Young breeding programs in developing countries, like the Chibas sorghum breeding program in Haiti, face the challenge of increasing genetic gain with limited resources. Implementing genomic selection (GS) could increase genetic gain, but optimization of GS is needed to account for these programs’ unique challenges and advantages. Here, we used simulations to identify conditions under which genomic-assisted recurrent selection (GARS) would be more effective than phenotypic recurrent selection (PRS) in small new breeding programs. We compared genetic gain, cost per unit gain, genetic variance, and prediction accuracy of GARS (two or three cycles per year) vs. PRS (one cycle per year) assuming various breeding population sizes and trait genetic architectures. For oligogenic architecture, the maximum relative genetic gain advantage of GARS over PRS was 12–88%, which was observed only during the first few cycles. For the polygenic architecture, GARS provided maximum relative genetic gain advantage of 26–165%, and was always superior to PRS. Average prediction accuracy declines substantially after several cycles of selection, suggesting the prediction models should be updated regularly. Updating prediction models every year increased the genetic gain by up to 33–39% compared to no-update scenarios. For small populations and oligogenic traits, cost per unit gain was lower in PRS than GARS. However, with larger populations and polygenic traits cost per unit gain was up to 67% lower in GARS than PRS. Collectively, the simulations suggest that GARS could increase the genetic gain in small young breeding programs by accelerating the breeding cycles and enabling evaluation of larger populations.

Harnessing diversity from germplasm collections is more feasible today because of the development of lower-cost and higherthroughput genotyping methods. However, the cost of phenotyping is still generally high, so efficient methods of sampling and exploiting useful diversity are needed. Genomic selection (GS) has the potential to enhance the use of desirable genetic variation in germplasm collections through predicting the genomic estimated breeding values (GEBVs) for all traits that have been measured. Here, we evaluated the effects of various scenarios of population genetic properties and marker density on the accuracy of GEBVs in the context of applying GS for wheat (Triticum aestivum L.) germplasm use. Empirical data for adult plant resistance to stripe rust (Puccinia striiformis f. sp. tritici) collected on 1163 spring wheat accessions and genotypic data based on the wheat 9K single nucleotide polymorphism (SNP) iSelect assay were used for various genomic prediction tests. Unsurprisingly, the results of the cross-validation tests demonstrated that prediction accuracy increased with an increase in training population size and marker density. It was evident that using all the available markers (5619) was unnecessary for capturing the trait variation in the germplasm collection, with no further gain in prediction accuracy beyond 1 SNP per 3.2 cM (~1850 markers), which is close to the linkage disequilibrium decay rate in this population. Collectively, our results suggest that larger germplasm collections may be efficiently sampled via lower-density genotyping methods, whereas genetic relationships between the training and validation populations remain critical when exploiting GS to select from germplasm collections.

Pearl millet [Cenchrus americanus (L.) Morrone syn. Pennisetum glaucum (L.) R. Br.] is one of the most extensively cultivated cereals in the world, after wheat (Triticum aestivum L.), maize (Zea mays L.), rice (Oryza sativa L.), barley (Hordeum vulgare L.), and sorghum [Sorghum bicolor (L.) Moench]. It is the main component of traditional farming systems and a staple food in the arid and semiarid regions of Africa and southern Asia. However, its genetic improvement is lagging behind other major cereals and the yield is still low. Genotyping-by-sequencing (GBS)-based single-nucleotide polymorphism (SNP) markers were screened on a total of 398 accessions from different geographic regions to assess genetic diversity, population structure, and linkage disequilibrium (LD). By mapping the GBS reads to the reference genome sequence, 82,112 genome-wide SNPs were discovered. The telomeric regions of the chromosomes have the higher SNP density than in pericentromeric regions. Model-based clustering analysis of the population revealed a hierarchical genetic structure of six subgroups that mostly overlap with the geographic origins or sources of the genotypes but with differing levels of admixtures. A neighbor-joining phylogeny analysis revealed that germplasm from western Africa rooted the dendrogram with much diversity within each subgroup. Greater LD decay was observed in the west-African subpopulation than in the other subpopulations, indicating a long history of recombination among landraces. Also, genome scan of genetic differentiatation detected different selection histories among subpopulations. These results have potential application in the development of genomic-assisted breeding in pearl millet and heterotic grouping of the lines for improved hybrid performance.

Stripe rust, caused by Puccinia striiformis Westend. f. sp. tritici Erikss. (Pst) remains one of the most significant diseases of wheat worldwide. We investigated stripe rust resistance by genome-wide association analysis (GWAS) in 959 spring wheat accessions from the United States Department of Agriculture-Agricultural Research Service National Small Grains Collection, representing major global production environments. The panel was characterized for field resistance in multi-environment field trials and seedling resistance under greenhouse conditions. A genome-wide set of 5,619 informative SNP markers were used to examine the population structure, linkage disequilibrium and marker-trait associations in the germplasm panel. Based on model-based analysis of population structure and hierarchical Ward clustering algorithm, the accessions were clustered into two major subgroups. These subgroups were largely separated according to geographic origin and improvement status of the accessions. A significant correlation was observed between the population sub-clusters and response to stripe rust infection. We identified 11 and 7 genomic regions with significant associations with stripe rust resistance at adult plant and seedling stages, respectively, based on a false discovery rate multiple correction method. The regions harboring all, except three, of the QTL identified from the field and greenhouse studies overlap with positions of previously reported QTL. Further work should aim at validating the identified QTL using proper germplasm and populations to enhance their utility in marker assisted breeding.

BackgroundThe narrow genetic basis of resistance in modern wheat cultivars and the strong selection response of pathogen populations have been responsible for periodic and devastating epidemics of the wheat rust diseases. Characterizing new sources of resistance and incorporating multiple genes into elite cultivars is the most widely accepted current mechanism to achieve durable varietal performance against changes in pathogen virulence. Here, we report a high-density molecular characterization and genome-wide association study (GWAS) of stripe rust and stem rust resistance in 190 Ethiopian bread wheat lines based on phenotypic data from multi-environment field trials and seedling resistance screening experiments. A total of 24,281 single nucleotide polymorphism (SNP) markers filtered from the wheat 90 K iSelect genotyping assay was used to survey Ethiopian germplasm for population structure, genetic diversity and marker-trait associations.ResultsUpon screening for field resistance to stripe rust in the Pacific Northwest of the United States and Ethiopia over multiple growing seasons, and against multiple races of stripe rust and stem rust at seedling stage, eight accessions displayed resistance to all tested races of stem rust and field resistance to stripe rust in all environments. Our GWAS results show 15 loci were significantly associated with seedling and adult plant resistance to stripe rust at false discovery rate (FDR)-adjusted probability (P) <0.10. GWAS also detected 9 additional genomic regions significantly associated (FDR-adjusted P < 0.10) with seedling resistance to stem rust in the Ethiopian wheat accessions. Many of the identified resistance loci were mapped close to previously identified rust resistance genes; however, three loci on the short arms of chromosomes 5A and 7B for stripe rust resistance and two on chromosomes 3B and 7B for stem rust resistance may be novel.ConclusionOur results demonstrate that considerable genetic variation resides within the landrace accessions that can be utilized to broaden the genetic base of rust resistance in wheat breeding germplasm. The molecular markers identified in this study should be useful in efficiently targeting the associated resistance loci in marker-assisted breeding for rust resistance in Ethiopia and other countries.Electronic supplementary materialThe online version of this article (doi:10.1186/s12870-017-1082-7) contains supplementary material, which is available to authorized users.

Successful management and utilization of increasingly large genomic datasets is essential for breeding programs to increase genetic gain and accelerate cultivar development. To help with data management and storage, we developed a sorghum Practical Haplotype Graph (PHG) pangenome database that stores all identified haplotypes and variant information for a given set of individuals. We developed two PHGs in sorghum, one with 24 individuals and another with 398 individuals, that reflect the diversity across genic regions of the sorghum genome. 24 founders of the Chibas sorghum breeding program were sequenced at low coverage (0.01x) and processed through the PHG to identify genome-wide variants. The PHG called SNPs with only 5.9% error at 0.01x coverage -only 3% lower than its accuracy when calling SNPs from 8x coverage sequence. Additionally, 207 progeny from the Chibas genomic selection (GS) training population were sequenced and processed through the PHG. Missing genotypes in the progeny were imputed from the parental haplotypes available in the PHG and used for genomic prediction. Mean prediction accuracies with PHG SNP calls range from 0.57-0.73 for different traits, and are similar to prediction accuracies obtained with genotyping-by-sequencing (GBS) or markers from sequencing targeted amplicons (rhAmpSeq). This study provides a proof of concept for using a sorghum PHG to call and impute SNPs from low-coverage sequence data and also shows that the PHG can unify genotype calls from different sequencing platforms. By reducing the amount of input sequence needed, the PHG has the potential to decrease the cost of genotyping for genomic selection, making GS more feasible and facilitating larger breeding populations that can capture maximum recombination. Our results demonstrate that the PHG is a useful research and breeding tool that can maintain variant information from a diverse group of taxa, store sequence data in a condensed but readily accessible format, unify genotypes from different genotyping methods, and provide a cost-effective option for genomic selection for any species.

Stripe rust, caused by Puccinia striiformis f. sp. tritici, is one of the most important diseases of wheat in Ethiopia. In total, 97 isolates were recovered from stripe rust samples collected in Ethiopia in 2013 and 2014. These isolates were tested on a set of 18 Yr single-gene differentials for characterization of races and 7 supplementary differentials for additional information of virulence. Of 18 P. striiformis f. sp. tritici races identified, the 5 most predominant races were PSTv-105 (21.7%), PSTv-106 (17.5%), PSTv-107 (11.3%), PSTv-76 (10.3%), and PSTv-41 (6.2%). High frequencies (>40%) were detected for virulence to resistance genes Yr1, Yr2, Yr6, Yr7, Yr8, Yr9, Yr17, Yr25, Yr27, Yr28, Yr31, Yr43, Yr44, YrExp2, and YrA. Low frequencies (<40%) were detected for virulence to Yr10, Yr24, Yr32, YrTr1, Hybrid 46, and Vilmorin 23. None of the isolates were virulent to Yr5, Yr15, YrSP, and YrTye. Among the six collection regions, Arsi Robe and Tiyo had the highest virulence diversities, followed by Bekoji, while Bale and Holeta had the lowest. Evaluation of 178 Ethiopian wheat cultivars and landraces with two of the Ethiopian races and three races from the United States indicated that the Ethiopian races were more virulent on the germplasm than the predominant races of the United States. Thirteen wheat cultivars or landraces that were resistant or moderately resistant to all five tested races should be useful for breeding wheat cultivars with resistance to stripe rust in both countries.

Successful management and utilization of increasingly large genomic datasets is essential for breeding programs to accelerate cultivar development. To help with this, we developed a Sorghum bicolor Practical Haplotype Graph (PHG) pangenome database that stores haplotypes and variant information. We developed two PHGs in sorghum that were used to identify genome-wide variants for 24 founders of the Chibas sorghum breeding program from 0.01x sequence coverage. The PHG called single nucleotide polymorphisms (SNPs) with 5.9% error at 0.01x coverage-only 3% higher than PHG error when calling SNPs from 8x coverage sequence. Additionally, 207 progenies from the Chibas genomic selection (GS) training population were sequenced and processed through the PHG. Missing genotypes were imputed from PHG parental haplotypes and used for genomic prediction. Mean prediction accuracies with PHG SNP calls range from .57-.73 and are similar to prediction accuracies obtained with genotyping-by-sequencing or targeted amplicon sequencing (rhAmpSeq) markers. This study demonstrates the use of a sorghum PHG to