title: Network-guided sparse regression modeling for detection of gene-by-gene interactions.
abstract: BACKGROUND
  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.
  
  
  BACKGROUND
  The proposed method is implemented in R and available at http://math.bu.edu/people/kolaczyk/software.html.
  
  
  BACKGROUND
  chenlu@bu.edu
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: 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
authors.forename: Chen
authors.initials: C
authors.lastname: Lu
authors.forename: Jeanne
authors.initials: J
authors.lastname: Latourelle
authors.forename: George T
authors.initials: GT
authors.lastname: O'Connor
authors.forename: Josée
authors.initials: J
authors.lastname: Dupuis
authors.forename: Eric D
authors.initials: ED
authors.lastname: Kolaczyk
mesh_terms: D003198:Computer Simulation; D004843:Epistasis, Genetic; D055106:Genome-Wide Association Study; D006680:HLA Antigens / Q000235:genetics; D006801:Humans; D006967:Hypersensitivity / Q000097:blood / Q000235:genetics; D007073:Immunoglobulin E / Q000097:blood / Q000235:genetics; D020641:Polymorphism, Single Nucleotide; D012044:Regression Analysis
chemical_list: D006680:HLA Antigens; D007073:Immunoglobulin E