Genomic characterization of a core set of the USDA-NPGS Ethiopian sorghum germplasm collection: implications for germplasm conservation, evaluation, and utilization in crop improvement.

BACKGROUND: The USDA Agriculture Research Service National Plant Germplasm System (NPGS) preserves the largest sorghum germplasm collection in the world, which includes 7,217 accessions from the center of diversity in Ethiopia. The characterization of this exotic germplasm at a genome-wide scale will improve conservation efforts and its utilization in research and breeding programs. Therefore, we phenotyped a representative core set of 374 Ethiopian accessions at two locations for agronomic traits and characterized the genomes. RESULTS: Using genotyping-by-sequencing, we identified 148,476 single-nucleotide polymorphism (SNP) markers distributed across the entire genome. Over half of the alleles were rare (frequency < 0.05). The genetic profile of each accession was unique (i.e., no duplicates), and the average genetic distance among accessions was 0.70. Based on population structure and cluster analyses, we separated the collection into 11 populations with pairwise F ST values ranging from 0.11 to 0.47. In total, 198 accessions (53%) were assigned to one of these populations with an ancestry membership coefficient of larger than 0.60; these covered 90% of the total genomic variation. We characterized these populations based on agronomic and seed compositional traits. We performed a cluster analysis with the sorghum association panel based on 26,026 SNPs and determined that nine of the Ethiopian populations expanded the genetic diversity in the panel. Genome-wide association analysis demonstrated that these low-coverage data and the observed population structure could be employed for the genomic dissection of important phenotypes in this core set of Ethiopian sorghum germplasm. CONCLUSIONS: The NPGS Ethiopian sorghum germplasm is a genetically and phenotypically diverse collection comprising 11 populations with high levels of admixture. Genetic associations with agronomic traits can be used to improve the screening of exotic germplasm for selection of specific populations. We detected many rare alleles, suggesting that this germplasm contains potentially useful undiscovered alleles, but their discovery and characterization will require extensive effort. The genotypic data available for these accessions provide a valuable resource for sorghum breeders and geneticists to effectively improve crops.

Use of genomic prediction to screen sorghum B-lines in hybrid testcrosses.

Use of trifluoromethanesulfonamide (TFMSA), a male gametocide, increases the opportunities to identify promising B-lines because large quantities of F1 seed can be generated prior to the laborious task of B-line sterilization. Combining TFMSA technology with genomic selection could efficiently evaluate sorghum B-lines in hybrid combination to maximize the rates of genetic gain of the crop. This study used two recombinant inbred B-line populations, consisting of 217 lines, which were testcrossed to two R-lines to produce 434 hybrids. Each population of testcross hybrids were evaluated across five environments. Population-based genomic prediction models were assessed across environments using three different cross-validation (CV) schemes, each with 70% training and 30% validation sets. The validation schemes were as follows: CV1-hybrids chosen randomly for validation; CV2-B-lines were randomly chosen, and each chosen B-line had one of the two corresponding testcross hybrids randomly chosen for the validation; and CV3-B-lines were randomly chosen, and each chosen B-line had both corresponding testcross hybrids chosen for the validation. CV1 and CV2 presented the highest prediction accuracies; nonetheless, the prediction accuracies of the CV schemes were not statistically different in many environments. We determined that combining the B-line populations could improve prediction accuracies, and the genomic prediction models were able to effectively rank the poorest 70% of hybrids even when genomic prediction accuracies themselves were low. Results indicate that combining genomic prediction models and TFMSA technology can effectively aid breeders in predicting B-line hybrid performance in early generations prior to the laborious task of generating A/B-line pairs.

Genomic prediction can be used to screen sorghum B-lines for hybrid grain yield and days to mid-anthesis. Using genomic prediction and the chemical gametocide TFMSA can increase the rate of genetic gain in sorghum B-lines. Using testers to screen sorghum B-line populations is an effective method for screening with genomic prediction. Genomic prediction can effectively predict hybrid performance within and across populations of sorghum B-lines. The ability to accurately rank hybrid performance remained relatively consistent regardless of prediction accuracy.

Mapping QTLs and Identification of Genes Associated with Drought Resistance in Sorghum.

Water limits global agricultural production. Increases in global aridity, a growing human population, and the depletion of aquifers will only increase the scarcity of water for agriculture. Water is essential for plant growth and in areas that are prone to drought, the use of drought-resistant crops is a long-term solution for growing more food for more people with less water. Sorghum is well adapted to hot and dry environments and has been used as a dietary staple for millions of people. Increasing the drought resistance in sorghum hybrids with no impact on yield is a continual objective for sorghum breeders. In this review, we describe the loci, quantitative trait loci (QTLs), or genes that have been identified for traits involved in drought avoidance (water-use efficiency, cuticular wax synthesis, trichome development and morphology, root system architecture) and drought tolerance (compatible solutes, pre- and post-flowering drought tolerance). Many of these identified genes and QTL regions have not been tested in hybrids and the effect of these genes, or their interactions, on yield must be understood in normal and drought-stressed conditions to understand the strength and weaknesses of their utility.

Natural Variation in Synthesis and Catabolism Genes Influences Dhurrin Content in Sorghum.

Cyanogenic glucosides are natural compounds found in more than 1000 species of angiosperms that produce HCN and are deemed undesirable for agricultural use. However, these compounds are important components of the primary defensive mechanisms of many plant species. One of the best-studied cyanogenic glucosides is dhurrin [(S)-p-hydroxymandelonitrile-ß-D-glucopyranoside], which is produced primarily in sorghum [Sorghum bicolor (L.) Moench]. The biochemical basis for dhurrin metabolism is well established; however, little information is available on its genetic control. Here, we dissect the genetic control of leaf dhurrin content through a genome-wide association study (GWAS) using a panel of 700 diverse converted sorghum lines (conversion panel) previously subjected to pre-breeding and selected for short stature (∼1 m in height) and photoperiod insensitivity. The conversion panel was grown for 2 yr in three environments. Wide variation for leaf dhurrin content was found in the sorghum conversion panel, with the Caudatum group exhibiting the highest dhurrin content and the Guinea group showing the lowest dhurrin content. A GWAS using a mixed linear model revealed significant associations (a false discovery rate [FDR] < 0.05) close to both UGT 185B1 in the canonical biosynthetic gene cluster on chromosome 1 and close to the catabolic dhurrinase loci on chromosome 8. Dhurrin content was associated consistently with biosynthetic genes in the two N-fertilized environments, while dhurrin content was associated with catabolic loci in the environment without supplemental N. These results suggest that genes for both biosynthesis and catabolism are important in determining natural variation for leaf dhurrin in sorghum in different environments.