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Provitamin A biofortification and increased dry matter content are important breeding targets in cassava improvement programs worldwide. Biofortified varieties contribute to the alleviation of provitamin A deficiency, a leading cause of preventable blindness in developing countries. Dry matter content is a major component of dry yield and thus underlies overall variety performance and acceptability by growers, processors, and consumers. SNP markers linked to these traits have recently been discovered through several genome-wide association studies but have not been deployed for marker-assisted selection (MAS). Assessment of marker performance in diverse genetic backgrounds is an important step towards their deployment for routine MAS. In the present study, seven previously identified markers for these traits were converted to a robust set of uniplex allele-specific PCR assays and validated in two independent pre-breeding and breeding populations. These assays were efficient in discriminating marker genotypic classes and had an average call rate greater than 98%. A high correlation was observed between the predicted and observed carotenoid content as inferred by root yellowness intensity in the breeding (r = 0.92) and pre-breeding (r = 0.95) populations. On the other hand, dry matter content-markers had moderately low predictive accuracy in both populations (r < 0.40) due to the more quantitative nature of the trait. This work confirmed marker effectiveness in multiple backgrounds, therefore, further strengthening its value in cassava biofortification to ensure nutritional security as well as dry matter content productivity. Our study provides a framework to guide future marker development, thus leading to more routine use of markers in MAS in cassava improvement programs.

Over 800 million people across the tropics rely on cassava as a major source of calories. While the root dry matter content (RDMC) of this starchy root crop is important for both producers and consumers, characterization of RDMC by traditional methods is time-consuming and laborious for breeding programs. Alternate phenotyping methods have been proposed but lack the accuracy, cost, or speed ultimately needed for cassava breeding programs. For this reason, we investigated the use of a low-cost, handheld NIR spectrometer for field-based RDMC prediction in cassava. Oven-dried measurements of RDMC were paired with 21,044 scans of roots of 376 diverse clones from 10 field trials in Nigeria and grouped into training and test sets based on cross-validation schemes relevant to plant breeding programs. Mean partial least squares regression model performance ranged from R2p = 0.62 - 0.89 for within-trial predictions, which is within the range achieved with laboratory-grade spectrometers in previous studies. Relative to other factors, model performance was highly impacted by the inclusion of samples from the same environment in both the training and test sets. Random forest variable importance analysis of root spectra revealed increased importance in a region previously identified as predictive of water content in plants (~950 - 990 nm). With appropriate model calibration, the tested spectrometer will allow for field-based collection of spectral data with a smartphone for accurate RDMC prediction and potentially other quality traits, a step that could be easily integrated into existing harvesting workflows of cassava breeding programs.CORE IDEASA low-cost, handheld near-infrared spectrometer was tested for phenotyping of cassava rootsPlant breeding-relevant cross-validation schemes were used for predictionsHigh prediction accuracies were achieved for cassava root dry matter contentA spectral region predictive of plant water content was identified as important

Cassava is a root crop originating from South America and a major staple crop in the Tropics, including marginal environments. In this study, we focused on South American and African cassava germplasm and investigated the genetic architecture of Hydrogen Cyanide (HCN), a major component of tuber quality. HCN is a plant defense component against herbivory but also toxic for human consumption. We genotyped 3,354 landraces and modern breeding lines originating from 26 Brazilian states and 1,389 individuals were phenotypically characterized across multi-year trials for HCN. All plant material was subjected to high density genotyping using Genotyping-by-sequencing (GBS). We performed genome wide association mapping (GWAS) to characterize the genetic architecture and gene mapping of HCN. Field experiment revealed strong broad and narrow-sense trait heritability (0.82 and 0.41 respectively). Two major loci were identified, encoding for an ATPase and a MATE protein and contributing up to 7% and 30% of the cyanide concentration in roots, respectively. We developed diagnostic markers for breeding applications, validated trait architecture consistency in African germplasm and investigated further evidence for domestication of sweet and bitter cassava. Fine genomic loci characterization indicate; (i) a major role played by vacuolar transporter in regulating HCN content, (ii) co-domestication of sweet and bitter cassava major alleles to be geographical zone dependant, and (ii) major loci allele for high cyanide cassava in Manihot esculenta Crantz seems to originate from its ancestor, M. esculenta ssp. flabellifolia. Taken together these findings expand insights on cyanide in cassava and its glycosylated derivatives in plants.One-sentence summaryIdentification of an intracellular transporter gene and its allelic variation allow to point out cultivars with up to 30 percent decrease in cassava root cyanide content, toxic for human consumption.

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.

Cassava (Manihot esculenta Crantz) is a clonally propagated staple food crop in the tropics. Genomic selection (GS) reduces selection cycle times by the prediction of breeding value for selection of unevaluated lines based on genome-wide marker data. GS has been implemented at three breeding programs in sub-Saharan Africa. Initial studies provided promising estimates of predictive abilities in single populations using standard prediction models and scenarios. In the present study we expand on previous analyses by assessing the accuracy of seven prediction models for seven traits in three prediction scenarios: (1) cross-validation within each population, (2) cross-population prediction and (3) cross-generation prediction. We also evaluated the impact of increasing training population size by phenotyping progenies selected either at random or using a genetic algorithm. Cross-validation results were mostly consistent across breeding programs, with non-additive models like RKHS predicting an average of 10% more accurately. Accuracy was generally associated with heritability. Cross-population prediction accuracy was generally low (mean 0.18 across traits and models) but prediction of cassava mosaic disease severity increased up to 57% in one Nigerian population, when combining data from another related population. Accuracy across-generation was poorer than within (cross-validation) as expected, but indicated that accuracy should be sufficient for rapid-cycling GS on several traits. Selection of prediction model made some difference across generations, but increasing training population (TP) size was more important. In some cases, using a genetic algorithm, selecting one third of progeny could achieve accuracy equivalent to phenotyping all progeny. Based on the datasets analyzed in this study, it was apparent that the size of a training population (TP) has a significant impact on prediction accuracy for most traits. We are still in the early stages of GS in this crop, but results are promising, at least for some traits. The TPs need to continue to grow and quality phenotyping is more critical than ever. General guidelines for successful GS are emerging. Phenotyping can be done on fewer individuals, cleverly selected, making for trials that are more focused on the quality of the data collected.Abbreviations(GS)Genomic selection(GBS)genotype-by-sequencing(IITA)International Institute of Tropical Agriculture(NRCRI)National Root Crops Research Institute(NaCRRI)National Crops Resources Research Institute(GEBVs)genomic estimated breeding values(TP)training population(RTWT)fresh root weight(RTNO)root number(SHTWT)fresh shoot weight(HI)harvest index(DM)dry matter(CMD)content cassava mosaic disease(MCMDS)mean CMD severity(VIGOR)early vigor

Cassava (Manihot esculenta (L.) Crantz) is a starchy root crop cultivated in the tropics for fresh consumption and commercial processing. Dry matter content and micronutrient density, particularly of provitamin A – traits that are negatively correlated – are among the primary selection objectives in cassava breeding. This study aimed at identifying genetic markers associated with these traits and uncovering the potential underlying cause of their negative correlation – whether linkage and/or pleiotropy. A genome-wide association mapping using 672 clones genotyped at 72,279 SNP loci was carried out. Root yellowness was used indirectly to assess variation in carotenoid content. Two major loci for root yellowness was identified on chromosome 1 at positions 24.1 and 30.5 Mbp. A single locus for dry matter content that co-located with the 24.1 Mbp peak for carotenoid content was identified. Haplotypes at these loci explained a large proportion of the phenotypic variability. Evidence of mega-base-scale linkage disequilibrium around the major loci of the two traits and detection of the major dry matter locus in independent analysis for the white- and yellow-root subpopulations suggests that physical linkage rather that pleiotropy is more likely to be the cause of the negative correlation between the target traits. Moreover, candidate genes for carotenoid (phytoene synthase) and starch biosynthesis (UDP-glucose pyrophosphorylase and sucrose synthase) occurred in the vicinity of the identified locus at 24.1 Mbp. These findings elucidate on the genetic architecture of carotenoids and dry matter in cassava and provides an opportunity to accelerate genetic improvement of these traits.CORE IDEASCassava, a starchy root crop, is a major source of dietary calories in the tropics.Most varieties consumed are poor in micronutrients, including pro-vitamin A.These two traits are governed by few major loci on chromosome one.Genetic linkage, rather than pleiotropy, is the most likely cause of their negative correlation.

Cassava (Manihot esculenta Crantz) is an important staple food crop in Africa and South America, however, ubiquitous deleterious mutations may severely reduce its fitness. To evaluate these deleterious mutations in the cassava genome, we constructed a cassava haplotype map using deep sequencing from 241 diverse accessions and identified over 28 million segregating variants. We found that, 1) while domestication modified starch and ketone metabolism pathways for human consumption, the concomitant bottleneck and clonal propagation resulted in a large proportion of fixed deleterious amino acid changes, raised the number of deleterious mutations by 26%, and shifted the mutational burden towards common variants; 2) deleterious mutations are ineffectively purged due to limited recombination in cassava genome; 3) recent breeding efforts maintained the yield by masking the most damaging recessive mutations in the heterozygous state, but unable to purge the mutation burden, which should be a key target for future cassava breeding.

In clonally propagated crops, nonadditive genetic effects can be effectively exploited by the identification of superior genetic individuals as varieties. Cassava (Manihot esculenta Crantz) is a clonally propagated staple food crop that feeds hundreds of millions. We quantified the amount and nature of nonadditive genetic variation for three key traits in a breeding population of cassava from sub-Saharan Africa using additive and nonadditive genome-wide marker-based relationship matrices. We then assessed the accuracy of genomic prediction for total (additive plus nonadditive) genetic value. We confirmed previous findings based on diallel crosses that nonadditive genetic variation is significant for key cassava traits. Specifically, we found that dominance is particularly important for root yield and epistasis contributes strongly to variation in cassava mosaic disease (CMD) resistance. Further, we showed that total genetic value predicted observed phenotypes more accurately than additive only models for root yield but not for dry matter content, which is mostly additive or for CMD resistance, which has high narrow-sense heritability. We address the implication of these results for cassava breeding and put our work in the context of previous results in cassava, and other plant and animal species.

Cassava (Manihot esculenta) is a crucial, under-researched crop feeding millions worldwide, especially in Africa. Cassava mosaic disease (CMD) has plagued production in Africa for over a century. Bi-parental mapping studies suggest primarily a single major gene mediates resistance. To be certain and to potentially identify new loci we conducted the first genome-wide association mapping study in cassava with 6128 African breeding lines. We also assessed the accuracy of genomic selection to improve CMD resistance. We found a single region on chromosome 8 accounts for most resistance but also identified 13 small effect regions. We found evidence that two epistatic loci and/or alternatively multiple resistance alleles exist at major QTL. We identified two peroxidases and one thioredoxin as candidate genes. Genomic prediction of additive and total genetic merit was accurate for CMD and will be effective both for selecting parents and identifying highly resistant clones as varieties.