Dataset Information

Development of a Traceability System Based on a SNP Array for Large-Scale Production of High-Value White Spruce (Picea glauca).

ABSTRACT: 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.

SUBMITTER: Godbout J

PROVIDER: S-EPMC5524734 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Publications

Development of a Traceability System Based on a SNP Array for Large-Scale Production of High-Value White Spruce (<i>Picea glauca</i>).

Godbout Julie J Tremblay Laurence L Levasseur Caroline C Lavigne Patricia P Rainville André A Mackay John J Bousquet Jean J Isabel Nathalie N

Frontiers in plant science 20170725

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 la ...[more]

PMID: 28791035

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Project description:Tree improvement programs select genotypes for faster growth, at both early and late stages, to increase yields over unimproved material, and the improvement is frequently attributed to genetic control in growth parameters among genotypes. Underutilized genetic variability among genotypes also has the potential to ensure future gains are possible. However, the genetic variation in growth, physiology and hormone control among genotypes generated from different breeding strategies has not been well characterized in conifers. We assessed growth, biomass, gas exchange, gene expression and hormone levels in white spruce seedlings obtained from three different breeding strategies (controlled crosses, polymix pollination, open pollination) using parents grafted into a clonal seed orchard in Alberta, Canada. A pedigree-based best linear unbiased prediction (ABLUP) mixed model was implemented to quantify variability and narrow-sense heritability for target traits and the levels of several hormones and expression of gibberellin-related genes in apical internodes were also determined. Over the first 2-years of development, the estimated heritabilities for height, volume, total dry biomass, above ground dry biomass, root:shoot ratio and root length, varied between 0.10 and 0.21, with height having the highest value. The ABLUP values showed large genetic variability in growth and physiology traits both between families from different breeding strategies, and within families. The principal component analysis showed that developmental and hormonal traits explained 44.2% and 29.4% of the total phenotypic variation between the three different breeding strategies and two growth groups. In general, controlled crosses from the fast growth group showed the best apical growth, with more accumulation of indole-3-acetic acid, abscisic acid, phaseic acid, and a 4-fold greater gene expression of PgGA3ox1 in genotypes from controlled crosses versus those from open pollination, but in some cases, open-pollination from the fast and slow growth groups showed the best root development, higher water use efficiency (iWUE and δ13C) and more accumulation of zeatin and isopentenyladenosine. In conclusion, tree domestication can lead to trade-offs between growth, carbon allocation, photosynthesis, hormone levels and gene expression, and we encourage the use of this phenotypic variation identified in improved and unimproved trees to advance white spruce tree improvement programs.

Dataset Information

Development of a Traceability System Based on a SNP Array for Large-Scale Production of High-Value White Spruce (Picea glauca).

Publications

Development of a Traceability System Based on a SNP Array for Large-Scale Production of High-Value White Spruce (<i>Picea glauca</i>).

Similar Datasets

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