Showing papers by "International Maize and Wheat Improvement Center published in 2014"

Single nucleotide polymorphism genotyping using Kompetitive Allele Specific PCR (KASP): overview of the technology and its application in crop improvement

Abstract: Single nucleotide polymorphism (SNP) data can be obtained using one of the numerous uniplex or multiplex SNP genotyping platforms that combine a variety of chemistries, detection methods, and reaction formats. Kompetitive Allele Specific PCR (KASP) is one of the uniplex SNP genotyping platforms, and has evolved to be a global benchmark technology. However, there are no publications relating either to the technology itself or to its application in crop improvement programs. In this review, we provide an overview of the different aspects of the KASP genotyping platform, discuss its application in crop improvement, and compare it with the chip-based Illumina GoldenGate platform. The International Maize and Wheat Improvement Center routinely uses KASP, generating in excess of a million data points annually for crop improvement purposes. We found that (1) 81 % of the SNPs used in a custom-designed GoldenGate assay were transferable to KASP; (2) using KASP, negative controls (no template) consistently clustered together and rarely produced signals exceeding the threshold values for allele calling, in contrast to the situation observed using GoldenGate assays; (3) KASP’s average genotyping error in positive control DNA samples was 0.7–1.6 %, which is lower than that observed using GoldenGate (2.0–2.4 %); (4) KASP genotyping costs for marker-assisted recurrent selection were 7.9–46.1 % cheaper than those of the BeadXpress and GoldenGate platforms; and (5) KASP offers cost-effective and scalable flexibility in applications that require small to moderate numbers of markers, such as quality control analysis, quantitative trait loci (QTL) mapping in bi-parental populations, marker-assisted recurrent selection, marker-assisted backcrossing, and QTL fine mapping.

Limited potential of no-till agriculture for climate change mitigation

Abstract: No-till agriculture is generally considered good for soils, and probably also beneficial in relation to climate change adaptation. However, this Perspective argues that the potential for climate change mitigation through soil carbon sequestration that is possible from a change to no-till agriculture has been widely overstated.

Managing vulnerability to drought and enhancing livelihood resilience in sub-Saharan Africa: Technological, institutional and policy options

Abstract: Agriculture and the economies of Sub-Saharan Africa (SSA) are highly sensitive to climatic variability. Drought, in particular, represents one of the most important natural factors contributing to malnutrition and famine in many parts of the region. The overall impact of drought on a given country/region and its ability to recover from the resulting social, economic and environmental impacts depends on several factors. The economic, social and environmental impacts of drought are huge in SSA and the national costs and losses incurred threaten to undermine the wider economic and development gains made in the last few decades in the region. There is an urgent need to reduce the vulnerability of countries to climate variability and to the threats posed by climate change. This paper attempts to highlight the challenges of drought in SSA and reviews the current drought risk management strategies, especially the promising technological and policy options for managing drought risks to protect livelihoods and reduce vulnerability. The review suggests the possibilities of several ex ante and ex post drought management strategies in SSA although their effectiveness depends on agro-climatic and socio-economic conditions. Existing technological, policy and institutional risk management measures need to be strengthened and integrated to manage drought ex ante and to minimize the ex post negative effects for vulnerable households and regions. A proactive approach that combines promising technological, institutional and policy solutions to manage the risks within vulnerable communities implemented by institutions operating at different levels (community, sub-national, and national) is considered to be the way forward for managing drought and climate variability.

A reaction norm model for genomic selection using high-dimensional genomic and environmental data

Abstract: Key message New methods that incorporate the main and interaction effects of high-dimensional markers and of high-dimensional environmental covariates gave increased prediction accuracy of grain yield in wheat across and within environments.

Metabolome-based genome-wide association study of maize kernel leads to novel biochemical insights

Abstract: Plants produce a variety of metabolites that have a critical role in growth and development. Here we present a comprehensive study of maize metabolism, combining genetic, metabolite and expression profiling methodologies to dissect the genetic basis of metabolic diversity in maize kernels. We quantify 983 metabolite features in 702 maize genotypes planted at multiple locations. We identify 1,459 significant locus-trait associations (P≤1.8 × 10(-6)) across three environments through metabolite-based genome-wide association mapping. Most (58.5%) of the identified loci are supported by expression QTLs, and some (14.7%) are validated through linkage mapping. Re-sequencing and candidate gene association analysis identifies potential causal variants for five candidate genes involved in metabolic traits. Two of these genes were further validated by mutant and transgenic analysis. Metabolite features associated with kernel weight could be used as biomarkers to facilitate genetic improvement of maize.

Genomic prediction in CIMMYT maize and wheat breeding programs

Abstract: Genomic selection (GS) has been implemented in animal and plant species, and is regarded as a useful tool for accelerating genetic gains. Varying levels of genomic prediction accuracy have been obtained in plants, depending on the prediction problem assessed and on several other factors, such as trait heritability, the relationship between the individuals to be predicted and those used to train the models for prediction, number of markers, sample size and genotype × environment interaction (GE). The main objective of this article is to describe the results of genomic prediction in International Maize and Wheat Improvement Center's (CIMMYT's) maize and wheat breeding programs, from the initial assessment of the predictive ability of different models using pedigree and marker information to the present, when methods for implementing GS in practical global maize and wheat breeding programs are being studied and investigated. Results show that pedigree (population structure) accounts for a sizeable proportion of the prediction accuracy when a global population is the prediction problem to be assessed. However, when the prediction uses unrelated populations to train the prediction equations, prediction accuracy becomes negligible. When genomic prediction includes modeling GE, an increase in prediction accuracy can be achieved by borrowing information from correlated environments. Several questions on how to incorporate GS into CIMMYT's maize and wheat programs remain unanswered and subject to further investigation, for example, prediction within and between related bi-parental crosses. Further research on the quantification of breeding value components for GS in plant breeding populations is required.

Novel Methods to Optimize Genotypic Imputation for Low-Coverage, Next-Generation Sequence Data in Crop Plants

Abstract: Next-generation sequencing technology such as genotyping-bysequencing (GBS) made low-cost, but often low-coverage, wholegenome sequencing widely available. Extensive inbreeding in crop plants provides an untapped, high quality source of phased haplotypes for imputing missing genotypes. We introduce Full-Sib Family Haplotype Imputation (FSFHap), optimized for full-sib populations, and a generalized method, Fast Inbred Line Library ImputatioN (FILLIN), to rapidly and accurately impute missing genotypes in GBS-type data with ordered markers. FSFHap and FILLIN impute missing genotypes with high accuracy in GBS-genotyped maize (Zea mays L.) inbred lines and breeding populations, while Beagle v. 4 is still preferable for diverse heterozygous populations. FILLIN and FSFHap are implemented in TASSEL 5.0.

SNP genotyping: the KASP assay

Abstract: The KASP genotyping assay utilizes a unique form of competitive allele-specific PCR combined with a novel, homogeneous, fluorescence-based reporting system for the identification and measurement of genetic variation occurring at the nucleotide level to detect single nucleotide polymorphisms (SNPs) or inserts and deletions (InDels). The KASP technology is suitable for use on a variety of equipment platforms and provides flexibility in terms of the number of SNPs and the number of samples able to be analyzed. The KASP chemistry functions equally well in 96-, 384-, and 1,536-well microtiter plate formats and has been utilized over many years in large and small laboratories by users across the fields of human, animal, and plant genetics.

Genome sequencing of the high oil crop sesame provides insight into oil biosynthesis

Abstract: Background: Sesame, Sesamum indicum L., is considered the queen of oilseeds for its high oil content and quality, and is grown widely in tropical and subtropical areas as an important source of oil and protein. However, the molecular biology of sesame is largely unexplored. Results: Here, we report a high-quality genome sequence of sesame assembled de novo with a contig N50 of 52.2 kb and a scaffold N50 of 2.1 Mb, containing an estimated 27,148 genes. The results reveal novel, independent whole genome duplication and the absence of the Toll/interleukin-1 receptor domain in resistance genes. Candidate genes and oil biosynthetic pathways contributing to high oil content were discovered by comparative genomic and transcriptomic analyses. These revealed the expansion of type 1 lipid transfer genes by tandem duplication, the contraction of lipid degradation genes, and the differential expression of essential genes in the triacylglycerol biosynthesis pathway, particularly in the early stage of seed development. Resequencing data in 29 sesame accessions from 12 countries suggested that the high genetic diversity of lipid-related genes might be associated with the wide variation in oil content. Additionally, the results shed light on the pivotal stage of seed development, oil accumulation and potential key genes for sesamin production, an important pharmacological constituent of sesame. Conclusions: As an important species from the order Lamiales and a high oil crop, the sesame genome will facilitate future research on the evolution of eudicots, as well as the study of lipid biosynthesis and potential genetic improvement of sesame.

Genome-wide association mapping of yield and yield components of spring wheat under contrasting moisture regimes.

Abstract: A stable QTL that may be used in marker-assisted selection in wheat breeding programs was detected for yield, yield components and drought tolerance-related traits in spring wheat association mapping panel. Genome-wide association mapping has become a widespread method of quantitative trait locus (QTL) identification for many crop plants including wheat (Triticum aestivum L.). Its benefit over traditional bi-parental mapping approaches depends on the extent of linkage disequilibrium in the mapping population. The objectives of this study were to determine linkage disequilibrium decay rate and population structure in a spring wheat association mapping panel (n = 285–294) and to identify markers associated with yield and yield components, morphological, phenological, and drought tolerance-related traits. The study was conducted under fully irrigated and rain-fed conditions at Greeley, CO, USA and Melkassa, Ethiopia in 2010 and 2011 (five total environments). Genotypic data were generated using diversity array technology markers. Linkage disequilibrium decay rate extended over a longer genetic distance for the D genome (6.8 cM) than for the A and B genomes (1.7 and 2.0 cM, respectively). Seven subpopulations were identified with population structure analysis. A stable QTL was detected for grain yield on chromosome 2DS both under irrigated and rain-fed conditions. A multi-trait region significant for yield and yield components was found on chromosome 5B. Grain yield QTL on chromosome 1BS co-localized with harvest index QTL. Vegetation indices shared QTL with harvest index on chromosome 1AL and 5A. After validation in relevant genetic backgrounds and environments, QTL detected in this study for yield, yield components and drought tolerance-related traits may be used in marker-assisted selection in wheat breeding programs.

Climate-smart agriculture global research agenda: scientific basis for action

Abstract: Background: Climate-smart agriculture (CSA) addresses the challenge of meeting the growing demand for food, fibre and fuel, despite the changing climate and fewer opportunities for agricultural expansion on additional lands. CSA focuses on contributing to economic development, poverty reduction and food security; maintaining and enhancing the productivity and resilience of natural and agricultural ecosystem functions, thus building natural capital; and reducing trade-offs involved in meeting these goals. Current gaps in knowledge, work within CSA, and agendas for interdisciplinary research and science-based actions identified at the 2013 Global Science Conference on Climate-Smart Agriculture (Davis, CA, USA) are described here within three themes: (1) farm and food systems, (2) landscape and regional issues and (3) institutional and policy aspects. The first two themes comprise crop physiology and genetics, mitigation and adaptation for livestock and agriculture, barriers to adoption of CSA practices, climate risk management and energy and biofuels (theme 1); and modelling adaptation and uncertainty, achieving multifunctionality, food and fishery systems, forest biodiversity and ecosystem services, rural migration from climate change and metrics (theme 2). Theme 3 comprises designing research that bridges disciplines, integrating stakeholder input to directly link science, action and governance. Outcomes: In addition to interdisciplinary research among these themes, imperatives include developing (1) models that include adaptation and transformation at either the farm or landscape level; (2) capacity approaches to examine multifunctional solutions for agronomic, ecological and socioeconomic challenges; (3) scenarios that are validated by direct evidence and metrics to support behaviours that foster resilience and natural capital; (4) reductions in the risk that can present formidable barriers for farmers during adoption of new technology and practices; and (5) an understanding of how climate affects the rural labour force, land tenure and cultural integrity, and thus the stability of food production. Effective work in CSA will involve stakeholders, address governance issues, examine uncertainties, incorporate social benefits with technological change, and establish climate finance within a green development framework. Here, the socioecological approach is intended to reduce development controversies associated with CSA and to identify technologies, policies and approaches leading to sustainable food production and consumption patterns in a changing climate.

Evaluation of genomic selection training population designs and genotyping strategies in plant breeding programs using simulation

Abstract: Genomic selection offers great potential for increasing the rate of genetic improvement in plant breeding programs. This research used simulation to evaluate the effectiveness of dif-ferent strategies for genotyping and phenotyp-ing to enable genomic selection in early genera -tion individuals (e.g., F 2 ) in breeding programs involving biparental or similar (e.g., backcross or top cross) populations. By using phenotypes that were previously collected in other bipa-rental populations, selection decisions could be made without waiting for phenotypes that pertain directly to the selection candidate to be collected, a process that would take at least three growing seasons. If these phenotypes were collected in biparental populations that were closely related to the selection candidates, only a small number of markers (e.g., 200–500) and a small number of phenotypes (e.g., 1000) were needed to achieve effective accuracy of estimated breeding values. If these phenotypes were collected in biparental populations that were not closely related to the selection can-didates, as many as 10,000 markers and 5000 to 20,000 phenotypes were needed. Increasing marker density beyond 10,000 markers did not show benefit and in some scenarios reduced the accuracy of prediction. This study provides a guide, enabling resource allocation to be opti -mized between genotyping and phenotyping investment dependent on the population under development.J.M. Hickey, The Roslin Institute and Royal (Dick) School of Vet-erinary Studies, Univ. of Edinburgh, Easter Bush Research Center, Midlothian EH25 9RG, UK; S. Dreisigacker, Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), Apdo. 06600 Mexico D.F., Mexico; J. Crossa and K. Mathews, Biomet -rics and Statistics Unit, International Maize and Wheat Improvement Center (CIMMYT), Apdo. 06600 Mexico D.F., Mexico; S. Hearne, Genetic Resources Program, International Maize and Wheat Improve -ment Center (CIMMYT), Apdo. 06600 Mexico D.F., Mexico; R. Babu and B.M. Prasanna, Global Maize Program, International Maize and Wheat Improvement Center (CIMMYT), Apdo. 06600 Mexico D.F., Mexico; M. Grondona and A. Zambelli, Center for Biotechnol-ogy Research, Advanta Semillas, Ruta 226 Km 60.5, (7620) Balcarce, Argentina; V.S. Windhausen, Saaten Union Recherche, 160 Avenue de Flandre, 60190 Estrees Saint Denis, France; Gregor Gorjanc, Biotech-nical Faculty, University of Ljubljana, Domzale, Slovenia. Received 22 Mar. 2013. *Corresponding author (john.hickey@roslin.ed.ac.uk).

Genomic selection for quantitative adult plant stem rust resistance in wheat

Abstract: Quantitative adult plant resistance (APR) to stem rust (Puccinia graminis f. sp. tritici) is an important breeding target in wheat (Triticum aestivum L.) and a potential target for genomic selection (GS). To evaluate the relative importance of known APR loci in applying GS, we characterized a set of CIMMYT germplasm at important APR loci and on a genome-wide profile using genotyping-by-sequencing (GBS). Using this germplasm, we describe the genetic architecture and evaluate prediction models for APR using data from the international Ug99 stem rust screening nurseries. Prediction models incorporating markers linked to important APR loci and seedling phenotype scores as fixed effects were evaluated along with the classic prediction models: Multiple linear regression (MLR), Genomic best linear unbiased prediction (G-BLUP), Bayesian Lasso (BL), and Bayes Cp (BCp). We found the Sr2 region to play an important role in APR in this germplasm. A model using Sr2 linked markers as fixed effects in G-BLUP was more accurate than MLR with Sr2 linked markers (p-value = 0.12), and ordinary G-BLUP (p-value = 0.15). Incorporating seedling phenotype information as fixed effects in G-BLUP did not consistently increase accuracy. Overall, levels of prediction accuracy found in this study indicate that GS can be effectively applied to improve stem rust APR in this germplasm, and if genotypes at Sr2 linked markers are available, modeling these genotypes as fixed effects could lead to better predictions.

Lr67/Yr46 confers adult plant resistance to stem rust and powdery mildew in wheat

Abstract: Key message We demonstrate thatLr67/Yr46has pleiotropic effect on stem rust and powdery mildew resistance and is associated with leaf tip necrosis. Genes are designated asSr55, Pm46andLtn3, respectively.

Bridging the gap between marker-assisted and genomic selection of heading time and plant height in hybrid wheat

Abstract: Based on data from field trials with a large collection of 135 elite winter wheat inbred lines and 1604 F1 hybrids derived from them, we compared the accuracy of prediction of marker-assisted selection and current genomic selection approaches for the model traits heading time and plant height in a cross-validation approach. For heading time, the high accuracy seen with marker-assisted selection severely dropped with genomic selection approaches RR-BLUP (ridge regression best linear unbiased prediction) and BayesCπ, whereas for plant height, accuracy was low with marker-assisted selection as well as RR-BLUP and BayesCπ. Differences in the linkage disequilibrium structure of the functional and single-nucleotide polymorphism markers relevant for the two traits were identified in a simulation study as a likely explanation for the different trends in accuracies of prediction. A new genomic selection approach, weighted best linear unbiased prediction (W-BLUP), designed to treat the effects of known functional markers more appropriately, proved to increase the accuracy of prediction for both traits and thus closes the gap between marker-assisted and genomic selection.

A consensus map for Ug99 stem rust resistance loci in wheat.

Abstract: Key message This consensus map of stem rust genes, QTLs, and molecular markers will facilitate the identi‑ fication of new resistance genes and provide a resource of information for development of new markers for breeding wheat varieties resistant to Ug99.