Artificial intelligence in plant breeding.

Harnessing cutting-edge technologies to enhance crop productivity is a pivotal goal in modern plant breeding. Artificial intelligence (AI) is renowned for its prowess in big data analysis and pattern recognition, and is revolutionizing numerous scientific domains including plant breeding. We explore the wider potential of AI tools in various facets of breeding, including data collection, unlocking genetic diversity within genebanks, and bridging the genotype-phenotype gap to facilitate crop breeding. This will enable the development of crop cultivars tailored to the projected future environments. Moreover, AI tools also hold promise for refining crop traits by improving the precision of gene-editing systems and predicting the potential effects of gene variants on plant phenotypes. Leveraging AI-enabled precision breeding can augment the efficiency of breeding programs and holds promise for optimizing cropping systems at the grassroots level. This entails identifying optimal inter-cropping and crop-rotation models to enhance agricultural sustainability and productivity in the field.

Wheat genetic resources have avoided disease pandemics, improved food security, and reduced environmental footprints: A review of historical impacts and future opportunities.

The use of plant genetic resources (PGR)-wild relatives, landraces, and isolated breeding gene pools-has had substantial impacts on wheat breeding for resistance to biotic and abiotic stresses, while increasing nutritional value, end-use quality, and grain yield. In the Global South, post-Green Revolution genetic yield gains are generally achieved with minimal additional inputs. As a result, production has increased, and millions of hectares of natural ecosystems have been spared. Without PGR-derived disease resistance, fungicide use would have easily doubled, massively increasing selection pressure for fungicide resistance. It is estimated that in wheat, a billion liters of fungicide application have been avoided just since 2000. This review presents examples of successful use of PGR including the relentless battle against wheat rust epidemics/pandemics, defending against diseases that jump species barriers like blast, biofortification giving nutrient-dense varieties and the use of novel genetic variation for improving polygenic traits like climate resilience. Crop breeding genepools urgently need to be diversified to increase yields across a range of environments (>200 Mha globally), under less predictable weather and biotic stress pressure, while increasing input use efficiency. Given that the ~0.8 m PGR in wheat collections worldwide are relatively untapped and massive impacts of the tiny fraction studied, larger scale screenings and introgression promise solutions to emerging challenges, facilitated by advanced phenomic and genomic tools. The first translocations in wheat to modify rhizosphere microbiome interaction (reducing biological nitrification, reducing greenhouse gases, and increasing nitrogen use efficiency) is a landmark proof of concept. Phenomics and next-generation sequencing have already elucidated exotic haplotypes associated with biotic and complex abiotic traits now mainstreamed in breeding. Big data from decades of global yield trials can elucidate the benefits of PGR across environments. This kind of impact cannot be achieved without widescale sharing of germplasm and other breeding technologies through networks and public-private partnerships in a pre-competitive space.

Multi-omic characterization of the maize GPI synthesis mutant gwt1 with defects in kernel development.

BACKGROUND: Glycosylphosphatidylinositol (GPI) and GPI-anchored proteins (GAPs) are important for cell wall formation and reproductive development in Arabidopsis. However, monocot counterparts that function in kernel endosperm development have yet to be discovered. Here, we performed a multi-omic analysis to explore the function of GPI related genes on kernel development in maize. RESULTS: In maize, 48 counterparts of human GPI synthesis and lipid remodeling genes were identified, in which null mutation of the glucosaminyl-phosphatidylinositol O-acyltransferase1 gene, ZmGWT1, caused a kernel mutant (named gwt1) with defects in the basal endosperm transport layer (BETL). We performed plasma membrane (PM) proteomics to characterize the potential GAPs involved in kernel development. In total, 4,981 proteins were successfully identified in 10-DAP gwt1 kernels of mutant and wild-type (WT), including 1,638 membrane-anchored proteins with different posttranslational modifications. Forty-seven of the 256 predicted GAPs were differentially accumulated between gwt1 and WT. Two predicted BETL-specific GAPs (Zm00001d018837 and Zm00001d049834), which kept similar abundance at general proteome but with significantly decreased abundance at membrane proteome in gwt1 were highlighted. CONCLUSIONS: Our results show the importance of GPI and GAPs for endosperm development and provide candidate genes for further investigation of the regulatory network in which ZmGWT1 participates.

Maize tubulin folding cofactor B is required for cell division and cell growth through modulating microtubule homeostasis.

Tubulin folding cofactors (TFCs) are required for tubulin folding, α/ß tubulin heterodimer formation, and microtubule (MT) dynamics in yeast and mammals. However, the functions of their plant counterparts remain to be characterized. We identified a natural maize crumpled kernel mutant, crk2, which exhibits reductions in endosperm cell number and size, as well as embryo/seedling lethality. Map-based cloning and functional complementation confirmed that ZmTFCB is causal for the mutation. ZmTFCB is targeted mainly to the cytosol. It facilitates α-tubulin folding and heterodimer formation through sequential interactions with the cytosolic chaperonin-containing TCP-1 ε subunit ZmCCT5 and ZmTFCE, thus affecting the organization of both the spindle and phragmoplast MT array and the cortical MT polymerization and array formation, which consequently mediated cell division and cell growth. We detected a physical association between ZmTFCB and the maize MT plus-end binding protein END-BINDING1 (ZmEB1), indicating that ZmTFCB1 may modulate MT dynamics by sequestering ZmEB1. Our data demonstrate that ZmTFCB is required for cell division and cell growth through modulating MT homeostasis, an evolutionarily conserved machinery with some species-specific divergence.

Maize Lethal Necrosis disease: review of molecular and genetic resistance mechanisms, socio-economic impacts, and mitigation strategies in sub-Saharan Africa.

BACKGROUND: Maize lethal necrosis (MLN) disease is a significant constraint for maize producers in sub-Saharan Africa (SSA). The disease decimates the maize crop, in some cases, causing total crop failure with far-reaching impacts on regional food security. RESULTS: In this review, we analyze the impacts of MLN in Africa, finding that resource-poor farmers and consumers are the most vulnerable populations. We examine the molecular mechanism of MLN virus transmission, role of vectors and host plant resistance identifying a range of potential opportunities for genetic and phytosanitary interventions to control MLN. We discuss the likely exacerbating effects of climate change on the MLN menace and describe a sobering example of negative genetic association between tolerance to heat/drought and susceptibility to viral infection. We also review role of microRNAs in host plant response to MLN causing viruses as well as heat/drought stress that can be carefully engineered to develop resistant varieties using novel molecular techniques. CONCLUSIONS: With the dual drivers of increased crop loss due to MLN and increased demand of maize for food, the development and deployment of simple and safe technologies, like resistant cultivars developed through accelerated breeding or emerging gene editing technologies, will have substantial positive impact on livelihoods in the region. We have summarized the available genetic resources and identified a few large-effect QTLs that can be further exploited to accelerate conversion of existing farmer-preferred varieties into resistant cultivars.

Genetic diversity and population structure of early and extra-early maturing maize germplasm adapted to sub-Saharan Africa.

BACKGROUND: Assessment and effective utilization of genetic diversity in breeding programs is crucial for sustainable genetic improvement and rapid adaptation to changing breeding objectives. During the past two decades, the commercialization of the early and extra-early maturing cultivars has contributed to rapid expansion of maize into different agro-ecologies of sub-Saharan Africa (SSA) where maize has become an important component of the agricultural economy and played a vital role in food and nutritional security. The present study aimed at understanding the population structure and genetic variability among 439 early and extra-early maize inbred lines developed from three narrow-based and twenty-seven broad-based populations by the International Iinstitute of Tropical Agriculture Maize Improvement Program (IITA-MIP). These inbreds were genotyped using 9642 DArTseq-based single nucleotide polymorphism (SNP) markers distributed uniformly throughout the maize genome. RESULTS: About 40.8% SNP markers were found highly informative and exhibited polymorphic information content (PIC) greater than 0.25. The minor allele frequency and PIC ranged from 0.015 to 0.500 and 0.029 to 0.375, respectively. The STRUCTURE, neighbour-joining phylogenetic tree and principal coordinate analysis (PCoA) grouped the inbred lines into four major classes generally consistent with the selection history, ancestry and kernel colour of the inbreds but indicated a complex pattern of the genetic structure. The pattern of grouping of the lines based on the STRUCTURE analysis was in concordance with the results of the PCoA and suggested greater number of sub-populations (K = 10). Generally, the classification of the inbred lines into heterotic groups based on SNP markers was reasonably reliable and in agreement with defined heterotic groups of previously identified testers based on combining ability studies. CONCLUSIONS: Complete understanding of potential heterotic groups would be difficult to portray by depending solely on molecular markers. Therefore, planned crosses involving representative testers from opposing heterotic groups would be required to refine the existing heterotic groups. It is anticipated that the present set of inbreds could contribute new beneficial alleles for population improvement, development of hybrids and lines with potential to strengthen future breeding programs. Results of this study would help breeders in formulating breeding strategies for genetic enhancement and sustainable maize production in SSA.

Independent introductions and admixtures have contributed to adaptation of European maize and its American counterparts.

Through the local selection of landraces, humans have guided the adaptation of crops to a vast range of climatic and ecological conditions. This is particularly true of maize, which was domesticated in a restricted area of Mexico but now displays one of the broadest cultivated ranges worldwide. Here, we sequenced 67 genomes with an average sequencing depth of 18x to document routes of introduction, admixture and selective history of European maize and its American counterparts. To avoid the confounding effects of recent breeding, we targeted germplasm (lines) directly derived from landraces. Among our lines, we discovered 22,294,769 SNPs and between 0.9% to 4.1% residual heterozygosity. Using a segmentation method, we identified 6,978 segments of unexpectedly high rate of heterozygosity. These segments point to genes potentially involved in inbreeding depression, and to a lesser extent to the presence of structural variants. Genetic structuring and inferences of historical splits revealed 5 genetic groups and two independent European introductions, with modest bottleneck signatures. Our results further revealed admixtures between distinct sources that have contributed to the establishment of 3 groups at intermediate latitudes in North America and Europe. We combined differentiation- and diversity-based statistics to identify both genes and gene networks displaying strong signals of selection. These include genes/gene networks involved in flowering time, drought and cold tolerance, plant defense and starch properties. Overall, our results provide novel insights into the evolutionary history of European maize and highlight a major role of admixture in environmental adaptation, paralleling recent findings in humans.

The impact of sample selection strategies on genetic diversity and representativeness in germplasm bank collections.

BACKGROUND: Germplasm banks maintain collections representing the most comprehensive catalogue of native genetic diversity available for crop improvement. Users of germplasm banks are interested in a fixed number of samples representing as broadly as possible the diversity present in the wider collection. A relevant question is whether it is necessary to develop completely independent germplasm samples or it is possible to select nested sets from a pre-defined core set panel not from the whole collection. We used data from 15,384, maize landraces stored in the CIMMYT germplasm bank to study the impact on 8 diversity criteria and the sample representativeness of: (1) two core selection strategies, a statistical sampling (DM), or a numerical maximization method (CH); (2) selecting samples of varying sizes; and (3) selecting samples of different sizes independently of each other or in a nested manner. RESULTS: Sample sizes greater than 10% of the whole population size retained more than 75% of the polymorphic markers for all selection strategies and types of sample; lower sample sizes showed more variability (instability) among repetitions; the strongest effect of sample size was observed on the CH-independent combination. Independent and nested samples showed similar performance for all the criteria for the DM method, but there were differences between them for the CH method. The DM method achieved better approximations to the known values in the population than the CH method; 2-d multidimensional scaling plots of the collection and samples highlighted tendency of sample selection towards the extremes of diversity in the CH method, compared with sampling more representative of the overall genotypic distribution of diversity under the DM method. CONCLUSIONS: The use of core subsets of size greater than or equal to 10% of the whole collection satisfied well the requirement of representativeness and diversity. Nested samples showed similar diversity and representativeness characteristics as independent samples offering a cost effective method of sample definition for germplasm banks. For most criteria assessed the DM method achieved better approximations to the known values in the whole population than the CH method, that is, it generated more statistically representative samples from collections.

Identifying loci with breeding potential across temperate and tropical adaptation via EigenGWAS and EnvGWAS.

Understanding the genomic basis of adaptation in maize is important for gene discovery and the improvement of breeding germplasm, but much remains a mystery in spite of significant population genetics and archaeological research. Identifying the signals underpinning adaptation are challenging as adaptation often coincided with genetic drift, and the base genomic diversity of the species in massive. In this study, tGBS technology was used to genotype 1,143 diverse maize accessions including landraces collected from 20 countries and elite breeding lines of tropical lowland, highland, subtropical/midaltitude and temperate ecological zones. Based on 355,442 high-quality single nucleotide polymorphisms, 13 genomic regions were detected as being under selection using the bottom-up searching strategy, EigenGWAS. Of the 13 selection regions, 10 were first reported, two were associated with environmental parameters via EnvGWAS, and 146 genes were enriched. Combining large-scale genomic and ecological data in this diverse maize panel, our study supports a polygenic adaptation model of maize and offers a framework to enhance our understanding of both the mechanistic basis and the evolutionary consequences of maize domestication and adaptation. The regions identified here are promising candidates for further, targeted exploration to identify beneficial alleles and haplotypes for deployment in maize breeding.

Divergence with gene flow is driven by local adaptation to temperature and soil phosphorus concentration in teosinte subspecies (Zea mays parviglumis and Zea mays mexicana).

Patterns of genomic divergence between hybridizing taxa can be heterogeneous along the genome. Both differential introgression and local adaptation may contribute to this pattern. Here, we analysed two teosinte subspecies, Zea mays ssp. parviglumis and ssp. mexicana, to test whether their divergence has occurred in the face of gene flow and to infer which environmental variables have been important drivers of their ecological differentiation. We generated 9,780 DArTseqTM SNPs for 47 populations, and used an additional data set containing 33,454 MaizeSNP50 SNPs for 49 populations. With these data, we inferred features of demographic history and performed genome wide scans to determine the number of outlier SNPs associated with climate and soil variables. The two data sets indicate that divergence has occurred or been maintained despite continuous gene flow and/or secondary contact. Most of the significant SNP associations were to temperature and to phosphorus concentration in the soil. A large proportion of these candidate SNPs were located in regions of high differentiation that had been identified previously as putative inversions. We therefore propose that genomic differentiation in teosintes has occurred by a process of adaptive divergence, with putative inversions contributing to reduced gene flow between locally adapted populations.

Initiating maize pre-breeding programs using genomic selection to harness polygenic variation from landrace populations.

BACKGROUND: The limited genetic diversity of elite maize germplasms raises concerns about the potential to breed for new challenges. Initiatives have been formed over the years to identify and utilize useful diversity from landraces to overcome this issue. The aim of this study was to evaluate the proposed designs to initiate a pre-breeding program within the Seeds of Discovery (SeeD) initiative with emphasis on harnessing polygenic variation from landraces using genomic selection. We evaluated these designs with stochastic simulation to provide decision support about the effect of several design factors on the quality of resulting (pre-bridging) germplasm. The evaluated design factors were: i) the approach to initiate a pre-breeding program from the selected landraces, doubled haploids of the selected landraces, or testcrosses of the elite hybrid and selected landraces, ii) the genetic parameters of landraces and phenotypes, and iii) logistical factors related to the size and management of a pre-breeding program. RESULTS: The results suggest a pre-breeding program should be initiated directly from landraces. Initiating from testcrosses leads to a rapid reconstruction of the elite donor genome during further improvement of the pre-bridging germplasm. The analysis of accuracy of genomic predictions across the various design factors indicate the power of genomic selection for pre-breeding programs with large genetic diversity and constrained resources for data recording. The joint effect of design factors was summarized with decision trees with easy to follow guidelines to optimize pre-breeding efforts of SeeD and similar initiatives. CONCLUSIONS: Results of this study provide guidelines for SeeD and similar initiatives on how to initiate pre-breeding programs that aim to harness polygenic variation from landraces.

AlphaMPSim: flexible simulation of multi-parent crosses.

UNLABELLED: Multi-parent crosses of recombinant inbred lines exist in many species for fine-scale analysis of genome structure and marker-trait association. These populations encompass a wide range of crossing designs with varying potential. AlphaMPSim is a flexible simulation program that is efficiently designed for comparison of alternative designs for traits with varying genetic architectures and biallelic markers with densities up to full sequence. A large pool of founder haplotypes can be supplied by the user, or generated via integration with external coalescent simulation programs such as MaCS. From these, diverse founders for multi-parent designs can be generated automatically, and users can compare designs generated from diverse pedigrees. Full tracking of identity by descent status of alleles within the pedigree is undertaken, and output files are compatible with commonly available analysis packages in R. AVAILABILITY AND IMPLEMENTATION: Executable versions of AlphaMPSim for Mac and Linux and a user manual are available at http://www.roslin.ed.ac.uk/john-hickey/software-packages/.

LanguageScreen: The Development, Validation, and Standardization of an Automated Language Assessment App.

PURPOSE: Oral language skills provide a critical foundation for formal education and especially for the development of children's literacy (reading and spelling) skills. It is therefore important for teachers to be able to assess children's language skills, especially if they are concerned about their learning. We report the development and standardization of a mobile app-LanguageScreen-that can be used by education professionals to assess children's language ability. METHOD: The standardization sample included data from approximately 350,000 children aged 3;06 (years;months) to 8;11 who were screened for receptive and expressive language skills using LanguageScreen. Rasch scaling was used to select items of appropriate difficulty on a single unidimensional scale. RESULTS: LanguageScreen has excellent psychometric properties, including high reliability, good fit to the Rasch model, and minimal differential item functioning across key student groups. Girls outperformed boys, and children with English as an additional language scored less well compared to monolingual English speakers. CONCLUSIONS: LanguageScreen provides an easy-to-use, reliable, child-friendly means of identifying children with language difficulties. Its use in schools may serve to raise teachers' awareness of variations in language skills and their importance for educational practice.

Molecular characterization of doubled haploid lines derived from different cycles of the Iowa Stiff Stalk Synthetic (BSSS) maize population.

Molecular characterization of a given set of maize germplasm could be useful for understanding the use of the assembled germplasm for further improvement in a breeding program, such as analyzing genetic diversity, selecting a parental line, assigning heterotic groups, creating a core set of germplasm and/or performing association analysis for traits of interest. In this study, we used single nucleotide polymorphism (SNP) markers to assess the genetic variability in a set of doubled haploid (DH) lines derived from the unselected Iowa Stiff Stalk Synthetic (BSSS) maize population, denoted as C0 (BSSS(R)C0), the seventeenth cycle of reciprocal recurrent selection in BSSS (BSSS(R)C17), denoted as C17 and the cross between BSSS(R)C0 and BSSS(R)C17 denoted as C0/C17. With the aim to explore if we have potentially lost diversity from C0 to C17 derived DH lines and observe whether useful genetic variation in C0 was left behind during the selection process since C0 could be a reservoir of genetic diversity that could be untapped using DH technology. Additionally, we quantify the contribution of the BSSS progenitors in each set of DH lines. The molecular characterization analysis confirmed the apparent separation and the loss of genetic variability from C0 to C17 through the recurrent selection process. Which was observed by the degree of differentiation between the C0_DHL versus C17_DHL groups by Wright's F-statistics (FST). Similarly for the population structure based on principal component analysis (PCA) revealed a clear separation among groups of DH lines. Some of the progenitors had a higher genetic contribution in C0 compared with C0/C17 and C17 derived DH lines. Although genetic drift can explain most of the genetic structure genome-wide, phenotypic data provide evidence that selection has altered favorable allele frequencies in the BSSS maize population through the reciprocal recurrent selection program.

DNNGP, a deep neural network-based method for genomic prediction using multi-omics data in plants.

Genomic prediction is an effective way to accelerate the rate of agronomic trait improvement in plants. Traditional methods typically use linear regression models with clear assumptions; such methods are unable to capture the complex relationships between genotypes and phenotypes. Non-linear models (e.g., deep neural networks) have been proposed as a superior alternative to linear models because they can capture complex non-additive effects. Here we introduce a deep learning (DL) method, deep neural network genomic prediction (DNNGP), for integration of multi-omics data in plants. We trained DNNGP on four datasets and compared its performance with methods built with five classic models: genomic best linear unbiased prediction (GBLUP); two methods based on a machine learning (ML) framework, light gradient boosting machine (LightGBM) and support vector regression (SVR); and two methods based on a DL framework, deep learning genomic selection (DeepGS) and deep learning genome-wide association study (DLGWAS). DNNGP is novel in five ways. First, it can be applied to a variety of omics data to predict phenotypes. Second, the multilayered hierarchical structure of DNNGP dynamically learns features from raw data, avoiding overfitting and improving the convergence rate using a batch normalization layer and early stopping and rectified linear activation (rectified linear unit) functions. Third, when small datasets were used, DNNGP produced results that are competitive with results from the other five methods, showing greater prediction accuracy than the other methods when large-scale breeding data were used. Fourth, the computation time required by DNNGP was comparable with that of commonly used methods, up to 10 times faster than DeepGS. Fifth, hyperparameters can easily be batch tuned on a local machine. Compared with GBLUP, LightGBM, SVR, DeepGS and DLGWAS, DNNGP is superior to these existing widely used genomic selection (GS) methods. Moreover, DNNGP can generate robust assessments from diverse datasets, including omics data, and quickly incorporate complex and large datasets into usable models, making it a promising and practical approach for straightforward integration into existing GS platforms.

Genetic structure analysis and identifying key founder inbred lines in diverse elite sub-tropical maize inbred lines.

Understanding the genetic relationships between the key founder inbred lines and derived inbred lines could provide insight into the breeding history and the structure of genetic diversity of the available elite inbred lines with desirable target traits. The maize improvement program at the International Institute of Tropical Agriculture (IITA) analyzed the pedigree information of 623 sub-tropical maize inbred lines generated at the IITA maize breeding program to identify the key founder inbred lines. We also used 5032 SNP markers to assess the genetic similarities of the founder inbred lines with their progenies subsequently developed for specific target traits. The results of pedigree analysis and SNP markers-based similarity scores identified 20 key founder inbred lines with significant contributions to the development of drought tolerant, early maturing, productive, Striga resistant, provitamin A enriched, and quality protein maize inbred lines. In our breeding program, line TZMi501 belonging to a flint heterotic group (HGA), and TZMi407-S and TZMi214, representing the dent heterotic group (HGB), were identified as the most useful founder inbred lines. The 623 inbred lines were consistently separated into four clusters based on Ward's hierarchical clustering, structure, and principal component analyses, with the 20 founder inbred lines spread into all clusters. The founder inbred lines were more genetically related to the productive inbred lines but showed genetic divergence from the provitamin A enriched inbred lines. These results provide a better understanding of the breeding history of the sub-tropical maize inbred lines to facilitate parental selection aligned to existing heterotic groups for use in breeding programs targeting the improvement of essential traits in maize.

The role of transposon inverted repeats in balancing drought tolerance and yield-related traits in maize.

The genomic basis underlying the selection for environmental adaptation and yield-related traits in maize remains poorly understood. Here we carried out genome-wide profiling of the small RNA (sRNA) transcriptome (sRNAome) and transcriptome landscapes of a global maize diversity panel under dry and wet conditions and uncover dozens of environment-specific regulatory hotspots. Transgenic and molecular studies of Drought-Related Environment-specific Super eQTL Hotspot on chromosome 8 (DRESH8) and ZmMYBR38, a target of DRESH8-derived small interfering RNAs, revealed a transposable element-mediated inverted repeats (TE-IR)-derived sRNA- and gene-regulatory network that balances plant drought tolerance with yield-related traits. A genome-wide scan revealed that TE-IRs associate with drought response and yield-related traits that were positively selected and expanded during maize domestication. These results indicate that TE-IR-mediated posttranscriptional regulation is a key molecular mechanism underlying the tradeoff between crop environmental adaptation and yield-related traits, providing potential genomic targets for the breeding of crops with greater stress tolerance but uncompromised yield.

Redesigning crop varieties to win the race between climate change and food security.

Climate change poses daunting challenges to agricultural production and food security. Rising temperatures, shifting weather patterns, and more frequent extreme events have already demonstrated their effects on local, regional, and global agricultural systems. Crop varieties that withstand climate-related stresses and are suitable for cultivation in innovative cropping systems will be crucial to maximize risk avoidance, productivity, and profitability under climate-changed environments. We surveyed 588 expert stakeholders to predict current and novel traits that may be essential for future pearl millet, sorghum, maize, groundnut, cowpea, and common bean varieties, particularly in sub-Saharan Africa. We then review the current progress and prospects for breeding three prioritized future-essential traits for each of these crops. Experts predict that most current breeding priorities will remain important, but that rates of genetic gain must increase to keep pace with climate challenges and consumer demands. Importantly, the predicted future-essential traits include innovative breeding targets that must also be prioritized; for example, (1) optimized rhizosphere microbiome, with benefits for P, N, and water use efficiency, (2) optimized performance across or in specific cropping systems, (3) lower nighttime respiration, (4) improved stover quality, and (5) increased early vigor. We further discuss cutting-edge tools and approaches to discover, validate, and incorporate novel genetic diversity from exotic germplasm into breeding populations with unprecedented precision, accuracy, and speed. We conclude that the greatest challenge to developing crop varieties to win the race between climate change and food security might be our innovativeness in defining and boldness to breed for the traits of tomorrow.

Identification, validation and high-throughput genotyping of transcribed gene SNPs in cassava.

The availability of genomic resources can facilitate progress in plant breeding through the application of advanced molecular technologies for crop improvement. This is particularly important in the case of less researched crops such as cassava, a staple and food security crop for more than 800 million people. Here, expressed sequence tags (ESTs) were generated from five drought stressed and well-watered cassava varieties. Two cDNA libraries were developed: one from root tissue (CASR), the other from leaf, stem and stem meristem tissue (CASL). Sequencing generated 706 contigs and 3,430 singletons. These sequences were combined with those from two other EST sequencing initiatives and filtered based on the sequence quality. Quality sequences were aligned using CAP3 and embedded in a Windows browser called HarvEST:Cassava which is made available. HarvEST:Cassava consists of a Unigene set of 22,903 quality sequences. A total of 2,954 putative SNPs were identified. Of these 1,536 SNPs from 1,170 contigs and 53 cassava genotypes were selected for SNP validation using Illumina's GoldenGate assay. As a result 1,190 SNPs were validated technically and biologically. The location of validated SNPs on scaffolds of the cassava genome sequence (v.4.1) is provided. A diversity assessment of 53 cassava varieties reveals some sub-structure based on the geographical origin, greater diversity in the Americas as opposed to Africa, and similar levels of diversity in West Africa and southern, eastern and central Africa. The resources presented allow for improved genetic dissection of economically important traits and the application of modern genomics-based approaches to cassava breeding and conservation.