetic associations involving 108 genetic loci and then use ten disease constellations resulting from clustering analysis of diseases in the embedding space, as well as 30 common diseases, to demonstrate that these genetic associations can be used to robustly predict various morbidities.</p>
20 Authors
- Gengjie Jia
- Yu Li
- Xue Zhong
- Kanix Wang
- Milton Pividori
- Rabab Alomairy
- Aniello Esposito
- Hatem Ltaief
- Chikashi Terao
- Masato Akiyama
- Koichi Matsuda
- David E. Keyes
- Hae Kyung Im
- Takashi Gojobori
- Yoichiro Kamatani
- Michiaki Kubo
- Nancy J. Cox
- James Evans
- Xin Gao
- Andrey Rzhetsky
Enabling scientific discoveries that improve human health