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. 2015 Feb 21;16(1):105.
doi: 10.1186/s12864-015-1266-1.

Genetic diversity and trait genomic prediction in a pea diversity panel

Judith Burstin  1 , Pauline Salloignon  2 , Marianne Chabert-Martinello  3 , Jean-Bernard Magnin-Robert  4 , Mathieu Siol  5 , Françoise Jacquin  6 , Aurélie Chauveau  7   8 , Caroline Pont  9 , Grégoire Aubert  10 , Catherine Delaitre  11 , Caroline Truntzer  12 , Gérard Duc  13
Affiliations

Genetic diversity and trait genomic prediction in a pea diversity panel

Judith Burstin et al. BMC Genomics. .

Abstract

Background: Pea (Pisum sativum L.), a major pulse crop grown for its protein-rich seeds, is an important component of agroecological cropping systems in diverse regions of the world. New breeding challenges imposed by global climate change and new regulations urge pea breeders to undertake more efficient methods of selection and better take advantage of the large genetic diversity present in the Pisum sativum genepool. Diversity studies conducted so far in pea used Simple Sequence Repeat (SSR) and Retrotransposon Based Insertion Polymorphism (RBIP) markers. Recently, SNP marker panels have been developed that will be useful for genetic diversity assessment and marker-assisted selection.

Results: A collection of diverse pea accessions, including landraces and cultivars of garden, field or fodder peas as well as wild peas was characterised at the molecular level using newly developed SNP markers, as well as SSR markers and RBIP markers. The three types of markers were used to describe the structure of the collection and revealed different pictures of the genetic diversity among the collection. SSR showed the fastest rate of evolution and RBIP the slowest rate of evolution, pointing to their contrasted mode of evolution. SNP markers were then used to predict phenotypes -the date of flowering (BegFlo), the number of seeds per plant (Nseed) and thousand seed weight (TSW)- that were recorded for the collection. Different statistical methods were tested including the LASSO (Least Absolute Shrinkage ans Selection Operator), PLS (Partial Least Squares), SPLS (Sparse Partial Least Squares), Bayes A, Bayes B and GBLUP (Genomic Best Linear Unbiased Prediction) methods and the structure of the collection was taken into account in the prediction. Despite a limited number of 331 markers used for prediction, TSW was reliably predicted.

Conclusion: The development of marker assisted selection has not reached its full potential in pea until now. This paper shows that the high-throughput SNP arrays that are being developed will most probably allow for a more efficient selection in this species.

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Figures

Figure 1
Figure 1
Genetic positions of the markers used. When their map positions were available, the markers used in the present study were placed on the pea genetic map according to [25]. SSR (Simple Sequence Repeat) markers are in green and SNP (Single Nucleotide Polymorphism) markers are in red.
Figure 2
Figure 2
Correlations of phenotypes between the 2003 and 2007 field trials. (a) Thousand seed weight (g), (b) Sum of temperatures from sowing to beginning of flowering (degree.days) and (c) Seed Number per plant.
Figure 3
Figure 3
Heatmaps of genetic similarities calculated for the different marker types. Dendograms represent complete linkage clustering of the 372 accessions according to (a) SSR markers, (b and d) SNP markers, (c) RBIP markers. The 6 DAPC (Discriminant Analysis of Principal Component) groups (a, b, c) and the 3 INSTRUCT group (d) are represented by the left colored banner.
Figure 4
Figure 4
First plan of the DAPC (Discriminant Analysis of Principal Component) analysis revealed genetic diversity of the collection. The analysis was performed on SSR and SNP data. (a) Dots highlight parents of recombinant inbred line populations, and triangles highlight P.sativum elatius (violet), P.sativum abyssinicum (blue), P.sativum humile (green) and P. fulvum accessions (orange). Accessions are also represented according to their population type (b), sowing type (c), use type (d) and geographical origins (e).
Figure 5
Figure 5
Plots of observed vs predicted Thousand Seed Weight (TSW) values in the 2007 field experiment. Predicted values after (a) the Genomic Best Linear Unbiased Prediction (GBLUP) method, (b) the Least Absolute Shrinkage and Selection Operator (LASSO) method, (c) the Partial Least Squares (PLS) method taking into account the INSTRUCT structure; (d) the LASSO method taking into account the DAPC structure.
Figure 6
Figure 6
Correlation of SNP Prediction coefficients for TSW in 2003 and 2007 according to different statistical methods. (a) Least Absolute Shrinkage and Selection Operator (LASSO) method coefficients (b) Genomic Best Linear Unbiased Prediction (GBLUP) method coefficients and (c) Partial Least Squares (PLS) method coefficients.
Figure 7
Figure 7
Linkage disequilibrium ( r 2 ) as a function of genetic distance among linked markers, in cM.
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References

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