Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Analysis
  • Published:

Gene prioritization through genomic data fusion

An Erratum to this article was published on 01 June 2006

Abstract

The identification of genes involved in health and disease remains a challenge. We describe a bioinformatics approach, together with a freely accessible, interactive and flexible software termed Endeavour, to prioritize candidate genes underlying biological processes or diseases, based on their similarity to known genes involved in these phenomena. Unlike previous approaches, ours generates distinct prioritizations for multiple heterogeneous data sources, which are then integrated, or fused, into a global ranking using order statistics. In addition, it offers the flexibility of including additional data sources. Validation of our approach revealed it was able to efficiently prioritize 627 genes in disease data sets and 76 genes in biological pathway sets, identify candidates of 16 mono- or polygenic diseases, and discover regulatory genes of myeloid differentiation. Furthermore, the approach identified a novel gene involved in craniofacial development from a 2-Mb chromosomal region, deleted in some patients with DiGeorge-like birth defects. The approach described here offers an alternative integrative method for gene discovery.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Concept of prioritization by Endeavour.
Figure 2: Cross-validation results.
Figure 3: Cross-validation results.
Figure 4: In vitro functional validation of Endeavour.
Figure 5: Functional validation of Endeavour in zebrafish.

Similar content being viewed by others

References

  1. Quackenbush, J. Genomics. Microarrays—guilt by association. Science 302, 240–241 (2004).

    Article  Google Scholar 

  2. Kanehisa, M. & Bork, P. Bioinformatics in the post-sequence era. Nat. Genet. 33 Suppl. 305–310 (2003).

    Article  CAS  Google Scholar 

  3. Ball, C.A., Sherlock, G. & Brazma, A. Funding high-throughput data sharing. Nat. Biotechnol. 22, 1179–1183 (2004).

    Article  CAS  Google Scholar 

  4. Freudenberg, J. & Propping, P. A similarity-based method for genome-wide prediction of disease-relevant human genes. Bioinformatics 18 Suppl. 2, S110–S115 (2002).

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  7. Tiffin, N. et al. Integration of text- and data-mining using ontologies successfully selects disease gene candidates. Nucleic Acids Res. 33, 1544–1552 (2005).

    Article  CAS  Google Scholar 

  8. Adie, E.A., Adams, R.R., Evans, K.L., Porteous, D.J. & Pickard, B.S. Speeding disease gene discovery by sequence based candidate prioritization. BMC Bioinformatics 6, 55 (2005).

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  10. Kent, W.J. et al. Exploring relationships and mining data with the UCSC Gene Sorter. Genome Res. 15, 737–741 (2005).

    Article  CAS  Google Scholar 

  11. Altermann, E. & Klaenhammer, T.R. PathwayVoyager: pathway mapping using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. BMC Genomics 6, 60 (2005).

    Article  Google Scholar 

  12. Aerts, S. et al. TOUCAN 2: the all-inclusive open source workbench for regulatory sequence analysis. Nucleic Acids Res. 33, W393–W396 (2005).

    Article  CAS  Google Scholar 

  13. Aerts, S., Van Loo, P., Thijs, G., Moreau, Y. & De Moor, B. Computational detection of cis-regulatory modules. Bioinformatics 19 (Suppl 2), II5–II14 (2003).

    Article  Google Scholar 

  14. Tamayo, P. et al. Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. Proc. Natl. Acad. Sci. USA 96, 2907–2912 (1999).

    Article  CAS  Google Scholar 

  15. Stegmaier, K. et al. Gene expression-based high-throughput screening (GE-HTS) and application to leukemia differentiation. Nat. Genet. 36, 257–263 (2004).

    Article  CAS  Google Scholar 

  16. Pixley, F.J. et al. BCL6 suppresses RhoA activity to alter macrophage morphology and motility. J. Cell Sci. 118, 1873–1883 (2005).

    Article  CAS  Google Scholar 

  17. Galimi, F. et al. Hepatocyte growth factor is a regulator of monocyte-macrophage function. J. Immunol. 166, 1241–1247 (2001).

    Article  CAS  Google Scholar 

  18. Brown, N.J. et al. Fas death receptor signaling represses monocyte numbers and macrophage activation in vivo. J. Immunol. 173, 7584–7593 (2004).

    Article  CAS  Google Scholar 

  19. Scambler, P.J. The 22q11 deletion syndromes. Hum. Mol. Genet. 9, 2421–2426 (2000).

    Article  CAS  Google Scholar 

  20. Baldini, A. Dissecting contiguous gene defects: TBX1. Curr. Opin. Genet. Dev. 15, 279–284 (2005).

    Article  CAS  Google Scholar 

  21. Jerome, L.A. & Papaioannou, V.E. DiGeorge syndrome phenotype in mice mutant for the T-box gene, Tbx1. Nat. Genet. 27, 286–291 (2001).

    Article  CAS  Google Scholar 

  22. Merscher, S. et al. TBX1 is responsible for cardiovascular defects in velo-cardio-facial/DiGeorge syndrome. Cell 104, 619–629 (2001).

    Article  CAS  Google Scholar 

  23. Lindsay, E.A. et al. Tbx1 haploinsufficieny in the DiGeorge syndrome region causes aortic arch defects in mice. Nature 410, 97–101 (2001).

    Article  CAS  Google Scholar 

  24. Piotrowski, T. et al. The zebrafish van gogh mutation disrupts tbx1, which is involved in the DiGeorge deletion syndrome in humans. Development 130, 5043–5052 (2003).

    Article  CAS  Google Scholar 

  25. Rauch, A. et al. A novel 22q11.2 microdeletion in DiGeorge syndrome. Am. J. Hum. Genet. 64, 659–666 (1999).

    Article  CAS  Google Scholar 

  26. Graham, A. The development and evolution of the pharyngeal arches. J. Anat. 199, 133–141 (2001).

    Article  CAS  Google Scholar 

  27. Stalmans, I. et al. VEGF: a modifier of the del22q11 (DiGeorge) syndrome? Nat. Med. 9, 173–182 (2003).

    Article  CAS  Google Scholar 

  28. Glenisson, P. et al. TXTGate: profiling gene groups with text-based information. Genome Biol. 5, R43 (2004).

    Article  Google Scholar 

  29. Bader, G.D., Betel, D. & Hogue, C.W. BIND: the Biomolecular Interaction Network Database. Nucleic Acids Res. 31, 248–250 (2003).

    Article  CAS  Google Scholar 

  30. Aerts, S., Van Loo, P., Moreau, Y. & De Moor, B. A genetic algorithm for the detection of new cis-regulatory modules in sets of coregulated genes. Bioinformatics 20, 1974–1976 (2004).

    Article  CAS  Google Scholar 

  31. Stuart, J.M., Segal, E., Koller, D. & Kim, S.K. A gene-coexpression network for global discovery of conserved genetic modules. Science 302, 249–255 (2003).

    Article  CAS  Google Scholar 

  32. Westerfield, M. The Zebrafish Book. A Guide for the Laboratory Use of Zebrafish, (University of Oregon Press, Eugene, Oregon, 1994).

    Google Scholar 

  33. Kimmel, C.B. et al. The shaping of pharyngeal cartilages during early development of the zebrafish. Dev. Biol. 203, 245–263 (1998).

    Article  CAS  Google Scholar 

  34. Splawski, I. et al. Ca(V)1.2 calcium channel dysfunction causes a multisystem disorder including arrhythmia and autism. Cell 119, 19–31 (2004).

    Article  CAS  Google Scholar 

  35. Robinson, S.W. et al. Missense mutations in CRELD1 are associated with cardiac atrioventricular septal defects. Am. J. Hum. Genet. 72, 1047–1052 (2003).

    Article  CAS  Google Scholar 

  36. Hayashi, T. et al. Identification and functional analysis of a caveolin-3 mutation associated with familial hypertrophic cardiomyopathy. Biochem. Biophys. Res. Commun. 313, 178–184 (2004).

    Article  CAS  Google Scholar 

  37. Zimprich, A. et al. Mutations in LRRK2 cause autosomal-dominant parkinsonism with pleomorphic pathology. Neuron 44, 601–607 (2004).

    Article  CAS  Google Scholar 

  38. Zuchner, S. et al. Mutations in the pleckstrin homology domain of dynamin 2 cause dominant intermediate Charcot-Marie-Tooth disease. Nat. Genet. 37, 289–294 (2005).

    Article  Google Scholar 

  39. Munch, C. et al. Point mutations of the p150 subunit of dynactin (DCTN1) gene in ALS. Neurology 63, 724–726 (2004).

    Article  CAS  Google Scholar 

  40. Tian, X.L. et al. Identification of an angiogenic factor that when mutated causes susceptibility to Klippel-Trenaunay syndrome. Nature 427, 640–645 (2004).

    Article  CAS  Google Scholar 

  41. Bienengraeber, M. et al. ABCC9 mutations identified in human dilated cardiomyopathy disrupt catalytic KATP channel gating. Nat. Genet. 36, 382–387 (2004).

    Article  CAS  Google Scholar 

  42. Windpassinger, C. et al. Heterozygous missense mutations in BSCL2 are associated with distal hereditary motor neuropathy and Silver syndrome. Nat. Genet. 36, 271–276 (2004).

    Article  CAS  Google Scholar 

  43. Tonkin, E.T., Wang, T.J., Lisgo, S., Bamshad, M.J. & Strachan, T. NIPBL, encoding a homolog of fungal Scc2-type sister chromatid cohesion proteins and fly Nipped-B, is mutated in Cornelia de Lange syndrome. Nat. Genet. 36, 636–641 (2004).

    Article  CAS  Google Scholar 

  44. Krantz, I.D. et al. Exclusion of linkage to the CDL1 gene region on chromosome 3q26.3 in some familial cases of Cornelia de Lange syndrome. Am. J. Med. Genet. 101, 120–129 (2001).

    Article  CAS  Google Scholar 

  45. Wang, X. et al. Positional identification of TNFSF4, encoding OX40 ligand, as a gene that influences atherosclerosis susceptibility. Nat. Genet. 37, 365–372 (2005).

    Article  CAS  Google Scholar 

  46. Peltekova, V.D. et al. Functional variants of OCTN cation transporter genes are associated with Crohn disease. Nat. Genet. 36, 471–475 (2004).

    Article  CAS  Google Scholar 

  47. Aharon-Peretz, J., Rosenbaum, H. & Gershoni-Baruch, R. Mutations in the glucocerebrosidase gene and Parkinson's disease in Ashkenazi Jews. N. Engl. J. Med. 351, 1972–1977 (2004).

    Article  CAS  Google Scholar 

  48. Begovich, A.B. et al. A missense single-nucleotide polymorphism in a gene encoding a protein tyrosine phosphatase (PTPN22) is associated with rheumatoid arthritis. Am. J. Hum. Genet. 75, 330–337 (2004).

    Article  CAS  Google Scholar 

  49. Helgadottir, A. et al. The gene encoding 5-lipoxygenase activating protein confers risk of myocardial infarction and stroke. Nat. Genet. 36, 233–239 (2004).

    Article  CAS  Google Scholar 

  50. Bertram, L. et al. Family-based association between Alzheimer's disease and variants in UBQLN1. N. Engl. J. Med. 352, 884–894 (2005).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We wish to thank all groups and consortia that made their data freely available: Ensembl, NCBI (EntrezGene and Medline), Gene Ontology, BIND, KEGG, Atlas, InterPro, BioBase, the Disease Probabilities from Lopez-Bigas and Ouzounis9 and the Prospectr scores from Euan Adie8. Ouzounis8 and the Prospectr scores from Euan Adie9. We also thank the following people for their help in particular areas: Robert Vlietinck with the manuscript, Patrick Glenisson with text mining, Joke Allemeersch and Gert Thijs with the order statistics and Camilla Esguerra with the zebrafish experiments. S.A., D.L. and P.V.L. are sponsored by the Research Foundation Flanders (FWO). This work is supported by Flanders Institute for Biotechnology (VIB), Instituut voor de aanmoediging van Innovatie door Wetenschap en Technologie in Vlaanderen (IWT) (STWW-00162), Research Council KULeuven (GOA-Ambiorics, IDO genetic networks), FWO (G.0229.03 and G.0413.03), IUAP V-22, K.U.L. Excellentiefinanciering CoE SymBioSys (EF/05/007), EU NoE Biopattern and EU EST BIOPTRAIN to Y.M., and by the FWO (G.0405.06), GOA/2006/11 and GOA/2001/09, Squibb and EULSHB-CT-2004-503573 to P.C.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Stein Aerts.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Fig. 1

Variability of the performance of endeavour, evaluated for each data source. (PDF 44 kb)

Supplementary Fig. 2

Pairwise dependency of the data sources. (PDF 55 kb)

Supplementary Fig. 3

Endeavor is not biased to well-characterized genes. (PDF 318 kb)

Supplementary Table 1

Selection criteria and training sets for the 10 mono and 6 polygenic diseases. (PDF 75 kb)

Supplementary Table 2

Prioritization of 1048 test genes located on chromosome 3 using training genes of congenital heart defects (CHD), arrythmias (AR), and cardiomyopathies (CM). (PDF 93 kb)

Supplementary Methods (PDF 119 kb)

Supplementary Notes (PDF 87 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Aerts, S., Lambrechts, D., Maity, S. et al. Gene prioritization through genomic data fusion. Nat Biotechnol 24, 537–544 (2006). https://doi.org/10.1038/nbt1203

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nbt1203

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing