title: RegScan: a GWAS tool for quick estimation of allele effects on continuous traits and their combinations.
abstract: Genome-wide association studies are becoming computationally more demanding with the growing amounts of data. Combinatorial traits can increase the data dimensions beyond the computational capabilities of the current tools. We addressed this issue by creating an application for quick association analysis that is ten to hundreds of times faster than the leading fast methods. Our tool (RegScan) is designed for performing basic linear regression analysis with continuous traits maximally fast on large data sets. RegScan specifically targets association analysis of combinatorial traits in metabolomics. It can both generate and analyze the combinatorial traits efficiently. RegScan is capable of analyzing any number of traits together without the need to specify each trait individually. The main goal of the article is to show that RegScan can be the preferred analytical tool when large amounts of data need to be analyzed quickly using the allele frequency test.
  
  
  BACKGROUND
  Precompiled RegScan (all major platforms), source code, user guide and examples are freely available at www.biobank.ee/regscan.
  
  
  METHODS
  Qt 4.4.3 or newer for dynamic compilations.
authors.forename: Toomas
authors.initials: T
authors.lastname: Haller
authors.forename: Mart
authors.initials: M
authors.lastname: Kals
authors.forename: Tõnu
authors.initials: T
authors.lastname: Esko
authors.forename: Reedik
authors.initials: R
authors.lastname: Mägi
authors.forename: Krista
authors.initials: K
authors.lastname: Fischer
mesh_terms: D019295:Computational Biology / Q000379:methods*; D003627:Data Interpretation, Statistical; D005787:Gene Frequency; D055106:Genome-Wide Association Study / Q000379:methods*; D006801:Humans; D016014:Linear Models
keywords: GWAS; combinatorial traits; continuous traits; genome-wide analysis; linear regression; metabolomics