title: ParallABEL: an R library for generalized parallelization of genome-wide association studies.
abstract: BACKGROUND
  Genome-Wide Association (GWA) analysis is a powerful method for identifying loci associated with complex traits and drug response. Parts of GWA analyses, especially those involving thousands of individuals and consuming hours to months, will benefit from parallel computation. It is arduous acquiring the necessary programming skills to correctly partition and distribute data, control and monitor tasks on clustered computers, and merge output files.
  
  
  RESULTS
  Most components of GWA analysis can be divided into four groups based on the types of input data and statistical outputs. The first group contains statistics computed for a particular Single Nucleotide Polymorphism (SNP), or trait, such as SNP characterization statistics or association test statistics. The input data of this group includes the SNPs/traits. The second group concerns statistics characterizing an individual in a study, for example, the summary statistics of genotype quality for each sample. The input data of this group includes individuals. The third group consists of pair-wise statistics derived from analyses between each pair of individuals in the study, for example genome-wide identity-by-state or genomic kinship analyses. The input data of this group includes pairs of SNPs/traits. The final group concerns pair-wise statistics derived for pairs of SNPs, such as the linkage disequilibrium characterisation. The input data of this group includes pairs of individuals. We developed the ParallABEL library, which utilizes the Rmpi library, to parallelize these four types of computations. ParallABEL library is not only aimed at GenABEL, but may also be employed to parallelize various GWA packages in R. The data set from the North American Rheumatoid Arthritis Consortium (NARAC) includes 2,062 individuals with 545,080, SNPs' genotyping, was used to measure ParallABEL performance. Almost perfect speed-up was achieved for many types of analyses. For example, the computing time for the identity-by-state matrix was linearly reduced from approximately eight hours to one hour when ParallABEL employed eight processors.
  
  
  CONCLUSIONS
  Executing genome-wide association analysis using the ParallABEL library on a computer cluster is an effective way to boost performance, and simplify the parallelization of GWA studies. ParallABEL is a user-friendly parallelization of GenABEL.
authors.affiliation: Center for Genomics and Bioinformatics Research, Faculty of Science, Prince of Songkla University, Songkhla, 90112, Thailand. usangket@yahoo.com
authors.forename: Unitsa
authors.initials: U
authors.lastname: Sangket
authors.forename: Surakameth
authors.initials: S
authors.lastname: Mahasirimongkol
authors.forename: Wasun
authors.initials: W
authors.lastname: Chantratita
authors.forename: Pichaya
authors.initials: P
authors.lastname: Tandayya
authors.forename: Yurii S
authors.initials: YS
authors.lastname: Aulchenko
mesh_terms: D015723:Gene Library; D016678:Genome; D055106:Genome-Wide Association Study; D023281:Genomics / Q000379:methods*; D020641:Polymorphism, Single Nucleotide; D012984:Software