Key message: β-Carotene content in sweetpotato is associated with theOrangeandphytoene synthasegenes; due to physical linkage ofphytoene synthasewithsucrose synthase, β-carotene and starch content are negatively correlated. In populations depending on sweetpotato for food security, starch is an important source of calories, while β-carotene is an important source of provitamin A. The negative association between the two traits contributes to the low nutritional quality of sweetpotato consumed, especially in sub-Saharan Africa. Using a biparental mapping population of 315 F₁progeny generated from a cross between an orange-fleshed and a non-orange-fleshed sweetpotato variety, we identified two major quantitative trait loci (QTL) on linkage group (LG) three (LG3) and twelve (LG12) affecting starch, β-carotene, and their correlated traits, dry matter and flesh color. Analysis of parental haplotypes indicated that these two regions acted pleiotropically to reduce starch content and increase β-carotene in genotypes carrying the orange-fleshed parental haplotype at the LG3 locus.Phytoene synthaseandsucrose synthase,the rate-limiting and linked genes located within the QTL on LG3 involved in the carotenoid and starch biosynthesis, respectively, were differentially expressed in Beauregard versus Tanzania storage roots. TheOrangegene, the molecular switch for chromoplast biogenesis, located within the QTL on LG12 while not differentially expressed was expressed in developing roots of the parental genotypes. We conclude that these two QTL regions act together in acisandtransmanner to inhibit starch biosynthesis in amyloplasts and enhance chromoplast biogenesis, carotenoid biosynthesis, and accumulation in orange-fleshed sweetpotato. Understanding the genetic basis of this negative association between starch and β-carotene will inform future sweetpotato breeding strategies targeting sweetpotato for food and nutritional security.

Sweetpotato [Ipomoea batatas (L.) Lam] breeding is important for food security and health in East Africa (EA), and a breeding platform in Uganda provides national researchers and breeders in EA with true seed. Our objectives were to characterize genetic relationships among parental material used at the EA breeding platform. There were 135 parents and six check clones analyzed using 31 simple sequence repeat primers. An average of 7.13 alleles per primer was found, and Jaccard similarity coefficients were in the range of 0.298 to 1.00 with a mean of 0.542. Unweighted pair group cluster analysis placed most African parents in two main subclusters showing no association with morphology or geographical origin. The subclusters were also supported by principal coordinate analysis, derivative analysis of principal components, and population structure simulations. The analyzed breeding material from EA was highly genetically variable, grouped in two distinct genetic pools, and suitable to study heterosis exploiting breeding schemes.

Quality assurance and control (QA/QC) is an essential element of a breeding program’s optimization efforts towards increased genetic gains. Due to auto-hexaploid genome complexity, a low-cost marker platform for routine QA/QC in sweetpotato breeding programs is still unavailable. We used 662 parents of the International Potato Center (CIP)’s global breeding program spanning Peru, Uganda, Mozambique and Ghana, to develop a low-density highly informative single nucleotide polymorphism (SNP) marker set to be deployed for routine QA/QC. Segregation of the selected 30 SNPs (two SNPs per base chromosome) in a recombined breeding population was evaluated using 282 progeny from some of the parents above. The progeny were replicated from in-vitro, screenhouse and field, and the selected SNP-set was confirmed to identify relatively similar mislabeling error rates as a high density SNP-set of 10,159 markers. Six additional trait-specific markers were added to the selected SNP set from previous quantitative trait loci mapping studies. The 36-SNP set will be deployed for QA/QC in breeding pipelines and in fingerprinting of advanced clones or released varieties to monitor genetic gains in famers’ fields. The study also enabled evaluation of CIP’s global breeding population structure and the effect of some of the most devastating stresses like sweetpotato virus disease on genetic variation management. These results will inform future deployment of genomic selection in sweetpotato.

Sweet potato (Ipomoea batatas) ranks among the most important crops in the world and provides nutritional and economic sustainability for subsistence farmers in sub-Saharan Africa. Its production is mainly constrained by sweet potato virus disease (SPVD) caused by the coinfection of two positive-sense single-stranded RNA viruses, sweet potato chlorotic stunt virus (SPCSV) and sweet potato feathery mottle virus (SPFMV). Current understanding of sweet potato responses to SPCSV and SPFMV at the molecular level remains very limited. In this study, we performed deep sequencing of both messenger RNA (mRNA) and small RNA (sRNA) populations in an SPVD-susceptible cultivar 'Beauregard' upon viral infection, to identify biological pathways that contribute to both general and specific host responses to these important viral pathogens. We found that pathways related to stress response and signaling were significantly affected by viral infection. sRNA components of these pathways were predominantly affected in late stages of the coinfection by SPCSV and SPFMV. We identified several novel microRNAs that were responsive to viral infection, some of which were predicted to target nucleotide-binding site leucine-rich repeat (NBS-LRR) disease resistance genes. The downregulation of the salicylic acid-mediated defense response pathway in particular seems to be a result of the viral infection process, and can in part explain the susceptible nature of the 'Beauregard' cultivar.

Potato virus Y (PVY) and Phytophthora infestans (Mont.) de Bary that causes potato late blight (LB), pose serious constraints to cultivated potatoes due to significant yield reduction, and phenotyping for resistance remains challenging. Breeding operations for vegetatively propagated crops can lead to genotype mislabeling that, in turn, reduces genetic gains. Low-density and low-cost molecular marker assessment for phenotype prediction and quality control is a viable option for breeding programs. Here, we report on the development of kompetitive allele specific PCR (KASP) markers for LB and PVY resistance, and for routine quality control assessment of different breeding populations. Two KASP markers for LB resistance and two for PVY Ryadg were validated with an estimated assay power that ranged between 0.65 and 0.88. The developed QC KASP markers demonstrated the capability of discriminating tetraploid calls in breeding materials, including full-sibs and half-sibs. Routine implementation of the developed markers in a breeding program would assist with better allocation of resources and enable precise characterization of breeding material, thereby leading to increased genetic gains.

Breeders have made important efforts to develop genotypes able to resist virus attacks in sweetpotato, a major crop providing food security and poverty alleviation to smallholder farmers in many regions of Sub-Saharan Africa, Asia and Latin America. However, a lack of accurate objective quantitative methods for this selection target in sweetpotato prevents a consistent and extensive assessment of large breeding populations. In this study, an approach to characterize and classify resistance in sweetpotato was established by assessing total yield loss and virus load after the infection of the three most common viruses (SPFMV, SPCSV, SPLCV). Twelve sweetpotato genotypes with contrasting reactions to virus infection were grown in the field under three different treatments: pre-infected by the three viruses, un-infected and protected from re-infection, and un-infected but exposed to natural infection. Virus loads were assessed using ELISA, (RT-)qPCR, and loop-mediated isothermal amplification (LAMP) methods, and also through multispectral reflectance and canopy temperature collected using an unmanned aerial vehicle. Total yield reduction compared to control and the arithmetic sum of (RT-)qPCR relative expression ratios were used to classify genotypes into four categories: resistant, tolerant, susceptible, and sensitives. Using 14 remote sensing predictors, machine learning algorithms were trained to classify all plots under the said categories. The study found that remotely sensed predictors were effective in discriminating the different virus response categories. The results suggest that using machine learning and remotely sensed data, further complemented by fast and sensitive LAMP assays to confirm results of predicted classifications could be used as a high throughput approach to support virus resistance phenotyping in sweetpotato breeding.