Dataset Information

High-throughput SNP discovery and assay development in common bean.

ABSTRACT: BACKGROUND: Next generation sequencing has significantly increased the speed at which single nucleotide polymorphisms (SNPs) can be discovered and subsequently used as molecular markers for research. Unfortunately, for species such as common bean (Phaseolus vulgaris L.) which do not have a whole genome sequence available, the use of next generation sequencing for SNP discovery is much more difficult and costly. To this end we developed a method which couples sequences obtained from the Roche 454-FLX system (454) with the Illumina Genome Analyzer (GA) for high-throughput SNP discovery. RESULTS: Using a multi-tier reduced representation library we discovered a total of 3,487 SNPs of which 2,795 contained sufficient flanking genomic sequence for SNP assay development. Using Sanger sequencing to determine the validation rate of these SNPs, we found that 86% are likely to be true SNPs. Furthermore, we designed a GoldenGate assay which contained 1,050 of the 3,487 predicted SNPs. A total of 827 of the 1,050 SNPs produced a working GoldenGate assay (79%). CONCLUSIONS: Through combining two next generation sequencing techniques we have developed a method that allows high-throughput SNP discovery in any diploid organism without the need of a whole genome sequence or the creation of normalized cDNA libraries. The need to only perform one 454 run and one GA sequencer run allows high-throughput SNP discovery with sufficient sequence for assay development to be performed in organisms, such as common bean, which have limited genomic resources.

SUBMITTER: Hyten DL

PROVIDER: S-EPMC3091671 | biostudies-literature | 2010

REPOSITORIES: biostudies-literature

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High-throughput SNP discovery and assay development in common bean.

Hyten David L DL Song Qijian Q Fickus Edward W EW Quigley Charles V CV Lim Jong-Sung JS Choi Ik-Young IY Hwang Eun-Young EY Pastor-Corrales Marcial M Cregan Perry B PB

BMC genomics 20100816

<h4>Background</h4>Next generation sequencing has significantly increased the speed at which single nucleotide polymorphisms (SNPs) can be discovered and subsequently used as molecular markers for research. Unfortunately, for species such as common bean (Phaseolus vulgaris L.) which do not have a whole genome sequence available, the use of next generation sequencing for SNP discovery is much more difficult and costly. To this end we developed a method which couples sequences obtained from the Ro ...[more]

PMID: 20712881

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Project description:BackgroundSingle nucleotide polymorphisms (SNPs) as defined here are single base sequence changes or short insertion/deletions between or within individuals of a given species. As a result of their abundance and the availability of high throughput analysis technologies SNP markers have begun to replace other traditional markers such as restriction fragment length polymorphisms (RFLPs), amplified fragment length polymorphisms (AFLPs) and simple sequence repeats (SSRs or microsatellite) markers for fine mapping and association studies in several species. For SNP discovery from chromatogram data, several bioinformatics programs have to be combined to generate an analysis pipeline. Results have to be stored in a relational database to facilitate interrogation through queries or to generate data for further analyses such as determination of linkage disequilibrium and identification of common haplotypes. Although these tasks are routinely performed by several groups, an integrated open source SNP discovery pipeline that can be easily adapted by new groups interested in SNP marker development is currently unavailable.ResultsWe developed SNP-PHAGE (SNP discovery Pipeline with additional features for identification of common haplotypes within a sequence tagged site (Haplotype Analysis) and GenBank (-dbSNP) submissions. This tool was applied for analyzing sequence traces from diverse soybean genotypes to discover over 10,000 SNPs. This package was developed on UNIX/Linux platform, written in Perl and uses a MySQL database. Scripts to generate a user-friendly web interface are also provided with common queries for preliminary data analysis. A machine learning tool developed by this group for increasing the efficiency of SNP discovery is integrated as a part of this package as an optional feature. The SNP-PHAGE package is being made available open source at http://bfgl.anri.barc.usda.gov/ML/snp-phage/.ConclusionSNP-PHAGE provides a bioinformatics solution for high throughput SNP discovery, identification of common haplotypes within an amplicon, and GenBank (dbSNP) submissions. SNP selection and visualization are aided through a user-friendly web interface. This tool is useful for analyzing sequence tagged sites (STSs) of genomic sequences, and this software can serve as a starting point for groups interested in developing SNP markers.

Project description:BackgroundWith the advance of new massively parallel genotyping technologies, quantitative trait loci (QTL) fine mapping and map-based cloning become more achievable in identifying genes for important and complex traits. Development of high-density genetic markers in the QTL regions of specific mapping populations is essential for fine-mapping and map-based cloning of economically important genes. Single nucleotide polymorphisms (SNPs) are the most abundant form of genetic variation existing between any diverse genotypes that are usually used for QTL mapping studies. The massively parallel sequencing technologies (Roche GS/454, Illumina GA/Solexa, and ABI/SOLiD), have been widely applied to identify genome-wide sequence variations. However, it is still remains unclear whether sequence data at a low sequencing depth are enough to detect the variations existing in any QTL regions of interest in a crop genome, and how to prepare sequencing samples for a complex genome such as soybean. Therefore, with the aims of identifying SNP markers in a cost effective way for fine-mapping several QTL regions, and testing the validation rate of the putative SNPs predicted with Solexa short sequence reads at a low sequencing depth, we evaluated a pooled DNA fragment reduced representation library and SNP detection methods applied to short read sequences generated by Solexa high-throughput sequencing technology.ResultsA total of 39,022 putative SNPs were identified by the Illumina/Solexa sequencing system using a reduced representation DNA library of two parental lines of a mapping population. The validation rates of these putative SNPs predicted with low and high stringency were 72% and 85%, respectively. One hundred sixty four SNP markers resulted from the validation of putative SNPs and have been selectively chosen to target a known QTL, thereby increasing the marker density of the targeted region to one marker per 42 K bp.ConclusionsWe have demonstrated how to quickly identify large numbers of SNPs for fine mapping of QTL regions by applying massively parallel sequencing combined with genome complexity reduction techniques. This SNP discovery approach is more efficient for targeting multiple QTL regions in a same genetic population, which can be applied to other crops.

Project description:BackgroundSoybean cyst nematode (SCN) is the most economically devastating pathogen of soybean. Two resistance loci, Rhg1 and Rhg4 primarily contribute resistance to SCN race 3 in soybean. Peking and PI 88788 are the two major sources of SCN resistance with Peking requiring both Rhg1 and Rhg4 alleles and PI 88788 only the Rhg1 allele. Although simple sequence repeat (SSR) markers have been reported for both loci, they are linked markers and limited to be applied in breeding programs due to accuracy, throughput and cost of detection methods. The objectives of this study were to develop robust functional marker assays for high-throughput selection of SCN resistance and to differentiate the sources of resistance.ResultsBased on the genomic DNA sequences of 27 soybean lines with known SCN phenotypes, we have developed Kompetitive Allele Specific PCR (KASP) assays for two Single nucleotide polymorphisms (SNPs) from Glyma08g11490 for the selection of the Rhg4 resistance allele. Moreover, the genomic DNA of Glyma18g02590 at the Rhg1 locus from 11 soybean lines and cDNA of Forrest, Essex, Williams 82 and PI 88788 were fully sequenced. Pairwise sequence alignment revealed seven SNPs/insertion/deletions (InDels), five in the 6th exon and two in the last exon. Using the same 27 soybean lines, we identified one SNP that can be used to select the Rhg1 resistance allele and another SNP that can be employed to differentiate Peking and PI 88788-type resistance. These SNP markers have been validated and a strong correlation was observed between the SNP genotypes and reactions to SCN race 3 using a panel of 153 soybean lines, as well as a bi-parental population, F5-derived recombinant inbred lines (RILs) from G00-3213xLG04-6000.ConclusionsThree functional SNP markers (two for Rhg1 locus and one for Rhg4 locus) were identified that could provide genotype information for the selection of SCN resistance and differentiate Peking from PI 88788 source for most germplasm lines. The robust KASP SNP marker assays were developed. In most contexts, use of one or two of these markers is sufficient for high-throughput marker-assisted selection of plants that will exhibit SCN resistance.

Project description:BACKGROUND:Single nucleotide polymorphisms (SNP) have been applied as important molecular markers in genetics and breeding studies. The rapid advance of next generation sequencing (NGS) provides a high-throughput means of SNP discovery. However, SNP development is limited by the availability of reliable SNP discovery methods. Especially, the optimum assembler and SNP caller for accurate SNP prediction from next generation sequencing data are not known. RESULTS:Herein we performed SNP prediction based on RNA-seq data of peach and mandarin peel tissue under a comprehensive comparison of two paired-end read lengths (125?bp and 150?bp), five assemblers (Trinity, IDBA, oases, SOAPdenovo, Trans-abyss) and two SNP callers (GATK and GBS). The predicted SNPs were compared with the authentic SNPs identified via PCR amplification followed by gene cloning and sequencing procedures. A total of 40 and 240 authentic SNPs were presented in five anthocyanin biosynthesis related genes in peach and in nine carotenogenic genes in mandarin. Putative SNPs predicted from the same RNA-seq data with different strategies led to quite divergent results. The rate of false positive SNPs was significantly lower when the paired-end read length was 150?bp compared with 125?bp. Trinity was superior to the other four assemblers and GATK was substantially superior to GBS due to a low rate of missing authentic SNPs. The combination of assembler Trinity, SNP caller GATK, and the paired-end read length 150?bp had the best performance in SNP discovery with 100% accuracy both in peach and in mandarin cases. This strategy was applied to the characterization of SNPs in peach and mandarin transcriptomes. CONCLUSIONS:Through comparison of authentic SNPs obtained by PCR cloning strategy and putative SNPs predicted from different combinations of five assemblers, two SNP callers, and two paired-end read lengths, we provided a reliable and efficient strategy, Trinity-GATK with 150?bp paired-end read length, for SNP discovery from RNA-seq data. This strategy discovered SNP at 100% accuracy in peach and mandarin cases and might be applicable to a wide range of plants and other organisms.

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High-throughput SNP discovery and assay development in common bean.

Publications

High-throughput SNP discovery and assay development in common bean.

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