Summary of marker depths.

<p>Summary of marker depths.</p

Number of markers and average sequencing depth.

<p>The x-axes in A and B indicate individual recombinant inbred line plant accessions, and the y-axes indicate the number of markers (A) and average depth (B).</p

Number of markers in each of eight segregation patterns.

<p>Number of markers in each of eight segregation patterns.</p

A High-Density Genetic Map for Soybean Based on Specific Length Amplified Fragment Sequencing

<div><p>Soybean is an important oil seed crop, but very few high-density genetic maps have been published for this species. Specific length amplified fragment sequencing (SLAF-seq) is a recently developed high-resolution strategy for large scale <i>de novo</i> discovery and genotyping of single nucleotide polymorphisms. SLAF-seq was employed in this study to obtain sufficient markers to construct a high-density genetic map for soybean. In total, 33.10 Gb of data containing 171,001,333 paired-end reads were obtained after preprocessing. The average sequencing depth was 42.29 in the Dongnong594, 56.63 in the Charleston, and 3.92 in each progeny. In total, 164,197 high-quality SLAFs were detected, of which 12,577 SLAFs were polymorphic, and 5,308 of the polymorphic markers met the requirements for use in constructing a genetic map. The final map included 5,308 markers on 20 linkage groups and was 2,655.68 cM in length, with an average distance of 0.5 cM between adjacent markers. To our knowledge, this map has the shortest average distance of adjacent markers for soybean. We report here a high-density genetic map for soybean. The map was constructed using a recombinant inbred line population and the SLAF-seq approach, which allowed the efficient development of a large number of polymorphic markers in a short time. Results of this study will not only provide a platform for gene/quantitative trait loci fine mapping, but will also serve as a reference for molecular breeding of soybean.</p></div

SLAF markers numbers on each chromosome.

<p>SLAF markers numbers on each chromosome.</p

Description on basic characteristics of the 20 linkage groups.

<p>‘Gap≤5’ indicated the percentages of gaps in which the distance between adjacent markers was smaller than 5 cM.</p

Construction and Analysis of High-Density Linkage Map Using High-Throughput Sequencing Data

<div><p>Linkage maps enable the study of important biological questions. The construction of high-density linkage maps appears more feasible since the advent of next-generation sequencing (NGS), which eases SNP discovery and high-throughput genotyping of large population. However, the marker number explosion and genotyping errors from NGS data challenge the computational efficiency and linkage map quality of linkage study methods. Here we report the HighMap method for constructing high-density linkage maps from NGS data. HighMap employs an iterative ordering and error correction strategy based on a k-nearest neighbor algorithm and a Monte Carlo multipoint maximum likelihood algorithm. Simulation study shows HighMap can create a linkage map with three times as many markers as ordering-only methods while offering more accurate marker orders and stable genetic distances. Using HighMap, we constructed a common carp linkage map with 10,004 markers. The singleton rate was less than one-ninth of that generated by JoinMap4.1. Its total map distance was 5,908 cM, consistent with reports on low-density maps. HighMap is an efficient method for constructing high-density, high-quality linkage maps from high-throughput population NGS data. It will facilitate genome assembling, comparative genomic analysis, and QTL studies. HighMap is available at <a href="http://highmap.biomarker.com.cn/" target="_blank">http://highmap.biomarker.com.cn/</a>.</p></div

Modules of HighMap algorithm.

<p>A: The single-linkage clustering algorithm was used to partition the marker loci into linkage groups based on a pairwise modified independence LOD score for the recombination frequency. B and B': The ordering module combines Gibbs sampling, spatial sampling, and simulated annealing algorithm to order markers and estimate map distances. C: The error correction module identified singletons according to parental contribution of genotypes and eliminated them from the data using <i>k</i>-nearest neighbor algorithm. To order markers correctly, the processes of ordering and error correction were carried out iteratively. D: Heat maps and haplotype maps were constructed to evaluate map quality.</p

Capability of missing imputation and error correction of HighMap.

<p>Capability of missing imputation and error correction of HighMap.</p

SLAF-seq: An Efficient Method of Large-Scale <em>De Novo</em> SNP Discovery and Genotyping Using High-Throughput Sequencing

<div><p>Large-scale genotyping plays an important role in genetic association studies. It has provided new opportunities for gene discovery, especially when combined with high-throughput sequencing technologies. Here, we report an efficient solution for large-scale genotyping. We call it specific-locus amplified fragment sequencing (SLAF-seq). SLAF-seq technology has several distinguishing characteristics: i) deep sequencing to ensure genotyping accuracy; ii) reduced representation strategy to reduce sequencing costs; iii) pre-designed reduced representation scheme to optimize marker efficiency; and iv) double barcode system for large populations. In this study, we tested the efficiency of SLAF-seq on rice and soybean data. Both sets of results showed strong consistency between predicted and practical SLAFs and considerable genotyping accuracy. We also report the highest density genetic map yet created for any organism without a reference genome sequence, common carp in this case, using SLAF-seq data. We detected 50,530 high-quality SLAFs with 13,291 SNPs genotyped in 211 individual carp. The genetic map contained 5,885 markers with 0.68 cM intervals on average. A comparative genomics study between common carp genetic map and zebrafish genome sequence map showed high-quality SLAF-seq genotyping results. SLAF-seq provides a high-resolution strategy for large-scale genotyping and can be generally applicable to various species and populations.</p> </div