Cassava ( Manihot esculenta Crantz) is a clonally propagated staple food crop in the tropics. Genomic selection (GS) has been implemented at three breeding institutions in Africa to reduce cycle times. Initial studies provided promising estimates of predictive abilities. Here, we expand on previous analyses by assessing the accuracy of seven prediction models for seven traits in three prediction scenarios: cross‐validation within populations, cross‐population prediction and cross‐generation prediction. We also evaluated the impact of increasing the training population (TP) size by phenotyping progenies selected either at random or with a genetic algorithm. Cross‐validation results were mostly consistent across programs, with nonadditive models predicting of 10% better on average. Cross‐population accuracy was generally low (mean = 0.18) but prediction of cassava mosaic disease increased up to 57% in one Nigerian population when data from another related population were combined. Accuracy across generations was poorer than within‐generation accuracy, as expected, but accuracy for dry matter content and mosaic disease severity should be sufficient for rapid‐cycling GS. Selection of a prediction model made some difference across generations, but increasing TP size was more important. With a genetic algorithm, selection of one‐third of progeny could achieve an accuracy equivalent to phenotyping all progeny. We are in the early stages of GS for this crop but the results are promising for some traits. General guidelines that are emerging are that TPs need to continue to grow but phenotyping can be done on a cleverly selected subset of individuals, reducing the overall phenotyping burden.

Fatty acids in seeds affect seed germination and seedling vigour and fatty acid composition determines the quality of seed oil. In this study, quantitative trait locus (QTL) mapping of fatty acid and transcript abundance was integrated with gene network analysis to unravel the genetic regulation of seed fatty acid composition in a Brassica rapa doubled haploid population from a cross between a yellow sarson oil-type and a black seeded pak choi. The distribution of major QTLs for fatty acids showed a relationship with the fatty acid types: linkage group A03 for mono-unsaturated fatty acids (MUFAs), A04 for saturated fatty acids (SFAs) and A05 for poly-unsaturated fatty acids (PUFAs). Using a genetical genomics approach, expression QTL (eQTL) hotspots were found at major fatty acid QTLs on linkage groups A03, A04, A05 and A09. An eQTL-guided gene co-expression network of lipid metabolism related genes showed major hubs at the genes BrPLA2-ALPHA, BrWD-40, a number of seed storage protein genes and a transcription factor BrMD-2, suggesting essential roles for these genes in lipid metabolism. Three sub-networks were extracted for the economically important and most abundant fatty acids erucic-, oleic-, linoleic- and linolenic acid. Network analysis, combined with comparison of genome positions of cis- or trans- eQTLs with fatty acid QTLs, allowed identification of candidate genes for genetic regulation of these fatty acids. The generated insights in the genetic architecture of fatty acid composition and underlying complex gene regulatory networks in B. rapa seeds are discussed.

Genomic selection can increase the rate of genetic gain in crops through accumulation of positive alleles and reduce phenotyping costs by shortening the breeding cycle time. We performed genomic prediction for resistance to wheat rusts in tetraploid wheat accessions using three cross-validation with the objective of predicting: (1) rust resistance when individuals are not tested in all environments/locations, (2) the performance of lines across years, and (3) adult plant resistance (APR) of lines with bivariate models. The rationale for the latter is that seedling assays are faster and could increase prediction accuracy for APR. Predictions were derived from adult plant and seedling responses for leaf rust (Lr), stem rust (Sr) and stripe rust (Yr) in a panel of 391 accessions grown across multiple years and locations and genotyped using 16,483 single nucleotide polymorphisms. Different Bayesian models and genomic best linear unbiased prediction yielded similar accuracies for all traits. Site and year prediction accuracies for Lr and Yr ranged between 0.56–0.71 for Lr and 0.51–0.56 for Yr. While prediction accuracy for Sr was variable across different sites, accuracies for Yr were similar across different years and sites. The changes in accuracies can reflect higher genotype × environment (G × E) interactions due to climate or pathogenic variation. The use of seedling assays in genomic prediction was underscored by significant positive genetic correlations between all stage resistance (ASR) and APR (Lr: 0.45, Sr: 0.65, Yr: 0.50). Incorporating seedling phenotypes in the bivariate genomic approach increased prediction accuracy for all three rust diseases. Our work suggests that the underlying plant-host response to pathogens in the field and greenhouse screens is genetically correlated, but likely highly polygenic and therefore difficult to detect at the individual gene level. Overall, genomic prediction accuracies were in the range suitable for selection in early generations of the breeding cycle.

Background: Genomic prediction models were, in principle, developed to include all the available marker information; with this approach, these models have shown in various crops moderate to high predictive accuracies. Previous studies in cassava have demonstrated that, even with relatively small training populations and low-density GBS markers, prediction models are feasible for genomic selection. In the present study, we prioritized SNPs in close proximity to genome regions with biological importance for a given trait. We used a number of strategies to select variants that were then included in single and multiple kernel GBLUP models. Specifically, our sources of information were transcriptomics, GWAS, and immunity-related genes, with the ultimate goal to increase predictive accuracies for Cassava Brown Streak Disease (CBSD) severity. Results: We used single and multi-kernel GBLUP models with markers imputed to whole genome sequence level to accommodate various sources of biological information; fitting more than one kinship matrix allowed for differential weighting of the individual marker relationships. We applied these GBLUP approaches to CBSD phenotypes (i.e., root infection and leaf severity three and six months after planting) in a Ugandan Breeding Population (n = 955). Three means of exploiting an established RNAseq experiment of CBSD-infected cassava plants were used. Compared to the biology-agnostic GBLUP model, the accuracy of the informed multi-kernel models increased the prediction accuracy only marginally (1.78% to 2.52%). Conclusions: Our results show that markers imputed to whole genome sequence level do not provide enhanced prediction accuracies compared to using standard GBS marker data in cassava. The use of transcriptomics data and other sources of biological information resulted in prediction accuracies that were nominally superior to those obtained from traditional prediction models.

Promoter-proximal pausing and divergent transcription at promoters and enhancers, which are prominent features in animals, have been reported to be absent in plants based on a study of Arabidopsis thaliana . Here, our PRO-Seq analysis in cassava ( Manihot esculenta ) identified peaks of transcriptionally-engaged RNA polymerase II (Pol2) at both 5’ and 3’ ends of genes, consistent with paused or slowly-moving Pol2, and divergent transcription at potential intragenic enhancers. A full genome search for bi-directional transcription using an algorithm for enhancer detection developed in mammals (dREG) identified many enhancer candidates. These sites show distinct patterns of methylation and nucleotide variation based on genomic evolutionary rate profiling characteristic of active enhancers. Maize GRO-Seq data showed RNA polymerase occupancy at promoters and enhancers consistent with cassava but not Arabidopsis. Furthermore, putative enhancers in maize identified by dREG significantly overlapped with sites previously identified on the basis of open chromatin, histone marks, and methylation. We show that SNPs within these divergently transcribed intergenic regions predict significantly more variation in fitness and root composition than SNPs in chromosomal segments randomly ascertained from the same intergenic distribution, suggesting a functional importance of these sites on cassava. The findings shed new light on plant transcription regulation and its impact on development and plasticity.

Cassava is widely cultivated due to its high drought tolerance and high carbohydrate-containing storage roots. The lack of uniformity and irregular shape of storage roots within and between genotypes poses significant constraints on harvesting and post-harvest processing. Routine assessment of storage root size and shape in breeding plots relies on visual scores. Here, we phenotyped the Genetic gain and offspring (C1) populations from the International Institute of Tropical Agriculture (IITA) breeding program for root shape and size-related traits using image analysis of storage root photographs taken in the field. In our study, using univariate genome-wide association analysis, we detected for most shape and size related traits, significant QTL regions located on chromosomes 1 and 12. The QTL region on chromosome 12 has been previously associated, using IITA breeding populations, to cassava mosaic disease (CMD) resistance. Because the uniformity in size and shape of cassava roots is an important breeding goal, we calculated the standard deviation of individual root measurements per clone. The use of standard deviation measurements allowed the identification of new significant QTL for Perimeter, Feret and Aspect Ratio on chromosomes 6, 9 and 16. Using genomic prediction cross validation, the accuracies of root size and shape-related traits were lower than those previously reported for dry matter content (DM) and cassava mosaic virus resistance (CMD). Predictive accuracies of the mean values of root size and shape image-extracted traits were mostly higher than yield trait prediction accuracies in the C1 population. This study aimed to evaluate the feasibility of the image phenotyping protocol and to assess the use of genome-wide analyses for size and shape image-extracted traits. The methodology described here and the results obtained in this study are promising and open up the opportunity to apply high-throughput methods in cassava.