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Analyzing networks of phenotypes in complex diseases: methodology and applications in COPD

 
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Author
Chu, Jen-hwaHARVARD
Hersh, Craig PHARVARD
Castaldi, Peter JHARVARD
Cho, Michael HHARVARD
Raby, Benjamin AHARVARD
Laird, NanHARVARD
Bowler, Russell
Rennard, Stephen
Loscalzo, JosephHARVARD
Quackenbush, JohnHARVARDORCID  0000-0002-2702-5879
Silverman, Edwin KHARVARD
Note: Order does not necessarily reflect citation order of authors.
Published Version
https://doi.org/10.1186/1752-0509-8-78
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Citation
Chu, J., C. P. Hersh, P. J. Castaldi, M. H. Cho, B. A. Raby, N. Laird, R. Bowler, et al. 2014. “Analyzing networks of phenotypes in complex diseases: methodology and applications in COPD.” BMC Systems Biology 8 (1): 78. doi:10.1186/1752-0509-8-78. http://dx.doi.org/10.1186/1752-0509-8-78.
Abstract
Background: The investigation of complex disease heterogeneity has been challenging. Here, we introduce a network-based approach, using partial correlations, that analyzes the relationships among multiple disease-related phenotypes. Results: We applied this method to two large, well-characterized studies of chronic obstructive pulmonary disease (COPD). We also examined the associations between these COPD phenotypic networks and other factors, including case-control status, disease severity, and genetic variants. Using these phenotypic networks, we have detected novel relationships between phenotypes that would not have been observed using traditional epidemiological approaches. Conclusion: Phenotypic network analysis of complex diseases could provide novel insights into disease susceptibility, disease severity, and genetic mechanisms.
Other Sources
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4105829/pdf/
Terms of Use
This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page
http://nrs.harvard.edu/urn-3:HUL.InstRepos:12785842

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