Abstract
Pea is an important food and feed crop and a valuable component of low-input farming systems. Improving resistance to biotic and abiotic stresses is a major breeding target to enhance yield potential and regularity. Genomic selection (GS) has lately emerged as a promising technique to increase the accuracy and gain of marker-based selection. It uses genome-wide molecular marker data to predict the breeding values of candidate lines to selection. A collection of 339 genetic resource accessions (CRB339) was subjected to high-density genotyping using the GenoPea 13.2K SNP Array. Genomic prediction accuracy was evaluated for thousand seed weight (TSW), the number of seeds per plant (NSeed), and the date of flowering (BegFlo). Mean cross-environment prediction accuracies reached 0.83 for TSW, 0.68 for NSeed, and 0.65 for BegFlo. For each trait, the statistical method, the marker density, and/or the training population size and composition used for prediction were varied to investigate their effects on prediction accuracy: the effect was large for the size and composition of the training population but limited for the statistical method and marker density. Maximizing the relatedness between individuals in the training and test sets, through the CDmean-based method, significantly improved prediction accuracies. A cross-population cross-validation experiment was further conducted using the CRB339 collection as a training population set and nine recombinant inbred lines populations as test set. Prediction quality was high with mean Q2 of 0.44 for TSW and 0.59 for BegFlo. Results are discussed in the light of current efforts to develop GS strategies in pea.
Highlights
New breeding challenges imposed by rapidly-expanding world population and global climate change urge crop breeders to develop and utilize more efficient selection approaches
The genomic predictability of pea thousand seed weight (TSW), number of seeds per plant (NSeed), and beginning of flowering (BegFlo) was inspected with a limited number of SNP markers and have shown encouraging accuracies (Burstin et al, 2015)
As in Burstin et al (2015), TSW was better predicted than BegFlo and NSeed
Summary
New breeding challenges imposed by rapidly-expanding world population and global climate change urge crop breeders to develop and utilize more efficient selection approaches. Genomic selection (GS) is a revolutionary approach where a Genomic Prediction in Pea breeder’s selection is made on the basis of genomic estimated breeding values (GEBVs) obtained from genome-wide DNA marker information (Meuwissen et al, 2001). Prediction accuracy has been reported to depend on the genetic architecture of the trait (Jannink et al, 2010; Burstin et al, 2015), the number and the distribution of the genetic markers (Heffner et al, 2011; Poland et al, 2012; Heslot et al, 2013), and the size (Heffner et al, 2011; Jarquín et al, 2014) and composition of the training population (Rincent et al, 2012; Charmet et al, 2014)
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