Network biology concepts in complex disease comorbidities

Nature Reviews Genetics

Volumn 17, Issue 10, 2016, Pages 615-629

  Hu, Jessica Xin ^a   Thomas, Cecilia Engel ^a   Brunak, Søren ^a,b  

^a University of Copenhagen   (Denmark)

^b International Center for Research and Research Training in Endocrine Disruption of Male Reproduction and Child Health   (Denmark)

Author keywords

[No Author keywords available]

Indexed keywords

COMORBIDITY; COMPLEX DISEASE; DISEASE ASSOCIATION; DRUG REPOSITIONING; GENE REGULATORY NETWORK; GENETIC ASSOCIATION; GENOTYPE ENVIRONMENT INTERACTION; HERITABILITY; HUMAN; IMMUNOPATHOLOGY; MOLECULAR GENETICS; MOLECULAR MECHANICS; MORBIDITY; MULTIFACTORIAL GENETIC DISORDER; NEOPLASM; NON INSULIN DEPENDENT DIABETES MELLITUS; PHENOTYPE; PLEIOTROPY; PRIORITY JOURNAL; PROTEIN FUNCTION; REVIEW; SYSTEMS BIOLOGY; BIOLOGY; DISEASES; GENETICS; METABOLISM; PROCEDURES;

COMORBIDITY; COMPUTATIONAL BIOLOGY; DISEASE; HUMANS; METABOLIC NETWORKS AND PATHWAYS; SYSTEMS BIOLOGY;

EID: 84982946695     PISSN: 14710056     EISSN: 14710064     Source Type: Journal    
DOI: 10.1038/nrg.2016.87     Document Type: Review

References (172)

1. Tyson, J. J., Chen, K. &Novak, B. Network dynamics and cell physiology. Nat. Rev. Mol. Cell. Biol. 2, 908-916 (2001)
2. Paaby, A. B. &Rockman, M. V. The many faces of pleiotropy. Trends Genet. 29, 66-73 (2013)
3. Solovieff, N., Cotsapas, C., Lee, P. H., Purcell, S. M. &Smoller, J. W. Pleiotropy in complex traits: challenges and strategies. Nat. Rev. Genet. 14, 483-495 (2013)
4. Hodgkin, J. Seven types of pleiotropy. Int. J. Dev. Biol. 42, 501-505 (1998)
5. Pyeritz, R. E. Pleiotropy revisited: molecular explanations of a classic concept. Am. J. Med. Genet. 34, 124-134 (1989)
6. Wagner, G. P. &Zhang, J. The pleiotropic structure of the genotype-phenotype map: the evolvability of complex organisms. Nat. Rev. Genet. 12, 204-213 (2011)
7. Félix, M.-A. &Barkoulas, M. Pervasive robustness in biological systems. Nat. Rev. Genet. 16, 483-496 (2015)
8. Kitano, H. Towards a theory of biological robustness. Mol. Syst. Biol. 3, 137 (2007)
9. Diss, G. et al. Integrative avenues for exploring the dynamics and evolution of protein interaction networks. Curr. Opin. Biotechnol. 24, 775-783 (2013)
10. Ideker, T. &Krogan, N. J. Differential network biology. Mol. Syst. Biol. 8, 565 (2012)
11. Lambert, J.-P. et al. Mapping differential interactomes by affinity purification coupled with data-independent mass spectrometry acquisition. Nat. Methods 10, 1239-1245 (2013)
12. Greene, C. S. et al. Understanding multicellular function and disease with human tissue-specific networks. Nat. Genet. 47, 569-576 (2015)
13. Bindea, G. et al. Spatiotemporal dynamics of intratumoral immune cells reveal the immune landscape in human cancer. Immunity 39, 782-795 (2013)
14. Sun, S., Liu, Z., Zeng, T., Wang, Y. &Chen, L. Spatio-temporal analysis of type 2 diabetes mellitus based on differential expression networks. Sci. Rep. 3, 2268 (2013)
15. Bandyopadhyay, S. et al. Rewiring of genetic networks in response to DNA damage. Science 330, 1385-1389 (2010)
16. Bensimon, A., Heck, A. J. &Aebersold, R. Mass spectrometry-based proteomics and network biology. Annu. Rev. Biochem. 81, 379-405 (2012)
17. Bisson, N. et al. Selected reaction monitoring mass spectrometry reveals the dynamics of signaling through the GRB2 adaptor. Nat. Biotechnol. 29, 653-658 (2011)
18. Lee, M. J. et al. Sequential application of anticancer drugs enhances cell death by rewiring apoptotic signaling networks. Cell 149, 780-794 (2012)
19. Creixell, P. et al. Kinome-wide decoding of network attacking mutations driving cancer signaling. Cell 163, 202-217 (2015)
20. Gijsen, R. et al. Causes and consequences of comorbidity: a review. J. Clin. Epidemiol. 54, 661-674 (2001)
21. Von Lueder, T. G. &Atar, D. Comorbidities and polypharmacy. Heart Fail. Clin. 10, 367-372 (2014)
22. Feinstein, A. R. The pre-therapeutic classification co-morbidity in chronic disease. J. Chronic Dis. 23, 455-468 (1970)
23. Valderas, J. M., Sibbald, B. &Salisbury, C. Defining comorbidity: implications for understanding health and health services. Ann. Fam. Med. 7, 357-363 (2009)
24. Meghani, S. H. et al. The conceptualization and measurement of comorbidity: a review of the interprofessional discourse. Nurs. Res. Pract. 2013, 192782 (2013)
25. Loscalzo, J., Kohane, I. &Barabási, A.-L. Human disease classification in the postgenomic era: a complex systems approach to human pathobiology. Mol. Syst. Biol. 3, 124 (2007)
26. Maj, M. 'Psychiatric comorbidity': an artefact of current diagnostic systems? Br. J. Psychiatry 186, 182-184 (2005)
27. Radner, H., Yoshida, K., Smolen, J. S. &Solomon, D. H. Multimorbidity and rheumatic conditions-enhancing the concept of comorbidity. Nat. Rev. Rheumatol. 10, 252-256 (2014)
28. van den Akker, M., Buntinx, F. &Knottnerus, J. A. Comorbidity or multimorbidity. Eur. J. Gen. Pract. 2, 65-70 (2009)
29. Scanlon, P. H. Diabetic retinopathy. Medicine 38, 656-660 (2010)
30. Thomas, M. C. et al. Diabetic kidney disease. Nat. Rev. Dis. Prim. 1, 15038 (2015)
31. Barnes, P. J. et al. Chronic obstructive pulmonary disease. Nat. Rev. Dis. Prim. 362, 15076 (2015)
32. Mannino, D. &Kiri, V. Changing the burden of COPD mortality. Int. J. Chron. Obstruct. Pulmon. Dis. 1, 219-233 (2006)
33. Agustí, A. &Vestbo, J. Current controversies and future perspectives in chronic obstructive pulmonary disease. Am. J. Respir. Crit. Care Med. 184, 507-513 (2012)
34. Hidalgo, C. A, Blumm, N., Barabási, A.-L. &Christakis, N. A. A dynamic network approach for the study of human phenotypes. PLoS Comput. Biol. 5, e1000353 (2009)
35. Roque, F. S. et al. Using electronic patient records to discover disease correlations and stratify patient cohorts. PLoS Comput. Biol. 7, e1002141 (2011)
36. Jensen, A. B. et al. Temporal disease trajectories condensed from population-wide registry data covering 6.2 million patients. Nat. Commun. 5, 4022 (2014)
37. Oh, W. et al. Type 2 diabetes mellitus trajectories and associated risks. Big Data 4, 25-30 (2016)
38. Capobianco, E. &Liò, P. Comorbidity: a multidimensional approach. Trends Mol. Med. 19, 515-521 (2013)
39. Capobianco, E. &Liò, P. Comorbidity networks: beyond disease correlations. J. Complex. Networks 3, 319-332 (2015)
40. Gibson, G. Decanalization and the origin of complex disease. Nat. Rev. Genet. 10, 134-140 (2009)
41. Faner, R. et al. Molecular and clinical diseasome of comorbidities in exacerbated COPD patients. Eur. Respir. J. 46, 1001-1010 (2015)
42. Ibáñez, K., Boullosa, C., Tabarés-Seisdedos, R., Baudot, A. &Valencia, A. Molecular evidence for the inverse comorbidity between central nervous system disorders and cancers detected by transcriptomic meta-analyses. PLoS Genet. 10, e1004173 (2014)
43. Ellinghaus, D. et al. Analysis of five chronic inflammatory diseases identifies 27 new associations and highlights disease-specific patterns at shared loci. Nat. Genet. 48, 510-518 (2016)
44. Park, J., Lee, D.-S., Christakis, N. a &Barabási, A.-L. The impact of cellular networks on disease comorbidity. Mol. Syst. Biol. 5, 262 (2009)
45. Catalá-López, F. et al. Inverse and direct cancer comorbidity in people with central nervous system disorders: a meta-analysis of cancer incidence in 577,013 participants of 50 observational studies. Psychother. Psychosom. 83, 89-105 (2014)
46. Driver, J. a et al. Inverse association between cancer and Alzheimer's disease: results from the Framingham Heart Study. BMJ 344, e1442 (2012)
47. Tabarés-Seisdedos, R. et al. No paradox, no progress: inverse cancer comorbidity in people with other complex diseases. Lancet. Oncol. 12, 604-608 (2011)
48. Pernicova, I. &Korbonits, M. Metformin-mode of action and clinical implications for diabetes and cancer. Nat. Rev. Endocrinol. 10, 143-156 (2014)
49. Kohane, I. S. Deeper, longer phenotyping to accelerate the discovery of the genetic architectures of diseases. Genome Biol. 15, 115 (2014)
50. Robinson, P. N. Deep phenotyping for precision medicine. Hum. Mutat. 33, 777-780 (2012)
51. Miotto, R., Li, L., Kidd, B. A. &Dudley, J. T. Deep patient: an unsupervised representation to predict the future of patients from the electronic health records. Sci. Rep. 6, 26094 (2016)
52. Zheng, X. et al. Genome-wide copy-number variation study of psychosis in Alzheimer's disease. Transl. Psychiatry 5, e574 (2015)
53. Plate, L. in Festschrift zum sechzigsten Geburtstag Richard Hertwigs. 536-610 (in German) (Fischer, 1910)
54. Sivakumaran, S. et al. Abundant pleiotropy in human complex diseases and traits. Am. J. Hum. Genet. 89, 607-618 (2011)
55. Rzhetsky, A., Wajngurt, D., Park, N. &Zheng, T. Probing genetic overlap among complex human phenotypes. Proc. Natl Acad. Sci. USA 104, 11694-11699 (2007)
56. Cotsapas, C. et al. Pervasive sharing of genetic effects in autoimmune disease. PLoS Genet. 7, e1002254 (2011)
57. Parkes, M., Cortes, A., van Heel, D. A. &Brown, M. A. Genetic insights into common pathways and complex relationships among immune-mediated diseases. Nat. Rev. Genet. 14, 661-673 (2013)
58. Ellis, J. D. et al. Tissue-specific alternative splicing remodels protein-protein interaction networks. Mol. Cell 46, 884-892 (2012)
59. Drivas, T. G., Wojno, A. P., Tucker, B. A., Stone, E. M. &Bennett, J. Basal exon skipping and genetic pleiotropy: A predictive model of disease pathogenesis. Sci. Transl. Med. 10, 291ra97 (2015)
60. Dickey, T. H., Altschuler, S. E. &Wuttke, D. S. Single-stranded DNA-binding proteins: multiple domains for multiple functions. Structure 21, 1074-1084 (2013)
61. Bossi, A. &Lehner, B. Tissue specificity and the human protein interaction network. Mol. Syst. Biol. 5, 260 (2009)
62. Verstraeten, A., Alaerts, M., Van Laer, L. &Loeys, B. Marfan syndrome and related disorders: 25 years of gene discovery. Hum. Mutat. 37, 524-531 (2016)
63. Lomas, D. A. Does protease-antiprotease imbalance explain chronic obstructive pulmonary disease? Ann. Am. Thorac. Soc. 13, S130-S137 (2016)
64. Wong, T. Y., Cheung, C. M. G., Larsen, M., Sharma, S. &Simó, R. Diabetic retinopathy. Nat. Rev. Dis. Prim. 2, 16012 (2016)
65. Denny, J. C. et al. Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data. Nat. Biotechnol. 31, 1102-1111 (2013)
66. Bush W. S., Oetjens M. T., &Crawford, D. C. Unravelling the human genome-phenome relationship using phenome-wide association studies. Nat. Rev. Genet. 17, 129-145 (2016)
67. Rastegar-Mojarad, M., Ye, Z., Kolesar, J. M., Hebbring, S. J. &Lin, S. M. Opportunities for drug repositioning from phenome-wide association studies. Nat. Biotechnol. 33, 342-345 (2015)
68. Blair, D. R. et al. A nondegenerate code of deleterious variants in Mendelian loci contributes to complex disease risk. Cell 155, 70-80 (2013)
69. Han, B. et al. A method to decipher pleiotropy by detecting underlying heterogeneity driven by hidden subgroups applied to autoimmune and neuropsychiatric diseases. Nat. Genet. 48, 803-810 (2016)
70. Queitsch, C., Carlson, K. D. &Girirajan, S. Lessons from model organisms: phenotypic robustness and missing heritability in complex disease. PLoS Genet. 8, e1003041 (2012)
71. Kitano, H. Biological robustness. Nat. Rev. Genet. 5, 826-837 (2004)
72. Hsiao, T. L. &Vitkup, D. Role of duplicate genes in robustness against deleterious human mutations. PLoS Genet. 4, e1000014 (2008)
73. Queitsch, C., Sangster, T. A. &Lindquist, S. Hsp90 as a capacitor of phenotypic variation. Nature 417, 618-624 (2002)
74. Barabasi, A.-L., Oltvai, Z. N. Z. N. &Barabási, A.-L. Network biology: understanding the cell's functional organization. Nat. Rev. Genet. 5, 101-113 (2004)
75. Kitano, H. et al. Metabolic syndrome and robustness tradeoffs. Diabetes 53, S6-S15 (2004)
76. Eichler, E. E. et al. Missing heritability and strategies for finding the underlying causes of complex disease. Nat. Rev. Genet. 11, 446-450 (2010)
77. Shao, H. et al. Genetic architecture of complex traits: large phenotypic effects and pervasive epistasis. Proc. Natl Acad. Sci. USA 105, 19910-19914 (2008)
78. Zuk, O., Hechter, E., Sunyaev, S. R. &Lander, E. S. The mystery of missing heritability: genetic interactions create phantom heritability. Proc. Natl Acad. Sci. USA 109, 1193-1198 (2012)
79. Bloom, J. S., Ehrenreich, I. M., Loo, W. T., Lite, T.-L. V. &Kruglyak, L. Finding the sources of missing heritability in a yeast cross. Nature 494, 234-237 (2013)
80. Wang, Y. et al. Parameters in dynamic models of complex traits are containers of missing heritability. PLoS Comput. Biol. 8, e1002459 (2012)
81. Wagner, A. Causal drift, robust signaling, and complex disease. PLoS ONE 10, e0118413 (2015)
82. Paaby, A. B. &Rockman, M. V. Cryptic genetic variation: evolution's hidden substrate. Nat. Rev. Genet. 15, 247-258 (2014)
83. Chen, R. et al. Analysis of 589,306 genomes identifies individuals resilient to severe Mendelian childhood diseases. Nat. Biotechnol. 34, 531-538 (2016)
84. Narasimhan, V. M. et al. Health and population effects of rare gene knockouts in adult humans with related parents. Science 352, 474-477 (2016)
85. Harper, A. R., Nayee, S. &Topol, E. J. Protective alleles and modifier variants in human health and disease. Nat. Rev. Genet. 16, 689-701 (2015)
86. Shtir, C. et al. Exome-based case-control association study using extreme phenotype design reveals novel candidates with protective effect in diabetic retinopathy. Hum. Genet. 135, 193-200 (2016)
87. Zhuang, L. et al. The Leu72Met polymorphism of the GHRL gene prevents the development of diabetic nephropathy in Chinese patients with type 2 diabetes mellitus. Mol. Cell. Biochem. 387, 19-25 (2014)
88. Lapice, E. et al. The PPARγ2 Pro12Ala variant is protective against progression of nephropathy in people with type 2 diabetes. J. Transl. Med. 13, 85 (2015)
89. Heng, H. H. Q. Missing heritability and stochastic genome alterations. Nat. Rev. Genet. 11, 813 (2010)
90. Zhang, F., Gu, W., Hurles, M. E. &Lupski, J. R. Copy number variation in human health, disease, and evolution. Annu. Rev. Genom. Hum. Genet. 10, 451-481 (2009)
91. Girirajan, S., Campbell, C. D. &Eichler, E. E. Human copy number variation and complex genetic disease. Annu. Rev. Genet. 45, 203-226 (2011)
92. Zarrei, M., MacDonald, J. R., Merico, D. &Scherer, S. W. A copy number variation map of the human genome. Nat. Rev. Genet. 16, 172-183 (2015)
93. Mittelman, D., Sykoudis, K., Hersh, M., Lin, Y. &Wilson, J. H. Hsp90 modulates CAG repeat instability in human cells. Cell Stress Chaperones 15, 753-759 (2010)
94. Chen, G., Bradford, W. D., Seidel, C. W. &Li, R. Hsp90 stress potentiates rapid cellular adaptation through induction of aneuploidy. Nature 482, 246-250 (2012)
95. Shameer, K. et al. Translational bioinformatics in the era of real-time biomedical, health care and wellness data streams. Brief. Bioinform. http://dx.doi.org/ 10.1093/bib/bbv118 (2016)
96. Biotechnology, N. The coming era of human phenotyping. Nat. Biotechnol. 33, 567-567 (2015)
97. Schork, N. J. Personalized medicine: time for one-person trials. Nature 520, 609-611 (2015)
98. Muoio, D. M. &Newgard, C. B. Mechanisms of disease: molecular and metabolic mechanisms of insulin resistance and β-cell failure in type 2 diabetes. Nat. Rev. Mol. Cell. Biol. 9, 193-205 (2008)
99. Kaushik, A., Bhatia, Y., Ali, S. &Gupta, D. Gene network rewiring to study melanoma stage progression and elements essential for driving melanoma. PLoS ONE 10, e0142443 (2015)
100. Hou, L., Chen, M., Zhang, C. K., Cho, J. &Zhao, H. Guilt by rewiring: gene prioritization through network rewiring in genome wide association studies. Hum. Mol. Genet. 23, 2780-2790 (2014)
101. Zeng, T., Wang, D. C., Wang, X., Xu, F. &Chen, L. Prediction of dynamical drug sensitivity and resistance by module network rewiring-analysis based on transcriptional profiling. Drug Resist. Updat. 17, 64-76 (2014)
102. Shou, C. et al. Measuring the evolutionary rewiring of biological networks. PLoS Comput. Biol. 7, e1001050 (2011)
103. Holme, P. &Saramδki, J. Temporal networks. Phys. Rep. 519, 97-125 (2013)
104. Kim, J., Kim, I., Han, S. K., Bowie, J. U. &Kim, S. Network rewiring is an important mechanism of gene essentiality change. Sci. Rep. 2, 900 (2012)
105. Hein, M. Y. et al. A human interactome in three quantitative dimensions organized by stoichiometries and abundances. Cell 163, 712-723 (2015)
106. Kastritis, P. L. &Bonvin, A. M. J. J. On the binding affinity of macromolecular interactions: daring to ask why proteins interact. J. R. Soc. Interface 10, 20120835 (2013)
107. Kiel, C., Verschueren, E., Yang, J.-S. &Serrano, L. Integration of protein abundance and structure data reveals competition in the ErbB signaling network. Sci. Signal. 6, ra109 (2013)
108. Stingele, S. et al. Global analysis of genome, transcriptome and proteome reveals the response to aneuploidy in human cells. Mol. Syst. Biol. 8, 608 (2012)
109. Purvis, J. E. et al. p53 dynamics control cell fate. Science 336, 1440-1444 (2012)
110. Moni, M. A. &Liç, P. Network-based analysis of comorbidities risk during an infection: SARS &HIV case studies. BMC Bioinformatics 15, 333 (2014)
111. Albert, R., Jeong, H. &Barabasi, A. Error and attack tolerance of complex networks. Nature 406, 378-382 (2000)
112. Yu, H., Greenbaum, D., Xin Lu, H., Zhu, X. &Gerstein, M. Genomic analysis of essentiality within protein networks. Trends Genet. 20, 227-231 (2004)
113. Yu, H., Kim, P. M., Sprecher, E., Trifonov, V. &Gerstein, M. The importance of bottlenecks in protein networks: correlation with gene essentiality and expression dynamics. PLoS Comput. Biol. 3, e59 (2007)
114. Joy, M. P., Brock, A., Ingber, D. E. &Huang, S. High-betweenness proteins in the yeast protein interaction network. J. Biomed. Biotechnol. 2005, 96-103 (2005)
115. Hahn, M. W. &Kern, A. D. Comparative genomics of centrality and essentiality in three eukaryotic protein-interaction networks. Mol. Biol. Evol. 22, 803-806 (2005)
116. Cai, J. J., Borenstein, E. &Petrov, D. A. Broker genes in human disease. 2, 815-825 (2010)
117. Kolch, W., Halasz, M., Granovskaya, M. &Kholodenko, B. N. The dynamic control of signal transduction networks in cancer cells. Nat. Rev. Cancer 15, 515-527 (2015)
118. Promislow, D. E. L. Protein networks, pleiotropy and the evolution of senescence. Proc. Biol. Sci. 271, 1225-1234 (2004)
119. Costanzo, M. et al. The genetic landscape of a cell. Science 327, 425-431 (2010)
120. Levy, S. F. &Siegal, M. L. Network hubs buffer environmental variation in Saccharomyces cerevisiae. PLoS Biol. 6, 2588-2604 (2008)
121. Nguyen, T.-P., Liu, W. &Jordαn, F. Inferring pleiotropy by network analysis: linked diseases in the human PPI network. BMC Syst. Biol. 5, 179 (2011)
122. He, X. &Zhang, J. Toward a molecular understanding of pleiotropy. Genetics 173, 1885-1891 (2006)
123. Garcia-alonso, L. et al. The role of the interactome in the maintenance of deleterious variability in human populations. Mol. Syst. Biol. 10, 752 (2014)
124. Azevedo, H. &Moreira-Filho, C. A. Topological robustness analysis of protein interaction networks reveals key targets for overcoming chemotherapy resistance in glioma. Sci. Rep. 5, 16830 (2015)
125. Gutierrez-Arcelus, M., Rich, S. S. &Raychaudhuri, S. Autoimmune diseases-connecting risk alleles with molecular traits of the immune system. Nat. Rev. Genet. 17, 160-174 (2016)
126. Sollid, L. M. &Jabri, B. Triggers and drivers of autoimmunity: lessons from coeliac disease. Nat. Rev. Immunol. 13, 294-302 (2013)
127. Costenbader, K. H., Feskanich, D., Mandl, L. A. &Karlson, E. W. Smoking intensity, duration, and cessation, and the risk of rheumatoid arthritis in women. Am. J. Med. 119, 503.e1-503.e9 (2006)
128. Klareskog, L. et al. A new model for an etiology of rheumatoid arthritis: smoking may trigger HLA-DR (shared epitope)-restricted immune reactions to autoantigens modified by citrullination. Arthritis Rheum. 54, 38-46 (2006)
129. James, J. A. et al. Systemic lupus erythematosus in adults is associated with previous Epstein-Barr virus exposure. Arthritis Rheum. 44, 1122-1126 (2001)
130. Criswell, L. A. et al. Analysis of families in the Multiple Autoimmune Disease Genetics Consortium (MADGC) collection: the PTPN22 620W allele associates with multiple autoimmune phenotypes. Am. J. Hum. Genet. 76, 561-571 (2005)
131. Park, J.-H. et al. Estimation of effect size distribution from genome-wide association studies and implications for future discoveries. Nat. Genet. 42, 570-575 (2010)
132. Graham, R. R. et al. Three functional variants of IFN regulatory factor 5 (IRF5) define risk and protective haplotypes for human lupus. Proc. Natl Acad. Sci. USA 104, 6758-6763 (2007)
133. Dendrou, C. A. et al. Cell-specific protein phenotypes for the autoimmune locus IL2RA using a genotype-selectable human bioresource. Nat. Genet. 41, 1011-1015 (2009)
134. Kofler, D. M., Severson, C. A, Mousissian, N. &Jager, P. L. and Hafler, D. A. The CD6 multiple sclerosis susceptibility allele is associated with alterations in CD4+ T cell proliferation. J. Immunol. 187, 3286-3291 (2011)
135. Lewis, M. J. et al. UBE2L3 polymorphism amplifies NF-κB activation and promotes plasma cell development, linking linear ubiquitination to multiple autoimmune diseases. Am. J. Hum. Genet. 96, 221-234 (2015)
136. Fairfax, B. P. et al. Innate immune activity conditions the effect of regulatory variants upon monocyte gene expression. Science 343, 1246949 (2014)
137. Shi, J. et al. Emerging role and therapeutic implication of wnt signaling pathways in autoimmune diseases. J. Immunol. Res. 2016, 9392132 (2016)
138. Ding, Y., Shen, S., Lino, A. C., Curotto Lafaille, M. A. &Lafaille, J. J. And beta-catenin stabilization extends regulatory T cell survival and induces anergy in nonregulatory T cells. Nat. Med. 14, 162-169 (2008)
139. Suryawanshi, A. et al. Canonical wnt signaling in dendritic cells regulates Th1/Th17 responses and suppresses autoimmune neuroinflammation. J. Immunol. 194, 3295-3304 (2015)
140. Rabelo, F. d S. et al. The Wnt signaling pathway and rheumatoid arthritis. Autoimmun. Rev. 9, 207-210 (2010)
141. Biros, I. &Forrest, S. Spinal muscular atrophy: untangling the knot? J. Med. Genet. 36, 1-8 (1999)
142. Feldkφtter, M., Schwarzer, V., Wirth, R., Wienker, T. F. &Wirth, B. Quantitative analyses of SMN1 and SMN2 based on real-time lightCycler PCR: fast and highly reliable carrier testing and prediction of severity of spinal muscular atrophy. Am. J. Hum. Genet. 70, 358-368 (2002)
143. Mailman, M. D. et al. Molecular analysis of spinal muscular atrophy and modification of the phenotype by SMN2. Genet. Med. 4, 20-26 (2002)
144. Lillie, E. O. et al. The n-of-1 clinical trial: the ultimate strategy for individualizing medicine? Per. Med. 8, 161-173 (2011)
145. Liu, J. et al. Comorbidity analysis according to sex and age in hypertension patients in china. Int. J. Med. Sci. 13, 99-107 (2016)
146. Vogel, F. A preliminary estimate of the number of human genes. Nature 201, 847 (1964)
147. US Department of Health and Human Services. Understanding our genetic inheritance, The U.S. Human Genome Project: The first five years: fiscal years 1991-1995. (US Dept. of Energy,1990)
148. Pertea, M. &Salzberg, S. L. Between a chicken and a grape: estimating the number of human genes. Genome Biol. 11, 206 (2010)
149. Menche, J. Uncovering disease-disease relationships through the incomplete interactome. Science 347, 1257601 (2015)
150. Gstaiger, M. &Aebersold, R. Applying mass spectrometry-based proteomics to genetics, genomics and network biology. Nat. Rev. Genet. 10, 617-627 (2009)
151. Collins, B. C. et al. Quantifying protein interaction dynamics by SWATH mass spectrometry: application to the 14-3-3 system. Nat. Methods 10, 1246-1253 (2013)
152. Humphrey, S. J., Azimifar, S. B. &Mann, M. High-throughput phosphoproteomics reveals in vivo insulin signaling dynamics. Nat. Biotechnol. 33, 990-995 (2015)
153. Daub, H. et al. Kinase-selective enrichment enables quantitative phosphoproteomics of the kinome across the cell cycle. Mol. Cell 31, 438-448 (2008)
154. Chattopadhyay, P. K., Gierahn, T. M., Roederer, M. &Love, J. C. Single-cell technologies for monitoring immune systems. Nat. Immunol. 15, 128-135 (2014)
155. Barabasi, A. L. &Albert, R. Emergence of scaling in random networks. Science 286, 509-512 (1999)
156. Mitra, K., Carvunis, A.-R., Ramesh, S. K. &Ideker, T. Integrative approaches for finding modular structure in biological networks. Nat. Rev. Genet. 14, 719-732 (2013)
157. Kitano, H. A robustness-based approach to systems-oriented drug design. Nat. Rev. Drug Discov. 6, 202-210 (2007)
158. Baffy, G. &Loscalzo, J. Complexity and network dynamics in physiological adaptation: an integrated view. Physiol. Behav. 131, 49-56 (2014)
159. Lichtenberg, U. Dynamic complex formation during the yeast cell cycle. Science 307, 724-727 (2005)
160. Faisal, F. E. &Milenkovi, T. Dynamic networks reveal key players in aging. Bioinformatics 30, 1721-1729 (2014)
161. Luscombe, N. M. et al. Genomic analysis of regulatory network dynamics reveals large topological changes. Nature 431, 308-312 (2004)
162. Roguev, A. et al. Conservation and rewiring of functional modules revealed by an epistasis map in fission yeast. Science 322, 405-410 (2008)
163. Altelaar, F. M., Munoz, J. &Heck, A. J. R. Next-generation proteomics: towards an integrative view of proteome dynamics. Nat. Rev. Genet. 14, 35-48 (2013)
164. Barrios-Rodiles, M. et al. High-throughput mapping of a dynamic signaling network in mammalian cells. Science 307, 1621-1625 (2005)
165. Dalianis, H., Hassel, M. &Velupillai, S. The Stockholm EPR corpus: characteristics and some initial findings. 14th Int. Symp. Health Inf. Manag. Res. 219, 243-249 (2009)
166. McGeachie, M. J. et al. Joint GWAS analysis: comparing similar GWAS at different genomic resolutions identifies novel pathway associations with six complex diseases. Genom. Data 2, 202-211 (2014)
167. Pendergrass, S. a et al. Phenome-wide association study (PheWAS) for detection of pleiotropy within the Population Architecture using Genomics and Epidemiology (PAGE) Network. PLoS Genet. 9, e1003087 (2013)
168. Glymour, M. M., Tchetgen Tchetgen, E. J. &Robins, J. M. Credible Mendelian randomization studies: approaches for evaluating the instrumental variable assumptions. Am. J. Epidemiol. 175, 332-339 (2012)
169. Lawlor, D. A., Harbord, R. M., Sterne, J. A. C., Timpson, N. &Davey Smith, G. Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Stat. Med. 27, 1133-1163 (2008)
170. Assenov, Y., Ramírez, F., Schelhorn, S.-E., Lengauer, T. &Albrecht, M. Computing topological parameters of biological networks. Bioinformatics 24, 282-284 (2008)
171. Li, F. et al. PerturbationAnalyzer: a tool for investigating the effects of concentration perturbation on protein interaction networks. Bioinformatics 26, 275-277 (2010)
172. Zhang, B. et al. DDN: a caBIG® analytical tool for differential network analysis. Bioinformatics 27, 1036-1038 (2011)