title: Pathway-based analyses.
abstract: BACKGROUND
  New technologies for acquisition of genomic data, while offering unprecedented opportunities for genetic discovery, also impose severe burdens of interpretation and penalties for multiple testing.
  
  
  METHODS
  The Pathway-based Analyses Group of the Genetic Analysis Workshop 19 (GAW19) sought reduction of multiple-testing burden through various approaches to aggregation of highdimensional data in pathways informed by prior biological knowledge.
  
  
  RESULTS
  Experimental methods testedincluded the use of "synthetic pathways" (random sets of genes) to estimate power and false-positive error rate of methods applied to simulated data; data reduction via independent components analysis, single-nucleotide polymorphism (SNP)-SNP interaction, and use of gene sets to estimate genetic similarity; and general assessment of the efficacy of prior biological knowledge to reduce the dimensionality of complex genomic data.
  
  
  CONCLUSIONS
  The work of this group explored several promising approaches to managing high-dimensional data, with the caveat that these methods are necessarily constrained by the quality of external bioinformatic annotation.
authors: Department of Genetics, Texas Biomedical Research Institute, PO Box 760549, San Antonio, TX, 78245-0549, USA. jkent@txbiomed.org.
authors: Jack W
authors: JW
authors: Kent
mesh_terms: D019295:Computational Biology / Q000379:methods*; D015870:Gene Expression; D053263:Gene Regulatory Networks; D015894:Genome, Human; D006801:Humans; D020641:Polymorphism, Single Nucleotide