Dataset Information

Construction of Ultradense Linkage Maps with Lep-MAP2: Stickleback F2 Recombinant Crosses as an Example.

ABSTRACT: High-density linkage maps are important tools for genome biology and evolutionary genetics by quantifying the extent of recombination, linkage disequilibrium, and chromosomal rearrangements across chromosomes, sexes, and populations. They provide one of the best ways to validate and refine de novo genome assemblies, with the power to identify errors in assemblies increasing with marker density. However, assembly of high-density linkage maps is still challenging due to software limitations. We describe Lep-MAP2, a software for ultradense genome-wide linkage map construction. Lep-MAP2 can handle various family structures and can account for achiasmatic meiosis to gain linkage map accuracy. Simulations show that Lep-MAP2 outperforms other available mapping software both in computational efficiency and accuracy. When applied to two large F2-generation recombinant crosses between two nine-spined stickleback (Pungitius pungitius) populations, it produced two high-density (?6 markers/cM) linkage maps containing 18,691 and 20,054 single nucleotide polymorphisms. The two maps showed a high degree of synteny, but female maps were 1.5-2 times longer than male maps in all linkage groups, suggesting genome-wide recombination suppression in males. Comparison with the genome sequence of the three-spined stickleback (Gasterosteus aculeatus) revealed a high degree of interspecific synteny with a low frequency (<5%) of interchromosomal rearrangements. However, a fairly large (ca. 10 Mb) translocation from autosome to sex chromosome was detected in both maps. These results illustrate the utility and novel features of Lep-MAP2 in assembling high-density linkage maps, and their usefulness in revealing evolutionarily interesting properties of genomes, such as strong genome-wide sex bias in recombination rates.

SUBMITTER: Rastas P

PROVIDER: S-EPMC4758246 | biostudies-literature | 2015 Dec

REPOSITORIES: biostudies-literature

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Construction of Ultradense Linkage Maps with Lep-MAP2: Stickleback F2 Recombinant Crosses as an Example.

Rastas Pasi P Calboli Federico C F FC Guo Baocheng B Shikano Takahito T Merilä Juha J

Genome biology and evolution 20151214 1

High-density linkage maps are important tools for genome biology and evolutionary genetics by quantifying the extent of recombination, linkage disequilibrium, and chromosomal rearrangements across chromosomes, sexes, and populations. They provide one of the best ways to validate and refine de novo genome assemblies, with the power to identify errors in assemblies increasing with marker density. However, assembly of high-density linkage maps is still challenging due to software limitations. We de ...[more]

PMID: 26668116

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Project description:BackgroundGenotyping and sequencing technologies produce increasingly large numbers of genetic markers with potentially high rates of missing or erroneous data. Therefore, the construction of linkage maps is more and more complex. Moreover, the size of segregating populations remains constrained by cost issues and is less and less commensurate with the numbers of SNPs available. Thus, guaranteeing a statistically robust marker order requires that maps include only a carefully selected subset of SNPs.ResultsIn this context, the SeSAM software allows automatic genetic map construction using seriation and placement approaches, to produce (1) a high-robustness framework map which includes as many markers as possible while keeping the order robustness beyond a given statistical threshold, and (2) a high-density total map including the framework plus almost all polymorphic markers. During this process, care is taken to limit the impact of genotyping errors and of missing data on mapping quality. SeSAM can be used with a wide range of biparental populations including from outcrossing species for which phases are inferred on-the-fly by maximum-likelihood during map elongation. The package also includes functions to simulate data sets, convert data formats, detect putative genotyping errors, visualize data and map quality (including graphical genotypes), and merge several maps into a consensus. SeSAM is also suitable for interactive map construction, by providing lower-level functions for 2-point and multipoint EM analyses. The software is implemented in a R package including functions in C++.ConclusionsSeSAM is a fully automatic linkage mapping software designed to (1) produce a framework map as robust as desired by optimizing the selection of a subset of markers, and (2) produce a high-density map including almost all polymorphic markers. The software can be used with a wide range of biparental mapping populations including cases from outcrossing. SeSAM is freely available under a GNU GPL v3 license and works on Linux, Windows, and macOS platforms. It can be downloaded together with its user-manual and quick-start tutorial from ForgeMIA (SeSAM project) at https://forgemia.inra.fr/gqe-acep/sesam/-/releases.

Project description:BackgroundGenotyping-by-sequencing (GBS) provides affordable methods for genotyping hundreds of individuals using millions of markers. However, this challenges bioinformatic procedures that must overcome possible artifacts such as the bias generated by polymerase chain reaction duplicates and sequencing errors. Genotyping errors lead to data that deviate from what is expected from regular meiosis. This, in turn, leads to difficulties in grouping and ordering markers, resulting in inflated and incorrect linkage maps. Therefore, genotyping errors can be easily detected by linkage map quality evaluations.ResultsWe developed and used the Reads2Map workflow to build linkage maps with simulated and empirical GBS data of diploid outcrossing populations. The workflows run GATK, Stacks, TASSEL, and Freebayes for single-nucleotide polymorphism calling and updog, polyRAD, and SuperMASSA for genotype calling, as well as OneMap and GUSMap to build linkage maps. Using simulated data, we observed which genotype call software fails in identifying common errors in GBS sequencing data and proposed specific filters to better handle them. We tested whether it is possible to overcome errors in a linkage map using genotype probabilities from each software or global error rates to estimate genetic distances with an updated version of OneMap. We also evaluated the impact of segregation distortion, contaminant samples, and haplotype-based multiallelic markers in the final linkage maps. Through our evaluations, we observed that some of the approaches produce different results depending on the dataset (dataset dependent) and others produce consistent advantageous results among them (dataset independent).ConclusionsWe set as default in the Reads2Map workflows the approaches that showed to be dataset independent for GBS datasets according to our results. This reduces the number of required tests to identify optimal pipelines and parameters for other empirical datasets. Using Reads2Map, users can select the pipeline and parameters that best fit their data context. The Reads2MapApp shiny app provides a graphical representation of the results to facilitate their interpretation.

Dataset Information

Construction of Ultradense Linkage Maps with Lep-MAP2: Stickleback F2 Recombinant Crosses as an Example.

Publications

Construction of Ultradense Linkage Maps with Lep-MAP2: Stickleback F2 Recombinant Crosses as an Example.

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