Genetic diversity and trait genomic prediction in a pea diversity panel |
| |
| Authors: | Judith Burstin Pauline Salloignon Marianne Chabert-Martinello Jean-Bernard Magnin-Robert Mathieu Siol Fran?oise Jacquin Aurélie Chauveau Caroline Pont Grégoire Aubert Catherine Delaitre Caroline Truntzer Gérard Duc |
| |
| Institution: | .UMR1347, Agroecology, INRA, 17 rue de Sully, Dijon Cedex, 21065 France ;.Clinical and Innovation Proteomic Platform (CLIPP), CHU Dijon, Université de Bourgogne, 1 rue du Professeur Marion, Dijon, 21000 France ;.Present address: US EPGV, IG-CEA, Centre National de Génotypage, 2 rue Gaston Crémieux, Evry Cedex, 91057 France ;.UMR GDEC, Plateforme Gentyane, Clermont Ferrand, 63100 France |
| |
| Abstract: | BackgroundPea (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.ResultsA 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.ConclusionThe 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-1266-1) contains supplementary material, which is available to authorized users. |
| |
| Keywords: | |
|
|
