title: Fast computation for genome-wide association studies using boosted one-step statistics.
abstract: BACKGROUND
  Statistical analyses of genome-wide association studies (GWAS) require fitting large numbers of very similar regression models, each with low statistical power. Taking advantage of repeated observations or correlated phenotypes can increase this statistical power, but fitting the more complicated models required can make computation impractical.
  
  
  RESULTS
  In this article, we present simple methods that capitalize on the structure inherent in GWAS studies to dramatically speed up computation for a wide variety of problems, with a special focus on methods for correlated phenotypes.
  
  
  BACKGROUND
  The R package 'boss' is available on the Comprehensive R Archive Network (CRAN) at http://cran.r-project.org/web/packages/boss/
authors.affiliation: Department of Biostatistics, University of Washington, Seattle, WA 98195, USA. voorma@u.washington.edu
authors.forename: Arend
authors.initials: A
authors.lastname: Voorman
authors.forename: Ken
authors.initials: K
authors.lastname: Rice
authors.forename: Thomas
authors.initials: T
authors.lastname: Lumley
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D055106:Genome-Wide Association Study / Q000379:methods*; D005838:Genotype; D015233:Models, Statistical; D012984:Software