In Vitro vs In Silico Detected SNPs for the Development of a Genotyping Array: What Can We Learn from a Non-Model Species?

Camille Lepoittevin,Barbara Vornam,Christophe Plomion,Pauline Garnier-Géré,Franck Salin,María-Teresa Cervera,Luc Harvengt,Jean-Marc Frigerio,Pär K. Ingvarsson

doi:10.1371/journal.pone.0011034

Abstract

BackgroundThere is considerable interest in the high-throughput discovery and genotyping of single nucleotide polymorphisms (SNPs) to accelerate genetic mapping and enable association studies. This study provides an assessment of EST-derived and resequencing-derived SNP quality in maritime pine (Pinus pinaster Ait.), a conifer characterized by a huge genome size (∼23.8 Gb/C).Methodology/Principal FindingsA 384-SNPs GoldenGate genotyping array was built from i/ 184 SNPs originally detected in a set of 40 re-sequenced candidate genes (in vitro SNPs), chosen on the basis of functionality scores, presence of neighboring polymorphisms, minor allele frequencies and linkage disequilibrium and ii/ 200 SNPs screened from ESTs (in silico SNPs) selected based on the number of ESTs used for SNP detection, the SNP minor allele frequency and the quality of SNP flanking sequences. The global success rate of the assay was 66.9%, and a conversion rate (considering only polymorphic SNPs) of 51% was achieved. In vitro SNPs showed significantly higher genotyping-success and conversion rates than in silico SNPs (+11.5% and +18.5%, respectively). The reproducibility was 100%, and the genotyping error rate very low (0.54%, dropping down to 0.06% when removing four SNPs showing elevated error rates).Conclusions/SignificanceThis study demonstrates that ESTs provide a resource for SNP identification in non-model species, which do not require any additional bench work and little bio-informatics analysis. However, the time and cost benefits of in silico SNPs are counterbalanced by a lower conversion rate than in vitro SNPs. This drawback is acceptable for population-based experiments, but could be dramatic in experiments involving samples from narrow genetic backgrounds. In addition, we showed that both the visual inspection of genotyping clusters and the estimation of a per SNP error rate should help identify markers that are not suitable to the GoldenGate technology in species characterized by a large and complex genome.

Highlights

In the last few years, the development of high-throughput methods for the detection and genotyping of single nucleotide polymorphisms (SNPs) has led to a revolution in their use as molecular markers [1]
The time and cost benefits of in silico SNPs are counterbalanced by a lower conversion rate than in vitro SNPs
This drawback is acceptable for population-based experiments, but could be dramatic in experiments involving samples from narrow genetic backgrounds

Summary

Introduction

In the last few years, the development of high-throughput methods for the detection and genotyping of single nucleotide polymorphisms (SNPs) has led to a revolution in their use as molecular markers [1]. In silico discovery is the most obvious method for de novo SNP identification This approach mainly provides markers located in transcribed regions (mostly coding and 39UTR), it offers a low cost source of abundant SNPs and has been validated by large scale genotyping for a number of plant species including Arabidopsis [9], maize [10], grapevine [11], melon [12], tomato [13], spruce [14] or pine [15]. This study provides an assessment of EST-derived and resequencing-derived SNP quality in maritime pine (Pinus pinaster Ait.), a conifer characterized by a huge genome size (,23.8 Gb/C)

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