Dataset Information

A new haplotype block detection method for dense genome sequencing data based on interval graph modeling of clusters of highly correlated SNPs.

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.

SUBMITTER: Kim SA

PROVIDER: S-EPMC5860363 | biostudies-other | 2018 Feb

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

A new haplotype block detection method for dense genome sequencing data based on interval graph modeling of clusters of highly correlated SNPs.

Kim Sun Ah SA Cho Chang-Sung CS Kim Suh-Ryung SR Bull Shelley B SB Yoo Yun Joo YJ

Bioinformatics (Oxford, England) 20180201 3

<h4>Motivation</h4>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 d ...[more]

PMID: 29028986

Similar Datasets

Project description:BACKGROUND:The new sequencing technologies enable to scan very long and dense genetic sequences, obtaining datasets of genetic markers that are an order of magnitude larger than previously available. Such genetic sequences are characterized by common alleles interspersed with multiple rarer alleles. This situation has renewed the interest for the identification of haplotypes carrying the rare risk alleles. However, large scale explorations of the linkage-disequilibrium (LD) pattern to identify haplotype blocks are not easy to perform, because traditional algorithms have at least ?(n2) time and memory complexity. RESULTS:We derived three incremental optimizations of the widely used haplotype block recognition algorithm proposed by Gabriel et al. in 2002. Our most efficient solution, called MIG ++, has only ?(n) memory complexity and, on a genome-wide scale, it omits >80% of the calculations, which makes it an order of magnitude faster than the original algorithm. Differently from the existing software, the MIG ++ analyzes the LD between SNPs at any distance, avoiding restrictions on the maximal block length. The haplotype block partition of the entire HapMap II CEPH dataset was obtained in 457 hours. By replacing the standard likelihood-based D' variance estimator with an approximated estimator, the runtime was further improved. While producing a coarser partition, the approximate method allowed to obtain the full-genome haplotype block partition of the entire 1000 Genomes Project CEPH dataset in 44 hours, with no restrictions on allele frequency or long-range correlations. These experiments showed that LD-based haplotype blocks can span more than one million base-pairs in both HapMap II and 1000 Genomes datasets. An application to the North American Rheumatoid Arthritis Consortium (NARAC) dataset shows how the MIG ++ can support genome-wide haplotype association studies. CONCLUSIONS:The MIG ++ enables to perform LD-based haplotype block recognition on genetic sequences of any length and density. In the new generation sequencing era, this can help identify haplotypes that carry rare variants of interest. The low computational requirements open the possibility to include the haplotype block structure into genome-wide association scans, downstream analyses, and visual interfaces for online genome browsers.

Project description:The advent of genome-wide dense variation data provides an opportunity to investigate ancestry in unprecedented detail, but presents new statistical challenges. We propose a novel inference framework that aims to efficiently capture information on population structure provided by patterns of haplotype similarity. Each individual in a sample is considered in turn as a recipient, whose chromosomes are reconstructed using chunks of DNA donated by the other individuals. Results of this "chromosome painting" can be summarized as a "coancestry matrix," which directly reveals key information about ancestral relationships among individuals. If markers are viewed as independent, we show that this matrix almost completely captures the information used by both standard Principal Components Analysis (PCA) and model-based approaches such as STRUCTURE in a unified manner. Furthermore, when markers are in linkage disequilibrium, the matrix combines information across successive markers to increase the ability to discern fine-scale population structure using PCA. In parallel, we have developed an efficient model-based approach to identify discrete populations using this matrix, which offers advantages over PCA in terms of interpretability and over existing clustering algorithms in terms of speed, number of separable populations, and sensitivity to subtle population structure. We analyse Human Genome Diversity Panel data for 938 individuals and 641,000 markers, and we identify 226 populations reflecting differences on continental, regional, local, and family scales. We present multiple lines of evidence that, while many methods capture similar information among strongly differentiated groups, more subtle population structure in human populations is consistently present at a much finer level than currently available geographic labels and is only captured by the haplotype-based approach. The software used for this article, ChromoPainter and fineSTRUCTURE, is available from http://www.paintmychromosomes.com/.

Dataset Information

A new haplotype block detection method for dense genome sequencing data based on interval graph modeling of clusters of highly correlated SNPs.

Publications

A new haplotype block detection method for dense genome sequencing data based on interval graph modeling of clusters of highly correlated SNPs.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets