title: Relative performance of gene- and pathway-level methods as secondary analyses for genome-wide association studies.
abstract: BACKGROUND
  Despite the success of genome-wide association studies (GWAS), there still remains "missing heritability" for many traits. One contributing factor may be the result of examining one marker at a time as opposed to a group of markers that are biologically meaningful in aggregate. To address this problem, a variety of gene- and pathway-level methods have been developed to identify putative biologically relevant associations. A simulation was conducted to systematically assess the performance of these methods. Using genetic data from 4,500 individuals in the Wellcome Trust Case Control Consortium (WTCCC), case-control status was simulated based on an additive polygenic model. We evaluated gene-level methods based on their sensitivity, specificity, and proportion of false positives. Pathway-level methods were evaluated on the relationship between proportion of causal genes within the pathway and the strength of association.
  
  
  RESULTS
  The gene-level methods had low sensitivity (20-63%), high specificity (89-100%), and low proportion of false positives (0.1-6%). The gene-level program VEGAS using only the top 10% of associated single nucleotide polymorphisms (SNPs) within the gene had the highest sensitivity (28.6%) with less than 1% false positives. The performance of the pathway-level methods depended on their reliance upon asymptotic distributions or if significance was estimated in a competitive manner. The pathway-level programs GenGen, GSA-SNP and MAGENTA had the best performance while accounting for potential confounders.
  
  
  CONCLUSIONS
  Novel genes and pathways can be identified using the gene and pathway-level methods. These methods may provide valuable insight into the "missing heritability" of traits and provide biological interpretations to GWAS findings.
authors.affiliation: Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA. gwojcik@stanford.edu.|Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA. gwojcik@stanford.edu.
authors.forename: Genevieve L
authors.initials: GL
authors.lastname: Wojcik
authors.affiliation: Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA. wkao@jhsph.edu.
authors.forename: W H Linda
authors.initials: WH
authors.lastname: Kao
authors.affiliation: Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA. pduggal@jhu.edu.
authors.forename: Priya
authors.initials: P
authors.lastname: Duggal
mesh_terms: D016022:Case-Control Studies; D019295:Computational Biology / Q000379:methods; D005796:Genes; D055106:Genome-Wide Association Study / Q000379:methods*; D023281:Genomics / Q000379:methods*; D006801:Humans; D020641:Polymorphism, Single Nucleotide; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D015398:Signal Transduction