Nature Communications (Feb 2023)
Hypothesis-free phenotype prediction within a genetics-first framework
- Chang Lu,
- Jan Zaucha,
- Rihab Gam,
- Hai Fang,
- Ben Smithers,
- Matt E. Oates,
- Miguel Bernabe-Rubio,
- James Williams,
- Natalie Zelenka,
- Arun Prasad Pandurangan,
- Himani Tandon,
- Hashem Shihab,
- Raju Kalaivani,
- Minkyung Sung,
- Adam J. Sardar,
- Bastian Greshake Tzovoras,
- Davide Danovi,
- Julian Gough
Affiliations
- Chang Lu
- MRC Laboratory of Molecular Biology, Cambridge Biomedical Campus
- Jan Zaucha
- Department of Computer Science, University of Bristol
- Rihab Gam
- MRC Laboratory of Molecular Biology, Cambridge Biomedical Campus
- Hai Fang
- Department of Computer Science, University of Bristol
- Ben Smithers
- Department of Computer Science, University of Bristol
- Matt E. Oates
- Department of Computer Science, University of Bristol
- Miguel Bernabe-Rubio
- Centre for Gene Therapy and Regenerative Medicine, King’s College London, Guy’s Hospital
- James Williams
- Centre for Gene Therapy and Regenerative Medicine, King’s College London, Guy’s Hospital
- Natalie Zelenka
- Department of Computer Science, University of Bristol
- Arun Prasad Pandurangan
- MRC Laboratory of Molecular Biology, Cambridge Biomedical Campus
- Himani Tandon
- MRC Laboratory of Molecular Biology, Cambridge Biomedical Campus
- Hashem Shihab
- Department of Computer Science, University of Bristol
- Raju Kalaivani
- MRC Laboratory of Molecular Biology, Cambridge Biomedical Campus
- Minkyung Sung
- MRC Laboratory of Molecular Biology, Cambridge Biomedical Campus
- Adam J. Sardar
- Department of Computer Science, University of Bristol
- Bastian Greshake Tzovoras
- Université de Paris, INSERM U1284, Center for Research and Interdisciplinarity (CRI)
- Davide Danovi
- Centre for Gene Therapy and Regenerative Medicine, King’s College London, Guy’s Hospital
- Julian Gough
- MRC Laboratory of Molecular Biology, Cambridge Biomedical Campus
- DOI
- https://doi.org/10.1038/s41467-023-36634-6
- Journal volume & issue
-
Vol. 14,
no. 1
pp. 1 – 14
Abstract
Systematically predicting phenotypes or disease risks based on the information of an individual’s genetic variation remains an unsolved challenge. Here, the authors develop a knowledge-based approach for performing and evaluating hypothesis-free phenotype prediction directly from a human genome.
