Effectiveness of genomic selection for improving provitamin A carotenoid content and associated traits in cassava.
Ontology highlight
ABSTRACT:
SUBMITTER: Esuma W
PROVIDER: S-EPMC8496257 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Genomic selection (GS) has been used to optimize genetic gains when phenotypic selection is considered costly and difficult to measure. The objective of this work was to evaluate the efficiency and consistency of GS prediction for cassava yield traits (Manihot esculenta Crantz) using different methods, taking into account the effect of population structure. BLUPs and deregressed BLUPs were obtained for 888 cassava accessions and evaluated for fresh root yield, dry root yield and dry matter content in roots in 21 trials conducted from 2011 to 2016. The deregressed BLUPs obtained for the accessions from a 48K single nucleotide polymorphism dataset were used for genomic predictions based on the BayesB, BLASSO, RR-BLUP, G-BLUP and RKHS methods. The accessions' BLUPs were used in the validation step using four cross-validation strategies, taking into account population structure and different GS methods. Similar estimates of predictive ability and bias were identified for the different genomic selection methods in the first cross-validation strategy. Lower predictive ability was observed for fresh root yield (0.4569 -RR-BLUP to 0.4756-RKHS) and dry root yield (0.4689 -G-BLUP to 0.4818-RKHS) in comparison with dry matter content (0.5655 -BLASSO to 0.5670 -RKHS). However, the RKHS method exhibited higher efficiency and consistency in most of the validation scenarios in terms of prediction ability for fresh root yield and dry root yield. The correlations of the genomic estimated breeding values between the genomic selection methods were quite high (0.99-1.00), resulting in high coincidence of clone selection regardless of the genomic selection method. The deviance analyses within and between the validation clusters formed by the discriminant analysis of principal components were significant for all traits. Therefore, this study indicated that i) the prediction of dry matter content was more accurate compared to that of yield traits, possibly as a result of the smaller influence of non-additive genetic effects; ii) the RKHS method resulted in high and stable prediction ability in most of the validation scenarios; and iii) some kinship between the validation and training populations is desirable in order for genomic selection to succeed due to the significant effect of population structure on genomic selection predictions.
| S-EPMC6855463 | biostudies-literature
Project description:Cassava is a starchy root crop cultivated in the tropics for fresh consumption and commercial processing. Primary selection objectives in cassava breeding include dry matter content and micronutrient density, particularly provitamin A carotenoids. These traits are negatively correlated in the African germplasm. This study aimed at identifying genetic markers associated with these traits and uncovering whether linkage and/or pleiotropy were responsible for observed negative correlation. A genome-wide association mapping using 672 clones genotyped at 72,279 single nucleotide polymorphism (SNP) loci was performed. Root yellowness was used indirectly to assess variation in carotenoid content. Two major loci for root yellowness were identified on chromosome 1 at positions 24.1 and 30.5 Mbp. A single locus for dry matter content that colocated with the 24.1 Mbp peak for carotenoids was identified. Haplotypes at these loci explained 70 and 37% of the phenotypic variability for root yellowness and dry matter content, respectively. Evidence of megabase-scale linkage disequilibrium (LD) 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 () and starch biosynthesis ( and ) occurred in the vicinity of the identified locus at 24.1 Mbp. These findings elucidate the genetic architecture of carotenoids and dry matter in cassava and provide an opportunity to accelerate breeding of these traits.
| S-EPMC7822061 | biostudies-literature
Project description:Cassava improvement using traditional breeding strategies is slow due to the species' long breeding cycle. However, the use of genomic selection can lead to a shorter breeding cycle. This study aimed to estimate genetic parameters for productive traits based on pedigree (pedigree and phenotypic information) and genomic (markers and phenotypic information) analyses using biparental crosses at different stages of selection. A total of 290 clones were genotyped and phenotyped for fresh root yield (FRY), dry matter content (DMC), dry yield (DY), fresh shoot yield (FSY) and harvest index (HI). The clones were evaluated in clonal evaluation trials (CET), preliminary yield trials (PYT), advanced yield trials (AYT) and uniform yield trials (UYT), from 2013 to 2018 in ten locations. The breeding stages were analyzed as follows: one stage (CET), two stages (CET and PYT), three stages (CET, PYT and AYT) and four stages (CET, PYT, AYT and UYT). The genomic predictions were analyzed via k-fold cross-validation based on the genomic best linear unbiased prediction (GBLUP) considering a model with genetic additive effects and genotype × location interactions. Genomic and pedigree accuracies were moderate to high (0.56-0.72 and 0.62-0.78, respectively) for important starch-related traits such as DY and FRY; when considering one breeding stage (CET) with the aim of early selection, the genomic accuracies ranged from 0.60 (DMC) to 0.71 (HI). Moreover, the correlations between the genomic estimation breeding values of one-stage genomic analysis and the estimated breeding values of the four-stage (full data set) pedigree analysis were high for all traits as well as for a selection index including all traits. The results indicate great possibilities for genomic selection in cassava, especially for selection early in the breeding cycle (saving time and effort).
| S-EPMC6658084 | biostudies-literature
Project description:Provitamin A cassava clones were analysed for starch yield and critical starch quality attributes, to understand possible applications in the food industry. Total carotenoids content in the test clones ranged from 0.03-11.94 ?g g-1 of fresh root. Starch yield ranged from 8.4-33.2 % and correlated negatively (r = -0.588, P < 0.001) with carotenoids content. Amylose content (16.4-22.1%) didn't differ significantly (P ? 0.05) among the cassava clones. Meanwhile, total carotenoid content had significant negative correlations (P ? 0.05) with starch pasting temperature, peak time, setback viscosities and peak area. The reduced peak time and pasting temperatures in high-carotenoid cassava signifies reduction in energy requirements in yellow-fleshed roots when compared to white-fleshed cassava. This attribute is desirable for the food industry as it would reduce the overall cost of processing the cassava. Furthermore, final viscosities of starch from carotenoid-rich cassava were lower than those of white-fleshed roots, making provitamin A cassava suitable for soft food processing.
| S-EPMC6369266 | biostudies-literature
Project description:Cassava is cultivated due to its drought tolerance and high carbohydrate-containing storage roots. The lack of uniformity and irregular shape of storage roots poses constraints on harvesting and post-harvest processing. Here, we phenotyped the Genetic gain and offspring (C1) populations from the International Institute of Tropical Agriculture (IITA) breeding program using image analysis of storage root photographs taken in the field. In the genome-wide association analysis (GWAS), we detected for most shape and size-related traits, QTL on chromosomes 1 and 12. In a previous study, we found the QTL on chromosome 12 to be associated with cassava mosaic disease (CMD) resistance. Because the root uniformity is important for breeding, we calculated the standard deviation (SD) of individual root measurements per clone. With SD measurements we identified new significant QTL for Perimeter, Feret and Aspect Ratio on chromosomes 6, 9 and 16. Predictive accuracies of root size and shape image-extracted traits were mostly higher than yield trait prediction accuracies. This study aimed to evaluate the feasibility of the image phenotyping protocol and assess GWAS and genomic prediction for size and shape image-extracted traits. The methodology described and the results are promising and open up the opportunity to apply high-throughput methods in cassava.
| S-EPMC7224197 | biostudies-literature
Project description:Cassava ( 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.
| S-EPMC7822052 | biostudies-literature
Project description:OBJECTIVE:This study was conducted to test the efficiency of genomic selection for milk production traits in a Korean Holstein cattle population. METHODS:A total of 506,481 milk production records from 293,855 animals (2,090 heads with single nucleotide polymorphism information) were used to estimate breeding value by single step best linear unbiased prediction. RESULTS:The heritability estimates for milk, fat, and protein yields in the first parity were 0.28, 0.26, and 0.23, respectively. As the parity increased, the heritability decreased for all milk production traits. The estimated generation intervals of sire for the production of bulls (LSB) and that for the production of cows (LSC) were 7.9 and 8.1 years, respectively, and the estimated generation intervals of dams for the production of bulls (LDB) and cows (LDC) were 4.9 and 4.2 years, respectively. In the overall data set, the reliability of genomic estimated breeding value (GEBV) increased by 9% on average over that of estimated breeding value (EBV), and increased by 7% in cows with test records, about 4% in bulls with progeny records, and 13% in heifers without test records. The difference in the reliability between GEBV and EBV was especially significant for the data from young bulls, i.e. 17% on average for milk (39% vs 22%), fat (39% vs 22%), and protein (37% vs 22%) yields, respectively. When selected for the milk yield using GEBV, the genetic gain increased about 7.1% over the gain with the EBV in the cows with test records, and by 2.9% in bulls with progeny records, while the genetic gain increased by about 24.2% in heifers without test records and by 35% in young bulls without progeny records. CONCLUSION:More genetic gains can be expected through the use of GEBV than EBV, and genomic selection was more effective in the selection of young bulls and heifers without test records.
| S-EPMC7054615 | biostudies-literature
Project description:In this study, β-carotene concentrations in cassava storage roots were enhanced by co-expression of transgenes for deoxyxylulose-5-phosphate synthase (DXS) and bacterial phytoene synthase (crtB), mediated by the patatin type-1 promoter. Storage roots harvested from field-grown plants accumulated carotenoids to ≤50 μg/g DW, a 15- to 20-fold increase relative to roots from non-transgenic plants. Approximately 85-90% of these carotenoids accumulated as all-trans-β-carotene, the most nutritionally efficacious carotenoid. β-carotene-accumulating storage roots displayed delayed onset of post-harvest physiological deterioration, a major constraint limiting utilization of cassava products. Significant metabolite changes were detected in β-carotene enhanced storage roots. Most significantly, an inverse correlation was observed between β-carotene and dry matter contents, with reductions of 50% to 60% of dry matter content in the highest carotenoid accumulating storage roots of different cultivars. Further analysis confirmed concomitant reduction in starch content, and increased levels of total fatty acids, triacylglycerols, soluble sugars, and abscisic acid. Irish potato engineered to co-express DXS and crtB displayed a similar correlation between β-carotene accumulation, reduced dry matter and starch content, and elevated oil and soluble sugars in tubers. Transcriptome analyses revealed reduced expression of starch biosynthetic genes, ADP-glucose pyrophosphorylase genes, in transgenic, carotene-accumulating cassava roots relative to non-transgenic roots. These findings highlight unintended metabolic consequences of provitamin A biofortification of starch-rich organs and point to strategies for redirecting metabolic flux to restore starch production.
2017-06-22 | GSE100319 | GEO
Project description:Efforts are underway for development of crops with improved levels of provitamin A carotenoids to help combat dietary vitamin A deficiency. As a global staple crop with considerable variation in kernel carotenoid composition, maize (Zea mays L.) could have a widespread impact. We performed a genome-wide association study (GWAS) of quantified seed carotenoids across a panel of maize inbreds ranging from light yellow to dark orange in grain color to identify some of the key genes controlling maize grain carotenoid composition. Significant associations at the genome-wide level were detected within the coding regions of zep1 and lut1, carotenoid biosynthetic genes not previously shown to impact grain carotenoid composition in association studies, as well as within previously associated lcyE and crtRB1 genes. We leveraged existing biochemical and genomic information to identify 58 a priori candidate genes relevant to the biosynthesis and retention of carotenoids in maize to test in a pathway-level analysis. This revealed dxs2 and lut5, genes not previously associated with kernel carotenoids. In genomic prediction models, use of markers that targeted a small set of quantitative trait loci associated with carotenoid levels in prior linkage studies were as effective as genome-wide markers for predicting carotenoid traits. Based on GWAS, pathway-level analysis, and genomic prediction studies, we outline a flexible strategy involving use of a small number of genes that can be selected for rapid conversion of elite white grain germplasm, with minimal amounts of carotenoids, to orange grain versions containing high levels of provitamin A.
| S-EPMC4256781 | biostudies-literature
Project description:Cassava (Manihot esculenta Crantz) is an important staple food in sub-Saharan Africa. Breeding experiments were conducted at the International Institute of Tropical Agriculture in cassava to select elite parents. Taking into account the heterogeneity in the field while evaluating these trials can increase the accuracy in estimation of breeding values. We used an exploratory approach using the parametric spatial kernels Power, Spherical, and Gaussian to determine the best kernel for a given scenario. The spatial kernel was fit simultaneously with a genomic kernel in a genomic selection model. Predictability of these models was tested through a 10-fold cross-validation method repeated five times. The best model was chosen as the one with the lowest prediction root mean squared error compared to that of the base model having no spatial kernel. Results from our real and simulated data studies indicated that predictability can be increased by accounting for spatial variation irrespective of the heritability of the trait. In real data scenarios we observed that the accuracy can be increased by a median value of 3.4%. Through simulations, we showed that a 21% increase in accuracy can be achieved. We also found that Range (row) directional spatial kernels, mostly Gaussian, explained the spatial variance in 71% of the scenarios when spatial correlation was significant.
| S-EPMC5765366 | biostudies-literature