Dataset Information

Automated SNP detection from a large collection of white spruce expressed sequences: contributing factors and approaches for the categorization of SNPs.

ABSTRACT:

Background

High-throughput genotyping technologies represent a highly efficient way to accelerate genetic mapping and enable association studies. As a first step toward this goal, we aimed to develop a resource of candidate Single Nucleotide Polymorphisms (SNP) in white spruce (Picea glauca [Moench] Voss), a softwood tree of major economic importance.

Results

A white spruce SNP resource encompassing 12,264 SNPs was constructed from a set of 6,459 contigs derived from Expressed Sequence Tags (EST) and by using the bayesian-based statistical software PolyBayes. Several parameters influencing the SNP prediction were analysed including the a priori expected polymorphism, the probability score (PSNP), and the contig depth and length. SNP detection in 3' and 5' reads from the same clones revealed a level of inconsistency between overlapping sequences as low as 1%. A subset of 245 predicted SNPs were verified through the independent resequencing of genomic DNA of a genotype also used to prepare cDNA libraries. The validation rate reached a maximum of 85% for SNPs predicted with either PSNP > or = 0.95 or > or = 0.99. A total of 9,310 SNPs were detected by using PSNP > or = 0.95 as a criterion. The SNPs were distributed among 3,590 contigs encompassing an array of broad functional categories, with an overall frequency of 1 SNP per 700 nucleotide sites. Experimental and statistical approaches were used to evaluate the proportion of paralogous SNPs, with estimates in the range of 8 to 12%. The 3,789 coding SNPs identified through coding region annotation and ORF prediction, were distributed into 39% nonsynonymous and 61% synonymous substitutions. Overall, there were 0.9 SNP per 1,000 nonsynonymous sites and 5.2 SNPs per 1,000 synonymous sites, for a genome-wide nonsynonymous to synonymous substitution rate ratio (Ka/Ks) of 0.17.

Conclusion

We integrated the SNP data in the ForestTreeDB database along with functional annotations to provide a tool facilitating the choice of candidate genes for mapping purposes or association studies.

SUBMITTER: Pavy N

PROVIDER: S-EPMC1557672 | biostudies-literature | 2006 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Automated SNP detection from a large collection of white spruce expressed sequences: contributing factors and approaches for the categorization of SNPs.

Pavy Nathalie N Parsons Lee S LS Paule Charles C MacKay John J Bousquet Jean J

BMC genomics 20060706

<h4>Background</h4>High-throughput genotyping technologies represent a highly efficient way to accelerate genetic mapping and enable association studies. As a first step toward this goal, we aimed to develop a resource of candidate Single Nucleotide Polymorphisms (SNP) in white spruce (Picea glauca [Moench] Voss), a softwood tree of major economic importance.<h4>Results</h4>A white spruce SNP resource encompassing 12,264 SNPs was constructed from a set of 6,459 contigs derived from Expressed Seq ...[more]

PMID: 16824208

Similar Datasets

Project description:Biological material is at the forefront of research programs, as well as application fields such as breeding, aquaculture, and reforestation. While sophisticated techniques are used to produce this material, all too often, there is no strict monitoring during the "production" process to ensure that the specific varieties are the expected ones. Confidence rather than evidence is often applied when the time comes to start a new experiment or to deploy selected varieties in the field. During the last decade, genomics research has led to the development of important resources, which have created opportunities for easily developing tools to assess the conformity of the material along the production chains. In this study, we present a simple methodology that enables the development of a traceability system which, is in fact a by-product of previous genomic projects. The plant production system in white spruce (Picea glauca) is used to illustrate our purpose. In Quebec, one of the favored strategies to produce elite varieties is to use somatic embryogenesis (SE). In order to detect human errors both upstream and downstream of the white spruce production process, this project had two main objectives: (i) to develop methods that make it possible to trace the origin of plants produced, and (ii) to generate a unique genetic fingerprint that could be used to differentiate each embryogenic cell line and ensure its genetic monitoring. Such a system had to rely on a minimum number of low-cost DNA markers and be easy to use by non-specialists. An efficient marker selection process was operationalized by testing different classification methods on simulated datasets. These datasets were generated using in-house bioinformatics tools that simulated crosses involved in the breeding program for which genotypes from hundreds of SNP markers were already available. The rate of misidentification was estimated and various sources of mishandling or contamination were identified. The method can easily be applied to other production systems for which genomic resources are already available.

Dataset Information

Automated SNP detection from a large collection of white spruce expressed sequences: contributing factors and approaches for the categorization of SNPs.

Background

Results

Conclusion

Publications

Automated SNP detection from a large collection of white spruce expressed sequences: contributing factors and approaches for the categorization of SNPs.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets