Leveraging agrigenomics" for crop improvement"

Modern biotechnologies have dramatically reshaped the crop improvement research during the past decade. Biotechniques have become indispensable for efficient and effective development of new knowledge, processes, and products. IITA's biotechnology, strategized as three major themes― genomics, transgenics, and diagnostics, is directed toward the genetic improvement of staple food crops of Africa, such as cooking-banana, plantain, cassava, yam, and cowpea. This section provides some insights and progress in this program

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

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

Improving root characterisation for genomic prediction in cassava

Open Access Journal; Published online: 14 May 2020Cassava 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

Folk taxonomy and traditional management of cassava (Manihot esculenta Crantz) diversity in southern and central Benin

Cassava (Manihot esculenta Crantz) is an important food security crop for poor rural communities, particularly in Africa. At household level, cassava landraces used for cultivation are mainly selected based on farmers' interests, leading to very particular diversity evolution over generations. The structure, composition and factors influencing cassava diversity at that level is not well monitored and under documented. This study aimed at capturing and analyzing local knowledge on cassava genetic diversity and the key parameters affecting it in Benin, for better and sustainable local cassava genetic resources management. The methodological approach was based on field visits, interview using questionnaire and group discussion with farmers. Data were collected from one hundred and ninety eight (198) respondents and analyzed using descriptive statistics. The majority (82%) of the respondents were male, generally 20 to 80 years old. Positive correlation was found between cassava diversity maintained per household with cultivated area and household size (R2 = 0.162). Farmers used mainly stem and leaves characteristics to identify cassava varieties. Plant materials for next season were mostly selected according to the disease (mainly plant free of viral infection) status, size of the stem and number of nodes. The study revealed existence of a high diversity of cassava at the household level. However, various factors constrained cassava production and threats on cassava diversity were observed. Establishment of community field genebank, introduction of new varieties were some of the on-farm conservation strategies proposed by cassava farmers

Selection for resistance to cassava mosaic disease in African cassava germplasm using single nucleotide polymorphism markers

Cassava mosaic disease (CMD) is one of the main constraints that hamper cassava production. Breeding for varieties that are CMD resistant is a major aim in cassava breeding programmes. However, the use of the conventional approach has its limitations, including a lengthy growth cycle and a low multiplication rate of planting materials. To increase breeding efficiency as well as genetic gain of traits, SNP markers can be used to screen and identify resistant genotypes. The objective of this study was to predict the performance of 145 cassava genotypes from open-pollinated crosses for CMD resistance using molecular markers. Two SNP markers (S12_7926132 and S14_4626854), previously converted into Kompetitive allele-specific PCR (KASP) assays, as well as CMD incidence and severity scores, were used for selection. About 76% of the genotypes were revealed to be resistant to CMD based on phenotypic scores, while over 24% of the total population were found to be susceptible. Significant effects were observed for alleles associated with marker S12_7926132 while the other marker had nonsignificant effects. The predictive accuracy (true positives and true negatives) of the major CMD2 locus on chromosome 12 was 77% in the population used in this study. Our study provides insight into the potential use of marker-assisted selection for CMD resistance in cassava breeding programmes. Significance:• With an aim towards reducing the food insecurity rate in Africa, we report on the use of genetic tools for a fast and efficient release of new cassava varieties to benefit breeders, farmers and consumers, given the food and industrial importance of this staple crop.• This study adds tremendous knowledge to phenotypic and molecular screening for CMD resistance. The outcome will encourage breeders in various cassava breeding programmes to accelerate genetic gains as well as increase breeding accuracy and efficiency for CMD resistance

Genomic prediction and quantitative trait locus discovery in a cassava training population constructed from multiple breeding stages

Open Access Article; Published online: 11 Dec 2019Assembly of a training population (TP) is an important component of effective genomic selection‐based breeding programs. In this study, we examined the power of diverse germplasm assembled from two cassava (Manihot esculenta Crantz) breeding programs in Tanzania at different breeding stages to predict traits and discover quantitative trait loci (QTL). This is the first genomic selection and genome‐wide association study (GWAS) on Tanzanian cassava data. We detected QTL associated with cassava mosaic disease (CMD) resistance on chromosomes 12 and 16; QTL conferring resistance to cassava brown streak disease (CBSD) on chromosomes 9 and 11; and QTL on chromosomes 2, 3, 8, and 10 associated with resistance to CBSD for root necrosis. We detected a QTL on chromosome 4 and two QTL on chromosome 12 conferring dual resistance to CMD and CBSD. The use of clones in the same stage to construct TPs provided higher trait prediction accuracy than TPs with a mixture of clones from multiple breeding stages. Moreover, clones in the early breeding stage provided more reliable trait prediction accuracy and are better candidates for constructing a TP. Although larger TP sizes have been associated with improved accuracy, in this study, adding clones from Kibaha to those from Ukiriguru and vice versa did not improve the prediction accuracy of either population. Including the Ugandan TP in either population did not improve trait prediction accuracy. This study applied genomic prediction to understand the implications of constructing TP from clones at different breeding stages pooled from different locations on trait accuracy

Improving genomic prediction in cassava field experiments by accounting for interplot competition

Open Access JournalPlants' competing for available resources is an unavoidable phenomenon in a field. We conducted studies in cassava (Manihot esculenta Crantz) in order to understand the pattern of this competition. Taking into account the competitive ability of genotypes while selecting parents for breeding advancement or commercialization can be very useful. We assumed that competition could occur in two levels i) at the genotypic level, which we called as inter-clonal, and ii) at the plot level irrespective of the type of genotype, which we call as inter-plot competition or competition error. Modification in incidence matrices was applied in order to relate neighboring genotype/plot to the performance of a target genotype/plot with respect to its competitive ability. This was added into a genomic selection model to simultaneously predict the direct and competitive ability of a genotype. Predictability of the 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 (pRMSE) compared to that of the base model having no competitive component. Results from our real data studies indicated that less than 10% increase in accuracy was achieved with GS-inter-clonal competition model but this value reached up to 25% with a GS-competition error model. We also found that the competitive influence of a cassava clone is not just limited to the adjacent neighbors but spreads beyond them. Through simulations we found that a 26% increase of accuracy in estimating trait genotypic effect can be achieved even in the presence of high competitive variance