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. 2010 Jun 9;5(6):e11034.
doi: 10.1371/journal.pone.0011034.

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  1 , Jean-Marc Frigerio, Pauline Garnier-Géré, Franck Salin, María-Teresa Cervera, Barbara Vornam, Luc Harvengt, Christophe Plomion
Affiliations

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 et al. PLoS One. .

Abstract

Background: There 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 ( approximately 23.8 Gb/C).

Methodology/principal findings: A 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/significance: This 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.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. Examples of clustering observed for the P. pinaster SNP array.
Each dot represents the mean intensity derived from a population of beads for a single sample. The normalized R (y axis) is the normalized sum of intensities of the two dyes (Cy3 and Cy5), and the normalized Theta (x axis) is ((2/Л)Tan−1 (Cy5/Cy3)), where a normalized Theta value nearest 0 is a homozygous for allele A and a Theta value nearest 1 is homozygous for allele B. A/ classical pattern with three clusters for a SNP considered as successful and polymorphic. B and C/ “cluster compression” when both homozygous clusters are closer to each other than expected. In panel B, the clustering algorithm is able to distinguish the three clusters and gives a GenTrain score of 0.58, however this kind of pattern was considered as a genotyping failure in our analysis because one of the homozygous cluster normalized Theta value does not fall in the [0, 0.1] or [0.9, 1] ranges. In panel C the clustering algorithm was not able to distinguish the three clusters because of low separation scores, and the SNP was automatically considered as a genotyping failure because of its low GenTrain score. D and E/ SNPs interpreted as genotyping failures either because of abnormal Theta values (D) or because of the presence of subgroups in a cluster (E).
Figure 2
Figure 2. Distribution of the 200 in silico SNPs according to the number of ESTs considered for the detection.
Figure 3
Figure 3. Allele frequency spectrum for 257 successfully genotyped in vitro and in silico SNPs.
Figure 4
Figure 4. Genotyping success rate according to functionality score for the 384 SNPs of the assay.
The number of SNPs in each functionality score class is indicated above each bar.
See this image and copyright information in PMC

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