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Integrating phenotypic features and tissue-specific information to prioritize disease genes

  • Research Paper
  • Special Focus on Biomolecular Network Analysis and Application
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Abstract

Prioritization of candidate disease genes is crucial for improving medical care, and is one of the fundamental challenges in the post-genomic era. In recent years, different network-based methods for gene prioritization are proposed. Previous studies on gene prioritization show that tissue-specific protein-protein interaction (PPI) networks built by integrating PPIs with tissue-specific gene expression profiles can perform better than tissue-na¨ıve global PPI network. Based on the observations that diseases with similar phenotypes are likely to have common related genes, and genes associated with the same phenotype tend to interact with each other, we propose a method to prioritize disease genes based on a heterogeneous network built by integrating phenotypic features and tissue-specific information. In this heterogeneous network, the PPI network is built by integrating phenotypic features with a tissue-specific PPI network, and the disease network consists of the diseases that are associated with the same phenotype and tissue as the query disease. To determine the impacts of these two factors on gene prioritization, we test three typical network-based prioritization methods on heterogeneous networks consisting of combinations of different PPIs and disease networks built with or without phenotypic features and tissue-specific information. We also compare the proposed method with other tissuespecific networks. The results of case studies reveals that integrating phenotypic features with a tissue-specific PPI network improves the prioritization results. Moreover, the disease networks generated using our method not only show comparable performance with the widely used disease similarity dataset of 5080 human diseases, but are also effective for diseases that are not in the dataset.

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References

  1. Ritchie M D, Holzinger E R, Li R, et al. Methods of integrating data to uncover genotype-phenotype interactions. Nat Rev Genet, 2015, 16: 85–97

    Article  Google Scholar 

  2. Moreau Y, Tranchevent L-C. Computational tools for prioritizing candidate genes: boosting disease gene discovery. Nat Rev Genet, 2012, 13: 523–536

    Article  Google Scholar 

  3. Piro R M, Di Cunto F. Computational approaches to disease-gene prediction: rationale, classification and successes. FEBS J, 2012, 279: 678–696

    Article  Google Scholar 

  4. Wang X J, Gulbahce N, Yu H Y. Network-based methods for human disease gene prediction. Brief Funct Genomics, 2011, 10: 280–293

    Article  Google Scholar 

  5. Lan W, Wang J X, Li M, et al. Computational approaches for prioritizing candidate disease genes based on PPI networks. Tsinghua Sci Technol, 2015, 20: 500–512

    Article  MathSciNet  Google Scholar 

  6. Wu X B, Jiang R, Zhang M, et al. Network-based global inference of human disease genes. Mol Syst Biol, 2008, 4: 189

    Article  Google Scholar 

  7. Vanunu O, Magger O, Ruppin E, et al. Associating genes and protein complexes with disease via network propagation. PLoS Comput Biol, 2010, 6: e1000641

    Article  MathSciNet  Google Scholar 

  8. Li Y J, Patra J C. Genome-wide inferring gene-phenotype relationship by walking on the heterogeneous network. Bioinformatics, 2010, 26: 1219–1224

    Article  Google Scholar 

  9. Wang J X, Peng X Q, Peng W, et al. Dynamic protein interaction network construction and applications. Proteomics, 2014, 14: 338–352

    Article  Google Scholar 

  10. Gaulton K J, Mohlke K L, Vision T J. A computational system to select candidate genes for complex human traits. Bioinformatics, 2007, 23: 1132–1140

    Article  Google Scholar 

  11. Schlicker A, Lengauer T, Albrecht M. Improving disease gene prioritization using the semantic similarity of Gene Ontology terms. Bioinformatics, 2010, 26: i561–i567

    Article  Google Scholar 

  12. Linghu B, Snitkin E S, Hu Z, et al. Genome-wide prioritization of disease genes and identification of disease-disease associations from an integrated human functional linkage network. Genome Biol, 2009, 10: R91

    Article  Google Scholar 

  13. Franke L, van Bakel H, Fokkens L, et al. Reconstruction of a functional human gene network, with an application for prioritizing positional candidate genes. Amer J Hum Genet, 2006, 78: 1011–1025

    Article  Google Scholar 

  14. Robinson P N, Webber C. Phenotype ontologies and cross-species analysis for translational research. PLoS Genet, 2014, 10: e1004268

    Article  Google Scholar 

  15. Hwang S, Kim E, Yang S, et al. MORPHIN: a web tool for human disease research by projecting model organism biology onto a human integrated gene network. Nucl Acids Res, 2014, 42: W147–W153

    Article  Google Scholar 

  16. Winter E E, Goodstadt L, Ponting C P. Elevated rates of protein secretion, evolution, and disease among tissue-specific genes. Genome Res, 2004, 14: 54–61

    Article  Google Scholar 

  17. Chao E C, Lipkin S M. Molecular models for the tissue specificity of DNA mismatch repair-deficient carcinogenesis. Nucl Acids Res, 2006, 34: 840–852

    Article  Google Scholar 

  18. Magger O, Waldman Y Y, Ruppin E, et al. Enhancing the prioritization of disease-causing genes through tissue specific protein interaction networks. PLoS Comput Biol, 2012, 8: e1002690

    Article  Google Scholar 

  19. Prasad T S K, Goel R, Kandasamy K, et al. Human protein reference database2009 update. Nucl Acids Res, 2009, 37: D767–D772

    Article  Google Scholar 

  20. Barshir R, Basha O, Eluk A, et al. The tissuenet database of human tissue protein-protein interactions. Nucl Acids Res, 2013, 41: D841–D844

    Article  Google Scholar 

  21. Su A I, Wiltshire T, Batalov S, et al. A gene atlas of the mouse and human protein-encoding transcriptomes. Proc Nat Acad Sci USA, 2004, 101: 6062–6067

    Article  Google Scholar 

  22. Berglund L, Björling E, Oksvold P, et al. A genecentric human protein atlas for expression profiles based on antibodies. Mol Cell Proteom, 2008, 7: 2019–2027

    Article  Google Scholar 

  23. Bradley R K, Merkin J, Lambert N J, et al. Alternative splicing of RNA triplets is often regulated and accelerates proteome evolution. PLoS Biol, 2012, 10: e1001229

    Article  Google Scholar 

  24. Chatr-aryamontri A, Breitkreutz B-J, Oughtred R, et al. The BioGRID interaction database: 2015 update. Nucl Acids Res, 2015, 43: D470–D478

    Article  Google Scholar 

  25. Salwinski L, Miller C S, Smith A J, et al. The database of interacting proteins: 2004 update. Nucl Acids Res, 2004, 32: D449–D451

    Article  Google Scholar 

  26. Orchard S, Ammari M, Aranda B, et al. The MIntAct project—IntAct as a common curation platform for 11 molecular interaction databases. Nucl Acids Res, 2014, 42: D358–D363

    Article  Google Scholar 

  27. Licata L, Briganti L, Peluso D, et al. MINT, the molecular interaction database: 2012 update. Nucl Acids Res, 2012, 40: D857–D861

    Article  Google Scholar 

  28. Barshir R, Shwartz O, Smoly I Y, et al. Comparative analysis of human tissue interactomes reveals factors leading to tissue-specific manifestation of hereditary diseases. PLoS Comput Biol, 2014, 10: e1003632

    Article  Google Scholar 

  29. Greene C S, Krishnan A, Wong A K, et al. Understanding multicellular function and disease with human tissue-specific networks. Nat Genet, 2015, 47: 569–576

    Article  Google Scholar 

  30. Li M, Zhang J Y, Liu Q, et al. Prediction of disease-related genes based on weighted tissue-specific networks by using DNA methylation. BMC Med Genomics, 2014, 7: S4

    Article  Google Scholar 

  31. Ganegoda G U, Wang J X, Wu F-X, et al. Prediction of disease genes using tissue-specified gene-gene network. BMC Syst Biol, 2014, 8: S3

    Article  Google Scholar 

  32. Jacquemin T, Jiang R. Walking on a tissue-specific disease-protein-complex heterogeneous network for the discovery of disease-related protein complexes. BioMed Res Int, 2013, 2013: 455–458

    Article  Google Scholar 

  33. Robinson P, Krawitz P, Mundlos S. Strategies for exome and genome sequence data analysis in disease-gene discovery projects. Clin Genet, 2011, 80: 127–132

    Article  Google Scholar 

  34. Köhler S, Bauer S, Horn D, et al. Walking the interactome for prioritization of candidate disease genes. Amer J Hum Genet, 2008, 82: 949–958

    Article  Google Scholar 

  35. van Driel M A, Bruggeman J, Vriend G, et al. A text-mining analysis of the human phenome. Eur J Hum Genet, 2006, 14: 535–542

    Article  Google Scholar 

  36. Brunner H G, van Driel M A. From syndrome families to functional genomics. Nat Rev Genet, 2004, 5: 545–551

    Article  Google Scholar 

  37. Yang H, Robinson P N, Wang K. Phenolyzer: phenotype-based prioritization of candidate genes for human diseases. Nat Methods, 2015, 12: 841–843

    Article  Google Scholar 

  38. Javed A, Agrawal S, Ng P C. Phen-Gen: combining phenotype and genotype to analyze rare disorders. Nat Methods, 2014, 11: 935–937

    Article  Google Scholar 

  39. Chen Y, Jiang T, Jiang R. Uncover disease genes by maximizing information flow in the phenome-interactome network. Bioinformatics, 2011, 27: i167–i176

    Article  Google Scholar 

  40. Xie M Q, Hwang T, Kuang R. Prioritizing disease genes by bi-random walk. In: Proceedings of 16th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, Kuala Lumpur, 2012. 292–303

    Chapter  Google Scholar 

  41. Hamosh A, Scott A F, Amberger J S, et al. Online mendelian inheritance in man (OMIM), a knowledgebase of human genes and genetic disorders. Nucl Acids Res, 2005, 33: D514–D517

    Article  Google Scholar 

  42. Lage K, Hansen N T, Karlberg E O, et al. A large-scale analysis of tissue-specific pathology and gene expression of human disease genes and complexes. Proc Nat Acad Sci, 2008, 105: 20870–20875

    Article  Google Scholar 

  43. Basha O, Flom D, Barshir R, et al. MyProteinNet: build up-to-date protein interaction networks for organisms, tissues and user-defined contexts. Nucl Acids Res, 2015, 43: W258–W263

    Article  Google Scholar 

  44. Köhler S, Doelken S C, Mungall C J, et al. The human phenotype ontology project: linking molecular biology and disease through phenotype data. Nucl Acids Res, 2014, 42: D966–D974

    Article  Google Scholar 

  45. Resnik P. Using information content to evaluate semantic similarity in a taxonomy. In: Proceedings of the 14th International Joint Conference on Artificial Intelligence. San Francisco: Morgan Kaufmann Publishers Inc., 1995. 448–453

    Google Scholar 

  46. Schlicker A, Domingues F, Rahnenf¨uhrer J, et al. A new measure for functional similarity of gene products based on Gene Ontology. BMC Bioinform, 2006, 7: 302

    Article  Google Scholar 

  47. Guo X L, Gao L, Wei C S, et al. A computational method based on the integration of heterogeneous networks for predicting disease-gene associations. PLoS ONE, 2011, 6: e24171

    Article  Google Scholar 

  48. Zhou X Z, Menche J, Barabási A-L, et al. Human symptoms-disease network. Nat Commun, 2014, 5: 4212

    Google Scholar 

  49. Goh K-I, Cusick M E, Valle D, et al. The human disease network. Proc Nat Acad Sci, 2007, 104: 8685–8690

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. School of Computer Science and Technology, Xidian University, Xi’an, 710071, China

    Yue Deng, Lin Gao, Xingli Guo & Bingbo Wang

  2. School of Software, Xidian University, Xi’an, 710071, China

    Yue Deng

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  1. Yue Deng
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  2. Lin Gao
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  3. Xingli Guo
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  4. Bingbo Wang
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Correspondence to Lin Gao.

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Deng, Y., Gao, L., Guo, X. et al. Integrating phenotypic features and tissue-specific information to prioritize disease genes. Sci. China Inf. Sci. 59, 070101 (2016). https://doi.org/10.1007/s11432-016-5584-y

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