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

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 (P_SNP), 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 P_SNP ≥ 0.95 or ≥ 0.99. A total of 9,310 SNPs were detected by using P_SNP ≥ 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 Forest TreeDB database along with functional annotations to provide a tool facilitating the choice of candidate genes for mapping purposes or association studies.