Dataset Information

Lep-MAP: fast and accurate linkage map construction for large SNP datasets.

ABSTRACT:

Motivation

Current high-throughput sequencing technologies allow cost-efficient genotyping of millions of single nucleotide polymorphisms (SNPs) for hundreds of samples. However, the tools that are currently available for constructing linkage maps are not well suited for large datasets. Linkage maps of large datasets would be helpful in de novo genome assembly by facilitating comprehensive genome validation and refinement by enabling chimeric scaffold detection, as well as in family-based linkage and association studies, quantitative trait locus mapping, analysis of genome synteny and other complex genomic data analyses.

Results

We describe a novel tool, called Lepidoptera-MAP (Lep-MAP), for constructing accurate linkage maps with ultradense genome-wide SNP data. Lep-MAP is fast and memory efficient and largely automated, requiring minimal user interaction. It uses simultaneously data on multiple outbred families and can increase linkage map accuracy by taking into account achiasmatic meiosis, a special feature of Lepidoptera and some other taxa with no recombination in one sex (no recombination in females in Lepidoptera). We demonstrate that Lep-MAP outperforms other methods on real and simulated data. We construct a genome-wide linkage map of the Glanville fritillary butterfly (Melitaea cinxia) with over 40 000 SNPs. The data were generated with a novel in-house SOLiD restriction site-associated DNA tag sequencing protocol, which is described in the online supplementary material.

Availability and implementation

Java source code under GNU general public license with the compiled classes and the datasets are available from http://sourceforge.net/users/lep-map.

SUBMITTER: Rastas P

PROVIDER: S-EPMC4433499 | biostudies-literature | 2013 Dec

REPOSITORIES: biostudies-literature

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Publications

Lep-MAP: fast and accurate linkage map construction for large SNP datasets.

Rastas Pasi P Paulin Lars L Hanski Ilkka I Lehtonen Rainer R Auvinen Petri P

Bioinformatics (Oxford, England) 20130926 24

<h4>Motivation</h4>Current high-throughput sequencing technologies allow cost-efficient genotyping of millions of single nucleotide polymorphisms (SNPs) for hundreds of samples. However, the tools that are currently available for constructing linkage maps are not well suited for large datasets. Linkage maps of large datasets would be helpful in de novo genome assembly by facilitating comprehensive genome validation and refinement by enabling chimeric scaffold detection, as well as in family-base ...[more]

PMID: 24078685

Similar Datasets

Project description:BACKGROUND:High levels of pairwise linkage disequilibrium (LD) in single nucleotide polymorphism (SNP) array or whole-genome sequence data may affect both performance and efficiency of genomic prediction models. Thus, this warrants pruning of genotyping data for high LD. We developed an algorithm, named SNPrune, which enables the rapid detection of any pair of SNPs in complete or high LD throughout the genome. METHODS:LD, measured as the squared correlation between phased alleles (r2), can only reach a value of 1 when both loci have the same count of the minor allele. Sorting loci based on the minor allele count, followed by comparison of their alleles, enables rapid detection of loci in complete LD. Detection of loci in high LD can be optimized by computing the range of the minor allele count at another locus for each possible value of the minor allele count that can yield LD values higher than a predefined threshold. This efficiently reduces the number of pairs of loci for which LD needs to be computed, instead of considering all pairwise combinations of loci. The implemented algorithm SNPrune considered bi-allelic loci either using phased alleles or allele counts as input. SNPrune was validated against PLINK on two datasets, using an r2 threshold of 0.99. The first dataset contained 52k SNP genotypes on 3534 pigs and the second dataset contained simulated whole-genome sequence data with 10.8 million SNPs and 2500 animals. RESULTS:SNPrune removed a similar number of SNPs as PLINK from the pig data but SNPrune was almost 12 times faster than PLINK. From the simulated sequence data with 10.8 million SNPs, SNPrune removed 6.4 and 1.4 million SNPs due to complete and high LD. Results were very similar regardless of whether phased alleles or allele counts were used. Using allele counts and multi-threading with 10 threads, SNPrune completed the analysis in 21 min. Using a sliding window of up to 500,000 SNPs, PLINK removed ~ 43,000 less SNPs (0.6%) in the sequence data and SNPrune was 24 to 170 times faster, using one or ten threads, respectively. CONCLUSIONS:The SNPrune algorithm developed here is able to remove SNPs in high LD throughout the genome very efficiently in large datasets.

Dataset Information

Lep-MAP: fast and accurate linkage map construction for large SNP datasets.

Motivation

Results

Availability and implementation

Publications

Lep-MAP: fast and accurate linkage map construction for large SNP datasets.

Similar Datasets

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