Skip to main content
SpringerLink
Log in

Web Tools for the Prioritization of Candidate Disease Genes

  • Protocol
  • First Online:
In Silico Tools for Gene Discovery

Part of the book series: Methods in Molecular Biology ((MIMB,volume 760))

Abstract

Despite increasing sequencing capacity, genetic disease investigation still frequently results in the identification of loci containing multiple candidate disease genes that need to be tested for involvement in the disease. This process can be expedited by prioritizing the candidates prior to testing. Over the last decade, a large number of computational methods and tools have been developed to assist the clinical geneticist in prioritizing candidate disease genes. In this chapter, we give an overview of computational tools that can be used for this purpose, all of which are freely available over the web.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Altshuler, D., Daly, M. J., Lander, E. S. (2008) Genetic mapping in human disease. Science 322, 881–888.

    Article  PubMed  CAS  Google Scholar 

  2. Kann, M. G. (2010) Advances in translational bioinformatics: computational approaches for the hunting of disease genes. Brief Bioinform 11, 96–110.

    Article  PubMed  CAS  Google Scholar 

  3. Oti, M., Brunner, H. G. (2007) The modular nature of genetic diseases. Clin Genet 71, 1–11.

    Article  PubMed  CAS  Google Scholar 

  4. Tiffin, N., Andrade-Navarro, M. A., Perez-Iratxeta, C. (2009) Linking genes to diseases: it’s all in the data. Genome Med 1, 77.

    Article  PubMed  Google Scholar 

  5. van Driel, M. A., Brunner, H. G. (2006) Bioinformatics methods for identifying candidate disease genes. Hum Genomics 2, 429–432.

    PubMed  Google Scholar 

  6. Tranchevent, L., Capdevila, F. B., Nitsch, D., De Moor, B., De Causmaecker, P., Moreau, Y. (2010) A guide to web tools to prioritize candidate genes. Brief Bioinform 11, 1–11.

    Article  Google Scholar 

  7. Yilmaz, S., Jonveaux, P., Bicep, C., et al. (2009) Gene-disease relationship discovery based on model-driven data integration and database view definition. Bioinformatics 25, 230–236.

    Article  PubMed  CAS  Google Scholar 

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

    Article  PubMed  CAS  Google Scholar 

  9. Shriner, D., Baye, T. M., Padilla, M. A., et al. (2008) Commonality of functional annotation: a method for prioritization of candidate genes from genome-wide linkage studies. Nucleic Acids Res 36, e26.

    Article  PubMed  Google Scholar 

  10. Li, Y., Patra, J. C. (2010) Integration of multiple data sources to prioritize candidate genes using discounted rating system. BMC Bioinformatics 11(Suppl 1), S20.

    Article  PubMed  Google Scholar 

  11. McEachin, R. C., Keller, B. J. (2009) Identifying hypothetical genetic influences on complex disease phenotypes. BMC Bioinformatics 10(Suppl 2), S13.

    Article  PubMed  Google Scholar 

  12. Turner, F. S., Clutterbuck, D. R., Semple, C. A. (2003) POCUS: mining genomic sequence annotation to predict disease genes. Genome Biol 4, R75.

    Article  PubMed  Google Scholar 

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

    Article  PubMed  Google Scholar 

  14. Ala, U., Piro, R. M., Grassi, E., et al. (2008) Prediction of human disease genes by human-mouse conserved coexpression analysis. PLoS Comput Biol 4, e1000043.

    Article  PubMed  Google Scholar 

  15. Care, M. A., Bradford, J. R., Needham, C. J., et al. (2009) Combining the interactome and deleterious SNP predictions to improve disease gene identification. Hum Mutat 30, 485–492.

    Article  PubMed  CAS  Google Scholar 

  16. Freudenberg, J., Propping, P. (2002) A similarity-based method for genome-wide prediction of disease-relevant human genes. Bioinformatics 18(Suppl 2), S110–115.

    Article  Google Scholar 

  17. Karni, S., Soreq, H., Sharan, R. (2009) A network-based method for predicting disease-causing genes. J Comput Biol 16, 181–189.

    Article  PubMed  CAS  Google Scholar 

  18. Lage, K., Karlberg, E. O., Storling, Z. M., et al. (2007) A human phenome-interactome network of protein complexes implicated in genetic disorders. Nat Biotechnol 25, 309–316.

    Article  PubMed  CAS  Google Scholar 

  19. Li, Y., Agarwal, P. (2009) A pathway-based view of human diseases and disease relationships. PLoS One 4, e4346.

    Article  PubMed  Google Scholar 

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

    Article  PubMed  Google Scholar 

  21. Oti, M., Snel, B., Huynen, M. A., Brunner, H. G. (2006) Predicting disease genes using protein-protein interactions. J Med Genet 43, 691–698.

    Article  PubMed  CAS  Google Scholar 

  22. Oti, M., van Reeuwijk, J., Huynen, M. A., Brunner, H. G. (2008) Conserved co-expression for candidate disease gene prioritization. BMC Bioinformatics 9, 208.

    Article  PubMed  Google Scholar 

  23. Tiffin, N., Kelso, J. F., Powell, A. R., et al. (2005) Integration of text- and data-mining using ontologies successfully selects disease gene candidates. Nucleic Acids Res 33, 1544–1552.

    Article  PubMed  CAS  Google Scholar 

  24. Lopez-Bigas, N., Ouzounis, C. A. (2004) Genome-wide identification of genes likely to be involved in human genetic disease. Nucleic Acids Res 32, 3108–3114.

    Article  PubMed  CAS  Google Scholar 

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

    Article  PubMed  CAS  Google Scholar 

  26. Sadasivam, R. S., Sundar, G., Vaughan, L. K., et al. (2009) Genetic region characterization (Gene RECQuest) – software to assist in identification and selection of candidate genes from genomic regions. BMC Res Notes 2, 201.

    Article  PubMed  Google Scholar 

  27. Aerts, S., Lambrechts, D., Maity, S., et al. (2006) Gene prioritization through genomic data fusion. Nat Biotechnol 24, 537–544.

    Article  PubMed  CAS  Google Scholar 

  28. Hutz, J. E., Kraja, A. T., McLeod, H. L., Province, M. A. (2008) CANDID: a flexible method for prioritizing candidate genes for complex human traits. Genet Epidemiol 32, 779–790.

    Article  PubMed  Google Scholar 

  29. Elbers, C. C., van Eijk, K. R., Franke, L., et al. (2009) Using genome-wide pathway analysis to unravel the etiology of complex diseases. Genet Epidemiol 33, 419–431.

    Article  PubMed  Google Scholar 

  30. Pan, W. (2008) Network-based model weighting to detect multiple loci influencing complex diseases. Hum Genet 124, 225–234.

    Article  PubMed  Google Scholar 

  31. Perry, J. R., McCarthy, M. I., Hattersley, A. T., et al. (2009) Interrogating type 2 diabetes genome-wide association data using a biological pathway-based approach. Diabetes 58, 1463–1467.

    Article  PubMed  CAS  Google Scholar 

  32. Torkamani, A., Schork, N. J. (2009) Pathway and network analysis with high-density allelic association data. Methods Mol Biol 563, 289–301.

    Article  PubMed  CAS  Google Scholar 

  33. Torkamani, A., Topol, E. J., Schork, N. J. (2008) Pathway analysis of seven common diseases assessed by genome-wide association. Genomics 92, 265–272.

    Article  PubMed  CAS  Google Scholar 

  34. Wang, K., Li, M., Bucan, M. (2007) Pathway-Based Approaches for Analysis of Genomewide Association Studies. Am J Hum Genet 81, 1278–1283.

    Article  PubMed  CAS  Google Scholar 

  35. Ashburner, M., Ball, C. A., Blake, J. A., et al. (2000) Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 25, 25–29.

    Article  PubMed  CAS  Google Scholar 

  36. Oti, M., Huynen, M. A., Brunner, H. G. (2008) Phenome connections. Trends Genet 24, 103–106.

    Article  PubMed  CAS  Google Scholar 

  37. Oti, M., Huynen, M. A., Brunner, H. G. (2009) The biological coherence of human phenome databases. Am J Hum Genet 85, 801–808.

    Article  PubMed  CAS  Google Scholar 

  38. Kobayashi, N., Toyoda, T. (2008) Statistical search on the Semantic Web. Bioinformatics 24, 1002–1010.

    Article  PubMed  CAS  Google Scholar 

  39. Elbers, C. C., Onland-Moret, N. C., Franke, L., et al. (2007) A strategy to search for common obesity and type 2 diabetes genes. Trends Endocrinol Metab 18, 19–26.

    Article  PubMed  CAS  Google Scholar 

  40. Teber, E. T., Liu, J. Y., Ballouz, S., et al. (2009) Comparison of automated candidate gene prediction systems using genes implicated in type 2 diabetes by genome-wide association studies. BMC Bioinfo 10(Suppl 1), S69.

    Article  Google Scholar 

  41. Thornblad, T. A., Elliott, K. S., Jowett, J., Visscher, P. M. (2007) Prioritization of positional candidate genes using multiple web-based software tools. Twin Res Hum Genet 10, 861–870.

    Article  PubMed  Google Scholar 

  42. Tiffin, N., Adie, E., Turner, F., et al. (2006) Computational disease gene identification: a concert of methods prioritizes type 2 diabetes and obesity candidate genes. Nucleic Acids Res 34, 3067–3081.

    Article  PubMed  CAS  Google Scholar 

  43. Tiffin, N., Okpechi, I., Perez-Iratxeta, C., et al. (2008) Prioritization of candidate disease genes for metabolic syndrome by computational analysis of its defining phenotypes. Physiol Genomics 35, 55–64.

    Article  PubMed  CAS  Google Scholar 

  44. Thiel, C. T., Horn, D., Zabel, B., et al. (2005) Severely incapacitating mutations in patients with extreme short stature identify RNA-processing endoribonuclease RMRP as an essential cell growth regulator. Am J Hum Genet 77, 795–806.

    Article  PubMed  CAS  Google Scholar 

  45. Sparrow, D. B., Guillen-Navarro, E., Fatkin, D., Dunwoodie, S. L. (2008) Mutation of Hairy-and-Enhancer-of-Split-7 in humans causes spondylocostal dysostosis. Hum Mol Genet 17, 3761–3766.

    Article  PubMed  CAS  Google Scholar 

  46. Tremblay, K., Lemire, M., Potvin, C., et al. (2008) Genes to diseases (G2D) computational method to identify asthma candidate genes, PLoS One 3, e2907.

    Article  PubMed  Google Scholar 

  47. Aerts, S., Vilain, S., Hu, S., et al. (2009) Integrating computational biology and forward genetics in Drosophila. PLoS Genet 5, e1000351.

    Article  PubMed  Google Scholar 

  48. Tranchevent, L. C., Barriot, R., Yu, S., et al. (2008) ENDEAVOUR update: a web resource for gene prioritization in multiple species. Nucleic Acids Res 36, W377–384.

    Article  Google Scholar 

  49. Smedley, D., Haider, S., Ballester, B., et al. (2009) BioMart – biological queries made easy. BMC Genomics 10, 22.

    Article  PubMed  Google Scholar 

  50. Woollard, P. M. (2010) Asking complex questions of the genome without programming. Methods Mol Biol 628, 39–52.

    Article  PubMed  CAS  Google Scholar 

  51. Huang da, W., Sherman, B. T., Lempicki, R. A. (2009) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 4, 44–57.

    Article  PubMed  Google Scholar 

  52. Jensen, L. J., Kuhn, M., Stark, M., et al. (2009) STRING 8 – a global view on proteins and their functional interactions in 630 organisms. Nucleic Acids Res 37, D412–416.

    Article  Google Scholar 

  53. Hoffmann, R., Valencia, A. (2004) A gene network for navigating the literature. Nat Genet 36, 664.

    Article  PubMed  CAS  Google Scholar 

  54. Halling-Brown, M., Shepherd, A. J. (2008) Constructing computational pipelines. Methods Mol Biol 453, 451–470.

    Article  PubMed  CAS  Google Scholar 

  55. Fisher, P., Noyes, H., Kemp, S., et al. (2009) A systematic strategy for the discovery of candidate genes responsible for phenotypic variation. Methods Mol Biol 573, 329–345.

    Article  PubMed  CAS  Google Scholar 

  56. Van Vooren, S., Thienpont, B., Menten, B., et al. (2007) Mapping biomedical concepts onto the human genome by mining literature on chromosomal aberrations. Nucleic Acids Res 35, 2533–2543.

    Article  PubMed  Google Scholar 

  57. Wu, X., Jiang, R., Zhang, M. Q., Li, S. (2008) Network-based global inference of human disease genes. Mol Syst Biol 4, 189.

    Article  PubMed  Google Scholar 

  58. Wu, X., Liu, Q., Jiang, R. (2009) Align human interactome with phenome to identify causative genes and networks underlying disease families. Bioinformatics 25, 98–104.

    Article  PubMed  CAS  Google Scholar 

  59. Hristovski, D., Peterlin, B., Mitchell, J. A., Humphrey, S. M. (2005) Using literature-based discovery to identify disease candidate genes. Int J Med Inform 74, 289–298.

    Article  PubMed  Google Scholar 

  60. Seelow, D., Schwarz, J. M., Schuelke, M. (2008) GeneDistiller--distilling candidate genes from linkage intervals. PLoS One 3, e3874.

    Article  PubMed  Google Scholar 

  61. Yu, W., Wulf, A., Liu, T., et al. (2008) Gene Prospector: an evidence gateway for evaluating potential susceptibility genes and interacting risk factors for human diseases. BMC Bioinformatics 9, 528.

    Article  PubMed  Google Scholar 

  62. Perez-Iratxeta, C., Bork, P., Andrade, M. A. (2002) Association of genes to genetically inherited diseases using data mining. Nat Genet 31, 316–319.

    PubMed  CAS  Google Scholar 

  63. van Driel, M. A., Cuelenaere, K., Kemmeren, P. P., et al. (2003) A new web-based data mining tool for the identification of candidate genes for human genetic disorders. Eur J Hum Genet 11, 57–63.

    Article  PubMed  Google Scholar 

  64. Kohler, S., Bauer, S., Horn, D., Robinson, P. N. (2008) Walking the interactome for prioritization of candidate disease genes. Am J Hum Genet 82, 949–958.

    Article  PubMed  Google Scholar 

  65. George, R. A., Liu, J. Y., Feng, L. L., et al. (2006) Analysis of protein sequence and interaction data for candidate disease gene prediction. Nucleic Acids Res 34, e130.

    Article  PubMed  Google Scholar 

  66. Masseroli, M., Martucci, D., Pinciroli, F. (2004) GFINDer: Genome Function INtegrated Discoverer through dynamic annotation, statistical analysis, and mining. Nucleic Acids Res 32, W293–300.

    Article  Google Scholar 

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

    Article  PubMed  Google Scholar 

  68. Xiong, Q., Qiu, Y., Gu, W. (2008) PGMapper: a web-based tool linking phenotype to genes. Bioinformatics 24, 1011–1013.

    Article  PubMed  CAS  Google Scholar 

  69. Radivojac, P., Peng, K., Clark, W. T., et al. (2008) An integrated approach to inferring gene-disease associations in humans. Proteins 72, 1030–1037.

    Article  PubMed  CAS  Google Scholar 

  70. Cheng, D., Knox, C., Young, N., et al. (2008) PolySearch: a web-based text mining system for extracting relationships between human diseases, genes, mutations, drugs and metabolites. Nucleic Acids Res 36, W399–405.

    Article  Google Scholar 

  71. Yoshida, Y., Makita, Y., Heida, N., et al. (2009) PosMed (Positional Medline): prioritizing genes with an artificial neural network comprising medical documents to accelerate positional cloning. Nucleic Acids Res 37, W147–W152.

    Article  PubMed  CAS  Google Scholar 

  72. Yue, P., Melamud, E., Moult, J. (2006) SNPs3D: candidate gene and SNP selection for association studies. BMC Bioinformatics 7, 166.

    Article  PubMed  Google Scholar 

  73. Adie, E. A., Adams, R. R., Evans, K. L., et al. (2006) SUSPECTS: enabling fast and effective prioritization of positional candidates. Bioinformatics 22, 773–774.

    Article  PubMed  CAS  Google Scholar 

  74. Gefen, A., Cohen, R., Birk, O. S. (2010) Syndrome to gene (S2G): in-silico identification of candidate genes for human diseases. Hum Mutat 31, 229–236.

    Article  PubMed  CAS  Google Scholar 

  75. Rossi, S., Masotti, D., Nardini, C., et al. (2006) TOM: a web-based integrated approach for identification of candidate disease genes. Nucleic Acids Res 34, W285–292.

    Article  Google Scholar 

  76. Chen, J., Bardes, E. E., Aronow, B. J., Jegga, A. G. (2009) ToppGene Suite for gene list enrichment analysis and candidate gene prioritization. Nucleic Acids Res 37, W305–W311.

    Article  PubMed  CAS  Google Scholar 

Download references

Acknowledgments

The authors would like to acknowledge the support of the St. Vincent’s Clinic Foundation to M.A.W. and the Australian National Health and Medical Research Council Project Grant 635512 to M.A.W. and M.O.

Author information

Authors and Affiliations

  1. Structural and Computational Biology Division, Victor Chang Cardiac Research Institute, 2010, Darlinghurst, NSW, Australia

    Martin Oti, Sara Ballouz & Merridee A. Wouters

  2. School of Computer Science and Engineering, University of New South Wales, 2052, Kensington, NSW, Australia

    Sara Ballouz

  3. School of Medical Sciences, University of New South Wales, 2052, Kensington, NSW, Australia

    Merridee A. Wouters

  4. School of Life & Environmental Sciences, Deakin University, Geelong 3220, Victoria, Australia

    Merridee A. Wouters

Authors
  1. Martin Oti
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sara Ballouz
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Merridee A. Wouters
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Merridee A. Wouters .

Editor information

Editors and Affiliations

  1. Royal Prince Alfred Hospital, Dept. of Molecular & Clinical Genetics, University of Sydney, Missenden Road, Camperdown, 2050, New South Wales, Australia

    Bing Yu

  2. Royal Prince Alfred Hospital, Dept. Molecular & Clinical Genetics, University of Sydney, Missenden Road, Camperdown, 2050, New South Wales, Australia

    Marcus Hinchcliffe

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer Science+Business Media, LLC

About this protocol

Cite this protocol

Oti, M., Ballouz, S., Wouters, M.A. (2011). Web Tools for the Prioritization of Candidate Disease Genes. In: Yu, B., Hinchcliffe, M. (eds) In Silico Tools for Gene Discovery. Methods in Molecular Biology, vol 760. Humana Press. https://doi.org/10.1007/978-1-61779-176-5_12

Download citation

  • DOI: https://doi.org/10.1007/978-1-61779-176-5_12

  • Published:

  • Publisher Name: Humana Press

  • Print ISBN: 978-1-61779-175-8

  • Online ISBN: 978-1-61779-176-5

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation