The high-dimensional space of human diseases built from diagnosis records and mapped to genetic loci

Gengjie Jia; Yu Li; Xue Zhong; Kanix Wang; Milton Pividori; Rabab M. 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

doi:10.1038/s43588-023-00453-y

The high-dimensional space of human diseases built from diagnosis records and mapped to genetic loci

Gengjie Jia, Yu Li, Xue Zhong, Kanix Wang, Milton Pividori, Rabab M. 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

Research output: Contribution to journal › Article › peer-review

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.

Original language	English (US)
Journal	Nature Computational Science
DOIs	https://doi.org/10.1038/s43588-023-00453-y
State	Published - May 22 2023

Bibliographical note

KAUST Repository Item: Exported on 2023-05-25
Acknowledged KAUST grant number(s): FCC/1/1976-26-01, FCS/1/4102-02-01, REI/1/0018-01-01, REI/1/4473-01-01
Acknowledgements: We thank E. Gannon and M. Rzhetsky for comments on earlier versions of this manuscript, and many volunteers whose data are used in this study. This work was funded by the DARPA Big Mechanism program under ARO contract W911NF1410333, by National Institutes of Health grants R01HL122712, 1P50MH094267 and U01HL108634-01, and by a gift from L. and K. Dauten to A.R. Additional support came from King Abdullah University of Science and Technology, award numbers FCS/1/4102-02-01, FCC/1/1976-26-01, REI/1/0018-01-01 and REI/1/4473-01-01 to X.G.; and came from Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, and the Central Public-interest Scientific Institution Basal Research Fund (11024316000202300001) to G.J. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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10.1038/s43588-023-00453-y

https://repository.kaust.edu.sa/bitstream/10754/692028/1/s43588-023-00453-y%20%281%29.pdf

Cite this

Jia, G., Li, Y., Zhong, X., Wang, K., Pividori, M., Alomairy, R. M., Esposito, A., Ltaief, H., Terao, C., Akiyama, M., Matsuda, K., Keyes, D. E., Im, H. K., Gojobori, T., Kamatani, Y., Kubo, M., Cox, N. J., Evans, J., Gao, X., & Rzhetsky, A. (2023). The high-dimensional space of human diseases built from diagnosis records and mapped to genetic loci. Nature Computational Science. https://doi.org/10.1038/s43588-023-00453-y

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title = "The high-dimensional space of human diseases built from diagnosis records and mapped to genetic loci",

abstract = "Human diseases are traditionally studied as singular, independent entities, limiting researchers{\textquoteright} 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{\textquoteright} 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.",

author = "Gengjie Jia and Yu Li and Xue Zhong and Kanix Wang and Milton Pividori and Alomairy, {Rabab M.} and Aniello Esposito and Hatem Ltaief and Chikashi Terao and Masato Akiyama and Koichi Matsuda and Keyes, {David E.} and Im, {Hae Kyung} and Takashi Gojobori and Yoichiro Kamatani and Michiaki Kubo and Cox, {Nancy J.} and James Evans and Xin Gao and Andrey Rzhetsky",

note = "KAUST Repository Item: Exported on 2023-05-25 Acknowledged KAUST grant number(s): FCC/1/1976-26-01, FCS/1/4102-02-01, REI/1/0018-01-01, REI/1/4473-01-01 Acknowledgements: We thank E. Gannon and M. Rzhetsky for comments on earlier versions of this manuscript, and many volunteers whose data are used in this study. This work was funded by the DARPA Big Mechanism program under ARO contract W911NF1410333, by National Institutes of Health grants R01HL122712, 1P50MH094267 and U01HL108634-01, and by a gift from L. and K. Dauten to A.R. Additional support came from King Abdullah University of Science and Technology, award numbers FCS/1/4102-02-01, FCC/1/1976-26-01, REI/1/0018-01-01 and REI/1/4473-01-01 to X.G.; and came from Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, and the Central Public-interest Scientific Institution Basal Research Fund (11024316000202300001) to G.J. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.",

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month = may,

day = "22",

doi = "10.1038/s43588-023-00453-y",

language = "English (US)",

journal = "Nature Computational Science",

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Jia, G, Li, Y, Zhong, X, Wang, K, Pividori, M, Alomairy, RM, Esposito, A, Ltaief, H, Terao, C, Akiyama, M, Matsuda, K, Keyes, DE, Im, HK, Gojobori, T, Kamatani, Y, Kubo, M, Cox, NJ, Evans, J, Gao, X & Rzhetsky, A 2023, 'The high-dimensional space of human diseases built from diagnosis records and mapped to genetic loci', Nature Computational Science. https://doi.org/10.1038/s43588-023-00453-y

TY - JOUR

T1 - The high-dimensional space of human diseases built from diagnosis records and mapped to genetic loci

AU - Jia, Gengjie

AU - Li, Yu

AU - Zhong, Xue

AU - Wang, Kanix

AU - Pividori, Milton

AU - Alomairy, Rabab M.

AU - Esposito, Aniello

AU - Ltaief, Hatem

AU - Terao, Chikashi

AU - Akiyama, Masato

AU - Matsuda, Koichi

AU - Keyes, David E.

AU - Im, Hae Kyung

AU - Gojobori, Takashi

AU - Kamatani, Yoichiro

AU - Kubo, Michiaki

AU - Cox, Nancy J.

AU - Evans, James

AU - Gao, Xin

AU - Rzhetsky, Andrey

N1 - KAUST Repository Item: Exported on 2023-05-25 Acknowledged KAUST grant number(s): FCC/1/1976-26-01, FCS/1/4102-02-01, REI/1/0018-01-01, REI/1/4473-01-01 Acknowledgements: We thank E. Gannon and M. Rzhetsky for comments on earlier versions of this manuscript, and many volunteers whose data are used in this study. This work was funded by the DARPA Big Mechanism program under ARO contract W911NF1410333, by National Institutes of Health grants R01HL122712, 1P50MH094267 and U01HL108634-01, and by a gift from L. and K. Dauten to A.R. Additional support came from King Abdullah University of Science and Technology, award numbers FCS/1/4102-02-01, FCC/1/1976-26-01, REI/1/0018-01-01 and REI/1/4473-01-01 to X.G.; and came from Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, and the Central Public-interest Scientific Institution Basal Research Fund (11024316000202300001) to G.J. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

PY - 2023/5/22

Y1 - 2023/5/22

N2 - 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.

AB - 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.

UR - http://hdl.handle.net/10754/692028

UR - https://www.nature.com/articles/s43588-023-00453-y

U2 - 10.1038/s43588-023-00453-y

DO - 10.1038/s43588-023-00453-y

M3 - Article

C2 - 38177845

SN - 2662-8457

JO - Nature Computational Science

JF - Nature Computational Science

ER -

The high-dimensional space of human diseases built from diagnosis records and mapped to genetic loci

Abstract

Bibliographical note

UN SDGs

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