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ARS Home » Midwest Area » Urbana, Illinois » Soybean/maize Germplasm, Pathology, and Genetics Research » Research » Publications at this Location » Publication #214062

Title: Genomic DNA sequence comparison between two inbred soybean cyst nematode biotypes facilitated by massively parallel 454 microbead sequencing

Author
item BEKAL, S - UNIV OF ILLINOIS
item CRAIG, J - UNIV OF ILLINOIS
item HUDSON, M - UNIV OF ILLINOISS
item NIBLACK, T - UNIV OF ILLINOIS
item Domier, Leslie
item LAMBERT, K - UNIV OF ILLINOIS

Submitted to: Molecular Genetics and Genomics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/3/2008
Publication Date: 5/1/2008
Citation: Bekal, S., Craig, J.P., Hudson, M.E., Niblack, T.L., Domier, L.L., Lambert, K.N. 2008. Genomic DNA sequence comparison between two inbred soybean cyst nematode biotypes facilitated by massively parallel 454 microbead sequencing. Molecular Genetics and Genomics. 5:535-543.

Interpretive Summary: Soybean cyst nematode (SCN) is a microscopic parasitic worm that infects and damages roots of soybean plants. In the Midwestern United States, SCN is the most economically important pathogen of soybean. Soybean cultivars have been developed that are resistant to many SCN biotypes. However, in the field, SCN populations are mixtures of several different biotypes, some of which are capable of reproducing even on SCN-resistant soybean cultivars. Consequently, SCN populations can adapt to soybean cultivars with similar resistance genes as more virulent biotypes predominate within a population. The goal of this study was to develop methods to rapidly characterize the structure of SCN field populations so that producers can make informed decisions about which soybean cultivars to plant in order to best manage SCN populations and minimize yield losses. These studies found that massively parallel genome sequencing was a relatively inexpensive method to generate information about the genetic diversity of SCN individuals. The procedures described will be used to create genetic markers to simultaneously sample the diversity of hundreds of regions on SCN chromosomes to describe the proportions of different biotypes within a particular geographic area. These results will be used by scientists interested in soybean cyst nematode genomics and single nucleotide polymorphism (SNP) identification.

Technical Abstract: Heterodera glycines, the soybean cyst nematode (SCN), is the most important pathogen of soybean in the Midwestern United States. Genomic DNA sequence information for this nematode is limited and thus progress in devising genomic approaches to control this pathogen has been slow. To remedy this problem, genomic DNA sequence was collected from two inbred biotypes of SCN using a low-cost, massively parallel micro-bead DNA sequencing approach, developed by 454 Life Sciences. In this study, a total of over 400 million base pairs of DNA sequence were collected from two SCN biotypes. To assess the quality of this micro-bead DNA sequence, an SCN bacterial artificial chromosome (BAC) was used as a reference sequence. The BAC clone, sequenced using conventional dideoxy sequencing, was compared to the micro-bead sequence data sets; showing a 88% coverage with 95% sequence identity. Comparison of the micro-bead and BAC sequences allowed the mapping of high and low copy regions. The frequency and location of repetitive DNA in the BAC sequence was similar for both biotypes. In addition, numerous single nucleotide polymorphisms (SNPs) and single base insertion/deletions (indels) were identified between the BAC and micro-bead sequences. Selected resequencing of the SCN genome revealed that most indels were sequenceing errors; however, 84% of SNPs were correct. Comparison of low-copy micro-bead sequences at the whole genome level produced 9427 putative SNPs with an 80% verification rate upon resequencing. We conclude from this study that the quality of the micro-bead sequence was sufficient to find SNPs between two SCN biotypes. Thus, micro-bead sequencing will be a useful tool for large-scale SNP discovery for the production of high-resolution genetic analysis of SCN.