Kim SA, Cho CS, Kim SR, Bull SB, Yoo YJ. A new haplotype block detection method for dense genome sequencing data based on interval graph modeling of clusters of highly correlated SNPs.
Bioinformatics 2018;
34:388-397. [PMID:
29028986 PMCID:
PMC5860363 DOI:
10.1093/bioinformatics/btx609]
[Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2016] [Revised: 09/11/2017] [Accepted: 09/28/2017] [Indexed: 11/13/2022] Open
Abstract
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
yyoo@snu.ac.kr.
Supplementary information
Supplementary data are available at Bioinformatics online.
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