SUMMARY: For the analysis of high-throughput genomic data produced by next-generation sequencing (NGS) technologies, researchers need to identify linkage disequilibrium (LD) structure in the genome. In this work, we developed an R package gpart which provides clustering algorithms to define LD blocks or analysis units consisting of SNPs. The visualization tool in gpart can display the LD structure and gene positions for up to 20 000 SNPs in one image. The gpart functions facilitate construction of LD blocks and SNP partitions for vast amounts of genome sequencing data within reasonable time and memory limits in personal computing environments. AVAILABILITY AND IMPLEMENTATION: The R package is available at https://bioconductor.org/packages/gpart. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Motivation: Linkage disequilibrium (LD) block construction is required for research in population genetics and genetic epidemiology, including specification of sets of single nucleotide polymorphisms (SNPs) for analysis of multi-SNP based association and identification of haplotype blocks in high density sequencing data. Existing methods based on a narrow sense definition do not allow intermediate regions of low LD between strongly associated SNP pairs and tend to split high density SNP data into small blocks having high between-block correlation. Results: We present Big-LD, a block partition method based on interval graph modeling of LD bins which are clusters of strong pairwise LD SNPs, not necessarily physically consecutive. Big-LD uses an agglomerative approach that starts by identifying small communities of SNPs, i.e. the SNPs in each LD bin region, and proceeds by merging these communities. We determine the number of blocks using a method to find maximum-weight independent set. Big-LD produces larger LD blocks compared to existing methods such as MATILDE, Haploview, MIG ++, or S-MIG ++ and the LD blocks better agree with recombination hotspot locations determined by sperm-typing experiments. The observed average runtime of Big-LD for 13 288 240 non-monomorphic SNPs from 1000 Genomes Project autosome data (286 East Asians) is about 5.83 h, which is a significant improvement over the existing methods. Availability and implementation: Source code and documentation are available for download at http://github.com/sunnyeesl/BigLD. Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

Linkage disequilibrium structure (LD) is the main source of the study of population genetics and disease-gene association. Especially, analyzing extended long haplotypes carrying a derived allele and examining LD block patterns can provide evidence for positive selection. We investigated the LD block structure of East Asian, European, and African populations for the previously reported sites of positive selection by comparing LD block construction results based on 1000 Genomes Project data. We confirmed that differences of LD block size in EDAR, LCT, PCDH15, and LARGE region among different populations is consistent with previous reports. We found new evidence for positive selection in SLC30A19, PDE11A and BCAS3 in East Asian and European populations based on the LD block patterns.