PMID- 22917479
OWN - NLM
STAT- MEDLINE
DCOM- 20130206
LR  - 20211021
IS  - 1557-8100 (Electronic)
IS  - 1536-2310 (Print)
IS  - 1536-2310 (Linking)
VI  - 16
IP  - 10
DP  - 2012 Oct
TI  - Predicting disease-related subnetworks for type 1 diabetes using a new network 
      activity score.
PG  - 566-78
LID - 10.1089/omi.2012.0029 [doi]
AB  - In this study we investigated the advantage of including network information in 
      prioritizing disease genes of type 1 diabetes (T1D). First, a naive Bayesian 
      network (NBN) model was developed to integrate information from multiple data 
      sources and to define a T1D-involvement probability score (PS) for each 
      individual gene. The algorithm was validated using known functional candidate 
      genes as a benchmark. Genes with higher PS were found to be more likely to appear 
      in T1D-related publications. Next a new network activity metric was proposed to 
      evaluate the T1D relevance of protein-protein interaction (PPI) subnetworks. The 
      metric considered the contribution both from individual genes and from network 
      topological characteristics. The predictions were confirmed by several 
      independent datasets, including a genome wide association study (GWAS), and two 
      large-scale human gene expression studies. We found that novel candidate genes in 
      the T1D subnetworks showed more significant associations with T1D than genes 
      predicted using PS alone. Interestingly, most novel candidates were not encoded 
      within the human leukocyte antigen (HLA) region, and their expression levels 
      showed correlation with disease only in cohorts with low-risk HLA genotypes. The 
      results suggested the importance of mapping disease gene networks in dissecting 
      the genetics of complex diseases, and offered a general approach to network-based 
      disease gene prioritization from multiple data sources.
FAU - Gao, Shouguo
AU  - Gao S
AD  - Department of Physics, the University of Alabama at Birmingham, Birmingham, 
      Alabama 35294, USA.
FAU - Jia, Shuang
AU  - Jia S
FAU - Hessner, Martin J
AU  - Hessner MJ
FAU - Wang, Xujing
AU  - Wang X
LA  - eng
GR  - R01 AI078713/AI/NIAID NIH HHS/United States
GR  - ImNIH/Intramural NIH HHS/United States
GR  - R01DK080100/DK/NIDDK NIH HHS/United States
GR  - R01AI078713/AI/NIAID NIH HHS/United States
PT  - Journal Article
PT  - Research Support, N.I.H., Intramural
PT  - Research Support, Non-U.S. Gov't
DEP - 20120823
PL  - United States
TA  - OMICS
JT  - Omics : a journal of integrative biology
JID - 101131135
SB  - IM
MH  - *Algorithms
MH  - Area Under Curve
MH  - Bayes Theorem
MH  - Computer Simulation
MH  - Diabetes Mellitus, Type 1/*genetics
MH  - *Gene Regulatory Networks
MH  - Genetic Linkage
MH  - Genome-Wide Association Study/*methods
MH  - Humans
MH  - *Models, Genetic
MH  - ROC Curve
MH  - Software
MH  - Transcriptome
PMC - PMC3459426
EDAT- 2012/08/25 06:00
MHDA- 2013/02/07 06:00
PMCR- 2013/10/01
CRDT- 2012/08/25 06:00
PHST- 2012/08/25 06:00 [entrez]
PHST- 2012/08/25 06:00 [pubmed]
PHST- 2013/02/07 06:00 [medline]
PHST- 2013/10/01 00:00 [pmc-release]
AID - 10.1089/omi.2012.0029 [pii]
AID - 10.1089/omi.2012.0029 [doi]
PST - ppublish
SO  - OMICS. 2012 Oct;16(10):566-78. doi: 10.1089/omi.2012.0029. Epub 2012 Aug 23.