ParallABEL: an R library for generalized parallelization of genome-wide association studies

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 o...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Unitsa Sangket, Surakameth Mahasirimongkol, Wasun Chantratita, Pichaya Tandayya, Yurii S. Aulchenko
• Keywords: BMCBI 2010, Group, Input Data, Statistics