title: GWAlpha: genome-wide estimation of additive effects (alpha) based on trait quantile distribution from pool-sequencing experiments.
abstract: Sequencing pools of individuals (Pool-Seq) is a cost-effective way to gain insight into the genetics of complex traits, but as yet no parametric method has been developed to both test for genetic effects and estimate their magnitude. Here, we propose GWAlpha, a flexible method to obtain parametric estimates of genetic effects genome-wide from Pool-Seq experiments.
  
  
  
  We showed that GWAlpha powerfully replicates the results of Genome-Wide Association Studies (GWAS) from model organisms. We perform simulation studies that illustrate the effect on power of sample size and number of pools and test the method on different experimental data.
  
  
  
  GWAlpha is implemented in python, designed to run on Linux operating system and tested on Mac OS. It is freely available at https://github.com/aflevel/GWAlpha .
  
  
  
  afournier@unimelb.edu.au.
  
  
  
  Supplementary data are available at Bioinformatics online.
authors.affiliation: School of BioSciences and Centre for Systems Genomics.
authors.forename: Alexandre
authors.initials: A
authors.lastname: Fournier-Level
authors.affiliation: School of BioSciences and Centre for Systems Genomics.
authors.forename: Charles
authors.initials: C
authors.lastname: Robin
authors.affiliation: School of BioSciences and Centre for Systems Genomics.|School of Mathematics and Statistics, The University of Melbourne, Parkville 3010, Australia.
authors.forename: David J
authors.initials: DJ
authors.lastname: Balding
mesh_terms: D016678:Genome; D055106:Genome-Wide Association Study / Q000379:methods*; D010641:Phenotype; D018401:Sample Size; D012984:Software