A Dynamic Network Approach for the Study of Human Phenotypes

Abstract

The use of networks to integrate different genetic, proteomic, and metabolic datasets has been proposed as a viable path toward elucidating the origins of specific diseases. Here we introduce a new phenotypic database summarizing correlations obtained from the disease history of more than 30 million patients in a Phenotypic Disease Network (PDN). We present evidence that the structure of the PDN is relevant to the understanding of illness progression by showing that (1) patients develop diseases close in the network to those they already have; (2) the progression of disease along the links of the network is different for patients of different genders and ethnicities; (3) patients diagnosed with diseases which are more highly connected in the PDN tend to die sooner than those affected by less connected diseases; and (4) diseases that tend to be preceded by others in the PDN tend to be more connected than diseases that precede other illnesses, and are associated with higher degrees of mortality. Our findings show that disease progression can be represented and studied using network methods, offering the potential to enhance our understanding of the origin and evolution of human diseases. The dataset introduced here, released concurrently with this publication, represents the largest relational phenotypic resource publicly available to the research community.

Full text

Its licence does not allow us to show the full text. Read it at PubMed Central:

References 33

1. 1.

   Goh K-I, Cusick ME, Valle D, Childs B, Vidal M, et al. The human disease network. Proc Natl Acad Sci U S A. 2007;104:8685-8690.

   Not in our library10.1073/pnas.0701361104

2. 2.

   Feldman I, Rzhetsky A, Vitkup D. Network properties of genes harboring inherited disease mutations. Proc Natl Acad Sci U S A. 2008;105:4323-4328.

   Not in our library10.1073/pnas.0701722105

3. 3.

   Rual JF, Venkatesan K, Hao T, Hirozane-Kishikawa T, Dricot A, et al. Towards a proteome-scale map of the human protein-protein interaction network. Nature. 2005;7062:1173-1178.

   Not in our library10.1038/nature04209

4. 4.

   Stelzl U, Worm U, Lalowski M, Haenig C, Brembeck FH, et al. A Human Protein-Protein Interaction Network: A Resource for Annotating the Proteome. Cell. 2005;122:957-968.

   Not in our library10.1016/j.cell.2005.08.029

5. 5.

   Lim J, Hao T, Shaw C, Patel AJ, Szabó G, et al. A Protein–Protein Interaction Network for Human Inherited Ataxias and Disorders of Purkinje Cell Degeneration. Cell. 2006;125:801-814.

   Not in our library10.1016/j.cell.2006.03.032

6. 6.

   Goehler H, Lalowski M, Stelzl U, Waelter S, Stroedicke M, et al. A protein interaction network links GIT1, an enhancer of huntingtin aggregation, to Huntington's disease. Moll Cell. 2004;15:853-865.

   Not in our library10.1016/j.molcel.2004.09.016

7. 7.

   Calvano SE, Xiao W, Richards DR, Felciano RM, Baker HV, et al. A network-based analysis of systemic inflammation in humans. Nature. 2005;437:1032-1037.

   Not in our library10.1038/nature03985

8. 8.

   Pujana MA, Han JD, Starita LM, Stevens KN, Tewari M, et al. Network modeling links breast cancer susceptibility and centrosome dysfunction. Nature Genetics. 2007;39:1338-1349.

   Not in our library10.1038/ng.2007.2

9. 9.

   Oldham MC, Horvath S, Geschwind DH. Conservation and evolution of gene coexpression networks in human and chimpanzee brains. Proc Natl Acad Sci U S A. 2006;103:17973-17978.

   Not in our library10.1073/pnas.0605938103

10. 10.

    Lee DS, Park J, Kay KA, Christakis NA, Oltvai ZN, et al. The implications of human metabolic network topology for disease comorbidity. Proc Natl Acad Sci U S A. 2008;105:9880-9885.

    Not in our library10.1073/pnas.0802208105

11. 11.

    Iezzoni LI, Heeren T, Foley SM, Daley J, Hughes J, et al. Chronic conditions and risk of in-hospital death. Health Services Research. 1994;29:435-460.

    Not in our library

12. 12.

    Schneeweiss S, Wang PS, Avorn J, Glynn RJ. Improved Comorbidity Adjustment for Predicting Mortality in Medicare Populations. Health Services Research. 2003;38:1103-1120.

    Not in our library10.1111/1475-6773.00165

13. 13.

    Rzhetsky A, Wajngurt D, Park N, Zheng T. Probing genetic overlap among complex human phenotypes. Proc Natl Acad Sci U S A. 2007;104:11694-11699.

    Not in our library10.1073/pnas.0704820104

14. 14.

    Zhang J, Iwashyna TJ, Christakis NA. The Performance of Different Lookback Periods and Sources of Information for Charlson Comorbidity Adjustment in Medicare Claims. Medical Care. 1999;37:1128-1139.

    Not in our library10.1097/00005650-199911000-00005

15. 15.

    Cooper GS, Yuan Z, Stange KC, Dennis LK, Amini SB, et al. The sensitivity of Medicare claims data for case ascertainment of six common cancers. Medical Care. 1999;37:436-44.

    Not in our library10.1097/00005650-199905000-00003

16. 16.

    Benesch C, Witter DM, Wilder AL, Duncan PW, Samsa GP, et al. Inaccuracy of the International Classification of Disease (ICD-9-CM) in identifiying the diagnosis of ischemic cerebrovascular disease. Neurology. 1997;49:660-664.

    Not in our library10.1212/wnl.49.3.660

17. 17.

    Lauderdale D, Furner SE, Miles TP, Goldberg J. Epidemiological uses of Medicare data. American Journal of Epidemiology. 1993;15:319-327.

    Not in our library10.1093/oxfordjournals.epirev.a036123

18. 18.

    Mitchell JB, Bubolz T, Paul JE, Pashos CL, Escarce JJ, et al. Using Medicare claims for outcomes research. Medical Care. 1994;32:S38-JS51.

    Not in our library

19. 19.

    Hatten J. Medicare's Common Denominator: The Covered Population. Health Care Financing Review. 1980;2:53-64.

    Not in our library

20. 20.

    Kobberling J, Tattersall R. The Genetics of Diabetes Mellitus. 1982.

    Not in our library

21. 21.

    Lusis AJ, Mar R, Pajukanta P. Genetics of atherosclerosis. Annu Rev Genomics Hum Genet. 2004;5:189-218.

    Not in our library10.1146/annurev.genom.5.061903.175930

22. 22.

    Craddock N, O' Donovan MC, Owen MJ. The genetics of schizophrenia and bipolar disorder: dissecting psychosis. J Med Genet. 2005;42:193-204.

    Not in our library10.1136/jmg.2005.030718

23. 23.

    McGuffin P, Rijsdijk F, Andrew M, Sham P, Katz R, et al. The heritability of bipolar affective disorder and the genetic relationships to unipolar depression. Arch Gen Psychiatry. 2003;60:497-502.

    Not in our library10.1001/archpsyc.60.5.497

24. 24.

    Wordsworth P, Bell J. Polygenic susceptibility in rheumatoid arthritis. Ann Rheum Dis. 1991;50:343-346.

    Not in our library10.1136/ard.50.6.343

25. 25.

    Genome-wide association study of 14000 cases of seven common diseases and 3000 shared controls. Nature. 2007;447:661-677.

    Not in our library10.1038/nature05911

26. 26.

    Grant SF, Thorleifsson G, Reynisdottir I, Benediktsson R, Manolescu A, et al. Variant of transcription factor 7-like 2 (TCF7L2) gene confers risk of type 2 diabetes. Nature Genetics. 2006;38:320-323.

    Not in our library10.1038/ng1732

27. 27.

    Begovich AB, Carlton VE, Honigberg LA, Schrodi SJ, Chokkalingam AP, 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. 2004;75:330-337.

    Not in our library10.1086/422827

28. 28.

    Hinks A, Eyre S, Barton A, Thomson W, Worthington J. Investigation of genetic variation across PTPN22 in UK rheumatoid arthritis (RA) patients. Ann Rheum Dis. 2006;66:683-686.

    Not in our library10.1136/ard.2006.060459

29. 29.

    Both RR were calculated using ICD9 codes at the 5-digit level for the entire study population

    Not in our library

30. 30.

    Hidalgo CA, Klinger B, Barabasi A-L, Hausmann R. The product space conditions the development of nations. Science. 2007;317:482-487.

    Not in our library10.1126/science.1144581

31. 31.

    Barrat A, Barthelemy M, Pastor-Satorras, Vespignani A. The architecture of complex weighted networks. Proc Natl Acad Sci U S A. 2004;101:3747-3752.

    Not in our library10.1073/pnas.0400087101

32. 32.

    Ahnert SE, Garlaschelli D, Fink TM, Caldarelli G. An ensemble approach to the Analysis of Weighted Networks. Physical Review E. 2007;76:016101.

    Not in our library10.1103/physreve.76.016101

33. 33.

    Hidalgo CA. Thinking Outside the Cube. Physics World. 2008;21:34-37.

    Not in our library

Cited by papers we hold 13

• Aging, longevity, and healthy aging: the public health approach · 2025
  No notice
• Genomics and phenomics of body mass index reveals a complex disease network · 2022
  No notice
• Debutant iOS app and gene-disease complexities in clinical genomics and precision medicine · 2019
  Not checked
• The translational network for metabolic disease – from protein interaction to disease co-occurrence · 2019
  Not checked
• Population-wide analysis of differences in disease progression patterns in men and women · 2019
  No notice
• Network Modules of the Cross-Species Genotype-Phenotype Map Reflect the Clinical Severity of Human Diseases. · 2015
  Not checked
• Systems Medicine 2.0: Potential Benefits of Combining Electronic Health Care Records With Systems Science Models · 2015
  Not checked
• Heterogeneous Network Edge Prediction: A Data Integration Approach to Prioritize Disease-Associated Genes · 2015
  No notice
• Modules, networks and systems medicine for understanding disease and aiding diagnosis · 2014
  No notice
• Temporal disease trajectories condensed from population-wide registry data covering 6.2 million patients · 2014
  No notice
• A probabilistic model for reducing medication errors. · 2013
  Not checked
• Using Electronic Patient Records to Discover Disease Correlations and Stratify Patient Cohorts · 2011
  No notice
• The Small World of Psychopathology · 2011
  No notice

Reads

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.