International Maize and Wheat Improvement Center

About: International Maize and Wheat Improvement Center is a nonprofit organization based out in Texcoco, Mexico. It is known for research contribution in the topics: Population & Agriculture. The organization has 1976 authors who have published 4799 publications receiving 218390 citations.

Detection and characterization of fungus (Magnaporthe oryzae pathotype Triticum) causing wheat blast disease on rain-fed grown wheat (Triticum aestivum L.) in Zambia.

Abstract: Wheat blast caused by Magnaporthe oryzae pathotype Triticum (MoT) is a threat to wheat production especially in the warmer-humid environments. In Zambia, wheat blast symptoms were observed for the first time on wheat (Triticum aestivum L.) grown in experimental plots and five farmers' fields in Mpika district of Muchinga Province during the 2017-18 rainy season. Infected plants showed the typical wheat blast symptoms with the spike becoming partially or completely bleached with the blackening of the rachis in a short span of time. Incidence of blast symptoms on nearly all wheat heads was high and ranged from 50 to 100%. Examination of diseased plant leaves showed the presence of elliptical, grayish to tan necrotic lesions with dark borders on the leaf often mixed with other foliar diseases. A study was conducted to isolate and identify the causal pathogen(s) using classical and molecular methods and determine the pathogenicity of the detected disease causal agent. Morphobiometrical determination of causal pathogen revealed conidia with characteristic pear shaped 2-septate hyaline spores associated with M. oryzae species. Preliminary polymerase chain reaction screening of six isolates obtained from wheat blast infected samples with diagnostic primers (MoT3F/R) was conducted at ZARI, Zambia, and subsequent analysis of two isolates with MoT3F/R and C17F/R was performed at USDA-ARS, USA. Both experiments confirmed that MoT is the causal agent of wheat blast in Zambia. Further, pathogenicity tests performed with pure culture isolates from samples WS4 and WS5 produced typical blast symptoms on all the six inoculated wheat genotypes. Results of this study indicate that MoT is causing wheat blast in rain-fed wheat grown in Zambia, thus making it the first report of MoT in Zambia and Africa. This inter-continental movement of the pathogen (disease) has serious implication for wheat production and trade that needs to be urgently addressed.

Multi-environment analysis and improved mapping of a yield-related QTL on chromosome 3B of wheat

Abstract: Improved mapping, multi-environment quantitative trait loci (QTL) analysis and dissection of allelic effects were used to define a QTL associated with grain yield, thousand grain weight and early vigour on chromosome 3BL of bread wheat (Triticum aestivum L.) under abiotic stresses. The QTL had pleiotropic effects and showed QTL x environment interactions across 21 diverse environments in Australia and Mexico. The occurrence and the severity of water deficit combined with high temperatures during the growing season affected the responsiveness of this QTL, resulting in a reversal in the direction of allelic effects. The influence of this QTL can be substantial, with the allele from one parent (RAC875) increasing grain yield by up to 12.5 % (particularly in environments where both heat and drought stress occurred) and the allele from the other parent (Kukri) increasing grain yield by up to 9 % in favourable environments. With the application of additional markers and the genotyping of additional recombinant inbred lines, the genetic map in the QTL region was refined to provide a basis for future positional cloning.

Comparison of molecular markers and coefficients of parentage for the analysis of genetic diversity among spring bread wheat accessions

Abstract: The comparison of different methods of estimating genetic diversity could define their usefulness in plant breeding and genetic improvement programs. This study evaluates and compares the genetic diversity of 70 spring wheat accessions representing a broad genetic pool based on molecular markers and parentage relationships. The sample was composed of 32 accessions from the International Maize and Wheat Improvement Center (CIMMYT) and 38 from other breeding programs worldwide. Eight AFLP-primer combinations and 37 pairs of SSR primers were used to characterize the accessions and the Coefficients of Parentage (COP) were calculated from registered pedigrees. The average genealogical (COP) similarity (0.09 with a range of 0.0–1.0) was low in comparison to similarity calculated using SSR markers (0.41 with a range of 0.15–0.88) and AFLP markers (0.70 with a range of 0.33–0.98). Correlation between the genealogical similarity matrix (excluding accessions with COPs = 0) and the matrices of genetic similarity based on molecular markers was 0.34≤r≤0.46 (p <0.05). It is concluded that AFLP and SSR markers are generally in agreement with estimates of diversity measured using COPs, especially when complete pedigree data are available. However, markers may provide a more correct estimate due to some unrealistic assumptions made when calculating COPs, such as absence of selection. Furthermore, both COP and marker distances indicate that CIMMYT accessions are different from the worldwide group of accessions.

Multi-environment Genomic Prediction of Plant Traits Using Deep Learners With Dense Architecture

Abstract: Genomic selection is revolutionizing plant breeding and therefore methods that improve prediction accuracy are useful. For this reason, active research is being conducted to build and test methods from other areas and adapt them to the context of genomic selection. In this paper we explore the novel deep learning (DL) methodology in the context of genomic selection. We compared DL methods with densely connected network architecture to one of the most often used genome-enabled prediction models: Genomic Best Linear Unbiased Prediction (GBLUP). We used nine published real genomic data sets to compare a fraction of all possible deep learning models to obtain a "meta picture" of the performance of DL methods with densely connected network architecture. In general, the best predictions were obtained with the GBLUP model when genotype×environment interaction (G×E) was taken into account (8 out of 9 data sets); when the interactions were ignored, the DL method was better than the GBLUP in terms of prediction accuracy in 6 out of the 9 data sets. For this reason, we believe that DL should be added to the data science toolkit of scientists working on animal and plant breeding. This study corroborates the view that there are no universally best prediction machines.

The Modern Plant Breeding Triangle: Optimizing the Use of Genomics, Phenomics, and Enviromics Data.

Abstract: Continued increases in genetic gain demonstrate the success of established public and private plant breeding programs. Nevertheless, in the last two decades, a growing body of modern technologies has been developed and now awaits efficient integration into traditional breeding pipelines. This integration offers attractive benefits, yet comes with the challenges of making modifications in established and operational systems, a recent example of which is rice breeding (Collard et al., 2019). Newly available technologies, genomics rapid cycling (Crossa et al., 2017), high throughput phenotyping (HTP, phenomics) (Montesinos-Lopez et al., 2017) and historical descriptions of environmental relatedness (enviromics) (Costa-Neto et al., 2020a,b; Resende et al., 2020; Rogers et al., 2021) are crucial to improving conventional breeding schemes and increasing genetic gain. Integrating these new technologies into routine breeding pipelines will support the delivery of cultivars with robust yields in the face of the expected unfavorable future environmental conditions caused by climate change and the consequently increased occurrence of biotic and abiotic stresses. Here, we briefly describe the use of these technologies and their implementation to provide cost-effective and time-saving approaches to plant breeding. We also give an overview of the interconnections between these techniques. Finally, we envision future perspectives to implement a more interconnected breeding approach that takes advantage of the so-called modern plant breeding triangle: integrating genomics, phenomics, and enviromics. Why Genomics for Improving Breeding? One of the most popular uses of genomics in breeding is the prediction of breeding values. Genomic selection (GS) reduces cycle time, increases the accuracy of estimated breeding values and improves selection accuracy. For instance, in maize, the effectiveness of GS has been proven for the case of bi-parental populations (Massman et al., 2013; Beyene et al., 2015; Vivek et al., 2017), as well as in multi-parental populations (Zhang et al., 2017). Its use has also been documented in species with long generation times such as trees (Grattapaglia et al., 2018) and dairy cattle breeding, where the reduction of the breeding cycle has increased the response to selection in comparison with the progeny testing system (Garcia-Ruiz et al., 2016). Genomic selection has been implemented in many crops, including wheat, chickpea, cassava and rice (Roorkiwal et al., 2016; Crossa et al., 2017; Wolfe et al., 2017; Huang et al., 2019), and the number of programs that are moving from “conventional” to GS is growing. Results in wheat show that genomic predictions used early in the breeding cycle led to a substantial increase in performance in later generations (Bonnett et al., 2021 this issue). Defining Foundational Core Parents for Genomic Selection-Assisted Breeding In genomic selection, the optimization of the training set composition is an important topic because training and testing sets should be genetically related in such a way that the genetic diversity present in the testing set could be covered and captured by the diversity in the training set. Breeding programs must start forming initial foundational core parents (training populations) that represent the genetic diversity found in the current progeny and conform to the testing population(s) to the greatest extent possible (Hickey et al., 2012). These foundational parents should be extensively phenotyped in different target populations of environments and genotyped with high-density marker systems. These training sets of foundation parents will be able to produce a model with a high accuracy for current highly selected progenies (Zhang et al., 2017).