title: Advanced complex trait analysis.
abstract: BACKGROUND
  The Genome-wide Complex Trait Analysis (GCTA) software package can quantify the contribution of genetic variation to phenotypic variation for complex traits. However, as those datasets of interest continue to increase in size, GCTA becomes increasingly computationally prohibitive. We present an adapted version, Advanced Complex Trait Analysis (ACTA), demonstrating dramatically improved performance.
  
  
  RESULTS
  We restructure the genetic relationship matrix (GRM) estimation phase of the code and introduce the highly optimized parallel Basic Linear Algebra Subprograms (BLAS) library combined with manual parallelization and optimization. We introduce the Linear Algebra PACKage (LAPACK) library into the restricted maximum likelihood (REML) analysis stage. For a test case with 8999 individuals and 279,435 single nucleotide polymorphisms (SNPs), we reduce the total runtime, using a compute node with two multi-core Intel Nehalem CPUs, from ∼17 h to ∼11 min.
  
  
  METHODS
  The source code is fully available under the GNU Public License, along with Linux binaries. For more information see http://www.epcc.ed.ac.uk/software-products/acta.
  
  
  BACKGROUND
  a.gray@ed.ac.uk
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: EPCC, The University of Edinburgh, Edinburgh EH9 3JZ, UK. a.gray@ed.ac.uk
authors.forename: A
authors.initials: A
authors.lastname: Gray
authors.forename: I
authors.initials: I
authors.lastname: Stewart
authors.forename: A
authors.initials: A
authors.lastname: Tenesa
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D005838:Genotype; D006801:Humans; D016013:Likelihood Functions; D016014:Linear Models; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide; D012984:Software