PMID- 23599501
OWN - NLM
STAT- MEDLINE
DCOM- 20131104
LR  - 20220309
IS  - 1367-4811 (Electronic)
IS  - 1367-4803 (Print)
IS  - 1367-4803 (Linking)
VI  - 29
IP  - 10
DP  - 2013 May 15
TI  - Network-guided sparse regression modeling for detection of gene-by-gene 
      interactions.
PG  - 1241-9
LID - 10.1093/bioinformatics/btt139 [doi]
AB  - MOTIVATION: Genetic variants identified by genome-wide association studies to 
      date explain only a small fraction of total heritability. Gene-by-gene 
      interaction is one important potential source of unexplained total heritability. 
      We propose a novel approach to detect such interactions that uses penalized 
      regression and sparse estimation principles, and incorporates outside biological 
      knowledge through a network-based penalty. RESULTS: We tested our new method on 
      simulated and real data. Simulation showed that with reasonable outside 
      biological knowledge, our method performs noticeably better than stage-wise 
      strategies (i.e. selecting main effects first, and interactions second, from 
      those main effects selected) in finding true interactions, especially when the 
      marginal strength of main effects is weak. We applied our method to Framingham 
      Heart Study data on total plasma immunoglobulin E (IgE) concentrations and found 
      a number of interactions among different classes of human leukocyte antigen genes 
      that may interact to influence the risk of developing IgE dysregulation and 
      allergy. AVAILABILITY: The proposed method is implemented in R and available at 
      http://math.bu.edu/people/kolaczyk/software.html. CONTACT: chenlu@bu.edu 
      SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics 
      online.
FAU - Lu, Chen
AU  - Lu C
AD  - Department of Biostatistics, Boston University School of Public Health, Pulmonary 
      Center, Department of Medicine and Department of Neurology, Boston University 
      School of Medicine, Boston, MA, USA. chenlu@bu.edu
FAU - Latourelle, Jeanne
AU  - Latourelle J
FAU - O'Connor, George T
AU  - O'Connor GT
FAU - Dupuis, Josee
AU  - Dupuis J
FAU - Kolaczyk, Eric D
AU  - Kolaczyk ED
LA  - eng
GR  - ES020827/ES/NIEHS NIH HHS/United States
GR  - AG028321/AG/NIA NIH HHS/United States
GR  - N01 HC25195/HC/NHLBI NIH HHS/United States
GR  - GM078987/GM/NIGMS NIH HHS/United States
GR  - P01 AI050516/AI/NIAID NIH HHS/United States
GR  - DK078616/DK/NIDDK NIH HHS/United States
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
PT  - Research Support, Non-U.S. Gov't
DEP - 20130418
PL  - England
TA  - Bioinformatics
JT  - Bioinformatics (Oxford, England)
JID - 9808944
RN  - 0 (HLA Antigens)
RN  - 37341-29-0 (Immunoglobulin E)
SB  - IM
MH  - Computer Simulation
MH  - *Epistasis, Genetic
MH  - Genome-Wide Association Study
MH  - HLA Antigens/genetics
MH  - Humans
MH  - Hypersensitivity/blood/genetics
MH  - Immunoglobulin E/blood/genetics
MH  - Polymorphism, Single Nucleotide
MH  - *Regression Analysis
PMC - PMC3711507
EDAT- 2013/04/20 06:00
MHDA- 2013/11/05 06:00
PMCR- 2014/05/15
CRDT- 2013/04/20 06:00
PHST- 2013/04/20 06:00 [entrez]
PHST- 2013/04/20 06:00 [pubmed]
PHST- 2013/11/05 06:00 [medline]
PHST- 2014/05/15 00:00 [pmc-release]
AID - btt139 [pii]
AID - 10.1093/bioinformatics/btt139 [doi]
PST - ppublish
SO  - Bioinformatics. 2013 May 15;29(10):1241-9. doi: 10.1093/bioinformatics/btt139. 
      Epub 2013 Apr 18.