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Inferring multi-organ genetic connections using imaging and clinical data through Mendelian randomization

Nature Biomedical Engineering (2025)Cite this article

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Abstract

Understanding the complex relationships among major clinical outcomes and the interplay among multiple organs remains a considerable challenge. By using imaging phenotypes, we can characterize the functional and structural architecture of major human organs. Mendelian randomization (MR) provides a valuable framework for uncovering robust relationships between phenotypes by leveraging genetic variants as instrumental variables. Here we conduct a systematic multi-organ MR analysis involving 402 imaging traits and 372 clinical outcomes. Our analysis reveals 184 MR associations for 58 diseases and 56 imaging traits across various organs, tissues and systems, including the brain, heart, liver, kidney, lung, pancreas, spleen, adipose tissue and skeletal system. We identify intra-organ MR connections, such as the putative bidirectional genetic links between Alzheimer’s disease and brain function, and interorgan associations, such as heart diseases and brain health. Metabolic disorders, such as diabetes, show genetically rooted putative MR effects across multiple organs. These findings shed light on the genetic links spanning multiple organs, providing targets for future mechanistic follow-up for clinical disease research.

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Fig. 1: Overview of study design and findings.
Fig. 2: Selected MR associations between clinical outcomes and brain imaging traits.
Fig. 3: Selected MR associations between clinical endpoints and heart imaging traits.
Fig. 4: Selected MR associations between clinical endpoints and abdominal imaging traits.
Fig. 5: Selected MR associations between clinical endpoints and skeleton imaging traits.

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Data availability

We used summary-level GWAS data in this study, which can be obtained from the FinnGen project (https://www.finngen.fi/en/access_results), BIG-KP (https://bigkp.org/) and Heart-KP (https://heartkp.org/), and project-specific resources are detailed in refs. 3 and 8.

Code availability

We used publicly available software and tools. Our analysis code is available on Zenodo at https://doi.org/10.5281/zenodo.16518650 (ref. 149).

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Acknowledgements

Research reported in this publication was supported by the National Institute of Mental Health under award number R01MH136055 (B.Z.) and National Institute on Aging under award numbers RF1AG082938 (B.Z. and H.Z.) and R01AG085581 (B.Z. and H.Z.). Assistance for this project was provided by the UNC Intellectual and Developmental Disabilities Research Center (NICHD; P50 HD103573; H.Z.), and by grants RF1AG098697 (H.Z.), R01AR082684 (H.Z.), OT2OD038045-01 (H.Z.) and K01AG095286 (T.L.). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The study has also been partially supported by funding from the Purdue University Statistics Department, Department of Statistics and Data Science at the University of Pennsylvania, Wharton Dean’s Research Fund, Analytics at Wharton, Wharton AI and Analytics Initiative, Perelman School of Medicine CCEB Innovation Center Grant and the University Research Foundation Grant (B.Z.). This research has been conducted using summary-level data from the UKB study and the FinnGen research project. We thank the individuals who participated in the UKB and FinnGen studies for their contribution and the research teams for their efforts in collecting, processing and disseminating these datasets. We thank the research computing groups at the University of North Carolina at Chapel Hill, Purdue University and the Wharton School of the University of Pennsylvania for providing computational resources and support that have contributed to these research results.

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Authors and Affiliations

  1. Department of Statistics, Purdue University, West Lafayette, IN, USA

    Juan Shu, Carlos Copana, Xiaochen Yang, Yujue Li & Bowei Xi

  2. Department of Nuclear Medicine, First Hospital of Shanxi Medical University, Taiyuan, China

    Rong Zheng

  3. Division of Cardiovascular Medicine, Hospital of the University of Pennsylvania, Philadelphia, PA, USA

    Julio Chirinos

  4. Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Julio Chirinos

  5. Department of Computer Science, Purdue University, West Lafayette, IN, USA

    Bingxuan Li & Xiyao Wang

  6. Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA, USA

    Zirui Fan, Yilin Yang & Bingxin Zhao

  7. Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Tengfei Li

  8. Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Tengfei Li

  9. Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Hongtu Zhu

  10. Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Hongtu Zhu

  11. Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Hongtu Zhu

  12. Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Hongtu Zhu

  13. Applied Mathematics and Computational Science Graduate Group, University of Pennsylvania, Philadelphia, PA, USA

    Bingxin Zhao

  14. Center for AI and Data Science for Integrated Diagnostics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Bingxin Zhao

  15. Penn Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Bingxin Zhao

  16. Population Aging Research Center, University of Pennsylvania, Philadelphia, PA, USA

    Bingxin Zhao

  17. Institute for Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA

    Bingxin Zhao

  18. Penn Center for Eye-Brain Health, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Bingxin Zhao

  19. Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Bingxin Zhao

Authors
  1. Juan Shu
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  2. Rong Zheng
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Contributions

J.S. and B.Z. designed the study. J.S., C.C., B.L., Z.F., X.Y., Y.Y, X.W. and Y.L. analysed the data. R.Z. and J.C. helped interpret the findings. B.X., T.L. and H.Z. provided feedback on the results. J.S. and B.Z. wrote the paper with feedback from all authors.

Corresponding authors

Correspondence to Hongtu Zhu or Bingxin Zhao.

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Nature Biomedical Engineering thanks Marios Georgakis and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Supplementary Note, Figs. 1–14 and legends for Tables 1–17.

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Supplementary Tables 1–17 provide data descriptions and detailed MR results.

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Shu, J., Zheng, R., Chirinos, J. et al. Inferring multi-organ genetic connections using imaging and clinical data through Mendelian randomization. Nat. Biomed. Eng (2025). https://doi.org/10.1038/s41551-025-01554-x

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