Document Detail


Partial correlation network analyses to detect altered gene interactions in human disease: using preeclampsia as a model.
MedLine Citation:
PMID:  20931231     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Differences in gene expression between cases and controls have been identified for a number of human diseases. However, the underlying mechanisms of transcriptional regulation remain largely unknown. Beyond comparisons of absolute or relative expression levels, disease states may be associated with alterations in the observed correlational patterns among sets of genes. Here we use partial correlation networks aiming to compare the transcriptional co-regulation for 222 genes that are differentially expressed in decidual tissues between preeclampsia (PE) cases and non-PE controls. Partial correlation coefficients (PCCs) have been calculated in cases (N = 37) and controls (N = 58) separately. For all PCCs, we tested if they were significant non-zero in the cases and controls separately. In addition, to examine if a given PCC is different between the cases and controls, we tested if the difference between two PCCs were significant non-zero. In the group with PE cases, only five PCCs were significant (FDR p value ≤ 0.05), of which none were significantly different from the PCCs in the controls. However, in the controls we identified a total of 56 statistically significant PCCs (FDR p value ≤ 0.05), of which 31 were also significantly different (FDR p value ≤ 0.05) from the PCCs in the PE cases. The identified partial correlation networks included genes that are potentially relevant for developing PE, including both known susceptibility genes (EGFL7, HES1) and novel candidate genes (CFH, NADSYN1, DBP, FIGLA). Our results might suggest that disturbed interactions, or higher order relationships between these genes play an important role in developing the disease.
Authors:
Asa Johansson; Mari Løset; Siv B Mundal; Matthew P Johnson; Katy A Freed; Mona H Fenstad; Eric K Moses; Rigmor Austgulen; John Blangero
Publication Detail:
Type:  Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't     Date:  2010-10-08
Journal Detail:
Title:  Human genetics     Volume:  129     ISSN:  1432-1203     ISO Abbreviation:  Hum. Genet.     Publication Date:  2011 Jan 
Date Detail:
Created Date:  2011-01-03     Completed Date:  2011-02-02     Revised Date:  2013-07-03    
Medline Journal Info:
Nlm Unique ID:  7613873     Medline TA:  Hum Genet     Country:  Germany    
Other Details:
Languages:  eng     Pagination:  25-34     Citation Subset:  IM    
Affiliation:
Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology (NTNU), Kvinne-barn senteret, 1.etg. Øst, 7006 Trondheim, Norway. asa.johansson@ucr.uu.se
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MeSH Terms
Descriptor/Qualifier:
Female
Gene Expression Profiling
Gene Expression Regulation*
Gene Regulatory Networks*
Humans
Infant, Newborn
Male
Models, Genetic
Pre-Eclampsia / genetics*
Pregnancy
Transcription, Genetic
Grant Support
ID/Acronym/Agency:
MH59490/MH/NIMH NIH HHS; R37 MH059490-12/MH/NIMH NIH HHS
Comments/Corrections

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