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

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

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

Composition of the genetic clusters defined by STRUCTURE.

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

Statistics of the iSelect consensus genetic map.

<p>Statistics of the iSelect consensus genetic map.</p

Population structure in the iCore.

<p>(A) Plot of Ancestry estimates for <i>k</i> = 5. Each bar represents the estimated membership coefficients for each accession in each of the five subpopulations (represented by different colors). (B) Geographical distribution of the accessions belonging to the iCore. A membership coefficient>0.8 was used to assign accessions (represented by circles) to the five subpopulations, and the remaining accessions were assigned to an ‘admixed’ group.</p

Distribution and extent of linkage disequilibrium along the barley chromosomes.

<p>The –log₁₀ of the logistic regression <i>p</i>-values between any pair of SNPs located 1–2 cM apart (A) and 4–5 cM apart (B) are displayed.</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

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