ParaHaplo: A program package for haplotype-based whole-genome association study using parallel computing

We developed a set of computer programs for the parallel computation of accurate P-values in haplotype-based GWAS. Our program, ParaHaplo, is intended for workstation clusters using the Intel Message Passing Interface (MPI). We compared the performance of our algorithm to that of the regular permutation test on JPT and CHB of HapMap.ParaHaplo can detect smaller differences between 2 populations than SNP-based GWAS. We also found that parallel-computing techniques made ParaHaplo 100-fold faster than a non-parallel version of the program.ParaHaplo is a useful tool in conducting haplotype-based GWAS. Since the data sizes of such projects continue to increase, the use of fast computations with parallel computing--such as that used in ParaHaplo--will become increasingly important. The executable binaries and program sources of ParaHaplo are available at the following address: http://sourceforge.jp/projects/parallelgwas/?_sl=1 webciteRecent advances in high-throughput genotyping technologies have allowed us to test allele frequency differences between case and control populations on a genome-wide scale [1]. Genome-wide association studies (GWAS) are used to compare the frequency of alleles or genotypes of a particular variant between disease cases and controls, across a given genome. A common approach is to test for differences in the allele frequencies of every single-nucleotide polymorphism (SNP) between the case and the control populations, by using the chi-square test [2-4]. The chi-square test uses the Pearson score, which increases as the difference in allele frequency between 2 populations increase. The chi-square test evaluates the Pearson score by way of the chi-square distribution.One crucial problem in conducting SNP-based GWAS is performing corrections for multiple comparisons. A Bonferroni correction for a P-value is usually used to account for multiple testing under the assumption that all SNPs are independent. When SNP loci are in linkage disequilibrium, Bo