Rapid analyses of dry matter content and carotenoids in fresh cassava roots using a portable visible and near infrared spectrometer (Vis/NIRS)

Portable Vis/NIRS are flexible tools for fast and unbiased analyses of constituents with minimal sample preparation. This study developed calibration models for dry matter content (DMC) and carotenoids in fresh cassava roots using a portable Vis/NIRS system. We examined the effects of eight data pre-treatment combinations on calibration models and assessed calibrations on processed and intact root samples. We compared Vis/NIRS derived-DMC to other phenotyping methods. The results of the study showed that the combination of standard normal variate and de-trend (SNVD) with first derivative calculated on two data points and no smoothing (SNVD+1111) was adequate for a robust model. Calibration performance was higher with processed than the intact root samples for all the traits although intact root models for some traits especially total carotenoid content (TCC) (R2c = 96%, R2cv = 90%, RPD = 3.6 and SECV = 0.63) were sufficient for screening purposes. Using three key quality traits as templates, we developed models with processed fresh root samples. Robust calibrations were established for DMC (R2c = 99%, R2cv = 95%, RPD = 4.5 and SECV = 0.9), TCC (R2c = 99%, R2cv = 91%, RPD = 3.5 and SECV = 2.1) and all Trans β-carotene (ATBC) (R2c = 98%, R2cv = 91%, RPD = 3.5 and SECV = 1.6). Coefficient of determination on independent validation set (R2p) for these traits were also satisfactory for ATBC (91%), TCC (88%) and DMC (80%). Compared to other methods, Vis/NIRS-derived DMC from both intact and processed roots had very high correlation (>0.95) with the ideal oven-drying than from specific gravity method (0.49). There was equally a high correlation (0.94) between the intact and processed Vis/NIRS DMC. Therefore, the portable Vis/NIRS could be employed for the rapid analyses of DMC and quantification of carotenoids in cassava for nutritional and breeding purposes

solGS: a webbased tool for genomic selection

Background: Genomic selection (GS) promises to improve accuracy in estimating breeding values and genetic gain for quantitative traits compared to traditional breeding methods. Its reliance on high-throughput genome-wide markers and statistical complexity, however, is a serious challenge in data management, analysis, and sharing. A bioinformatics infrastructure for data storage and access, and user-friendly web-based tool for analysis and sharing output is needed to make GS more practical for breeders. Results: We have developed a web-based tool, called solGS, for predicting genomic estimated breeding values (GEBVs) of individuals, using a Ridge-Regression Best Linear Unbiased Predictor (RR-BLUP) model. It has an intuitive web-interface for selecting a training population for modeling and estimating genomic estimated breeding values of selection candidates. It estimates phenotypic correlation and heritability of traits and selection indices of individuals. Raw data is stored in a generic database schema, Chado Natural Diversity, co-developed by multiple database groups. Analysis output is graphically visualized and can be interactively explored online or downloaded in text format. An instance of its implementation can be accessed at the NEXTGEN Cassava breeding database, http://cassavabase.org/solgs. Conclusions: solGS enables breeders to store raw data and estimate GEBVs of individuals online, in an intuitive and interactive workflow. It can be adapted to any breeding program.Background: Genomic selection (GS) promises to improve accuracy in estimating breeding values and genetic gain for quantitative traits compared to traditional breeding methods. Its reliance on high-throughput genome-wide markers and statistical complexity, however, is a serious challenge in data management, analysis, and sharing. A bioinformatics infrastructure for data storage and access, and user-friendly web-based tool for analysis and sharing output is needed to make GS more practical for breeders. Results: We have developed a web-based tool, called solGS, for predicting genomic estimated breeding values (GEBVs) of individuals, using a Ridge-Regression Best Linear Unbiased Predictor (RR-BLUP) model. It has an intuitive web-interface for selecting a training population for modeling and estimating genomic estimated breeding values of selection candidates. It estimates phenotypic correlation and heritability of traits and selection indices of individuals. Raw data is stored in a generic database schema, Chado Natural Diversity, co-developed by multiple database groups. Analysis output is graphically visualized and can be interactively explored online or downloaded in text format. An instance of its implementation can be accessed at the NEXTGEN Cassava breeding database, http://cassavabase.org/solgs. Conclusions: solGS enables breeders to store raw data and estimate GEBVs of individuals online, in an intuitive and interactive workflow. It can be adapted to any breeding program.Background: Genomic selection (GS) promises to improve accuracy in estimating breeding values and genetic gain for quantitative traits compared to traditional breeding methods. Its reliance on high-throughput genome-wide markers and statistical complexity, however, is a serious challenge in data management, analysis, and sharing. A bioinformatics infrastructure for data storage and access, and user-friendly web-based tool for analysis and sharing output is needed to make GS more practical for breeders. Results: We have developed a web-based tool, called solGS, for predicting genomic estimated breeding values (GEBVs) of individuals, using a Ridge-Regression Best Linear Unbiased Predictor (RR-BLUP) model. It has an intuitive web-interface for selecting a training population for modeling and estimating genomic estimated breeding values of selection candidates. It estimates phenotypic correlation and heritability of traits and selection indices of individuals. Raw data is stored in a generic database schema, Chado Natural Diversity, co-developed by multiple database groups. Analysis output is graphically visualized and can be interactively explored online or downloaded in text format. An instance of its implementation can be accessed at the NEXTGEN Cassava breeding database, http://cassavabase.org/solgs. Conclusions: solGS enables breeders to store raw data and estimate GEBVs of individuals online, in an intuitive and interactive workflow. It can be adapted to any breeding program.Background: Genomic selection (GS) promises to improve accuracy in estimating breeding values and genetic gain for quantitative traits compared to traditional breeding methods. Its reliance on high-throughput genome-wide markers and statistical complexity, however, is a serious challenge in data management, analysis, and sharing. A bioinformatics infrastructure for data storage and access, and user-friendly web-based tool for analysis and sharing output is needed to make GS more practical for breeders. Results: We have developed a web-based tool, called solGS, for predicting genomic estimated breeding values (GEBVs) of individuals, using a Ridge-Regression Best Linear Unbiased Predictor (RR-BLUP) model. It has an intuitive web-interface for selecting a training population for modeling and estimating genomic estimated breeding values of selection candidates. It estimates phenotypic correlation and heritability of traits and selection indices of individuals. Raw data is stored in a generic database schema, Chado Natural Diversity, co-developed by multiple database groups. Analysis output is graphically visualized and can be interactively explored online or downloaded in text format. An instance of its implementation can be accessed at the NEXTGEN Cassava breeding database, http://cassavabase.org/solgs. Conclusions: solGS enables breeders to store raw data and estimate GEBVs of individuals online, in an intuitive and interactive workflow. It can be adapted to any breeding program

Genetic correlation, genome-wide association and genomic prediction of portable NIRS predicted carotenoids in cassava roots

Open Access Journal; Published online: 04 Dec 2019Random forests (RF) was used to correlate spectral responses to known wet chemistry carotenoid concentrations including total carotenoid content (TCC), all-trans β-carotene (ATBC), violaxanthin (VIO), lutein (LUT), 15-cis beta-carotene (15CBC), 13-cis beta-carotene (13CBC), alpha-carotene (AC), 9-cis beta-carotene (9CBC), and phytoene (PHY) from laboratory analysis of 173 cassava root samples in Columbia. The cross-validated correlations between the actual and estimated carotenoid values using RF ranged from 0.62 in PHY to 0.97 in ATBC. The developed models were used to evaluate the carotenoids of 594 cassava clones with spectral information collected across three locations in a national breeding program (NRCRI, Umudike), Nigeria. Both populations contained cassava clones characterized as white and yellow. The NRCRI evaluated phenotypes were used to assess the genetic correlations, conduct genome-wide association studies (GWAS), and genomic predictions. Estimates of genetic correlation showed various levels of the relationship among the carotenoids. The associations between TCC and the individual carotenoids were all significant (P 0.75, except in LUT and PHY where r < 0.3). The GWAS revealed significant genomic regions on chromosomes 1, 2, 4, 13, 14, and 15 associated with variation in at least one of the carotenoids. One of the identified candidate genes, phytoene synthase (PSY) has been widely reported for variation in TCC in cassava. On average, genomic prediction accuracies from the single-trait genomic best linear unbiased prediction (GBLUP) and RF as well as from a multiple-trait GBLUP model ranged from ∼0.2 in LUT and PHY to 0.52 in TCC. The multiple-trait GBLUP model gave slightly higher accuracies than the single trait GBLUP and RF models. This study is one of the initial attempts in understanding the genetic basis of individual carotenoids and demonstrates the usefulness of NIRS in cassava improvement

Cassava haplotype map highlights fixation of deleterious mutations during clonal propagation

Article purchased; Published online: 17 April 2017Cassava (Manihot esculenta Crantz) is an important staple food crop in Africa and South America; however, ubiquitous deleterious mutations may severely decrease its fitness. To evaluate these deleterious mutations, we constructed a cassava haplotype map through deep sequencing 241 diverse accessions and identified >28 million segregating variants. We found that (i) although domestication has modified starch and ketone metabolism pathways to allow for human consumption, the concomitant bottleneck and clonal propagation have resulted in a large proportion of fixed deleterious amino acid changes, increased the number of deleterious alleles by 26%, and shifted the mutational burden toward common variants; (ii) deleterious mutations have been ineffectively purged, owing to limited recombination in the cassava genome; (iii) recent breeding efforts have maintained yield by masking the most damaging recessive mutations in the heterozygous state but have been unable to purge the mutation burden; such purging should be a key target in future cassava breeding

Genome-wide association and prediction reveals genetic architecture of cassava mosaic disease resistance and prospects for rapid genetic improvement

Published: 13 May 2016Cassava (Manihot esculenta Crantz) 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. Biparental mapping studies suggest primarily a single major gene mediates resistance. To investigate this genetic architecture, we conducted the first genome-wide association mapping study in cassava with up to 6128 genotyping-by-sequenced African breeding lines and 42,113 reference genome-mapped single-nucleotide polymorphism (SNP) markers. We found a single region on chromosome 8 that accounts for 30 to 66% of genetic resistance in the African cassava germplasm. Thirteen additional regions with small effects were also identified. Further dissection of the major quantitative trait locus (QTL) on chromosome 8 revealed the presence of two possibly epistatic loci and/or multiple resistance alleles, which may account for the difference between moderate and strong disease resistances in the germplasm. Search of potential candidate genes in the major QTL region identified two peroxidases and one thioredoxin. Finally, we found genomic prediction accuracy of 0.53 to 0.58 suggesting that genomic selection (GS) will be effective both for improving resistance in breeding populations and identifying highly resistant clones as varieties

Genome-wide association study of resistance to cassava green mite pest and related traits in cassava

Published: 26 July 2018Cassava green mite [CGM, Mononychellus tanajoa (Bondar)] is a dry-season pest that usually feeds on the underside of young leaves causing leaf chlorosis, stunted growth, and root yield reduction by 80%. Since cassava (Manihot esculenta Crantz) leaves and roots serve as a primary staple food source, a decline in cassava yield can lead to household food, nutrition, and income insecurity. To evaluate the existence of CGM resistance alleles in the available germplasm, a diversity panel of 845 advanced breeding lines obtained from IITA, CIAT, and the National Root Crops Research Institute (NRCRI) were evaluated for CGM severity (CGMS), leaf pubescence (LP), leaf retention (LR), stay green, shoot tip compactness, and shoot tip size. A genome-wide association mapping detected 35 single-nucleotide polymorphisms (SNPs) markers significantly associated with CGMS, LP, and LR on chromosome 8. Colocalization of the most significant SNP associated with CGMS, LP, and LR on chromosome 8 is possibly an indication of pleiotropy or the presence of closely linked genes that regulate these traits. Seventeen candidate genes were found to be associated to CGM resistance. These candidate genes were subdivided into seven categories according to their protein structure namely, Zn finger, pentatricopeptide, MYB, MADS, homeodomain, trichome birefringence-related protein, and ethylene-responsive transcription factor genes. This study revealed significant loci associated with CGM, not previously reported, which together represent potential sources for the ongoing effort to develop multiple pest- and disease-resistant cassava cultivars