Low-cost, handheld near-infrared spectroscopy for root dry matter content prediction in cassava

Open Access Journal; Published online: 31 Mar 2022Over 800 million people across the tropics rely on cassava (Manihot esculenta Crantz) 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 near-infrared spectrometer (740–1070 nm) for field-based RDMC prediction in cassava. Oven-dried measurements of RDMC were paired with 21,044 scans of roots of 376 diverse genotypes 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 affected by the inclusion of samples from the same environment in both the training and test sets. 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

Predicting starch content in cassava fresh roots using near-infrared spectroscopy

Open Access Journal; Published online: 08 Nov 2022The cassava starch market is promising in sub-Saharan Africa and increasing rapidly due to the numerous uses of starch in food industries. More accurate, high-throughput, and cost-effective phenotyping approaches could hasten the development of cassava varieties with high starch content to meet the growing market demand. This study investigated the effectiveness of a pocket-sized SCiO™ molecular sensor (SCiO) (740−1070 nm) to predict starch content in freshly ground cassava roots. A set of 344 unique genotypes from 11 field trials were evaluated. The predictive ability of individual trials was compared using partial least squares regression (PLSR). The 11 trials were aggregated to capture more variability, and the performance of the combined data was evaluated using two additional algorithms, random forest (RF) and support vector machine (SVM). The effect of pretreatment on model performance was examined. The predictive ability of SCiO was compared to that of two commercially available near-infrared (NIR) spectrometers, the portable ASD QualitySpec® Trek (QST) (350−2500 nm) and the benchtop FOSS XDS Rapid Content™ Analyzer (BT) (400−2490 nm). The heritability of NIR spectra was investigated, and important spectral wavelengths were identified. Model performance varied across trials and was related to the amount of genetic diversity captured in the trial. Regardless of the chemometric approach, a satisfactory and consistent estimate of starch content was obtained across pretreatments with the SCiO (correlation between the predicted and the observed test set, (R2 P): 0.84−0.90; ratio of performance deviation (RPD): 2.49−3.11, ratio of performance to interquartile distance (RPIQ): 3.24−4.08, concordance correlation coefficient (CCC): 0.91−0.94). While PLSR and SVM showed comparable prediction abilities, the RF model yielded the lowest performance. The heritability of the 331 NIRS spectra varied across trials and spectral regions but was highest (H2 > 0.5) between 871−1070 nm in most trials. Important wavelengths corresponding to absorption bands associated with starch and water were identified from 815 to 980 nm. Despite its limited spectral range, SCiO provided satisfactory prediction, as did BT, whereas QST showed less optimal calibration models. The SCiO spectrometer may be a cost-effective solution for phenotyping the starch content of fresh roots in resource-limited cassava breeding programs

High-resolution linkage map and chromosome-scale genome assembly for cassava (Manihot esculenta Crantz) from 10 populations

Cassava (Manihot esculenta Crantz) is a major staple crop in Africa, Asia, and South America, and its starchy roots provide nourishment for 800 million people worldwide. Although native to South America, cassava was brought to Africa 400–500 years ago and is now widely cultivated across sub-Saharan Africa, but it is subject to biotic and abiotic stresses. To assist in the rapid identification of markers for pathogen resistance and crop traits, and to accelerate breeding programs, we generated a framework map for M. esculenta Crantz from reduced representation sequencing [genotyping-by-sequencing (GBS)]. The composite 2412-cM map integrates 10 biparental maps (comprising 3480 meioses) and organizes 22,403 genetic markers on 18 chromosomes, in agreement with the observed karyotype. We used the map to anchor 71.9% of the draft genome assembly and 90.7% of the predicted protein-coding genes. The chromosome-anchored genome sequence will be useful for breeding improvement by assisting in the rapid identification of markers linked to important traits, and in providing a framework for genomic selectionenhanced breeding of this important crop.Bill and Melinda Gates Foundation (BMGF) Grant OPPGD1493. University of Arizona. CGIAR Research Program on Roots, Tubers, and Bananas. Next Generation Cassava Breeding grant OPP1048542 from BMGF and the United Kingdom Department for International Development. BMGF grant OPPGD1016 to IITA. National Institutes of Health S10 Instrumentation Grants S10RR029668 and S10RR027303.http://www.g3journal.orghb201

High-resolution mapping of resistance to cassava mosaic geminiviruses in cassava using genotypingbysequencing and its implications for breeding

Cassava mosaic disease (CMD), caused by different species of cassava mosaic geminiviruses (CMGs), is themost important disease of cassava in Africa and the Indian sub-continent. The cultivated cassava speciesis protected from CMD by polygenic resistance introgressed from the wild species Manihot glaziovii anda dominant monogenic type of resistance, named CMD2, discovered in African landraces. The ability ofthe monogenic resistance to confer high levels of resistance in different genetic backgrounds has ledrecently to its extensive usage in breeding across Africa as well as pre-emptive breeding in Latin Amer-ica. However, most of the landraces carrying the monogenic resistance are morphologically very similarand come from a geographically restricted area of West Africa, raising the possibility that the diversityof the single-gene resistance could be very limited, or even located at a single locus. Several mappingstudies, employing bulk segregant analysis, in different genetic backgrounds have reported additionalmolecular markers linked to supposedly new resistance genes. However, it is not possible to tell if theseare indeed new genes in the absence adequate genetic map framework or allelism tests. To address thisimportant question, a high-density single nucleotide polymorphism (SNP) map of cassava was developedthrough genotyping-by-sequencing a bi-parental mapping population (N = 180) that segregates for thedominant monogenic resistance to CMD. Virus screening using PCR showed that CMD symptoms andpresence of virus were strongly correlated (r = 0.98). Genome-wide scan and high-resolution compositeinterval mapping using 6756 SNPs uncovered a single locus with large effect (R2= 0.74). Projection ofthe previously published resistance-linked microsatellite markers showed that they co-occurred in thesame chromosomal location surrounding the presently mapped resistance locus. Moreover, their relativedistance to the mapped resistance locus correlated with the reported degree of linkage with the resis-tance phenotype. Cluster analysis of the landraces first shown to have this type of resistance revealedthat they are very closely related, if not identical. These findings suggest that there is a single source ofmonogenic resistance in the crop’s genepool tracing back to a common ancestral clone. In the absenceof further resistance diversification, the long-term effectiveness of the single gene resistance is knownto be precarious, given the potential to be overcome by CMGs due to their fast-paced evolutionary rate.However, combining the quantitative with the qualitative type of resistance may ensure that this resis-tance gene continues to offer protection to cassava, a crop that is depended upon by millions of peoplein Africa against the devastating onslaught of CMGs

Genetic mapping using genotypingbysequencing in the clonally propagated cassava

Published online: April 25, 2014Cassava (Manihot esculenta L.) is one of the most important food crops in the tropics, but yields are far below their potential. The gene pool of cassava contains natural genetic diversity relevant to many important breeding goals, but breeding progress has been slow, partly because of insuf-ficient genomic resources. As a first step toward implementing genomewide genetic studies that will facilitate rapid genetic gain through breed-ing, we genotyped-by-sequencing a set of 182 full-sibs population of cassava that segregated in several traits: resistance to the cassava mosaic disease (CMD) and yield under CMD pressure; increased carotenoid content in storage roots; color of stem exterior and anthocyanin pigmen-tation in the petioles, inner root skin, and api-cal leaves. Employing a rare-cutting restriction enzyme, PstI, in a genotyping-by-sequencing (GBS) library preparation, we obtained 2478 segregating single nucleotide polymorphisms (SNPs), of which 1257 passed standard filter-ing for missing genotypes and deviation from expected genotypic frequencies. We mapped 772 SNPs across 19 linkage groups and anchored 313 unique scaffolds from the version 4.1 of the cassava genome assembly. Most of the stud-ied morphological traits as well as resistance to CMD and root carotenoid content showed quali-tative inheritance. As expected, quantitative trait loci analysis for these traits revealed single loci surrounded by small confidence intervals. Yield under CMD was associated with the CMD resis-tance locus. We show that GBS is a powerful genotyping tool that provides a sufficient number of markers for unraveling the genetic architecture of Mendelian traits in cassava in addition to the development of a robust genetic map that can help anchor unassembled genomic scaffolds

High-resolution mapping of resistance to cassava mosaic geminiviruses in cassava using genotyping-by-sequencing and its implications for breeding

AbstractCassava mosaic disease (CMD), caused by different species of cassava mosaic geminiviruses (CMGs), is the most important disease of cassava in Africa and the Indian sub-continent. The cultivated cassava species is protected from CMD by polygenic resistance introgressed from the wild species Manihot glaziovii and a dominant monogenic type of resistance, named CMD2, discovered in African landraces. The ability of the monogenic resistance to confer high levels of resistance in different genetic backgrounds has led recently to its extensive usage in breeding across Africa as well as pre-emptive breeding in Latin America. However, most of the landraces carrying the monogenic resistance are morphologically very similar and come from a geographically restricted area of West Africa, raising the possibility that the diversity of the single-gene resistance could be very limited, or even located at a single locus. Several mapping studies, employing bulk segregant analysis, in different genetic backgrounds have reported additional molecular markers linked to supposedly new resistance genes. However, it is not possible to tell if these are indeed new genes in the absence adequate genetic map framework or allelism tests. To address this important question, a high-density single nucleotide polymorphism (SNP) map of cassava was developed through genotyping-by-sequencing a bi-parental mapping population (N=180) that segregates for the dominant monogenic resistance to CMD. Virus screening using PCR showed that CMD symptoms and presence of virus were strongly correlated (r=0.98). Genome-wide scan and high-resolution composite interval mapping using 6756 SNPs uncovered a single locus with large effect (R2=0.74). Projection of the previously published resistance-linked microsatellite markers showed that they co-occurred in the same chromosomal location surrounding the presently mapped resistance locus. Moreover, their relative distance to the mapped resistance locus correlated with the reported degree of linkage with the resistance phenotype. Cluster analysis of the landraces first shown to have this type of resistance revealed that they are very closely related, if not identical. These findings suggest that there is a single source of monogenic resistance in the crop's genepool tracing back to a common ancestral clone. In the absence of further resistance diversification, the long-term effectiveness of the single gene resistance is known to be precarious, given the potential to be overcome by CMGs due to their fast-paced evolutionary rate. However, combining the quantitative with the qualitative type of resistance may ensure that this resistance gene continues to offer protection to cassava, a crop that is depended upon by millions of people in Africa against the devastating onslaught of CMGs

Image_6_Predicting starch content in cassava fresh roots using near-infrared spectroscopy.jpeg

The cassava starch market is promising in sub-Saharan Africa and increasing rapidly due to the numerous uses of starch in food industries. More accurate, high-throughput, and cost-effective phenotyping approaches could hasten the development of cassava varieties with high starch content to meet the growing market demand. This study investigated the effectiveness of a pocket-sized SCiO™ molecular sensor (SCiO) (740−1070 nm) to predict starch content in freshly ground cassava roots. A set of 344 unique genotypes from 11 field trials were evaluated. The predictive ability of individual trials was compared using partial least squares regression (PLSR). The 11 trials were aggregated to capture more variability, and the performance of the combined data was evaluated using two additional algorithms, random forest (RF) and support vector machine (SVM). The effect of pretreatment on model performance was examined. The predictive ability of SCiO was compared to that of two commercially available near-infrared (NIR) spectrometers, the portable ASD QualitySpec® Trek (QST) (350−2500 nm) and the benchtop FOSS XDS Rapid Content™ Analyzer (BT) (400−2490 nm). The heritability of NIR spectra was investigated, and important spectral wavelengths were identified. Model performance varied across trials and was related to the amount of genetic diversity captured in the trial. Regardless of the chemometric approach, a satisfactory and consistent estimate of starch content was obtained across pretreatments with the SCiO (correlation between the predicted and the observed test set, (R2P): 0.84−0.90; ratio of performance deviation (RPD): 2.49−3.11, ratio of performance to interquartile distance (RPIQ): 3.24−4.08, concordance correlation coefficient (CCC): 0.91−0.94). While PLSR and SVM showed comparable prediction abilities, the RF model yielded the lowest performance. The heritability of the 331 NIRS spectra varied across trials and spectral regions but was highest (H2 > 0.5) between 871−1070 nm in most trials. Important wavelengths corresponding to absorption bands associated with starch and water were identified from 815 to 980 nm. Despite its limited spectral range, SCiO provided satisfactory prediction, as did BT, whereas QST showed less optimal calibration models. The SCiO spectrometer may be a cost-effective solution for phenotyping the starch content of fresh roots in resource-limited cassava breeding programs.</p

Image_9_Predicting starch content in cassava fresh roots using near-infrared spectroscopy.jpeg

The cassava starch market is promising in sub-Saharan Africa and increasing rapidly due to the numerous uses of starch in food industries. More accurate, high-throughput, and cost-effective phenotyping approaches could hasten the development of cassava varieties with high starch content to meet the growing market demand. This study investigated the effectiveness of a pocket-sized SCiO™ molecular sensor (SCiO) (740−1070 nm) to predict starch content in freshly ground cassava roots. A set of 344 unique genotypes from 11 field trials were evaluated. The predictive ability of individual trials was compared using partial least squares regression (PLSR). The 11 trials were aggregated to capture more variability, and the performance of the combined data was evaluated using two additional algorithms, random forest (RF) and support vector machine (SVM). The effect of pretreatment on model performance was examined. The predictive ability of SCiO was compared to that of two commercially available near-infrared (NIR) spectrometers, the portable ASD QualitySpec® Trek (QST) (350−2500 nm) and the benchtop FOSS XDS Rapid Content™ Analyzer (BT) (400−2490 nm). The heritability of NIR spectra was investigated, and important spectral wavelengths were identified. Model performance varied across trials and was related to the amount of genetic diversity captured in the trial. Regardless of the chemometric approach, a satisfactory and consistent estimate of starch content was obtained across pretreatments with the SCiO (correlation between the predicted and the observed test set, (R2P): 0.84−0.90; ratio of performance deviation (RPD): 2.49−3.11, ratio of performance to interquartile distance (RPIQ): 3.24−4.08, concordance correlation coefficient (CCC): 0.91−0.94). While PLSR and SVM showed comparable prediction abilities, the RF model yielded the lowest performance. The heritability of the 331 NIRS spectra varied across trials and spectral regions but was highest (H2 > 0.5) between 871−1070 nm in most trials. Important wavelengths corresponding to absorption bands associated with starch and water were identified from 815 to 980 nm. Despite its limited spectral range, SCiO provided satisfactory prediction, as did BT, whereas QST showed less optimal calibration models. The SCiO spectrometer may be a cost-effective solution for phenotyping the starch content of fresh roots in resource-limited cassava breeding programs.</p

Image_10_Predicting starch content in cassava fresh roots using near-infrared spectroscopy.jpeg

The cassava starch market is promising in sub-Saharan Africa and increasing rapidly due to the numerous uses of starch in food industries. More accurate, high-throughput, and cost-effective phenotyping approaches could hasten the development of cassava varieties with high starch content to meet the growing market demand. This study investigated the effectiveness of a pocket-sized SCiO™ molecular sensor (SCiO) (740−1070 nm) to predict starch content in freshly ground cassava roots. A set of 344 unique genotypes from 11 field trials were evaluated. The predictive ability of individual trials was compared using partial least squares regression (PLSR). The 11 trials were aggregated to capture more variability, and the performance of the combined data was evaluated using two additional algorithms, random forest (RF) and support vector machine (SVM). The effect of pretreatment on model performance was examined. The predictive ability of SCiO was compared to that of two commercially available near-infrared (NIR) spectrometers, the portable ASD QualitySpec® Trek (QST) (350−2500 nm) and the benchtop FOSS XDS Rapid Content™ Analyzer (BT) (400−2490 nm). The heritability of NIR spectra was investigated, and important spectral wavelengths were identified. Model performance varied across trials and was related to the amount of genetic diversity captured in the trial. Regardless of the chemometric approach, a satisfactory and consistent estimate of starch content was obtained across pretreatments with the SCiO (correlation between the predicted and the observed test set, (R2P): 0.84−0.90; ratio of performance deviation (RPD): 2.49−3.11, ratio of performance to interquartile distance (RPIQ): 3.24−4.08, concordance correlation coefficient (CCC): 0.91−0.94). While PLSR and SVM showed comparable prediction abilities, the RF model yielded the lowest performance. The heritability of the 331 NIRS spectra varied across trials and spectral regions but was highest (H2 > 0.5) between 871−1070 nm in most trials. Important wavelengths corresponding to absorption bands associated with starch and water were identified from 815 to 980 nm. Despite its limited spectral range, SCiO provided satisfactory prediction, as did BT, whereas QST showed less optimal calibration models. The SCiO spectrometer may be a cost-effective solution for phenotyping the starch content of fresh roots in resource-limited cassava breeding programs.</p