Predicting molecular mechanisms of hereditary diseases by using their tissue-selective manifestation

GTEx Consortium, Eyal Simonovsky, Moran Sharon, Maya Ziv, Omry Mauer

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


How do aberrations in widely expressed genes lead to tissue-selective hereditary diseases? Previous attempts to answer this question were limited to testing a few candidate mechanisms. To answer this question at a larger scale, we developed “Tissue Risk Assessment of Causality by Expression” (TRACE), a machine learning approach to predict genes that underlie tissue-selective diseases and selectivity-related features. TRACE utilized 4,744 biologically interpretable tissue-specific gene features that were inferred from heterogeneous omics datasets. Application of TRACE to 1,031 disease genes uncovered known and novel selectivity-related features, the most common of which was previously overlooked. Next, we created a catalog of tissue-associated risks for 18,927 protein-coding genes ( As proof-of-concept, we prioritized candidate disease genes identified in 48 rare-disease patients. TRACE ranked the verified disease gene among the patient's candidate genes significantly better than gene prioritization methods that rank by gene constraint or tissue expression. Thus, tissue selectivity combined with machine learning enhances genetic and clinical understanding of hereditary diseases.

Original languageEnglish
Article numbere11407
Number of pages20
JournalMolecular Systems Biology
Issue number2023
StatePublished - 26 May 2023


  • data integration
  • genomic medicine
  • machine learning
  • omics
  • tissue selectivity

ASJC Scopus subject areas

  • Information Systems
  • Biochemistry, Genetics and Molecular Biology (all)
  • Immunology and Microbiology (all)
  • Agricultural and Biological Sciences (all)
  • Computational Theory and Mathematics
  • Applied Mathematics


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