Expansion of the crop ontology by adding cassava trait ontology

Poster presented at CGIAR Generation Challenge Programme, General Research Meeting. Hyderabad (India), 21-25 Sep 201

Cassava improvement in the era of "agrigenomics": the road to nextgeneration breeding.

In the last 45 years, IITA has played a pivotal role in the genetic improvement of cassava for resourcepoor farmers in sub-Saharan Africa (SSA). More than 400 varieties have been developed that are not only high yielding but also resistant to diseases and pests. Many of these improved varieties have been extensively deployed in SSA and have helped to avert humanitarian crises caused by the viral disease pandemics that devastated local landraces in East and Central Africa

Marker-based estimates reveal significant non-additive effects in clonally propagated cassava (Manihot esculenta): implications for the prediction of total genetic value and the selection of varieties

Open Access JournalIn clonally propagated crops, non-additive 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 non-additive genetic variation for key traits in a breeding population of cassava from sub-Saharan Africa using additive and non-additive genome-wide marker-based relationship matrices. We then assessed the accuracy of genomic prediction of additive compared to total (additive plus non-additive) genetic value. We confirmed previous findings based on diallel populations, that non-additive genetic variation is significant, especially for yield traits. Further, we show that we total genetic value correlated more strongly to observed phenotypes than did additive value, although this is constrained by low broad-sense heritability and is not beneficial for traits with already high 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

Effects of β-carotene biofortified cassava grits (Mahihot esculenta Crantz) based-diets on retinol bioavailability and performance of broiler chicks

The B-carotene content of five cassava varieties and products as well as B-carotene bioavailability in cassava grit from TMS 01/1371 were undertaken using 240 one-day old Arbor acre broiler strain randomly divided into eight groups of 30 birds each. Each group comprised a triplicate of 10 birds each assigned in a completely randomized design. The eight dietary treatments were: Diets 1 and 8 had yellow and white maize respectively as the main energy source, while diets 2, 3 and 4 had maize replaced with cassava grit from TMS 01/1371 at 25%, 50% and 75%. Diets 5, 6 and 7 also had the maize contents similarly replaced with 25%, 50% and 75% grits from TME 419 respectively. Yellow maize, white maize, grits from TME 419 and TMS 01/1371 contained 238.33, 13.33, 6.67 and 108.33. B-carotene in the peeled fresh tuber of TMS 01/1412 (468.33), unpeeled fresh tuber (425.00), dried peeled tuber (391.67) and dried unpeeled cassava (323.33) were significantly higher (P < 0.05) than the corresponding values in TMS 01/1371 (416.67, 371.67, 311.67 and 283.33) and TMS 01/1368 (401.67, 350.00, 295.00 and 258.33). TME 419 cassava and products (peeled fresh tuber, unpeeled fresh tuber, dried peeled tuber and dried unpeeled tuber, garri, garri flour, grit, grit flour, peeling, peels and leaves) contained significantly lower (P < 0.05) levels of B-carotene. The FCR of chicks on diet 1 (1.50) was lowest (P < 0.05) while those on diet 8 (2.24) was highest. The main and interactive effects of cassava varieties and inclusion levels of grits on all indices of performance were significantly different (P < 0.05). Dietary B-carotene only correlated negatively with grits inclusion levels (P < 0.05) from TMS 01/1371 (r = 0.40). The B-carotene content of the diets when related to the inclusion levels of grits from TMS 01/1371 were both significantly negative linearly and quadratically. Regression equations were: (1) Y = 15.333 – 0.0530x (R2 = 0.16); (2) Y = 13.667 + 0.147x – 0.003x2 (R2 = 0.36) Conclusively, processing methods adopted in this study reduced B-carotene content of cassava grits which may have affected the bioavailability of retinoic acid in broiler chicks’

Regional Heritability Mapping provides insights into Dry matter (DM) Content in African white and yellow cassava populations

Open Access ArticleThe HarvestPlus program for cassava (Manihot esculenta Crantz) fortifies cassava with beta-carotene by breeding for carotene-rich tubers (yellow cassava). However, a negative correlation between yellowness and dry matter (DM) content has been identified. Here, we investigated the genetic control of DM in white and yellow cassava subpopulations. We used regional heritability mapping (RHM) to associate DM to genomic segments in both subpopulations. Significant segments were subjected to candidate gene analysis and we attempted to validate candidates using prediction accuracies. The RHM procedure was validated using a simulation approach. The RHM revealed significant hits for white cassava on chromosomes 1, 4, 5, 10, 17 and 18 while hits for the yellow were on chromosome 1. Candidate gene analysis revealed genes in the carbohydrate biosynthesis pathway including the plant serine-threonine protein kinases (SnRKs), UDP-glycosyltransferases, UDP-sugar transporters, invertases, pectinases, and regulons. Validation using 1252 unique identifiers from the SnRK gene family genome-wide recovered 50% of the predictive accuracy of whole genome SNPs for DM while validation using 53 likely (extracted from literature) genes from significant segments recovered 32%. Genes including an acid invertase, a neutral/alkaline invertase and a glucose-6-phosphate isomerase were validated based on an a priori list for the cassava starch pathway and also a fructose-biphosphate aldolase from the calvin cycle pathway. The power of the RHM procedure was estimated at 47 percent when the causal QTL generated 10% of the phenotypic variance with sample size of 451. Cassava DM genetics is complex. RHM may be useful for complex traits

Consumption trends of white cassava and consumer perceptions of yellow cassava in Ghana

Open Access JournalVitamin A deficiency has been one of the major nutritional problems for many countries where cassava is eaten as a major source of energy. In an attempt to help reduce the incidence of vitamin A deficiency, bio-fortified cassava which contains more pro-vitamin A carotenoids than the white cassava, has been introduced to such areas. This study therefore endeavored to find out how often Ghanaians ate cassava and its products, as well as what Ghanaian consumers knew about bio-fortified cassava and their willingness to consume it. A survey was done between the month of January and March using 287 participants in the Greater Accra Region of Ghana which gathered information on their demographics, and their frequencies of the consumption of cassava and its products. Data on the knowledge of the participants on yellow flesh cassava, and their willingness to accept it were also gathered. Logistic regression was used to determine the relationship between some demographic characteristics and knowledge and ‘willingness-to-accept’ biofortified cassava. The cassava product which was mostly consumed by the participants was gari. Sixty-three percent of the participants had no knowledge of bio-fortified cassava. About half of them were willing to accept the biofortified cassava, and more than half of the participants perceived that yellow cassava could be used for some white cassava products. Providing nutritional information and sensitizing consumers on the benefits of biofortified cassava can enhance its consumption in Ghana

Accuracies of univariate and multivariate genomic prediction models in African cassava

Open Access Journal; Published online:15 March 2017Background: Genomic selection (GS) promises to accelerate genetic gain in plant breeding programs especially for crop species such as cassava that have long breeding cycles. Practically, to implement GS in cassava breeding, it is necessary to evaluate different GS models and to develop suitable models for an optimized breeding pipeline. In this paper, we compared (1) prediction accuracies from a single-trait (uT) and a multi-trait (MT) mixed model for a singleenvironment genetic evaluation (Scenario 1), and (2) accuracies from a compound symmetric multi-environment model (uE) parameterized as a univariate multi-kernel model to a multivariate (ME) multi-environment mixed model that accounts for genotype-by-environment interaction for multi-environment genetic evaluation (Scenario 2). For these analyses, we used 16 years of public cassava breeding data for six target cassava traits and a fivefold cross-validation scheme with 10-repeat cycles to assess model prediction accuracies. Results: In Scenario 1, the MT models had higher prediction accuracies than the uT models for all traits and locations analyzed, which amounted to on average a 40% improved prediction accuracy. For Scenario 2, we observed that the ME model had on average (across all locations and traits) a 12% improved prediction accuracy compared to the uE model. Conclusions: We recommend the use of multivariate mixed models (MT and ME) for cassava genetic evaluation. These models may be useful for other plant species

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