Open Access Software

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

Unitsa Sangket1*, Surakameth Mahasirimongkol2, Wasun Chantratita3, Pichaya Tandayya4 and Yurii S Aulchenko56

Author Affiliations

1 Center for Genomics and Bioinformatics Research, Faculty of Science, Prince of Songkla University, Songkhla, 90112, Thailand

2 Medical Genetic Section, National Institute of Health, Department of Medical Sciences, Ministry of Public Health, Nonthaburi, 11000, Thailand

3 Department of Pathology, Faculty of Medicine, Ramathibodhi Hospital, Mahidol University, Bangkok, 10400, Thailand

4 Department of Computer Engineering, Faculty of Engineering, Prince of Songkla University, Songkhla, 90112, Thailand

5 Department of Epidemiology, Erasmus MC Rotterdam, Postbus 2040, 3000 CA Rotterdam, the Netherlands

6 Quantitative Integrative Genomics Group, Institute of Cytology & Genetics SD RAS, Novosibirsk 630090, Russia

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BMC Bioinformatics 2010, 11:217  doi:10.1186/1471-2105-11-217

Published: 29 April 2010



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.


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.


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.