title: NAM: association studies in multiple populations.
abstract: BACKGROUND
  Mixed linear models provide important techniques for performing genome-wide association studies. However, current models have pitfalls associated with their strong assumptions. Here, we propose a new implementation designed to overcome some of these pitfalls using an empirical Bayes algorithm.
  
  
  RESULTS
  Here we introduce NAM, an R package that allows user to take into account prior information regarding population stratification to relax the linkage phase assumption of current methods. It allows markers to be treated as a random effect to increase the resolution, and uses a sliding-window strategy to increase power and avoid double fitting markers into the model.
  
  
  METHODS
  NAM is an R package available in the CRAN repository. It can be installed in R by typing install.packages ('NAM').
  
  
  BACKGROUND
  krainey@purdue.edu.
  
  
  BACKGROUND
  Supplementary date are available at Bioinformatics online.
authors.affiliation: Department of Agronomy and.
authors.forename: Alencar
authors.initials: A
authors.lastname: Xavier
authors.affiliation: Department of Plant Science, University of California, Riverside, CA 92521, USA.
authors.forename: Shizhong
authors.initials: S
authors.lastname: Xu
authors.affiliation: Department of Animal Science, Purdue University, West Lafayette, IN 47907 and.
authors.forename: William M
authors.initials: WM
authors.lastname: Muir
authors.affiliation: Department of Agronomy and.
authors.forename: Katy Martin
authors.initials: KM
authors.lastname: Rainey
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D008040:Genetic Linkage; D055106:Genome-Wide Association Study / Q000379:methods*; D016014:Linear Models; D012984:Software