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

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)...

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Published inMolecular systems biology Vol. 19; no. 8; pp. e11407 - n/a
Main Authors Simonovsky, Eyal, Sharon, Moran, Ziv, Maya, Mauer, Omry, Hekselman, Idan, Jubran, Juman, Vinogradov, Ekaterina, Argov, Chanan M, Basha, Omer, Kerber, Lior, Yogev, Yuval, Segrè, Ayellet V, Im, Hae Kyung, Birk, Ohad, Rokach, Lior, Yeger‐Lotem, Esti
Format Journal Article
LanguageEnglish
Published London Nature Publishing Group UK 08.08.2023
EMBO Press
John Wiley and Sons Inc
Springer Nature
Subjects
Consortia
data integration
Datasets
Decision trees
Deep learning
Disease
EMBO10
EMBO16
EMBO22
Gene expression
Genes
Genetic disorders
genomic medicine
Hereditary diseases
Humans
Learning algorithms
Life Sciences
Machine Learning
Molecular modelling
Mutation
omics
Ontology
Physiology
Proteins
Questions
Risk assessment
Selectivity
Systems Biology
tissue selectivity
Tissues
genomic medicine
omics
machine learning
tissue selectivity
data integration
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Abstract 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 ( https://netbio.bgu.ac.il/trace/ ). 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. Synopsis An interpretable machine‐learning framework predicts disease genes for tissue‐selective hereditary diseases. The framework highlights known and novel tissue‐selectivity features and enhances genetic diagnosis of rare‐disease patients. An interpretable machine‐learning (ML) framework uses thousands of gene features to predict tissue‐associated disease genes. ML models highlight known and novel tissue‐selectivity mechanisms. An online catalogue of tissue‐associated risks for 18,927 protein‐coding genes in eight tissues is presented. The framework and catalogue enhance genetic diagnosis of rare‐disease patients. Graphical Abstract An interpretable machine‐learning framework predicts disease genes for tissue‐selective hereditary diseases. The framework highlights known and novel tissue‐selectivity features and enhances genetic diagnosis of rare‐disease patients.
AbstractList 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 (https://netbio.bgu.ac.il/trace/). 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.
Abstract 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 ( https://netbio.bgu.ac.il/trace/ ). 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. Synopsis image An interpretable machine‐learning framework predicts disease genes for tissue‐selective hereditary diseases. The framework highlights known and novel tissue‐selectivity features and enhances genetic diagnosis of rare‐disease patients. An interpretable machine‐learning (ML) framework uses thousands of gene features to predict tissue‐associated disease genes. ML models highlight known and novel tissue‐selectivity mechanisms. An online catalogue of tissue‐associated risks for 18,927 protein‐coding genes in eight tissues is presented. The framework and catalogue enhance genetic diagnosis of rare‐disease patients.
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 ( https://netbio.bgu.ac.il/trace/ ). 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. An interpretable machine‐learning framework predicts disease genes for tissue‐selective hereditary diseases. The framework highlights known and novel tissue‐selectivity features and enhances genetic diagnosis of rare‐disease patients.
Abstract 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 ( https://netbio.bgu.ac.il/trace/ ). 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.
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 (https://netbio.bgu.ac.il/trace/). 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. Synopsis An interpretable machine‐learning framework predicts disease genes for tissue‐selective hereditary diseases. The framework highlights known and novel tissue‐selectivity features and enhances genetic diagnosis of rare‐disease patients. An interpretable machine‐learning (ML) framework uses thousands of gene features to predict tissue‐associated disease genes. ML models highlight known and novel tissue‐selectivity mechanisms. An online catalogue of tissue‐associated risks for 18,927 protein‐coding genes in eight tissues is presented. The framework and catalogue enhance genetic diagnosis of rare‐disease patients. An interpretable machine‐learning framework predicts disease genes for tissue‐selective hereditary diseases. The framework highlights known and novel tissue‐selectivity features and enhances genetic diagnosis of rare‐disease patients.
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 ( https://netbio.bgu.ac.il/trace/ ). 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. Synopsis An interpretable machine‐learning framework predicts disease genes for tissue‐selective hereditary diseases. The framework highlights known and novel tissue‐selectivity features and enhances genetic diagnosis of rare‐disease patients. An interpretable machine‐learning (ML) framework uses thousands of gene features to predict tissue‐associated disease genes. ML models highlight known and novel tissue‐selectivity mechanisms. An online catalogue of tissue‐associated risks for 18,927 protein‐coding genes in eight tissues is presented. The framework and catalogue enhance genetic diagnosis of rare‐disease patients. Graphical Abstract An interpretable machine‐learning framework predicts disease genes for tissue‐selective hereditary diseases. The framework highlights known and novel tissue‐selectivity features and enhances genetic diagnosis of rare‐disease patients.
Author Yogev, Yuval
Jubran, Juman
Rokach, Lior
Vinogradov, Ekaterina
Sharon, Moran
Im, Hae Kyung
Kerber, Lior
Segrè, Ayellet V
Birk, Ohad
Simonovsky, Eyal
Basha, Omer
Yeger‐Lotem, Esti
Ziv, Maya
Hekselman, Idan
Argov, Chanan M
Mauer, Omry
AuthorAffiliation 4 The Broad Institute of MIT and Harvard Cambridge MA USA
7 Department of Software & Information Systems Engineering Ben‐Gurion University of the Negev Beer Sheva Israel
5 Section of Genetic Medicine, Department of Medicine The University of Chicago Chicago IL USA
6 The National Institute for Biotechnology in the Negev Ben‐Gurion University of the Negev Beer Sheva Israel
3 Ocular Genomics Institute, Massachusetts Eye and Ear Harvard Medical School Boston MA USA
1 Department of Clinical Biochemistry and Pharmacology Ben‐Gurion University of the Negev Beer Sheva Israel
2 Morris Kahn Laboratory of Human Genetics and the Genetics Institute at Soroka Medical Center, Faculty of Health Sciences Ben Gurion University of the Negev Beer Sheva Israel
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/37232043$$D View this record in MEDLINE/PubMed
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Sat Oct 05 04:40:58 EDT 2024
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Tue Nov 05 01:38:19 EST 2024
IsDoiOpenAccess true
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Issue 8
Keywords genomic medicine
omics
machine learning
tissue selectivity
data integration
Language English
License Attribution
2023 The Authors. Published under the terms of the CC BY 4.0 license.
This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
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Notes Membership of the GTEx Consortium appears in the Appendix
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SSID ssj0038182
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Snippet How do aberrations in widely expressed genes lead to tissue‐selective hereditary diseases? Previous attempts to answer this question were limited to testing a...
How do aberrations in widely expressed genes lead to tissue-selective hereditary diseases? Previous attempts to answer this question were limited to testing a...
Abstract How do aberrations in widely expressed genes lead to tissue‐selective hereditary diseases? Previous attempts to answer this question were limited to...
Abstract How do aberrations in widely expressed genes lead to tissue‐selective hereditary diseases? Previous attempts to answer this question were limited to...
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StartPage e11407
SubjectTerms Consortia
data integration
Datasets
Decision trees
Deep learning
Disease
EMBO10
EMBO16
EMBO22
Gene expression
Genes
Genetic disorders
genomic medicine
Hereditary diseases
Humans
Learning algorithms
Life Sciences
Machine Learning
Molecular modelling
Mutation
omics
Ontology
Physiology
Proteins
Questions
Risk assessment
Selectivity
Systems Biology
tissue selectivity
Tissues
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Title Predicting molecular mechanisms of hereditary diseases by using their tissue‐selective manifestation
URI https://link.springer.com/article/10.15252/msb.202211407
https://onlinelibrary.wiley.com/doi/abs/10.15252%2Fmsb.202211407
https://www.ncbi.nlm.nih.gov/pubmed/37232043
https://www.proquest.com/docview/2847222320
https://search.proquest.com/docview/2820030065
https://pubmed.ncbi.nlm.nih.gov/PMC10407743
https://doaj.org/article/2f83bd8335064584b8465fdd36d265bc
Volume 19
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