The USDA Barley Core Collection: Genetic Diversity, Population Structure, and Potential for Genome-Wide Association Studies

New sources of genetic diversity must be incorporated into plant breeding programs if they are to continue increasing grain yield and quality, and tolerance to abiotic and biotic stresses. Germplasm collections provide a source of genetic and phenotypic diversity, but characterization of these resources is required to increase their utility for breeding programs. We used a barley SNP iSelect platform with 7,842 SNPs to genotype 2,417 barley accessions sampled from the USDA National Small Grains Collection of 33,176 accessions. Most of the accessions in this core collection are categorized as landraces or cultivars/breeding lines and were obtained from more than 100 countries. Both STRUCTURE and principal component analysis identified five major subpopulations within the core collection, mainly differentiated by geographical origin and spike row number (an inflorescence architecture trait). Different patterns of linkage disequilibrium (LD) were found across the barley genome and many regions of high LD contained traits involved in domestication and breeding selection. The genotype data were used to define ‘mini-core’ sets of accessions capturing the majority of the allelic diversity present in the core collection. These ‘mini-core’ sets can be used for evaluating traits that are difficult or expensive to score. Genome-wide association studies (GWAS) of ‘hull cover’, ‘spike row number’, and ‘heading date’ demonstrate the utility of the core collection for locating genetic factors determining important phenotypes. The GWAS results were referenced to a new barley consensus map containing 5,665 SNPs. Our results demonstrate that GWAS and high-density SNP genotyping are effective tools for plant breeders interested in accessing genetic diversity in large germplasm collections

Genetic Diversity among Wheat Accessions from the USDA National Small Grains Collection

Accessions of common wheat (Triticum aestivum L. subsp. aestivum) from the USDA–ARS National Small Grains Collection (NSGC) are a resource for wheat scientists worldwide. The genetic diversity of the wheat core subset, representing approximately 10% of the collection’s 42,138 T. aestivum accessions, was examined using 390 diversity arrays technology (DArT) markers, 4941 single nucleotide polymorphisms (SNPs), and descriptor data. The marker profiles revealed duplicates, which were excluded to form an informative core (iCore) of 3230 accessions. The iCore population structure and diversity within various subgroups were examined with analysis of molecular variance, principal coordinate analysis, cluster analysis, and by ranking the contribution of individual accessions to overall diversity. Accession groups based on molecular marker data corresponded well to their geographic origin, and population structure was accounted for primarily by differences between Iranian landrace accessions and the rest of the accessions. Accessions classified as breeding lines were overrepresented among those ranked as most diverse based on SNP data, whereas Iranian landraces were underrepresented. Although less diverse as a group, Iranian landrace accessions had a higher frequency of resistance to bunt diseases and Russian wheat aphid compared with the iCore as a whole. The present study provides support for establishing core subsets based on geographic origin of accessions and will be a basis for further study of diversity among NSGC wheats

The USDA Barley Core Collection:Genetic Diversity, Population Structure, and Potential for Genome-Wide Association Studies

New sources of genetic diversity must be incorporated into plant breeding programs if they are to continue increasing grain yield and quality, and tolerance to abiotic and biotic stresses. Germplasm collections provide a source of genetic and phenotypic diversity, but characterization of these resources is required to increase their utility for breeding programs. We used a barley SNP iSelect platform with 7,842 SNPs to genotype 2,417 barley accessions sampled from the USDA National Small Grains Collection of 33,176 accessions. Most of the accessions in this core collection are categorized as landraces or cultivars/breeding lines and were obtained from more than 100 countries. Both STRUCTURE and principal component analysis identified five major subpopulations within the core collection, mainly differentiated by geographical origin and spike row number (an inflorescence architecture trait). Different patterns of linkage disequilibrium (LD) were found across the barley genome and many regions of high LD contained traits involved in domestication and breeding selection. The genotype data were used to define 'mini-core' sets of accessions capturing the majority of the allelic diversity present in the core collection. These 'mini-core' sets can be used for evaluating traits that are difficult or expensive to score. Genome-wide association studies (GWAS) of 'hull cover', 'spike row number', and 'heading date' demonstrate the utility of the core collection for locating genetic factors determining important phenotypes. The GWAS results were referenced to a new barley consensus map containing 5,665 SNPs. Our results demonstrate that GWAS and high-density SNP genotyping are effective tools for plant breeders interested in accessing genetic diversity in large germplasm collections

Disease and insect resistance in cultivated barley accessions from the USDA National Small Grains Collection

Cultivated barley (Hordeum vulgare subsp. vulgare L.) accessions from the USDA-ARS National Small Grains Collection (NSGC) have been tested systematically for the past 20 yr for disease and insect resistance. In this study, we analyzed the resistance to barley yellow dwarf (BYD), spot blotch (SB) caused by Cochliobolus sativus (Ito and Kuribayashi) Drechs. ex Dastur, net blotch (NB) caused by Pyrenophora teres f. teres Drechs., stripe rust (SR) caused by Puccinia striiformis Westend. f. sp. hordei, and Russian wheat aphid (RWA), Diuraphis noxia (Mordvilko), with respect to (i) geographic origin of resistant accessions, (ii) relationship to other NSGC descriptor data, and (iii) relationships among resistances. "Centers of concentration" for certain resistances were identified: eastern Africa for several diseases, western Turkey and the Caucasus for SR resistance, eastern Asia for adult plant resistance to NB, and south-central Asia for RWA resistance. Stripe rust resistance was also associated with accessions originating from high altitude in eastern Africa (Ethiopia). Various associations between resistances and grain descriptors, plant habit, and landrace status were also found. Forty-eight accessions showed multiple resistances on the basis of the field disease data and the RWA greenhouse data. Many of these resistant accessions were from Ethiopia, and many were of unknown origin. Stripe rust testing in California and Bolivia supported the conclusion that winter-habit accessions were more resistant to the disease than were spring-habit accessions. Information from this study will be used to guide future NSGC acquisition and evaluation efforts

Composition of the genetic clusters defined by STRUCTURE.

<p>Composition of the genetic clusters defined by STRUCTURE.</p

Significant SNPs showing the highest marker-trait associations for the phenotypes tested.

<p>The –log₁₀ of the FDR corrected <i>p-</i>values (<i>q</i>) for those markers are shown, together with the allele effects (allele in parenthesis) and the minor allele frequency (MAF) for each marker.</p

Principal Component Analysis (PCA) of the iCore and distribution of the ‘mini-core’ set in the first 4 PCs.

<p>The ‘mini-core’ set is shown in red and it is composed of the first 10% top-ranked accessions by their contribution to the polymporphism information content (PIC) value of the whole iCore.</p

Genetic differentiation between subpopulations 2, 3 and 4.

<p>(A) Genetic differentiation measured by <i>Φ</i>_PT for subpopulations 2, 3 and 4 (A). To identify which subpopulation is responsible for the high values of some markers, we run independent analyses of divergent selection for: (B) subpopulation 2 against subpopulations 3 and 4; (C) subpopulation 3 against subpopulations 2 and 4; and (D) subpopulation 4 against subpopulations 2 and 3. To help discriminate markers with higher values, the Y-axis displays <i>Φ</i>_PT to the power of 10.</p

Genome-wide association scans in the iCore.

<p>Manhattan plots of the GWAS for ‘hull cover’, ‘spike row number’, ‘heading date’ in the spring accessions, and ‘heading date’ in the winter accessions are shown. The horizontal axes indicate the consensus map position of each SNP (black dots), while the vertical axes indicate the −log₁₀ of the corrected <i>p</i> values (<i>q</i>). The dash line indicates the 0.05 threshold.</p