Abstract

Fusarium head blight (FHB) is an economically and environmentally concerning disease of wheat (Triticum aestivum L). A two-pronged approach of marker-assisted selection coupled with genomic selection has been suggested when breeding for FHB resistance. A historical dataset comprised of entries in the Southern Uniform Winter Wheat Scab Nursery (SUWWSN) from 2011 to 2021 was partitioned and used in genomic prediction. Two traits were curated from 2011 to 2021 in the SUWWSN: percent Fusarium damaged kernels (FDK) and deoxynivalenol (DON) content. Heritability was estimated for each trait-by-environment combination. A consistent set of check lines was drawn from each year in the SUWWSN, and k-means clustering was performed across environments to assign environments into clusters. Two clusters were identified as FDK and three for DON. Cross-validation on SUWWSN data from 2011 to 2019 indicated no outperforming training population in comparison to the combined dataset. Forward validation for FDK on the SUWWSN 2020 and 2021 data indicated a predictive accuracy r≈0.58$r \approx 0.58$ and r≈0.53$r \approx 0.53$, respectively. Forward validation for DON indicated a predictive accuracy of r≈0.57$r \approx 0.57$ and r≈0.45$r \approx 0.45$, respectively. Forward validation using environments in cluster one for FDK indicated a predictive accuracy of r≈0.65$r \approx 0.65$ and r≈0.60$r \approx 0.60$, respectively. Forward validation using environments in cluster one for DON indicated a predictive accuracy of r≈0.67$r \approx 0.67$ and r≈0.60$r \approx 0.60$, respectively. These results indicated that selecting environments based on check performance may produce higher forward prediction accuracies. This work may be used as a model for utilizing public resources for genomic prediction of FHB resistance traits across public wheat breeding programs.

Abstract

Melilotus officinalis is an important legume crop with forage and Chinese medicinal value. The unknown genome of M. officinalis restricted the domestication and utilization of the species and its germplasm resource diversity. A chromosome-scale assembly of the M. officinalis genome was assembled and analysed. The 976.27 Mb of genome was divided into eight chromosomes covering 99.16% of the whole genome. A total of 50022 genes were predicted in the genome. M. officinalis and Melilotus albus shared a common ancestor 0.5–5.65 million years ago (MYA). A genome-wide doubling event occurred 68.93 MYA according to the synonymous nucleotide-substitution values. A total of 552102 tandem repeats were predicted, and 46004 SSR primers of TRs with 10 or more base pairs were developed and designed. The elucidation of the M. officinalis genome provides a compelling model system for studying the genetic, evolutionary and biosynthesis of this legume.

Abstract

Bread wheat (Triticum aestivum L.) is a major crop and its genome is one of the largest ever assembled at reference-quality level. It is 15 Gb, hexaploid, with 85% of transposable elements (TEs). Wheat genetic diversity was mainly focused on genes and little is known about the extent of genomic variability affecting TEs, transposition rate, and the impact of polyploidy. Multiple chromosome-scale assemblies are now available for bread wheat and for its tetraploid and diploid wild relatives. In this study, we computed base pair-resolved, gene-anchored, whole genome alignments of A, B, and D lineages at different ploidy levels in order to estimate the variability that affects the TE space. We used assembled genomes of 13 T. aestivum cultivars (6x = AABBDD) and a single genome for Triticum durum (4x = AABB), Triticum dicoccoides (4x = AABB), Triticum urartu (2x = AA), and Aegilops tauschii (2x = DD). We show that 5%–34% of the TE fraction is variable, depending on the species divergence. Between 400 and 13,000 novel TE insertions per subgenome were detected. We found lineage-specific insertions for nearly all TE families in di-, tetra-, and hexaploids. No burst of transposition was observed and polyploidization did not trigger any boost of transposition. This study challenges the prevailing idea of wheat TE dynamics and is more in agreement with an equilibrium model of evolution.

Abstract

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.

Core Ideas

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.