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Sci Rep. 2017 Jul 24;7(1):6207. doi: 10.1038/s41598-017-04939-4.

Genetic and functional characterization of disease associations explains comorbidity.

Author information

1
Joint IRB-BSC-CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), 08028, Barcelona, Spain.
2
Structural Bioinformatics Group, GRIB, IMIM, Department of Experimental and Life Sciences, Universitat Pompeu Fabra, 08003, Barcelona, Catalonia, Spain.
3
Center for Complex Network Research and Department of Physics, Northeastern University, Boston, 02115, MA, USA.
4
Barcelona Institute for Global Health (ISGlobal), 08003, Barcelona, Catalonia, Spain.
5
Integrative Biomedical Informatics Group, GRIB, IMIM, Department of Experimental and Life Sciences, Universitat Pompeu Fabra, Barcelona, 08003, Catalonia, Spain.
6
Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, SY23 3EB, United Kingdom. naf4@aber.ac.uk.
7
Integrative Biomedical Informatics Group, GRIB, IMIM, Department of Experimental and Life Sciences, Universitat Pompeu Fabra, Barcelona, 08003, Catalonia, Spain. laura.furlong@upf.edu.
8
Structural Bioinformatics Group, GRIB, IMIM, Department of Experimental and Life Sciences, Universitat Pompeu Fabra, 08003, Barcelona, Catalonia, Spain. baldo.oliva@upf.edu.

Abstract

Understanding relationships between diseases, such as comorbidities, has important socio-economic implications, ranging from clinical study design to health care planning. Most studies characterize disease comorbidity using shared genetic origins, ignoring pathway-based commonalities between diseases. In this study, we define the disease pathways using an interactome-based extension of known disease-genes and introduce several measures of functional overlap. The analysis reveals 206 significant links among 94 diseases, giving rise to a highly clustered disease association network. We observe that around 95% of the links in the disease network, though not identified by genetic overlap, are discovered by functional overlap. This disease network portraits rheumatoid arthritis, asthma, atherosclerosis, pulmonary diseases and Crohn's disease as hubs and thus pointing to common inflammatory processes underlying disease pathophysiology. We identify several described associations such as the inverse comorbidity relationship between Alzheimer's disease and neoplasms. Furthermore, we investigate the disruptions in protein interactions by mapping mutations onto the domains involved in the interaction, suggesting hypotheses on the causal link between diseases. Finally, we provide several proof-of-principle examples in which we model the effect of the mutation and the change of the association strength, which could explain the observed comorbidity between diseases caused by the same genetic alterations.

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