Predictive Characterization for Seed Morphometric Traits for Genebank Accessions Using Genomic Selection
Impact factor: 2.686 (Year: 2020)
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Zakaria Kehel, Miguel Sanchez-Garcia, Adil El-Baouchi, Hafid Aberkane, Athanasios Tsivelikas, Chen Charles, Ahmed Amri. (17/3/2020). Predictive Characterization for Seed Morphometric Traits for Genebank Accessions Using Genomic Selection. Frontiers in Ecology and Evolution, 8, pp. 1-11.
Seed traits of bread wheat, including the seed size that is considered to be associated with early vigor of the crop and end-use quality, are valuable to farmers and breeders. In this study, a collection of 789 bread wheat landraces, held in-trust at the genebank of the International Center for Agricultural Research in the Dry Areas (ICARDA) were scanned for seed morphometric traits using GrainScan. Diversity analysis using the 12k DartSeq SNP markers revealed that these accessions can be grouped into ﬁve distinct clusters. To evaluate the performance for early selection from genebank accessions, we examined the accuracy of genomic selection models with genomic relationship that these landraces accounted for. Based on cross-validations, prediction accuracies for seed traits ranged from 0.64 for seed perimeter to 0.74 for seed width. The variability of prediction accuracies across random validations averaged at 0.14, with a range from 0.12 to 0.18, suggesting stable predictability and reproducible results even with a collection of much greater genetic diversity from genebank accessions. Adding the climatic relationship matrix between accessions based on passport information improved the predictive ability by 8%. Our results on seed traits demonstrated the capacity for estimating important agronomic phenotypes for genebank accessions directly based on genomic information, further advocating the advance in genomic technologies for identifying parental germplasm as potential donors of beneﬁcial alleles for introgression.