Display options
Cite
Darabos C, White MJ, Graham BE, et al. The multiscale backbone of the human phenotype network based on biological pathways. BioData Min. 2014;7(1):1doi: 10.1186/1756-0381-7-1.
Darabos, C., White, M. J., Graham, B. E., Leung, D. N., Williams, S. M., & Moore, J. H. (2014). The multiscale backbone of the human phenotype network based on biological pathways. BioData mining, 7(1), 1. https://doi.org/10.1186/1756-0381-7-1
Darabos, Christian, et al. "The multiscale backbone of the human phenotype network based on biological pathways." BioData mining vol. 7,1 (2014): 1. doi: https://doi.org/10.1186/1756-0381-7-1
Darabos C, White MJ, Graham BE, Leung DN, Williams SM, Moore JH. The multiscale backbone of the human phenotype network based on biological pathways. BioData Min. 2014 Jan 25;7(1):1. doi: 10.1186/1756-0381-7-1. PMID: 24460644; PMCID: PMC3924922.
Copy Download .nbib Format: NLM
Share it on

BioData Min. 2014 Jan 25;7(1):1. doi: 10.1186/1756-0381-7-1.

The multiscale backbone of the human phenotype network based on biological pathways.

BioData mining

Christian Darabos, Marquitta J White, Britney E Graham, Derek N Leung, Scott M Williams, Jason H Moore

Affiliations

  1. Department of Genetics, Institute for Quantitative Biomedical Sciences, Dartmouth College, Hanover, NH, USA. [email protected].

PMID: 24460644 PMCID: PMC3924922 DOI: 10.1186/1756-0381-7-1

Abstract

BACKGROUND: Networks are commonly used to represent and analyze large and complex systems of interacting elements. In systems biology, human disease networks show interactions between disorders sharing common genetic background. We built pathway-based human phenotype network (PHPN) of over 800 physical attributes, diseases, and behavioral traits; based on about 2,300 genes and 1,200 biological pathways. Using GWAS phenotype-to-genes associations, and pathway data from Reactome, we connect human traits based on the common patterns of human biological pathways, detecting more pleiotropic effects, and expanding previous studies from a gene-centric approach to that of shared cell-processes.

RESULTS: The resulting network has a heavily right-skewed degree distribution, placing it in the scale-free region of the network topologies spectrum. We extract the multi-scale information backbone of the PHPN based on the local densities of the network and discarding weak connection. Using a standard community detection algorithm, we construct phenotype modules of similar traits without applying expert biological knowledge. These modules can be assimilated to the disease classes. However, we are able to classify phenotypes according to shared biology, and not arbitrary disease classes. We present examples of expected clinical connections identified by PHPN as proof of principle.

CONCLUSIONS: We unveil a previously uncharacterized connection between phenotype modules and discuss potential mechanistic connections that are obvious only in retrospect. The PHPN shows tremendous potential to become a useful tool both in the unveiling of the diseases' common biology, and in the elaboration of diagnosis and treatments.

References

  1. Proc AMIA Symp. 2001;:81-5 - PubMed
  2. Mol Syst Biol. 2008;4:189 - PubMed
  3. PLoS Comput Biol. 2010 Feb 05;6(2):e1000662 - PubMed
  4. N Engl J Med. 1995 Mar 9;332(10):635-41 - PubMed
  5. Atherosclerosis. 2005 Jan;178(1):107-13 - PubMed
  6. Proc Natl Acad Sci U S A. 2006 Jun 6;103(23):8577-82 - PubMed
  7. Ann Hematol. 2006 Jul;85(7):415-23 - PubMed
  8. Br J Nutr. 2013 Mar 14;109(5):906-13 - PubMed
  9. Mol Psychiatry. 2006 Aug;11(8):721-36 - PubMed
  10. Int J Cardiol. 2012 Mar 8;155(2):217-22 - PubMed
  11. Brain. 2010 Apr;133(Pt 4):1163-72 - PubMed
  12. Wiley Interdiscip Rev Syst Biol Med. 2011 Nov-Dec;3(6):619-27 - PubMed
  13. Nat Med. 2007 Nov;13(11):1359-62 - PubMed
  14. Atherosclerosis. 2005 Mar;179(1):161-7 - PubMed
  15. Nature. 1998 Jun 4;393(6684):440-2 - PubMed
  16. Phys Rev E Stat Nonlin Soft Matter Phys. 2009 Nov;80(5 Pt 2):056117 - PubMed
  17. Ann Med. 2008;40(8):562-83 - PubMed
  18. Trends Genet. 2010 Feb;26(2):84-93 - PubMed
  19. Proc Natl Acad Sci U S A. 2009 Apr 21;106(16):6483-8 - PubMed
  20. Semin Pediatr Neurol. 2006 Sep;13(3):158-65 - PubMed
  21. Lancet. 1993 Sep 18;342(8873):697-9 - PubMed
  22. Circulation. 2001 Jul 3;104(1):19-24 - PubMed
  23. Food Nutr Bull. 2003 Dec;24(4 Suppl):S104-10 - PubMed
  24. J Thromb Haemost. 2011 Aug;9(8):1475-82 - PubMed
  25. Nutr Neurosci. 2010 Apr;13(2):54-70 - PubMed
  26. Am J Med. 2002 Dec 1;113(8):636-42 - PubMed
  27. Stroke. 2013 May;44(5):1433-5 - PubMed
  28. Phys Rev E Stat Nonlin Soft Matter Phys. 2007 Oct;76(4 Pt 2):046115 - PubMed
  29. Blood. 2009 Mar 26;113(13):2873-4 - PubMed
  30. PLoS One. 2009 Nov 30;4(11):e8090 - PubMed
  31. Eur Heart J. 2008 Sep;29(17):2125-32 - PubMed
  32. Adv Nutr. 2011 Mar;2(2):112-21 - PubMed
  33. Proc Natl Acad Sci U S A. 1993 Mar 1;90(5):1977-81 - PubMed
  34. Haematologica. 2003 Jan;88(1):94-108 - PubMed
  35. Eur Heart J. 2008 Sep;29(17):2067-9 - PubMed
  36. J Lipid Res. 1991 May;32(5):783-92 - PubMed
  37. J Am Med Inform Assoc. 2012 Mar-Apr;19(2):295-305 - PubMed
  38. Dev Med Child Neurol. 2012 Dec;54(12):1075-6 - PubMed
  39. Proc Natl Acad Sci U S A. 2007 May 22;104(21):8685-90 - PubMed
  40. Biotechnol Prog. 1999 May-Jun;15(3):296-303 - PubMed
  41. Hum Mol Genet. 2010 May 15;19(10):2068-78 - PubMed

Publication Types

Grant support