Creation and Validation of the Spanish Durum Wheat Core Collection

Abstract

Spanish wheat (Triticum spp.) landraces have a considerable polymorphism, containing many unique alleles, relative to other collections. The existence of a core collection is a favored approach for breeders to efficiently explore novel variation and enhance the use of germplasm. In this study, the Spanish durum wheat (Triticum turgidum L.) core collection (CC) was created using a population structure–based method, grouping accessions by subspecies and allocating the number of genotypes among populations according to the diversity of simple sequence repeat (SSR) markers. The CC of 94 genotypes was established, which accounted for 17% of the accessions in the entire collection. An alternative core collection (CH), with the same number of genotypes per subspecies and maximizing the coverage of SSR alleles, was assembled with the Core Hunter software. The quality of both core collections was compared with a random core collection and evaluated using geographic, agromorphological, and molecular marker data not previously used in the selection of genotypes. Both core collections had a high genetic representativeness, which validated their sampling strategies. Geographic and agromorphological variation, phenotypic correlations, and gliadin alleles of the original collection were more accurately depicted by the CC. Diversity arrays technology (DArT) markers revealed that the CC included genotypes less similar than the CH. Although more SSR alleles were retained by the CH (94%) than by the CC (91%), the results showed that the CC was better than CH for breeding purposes.

Development of a Multipurpose Core Collection of Bread Wheat Based on High-Throughput Genotyping Data

“…To determine the optimal number of entries needed to retain an acceptable proportion of alleles present in the primary set, we tried to find a "point of compromise" between gain of genetic variation and elimination of genetic redundancy in the core collection. The final size of 94 entries captured 96% of polymorphic DArT markers present in the PS, which is in agreement with other studies that have reported values between 70 and 98% using SNPs [47,48] and SSR [40,49,50]. This size represents 18% of the entire collection, and is within the range from 5 to 30% recommended for…”

“…The final size of 94 entries captured 96% of polymorphic DArT markers present in the PS, which is in agreement with other studies that have reported values between 70 and 98% using SNPs [47,48] and SSR [40,49,50]. This size represents 18% of the entire collection, and is within the range from 5 to 30% recommended for retaining a great part of the genetic variability with a manageable number of accessions (e.g., [5,40,51]).…”

“…Only Pop 4 was clearly separated, while some overlap was shown among the other three populations, especially within Pop 2, which was the largest population [27]. In contrast, the stratified sampling performed better than the M-strategy in the Spanish durum wheat collection [40]. Comparisons of the genetic structure of the two wheat collections revealed that bread wheat populations exhibited a higher level of admixture and less genetic differentiation (Population differentiation index Dest = 0.22 in durum and 0.17 in bread wheat) [27].…”

“…However, even after the removal of redundant accessions, the collection maintained at CRF-INIA comprising more than 500 landraces is too large for evaluation and use in most breeding programs. Defining a core collection is, therefore, a pre-requisite and valuable tool for utilizing this germplasm, particularly for more complicated phenotypic screens, as has been demonstrated by the Spanish core collections of barley and durum wheat [40,41]. Both core subsets have facilitated detailed study of some difficult to analyze traits such as yield performance, root architecture, disease resistance or characterization of low molecular glutenin subunits [42][43][44][45][46].…”

“…Other studies have shown that the CC created based on the M-strategy maximizes the genetic variability index [23,40], whereas the structured method yields subsets that better represent the distribution of the genetic variability of the initial collections [7,20,40,57]. Also, worse values for E-NE distances have been reported in collections developed using the M-strategy [23,40]. In the present study, however, the M-strategy performed better than the stratified method, by increasing genetic diversity and reducing redundancy [26].…”