title: Two novel pathway analysis methods based on a hierarchical model.
abstract: BACKGROUND
  Over the past few years several pathway analysis methods have been proposed for exploring and enhancing the analysis of genome-wide association data. Hierarchical models have been advocated as a way to integrate SNP and pathway effects in the same model, but their computational complexity has prevented them being applied on a genome-wide scale to date.
  
  
  METHODS
  We present two novel methods for identifying associated pathways. In the proposed hierarchical model, the SNP effects are analytically integrated out of the analysis, allowing computationally tractable model fitting to genome-wide data. The first method uses Bayes factors for calculating the effect of the pathways, whereas the second method uses a machine learning algorithm and adaptive lasso for finding a sparse solution of associated pathways.
  
  
  RESULTS
  The performance of the proposed methods was explored on both simulated and real data. The results of the simulation study showed that the methods outperformed some well-established association methods: the commonly used Fisher's method for combining P-values and also the recently published BGSA. The methods were applied to two genome-wide association study datasets that aimed to find the genetic structure of platelet function and body mass index, respectively. The results of the analyses replicated the results of previously published pathway analysis of these phenotypes but also identified novel pathways that are potentially involved.
  
  
  BACKGROUND
  An R package is under preparation. In the meantime, the scripts of the methods are available on request from the authors.
authors.affiliation: Medical Research Council Biostatistics Unit, Institute of Public Health, Cambridge, CB2 0SR, UK, JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, NIHR Cambridge Biomedical Research Centre, Cambridge Institute for Medical Research, University of Cambridge, Addenbrooke's Hospital, Cambridge, CB2 0XY, UK and Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK.
authors.forename: Marina
authors.initials: M
authors.lastname: Evangelou
authors.forename: Frank
authors.initials: F
authors.lastname: Dudbridge
authors.forename: Lorenz
authors.initials: L
authors.lastname: Wernisch
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D001792:Blood Platelets / Q000502:physiology; D015992:Body Mass Index; D003198:Computer Simulation; D055106:Genome-Wide Association Study / Q000379:methods*; D006801:Humans; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide