ParaHaplo 2.0: a program package for haplotype-estimation and haplotype-based whole-genome association study using parallel computing

We developed a program package for parallel computation of haplotype estimation. Our program package, ParaHaplo 2.0, is intended for use in workstation clusters using the Intel Message Passing Interface (MPI). We compared the performance of our algorithm to that of the regular permutation test on both Japanese in Tokyo, Japan and Han Chinese in Beijing, China of the HapMap dataset.Parallel version of ParaHaplo 2.0 can estimate haplotypes 100 times faster than a non-parallel version of the ParaHaplo.ParaHaplo 2.0 is an invaluable tool for conducting haplotype-based genome-wide association studies (GWAS). The need for fast haplotype estimation using parallel computing will become increasingly important as the data sizes of such projects continue to increase. The executable binaries and program sources of ParaHaplo are available at the following address: http://en.sourceforge.jp/projects/parallelgwas/releases/ webciteRecent advances in various 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 cases and controls for a particular disease across a given genome [2-4]. More than a million single-nucleotide polymorphisms (SNPs) are analyzed in SNP-based GWAS. One difficulty faced when conducting SNP-based GWAS is performing corrections for multiple comparisons. Under the assumption that all SNPs are independent, a Bonferroni correction for a P value is usually used to account for multiple tests. When SNP loci are in linkage disequilibrium, Bonferroni corrections are known to be too conservative [5]. As a result, SNP-based GWAS may exclude the truly significant SNPs from analysis [6].To cope with problems related to multiple comparisons in GWAS, haplotype-based algorithms were developed to correct for multiple comparisons at multiple SNP loci