Comparative Review of Infection in Maize and Wheat: Similarities in Resistance Mechanisms and Future Directions.

is one of the most important plant-pathogenic fungi that causes disease on wheat and maize, as it decreases yield in both crops and produces mycotoxins that pose a risk to human and animal health. Resistance to Fusarium head blight (FHB) in wheat is well studied and documented. However, resistance to Gibberella ear rot (GER) in maize is less understood, despite several similarities with FHB.

Implementing multi-trait genomic selection to improve grain milling quality in oats (Avena sativa L.).

Oats (Avena sativa L.) provide unique nutritional benefits and contribute to sustainable agricultural systems. Breeding high-value oat varieties that meet milling industry standards is crucial for satisfying the demand for oat-based food products.

New cycle, same old mistakes? Overlapping vs. discrete generations in long-term recurrent selection.

Background: Recurrent selection is a foundational breeding method for quantitative trait improvement. It typically features rapid breeding cycles that can lead to high rates of genetic gain. Usually, generations are discrete in recurrent selection, which means that breeding candidates are evaluated and considered for selection for only one cycle.

Multi-trait genomic selection can increase selection accuracy for deoxynivalenol accumulation resulting from fusarium head blight in wheat.

Multi-trait genomic prediction (MTGP) can improve selection accuracy for economically valuable 'primary' traits by incorporating data on correlated secondary traits. Resistance to Fusarium head blight (FHB), a fungal disease of wheat (Triticum aestivum L.) and barley (Hordeum vulgare L.

Identification of an Elite Core Panel as a Key Breeding Resource to Accelerate the Rate of Genetic Improvement for Irrigated Rice.

Rice genetic improvement is a key component of achieving and maintaining food security in Asia and Africa in the face of growing populations and climate change. In this effort, the International Rice Research Institute (IRRI) continues to play a critical role in creating and disseminating rice varieties with higher productivity. Due to increasing demand for rice, especially in Africa, there is a strong need to accelerate the rate of genetic improvement for grain yield.

Steady agronomic and genetic interventions are essential for sustaining productivity in intensive rice cropping.

Intensive systems with two or three rice ( L.) crops per year account for about 50% of the harvested area for irrigated rice in Asia. Any reduction in productivity or sustainability of these systems has serious implications for global food security.

Genomic Prediction of Yield Traits in Single-Cross Hybrid Rice ( L.).

Hybrid rice varieties can outyield the best inbred varieties by 15 - 30% with appropriate management. However, hybrid rice requires more inputs and management than inbred rice to realize a yield advantage in high-yielding environments. The development of stress-tolerant hybrid rice with lowered input requirements could increase hybrid rice yield relative to production costs.

Establishment method affects rice root plasticity in response to drought and its relationship with grain yield stability.

By responding to the variable soil environments in which they are grown, the roots of rice crops are likely to contribute to yield stability across a range of soil moistures, nutrient levels, and establishment methods. In this study, we explored different approaches to quantification of root plasticity and characterization of its relationship with yield stability. Using four different statistical approaches (plasticity index, slope, AMMI, and factor analytic) on a set of 17 genotypes including several recently-developed breeding lines targeted to dry direct-seeding, we identified only very few direct relationships between root plasticity and yield stability.

Heterosis and Hybrid Crop Breeding: A Multidisciplinary Review.

Although hybrid crop varieties are among the most popular agricultural innovations, the rationale for hybrid crop breeding is sometimes misunderstood. Hybrid breeding is slower and more resource-intensive than inbred breeding, but it allows systematic improvement of a population by recurrent selection and exploitation of heterosis simultaneously. Inbred parental lines can identically reproduce both themselves and their F progeny indefinitely, whereas outbred lines cannot, so uniform outbred lines must be bred indirectly through their inbred parents to harness heterosis.

1k-RiCA (1K-Rice Custom Amplicon) a novel genotyping amplicon-based SNP assay for genetics and breeding applications in rice.

Background: While a multitude of genotyping platforms have been developed for rice, the majority of them have not been optimized for breeding where cost, turnaround time, throughput and ease of use, relative to density and informativeness are critical parameters of their utility. With that in mind we report the development of the 1K-Rice Custom Amplicon, or 1k-RiCA, a robust custom sequencing-based amplicon panel of ~ 1000-SNPs that are uniformly distributed across the rice genome, designed to be highly informative within indica rice breeding pools, and tailored for genomic prediction in elite indica rice breeding programs.

Results: Empirical validation tests performed on the 1k-RiCA showed average marker call rates of 95% with marker repeatability and concordance rates of 99%.

Enhancing the rate of genetic gain in public-sector plant breeding programs: lessons from the breeder's equation.

The integration of new technologies into public plant breeding programs can make a powerful step change in agricultural productivity when aligned with principles of quantitative and Mendelian genetics. The breeder's equation is the foundational application of quantitative genetics to crop improvement. Guided by the variables that describe response to selection, emerging breeding technologies can make a powerful step change in the effectiveness of public breeding programs.

Hyperspectral Reflectance-Derived Relationship Matrices for Genomic Prediction of Grain Yield in Wheat.

Hyperspectral reflectance phenotyping and genomic selection are two emerging technologies that have the potential to increase plant breeding efficiency by improving prediction accuracy for grain yield. Hyperspectral cameras quantify canopy reflectance across a wide range of wavelengths that are associated with numerous biophysical and biochemical processes in plants. Genomic selection models utilize genome-wide marker or pedigree information to predict the genetic values of breeding lines.

High-throughput phenotyping platforms enhance genomic selection for wheat grain yield across populations and cycles in early stage.

Genomic selection (GS) models have been validated for many quantitative traits in wheat (Triticum aestivum L.) breeding. However, those models are mostly constrained within the same growing cycle and the extension of GS to the case of across cycles has been a challenge, mainly due to the low predictive accuracy resulting from two factors: reduced genetic relationships between different families and augmented environmental variances between cycles.

Combining High-Throughput Phenotyping and Genomic Information to Increase Prediction and Selection Accuracy in Wheat Breeding.

Genomics and phenomics have promised to revolutionize the field of plant breeding. The integration of these two fields has just begun and is being driven through big data by advances in next-generation sequencing and developments of field-based high-throughput phenotyping (HTP) platforms. Each year the International Maize and Wheat Improvement Center (CIMMYT) evaluates tens-of-thousands of advanced lines for grain yield across multiple environments.

Genomic Selection in Plant Breeding: Methods, Models, and Perspectives.

Genomic selection (GS) facilitates the rapid selection of superior genotypes and accelerates the breeding cycle. In this review, we discuss the history, principles, and basis of GS and genomic-enabled prediction (GP) as well as the genetics and statistical complexities of GP models, including genomic genotype×environment (G×E) interactions. We also examine the accuracy of GP models and methods for two cereal crops and two legume crops based on random cross-validation.

Comparison of Models and Whole-Genome Profiling Approaches for Genomic-Enabled Prediction of Septoria Tritici Blotch, Stagonospora Nodorum Blotch, and Tan Spot Resistance in Wheat.

The leaf spotting diseases in wheat that include Septoria tritici blotch (STB) caused by , Stagonospora nodorum blotch (SNB) caused by , and tan spot (TS) caused by pose challenges to breeding programs in selecting for resistance. A promising approach that could enable selection prior to phenotyping is genomic selection that uses genome-wide markers to estimate breeding values (BVs) for quantitative traits. To evaluate this approach for seedling and/or adult plant resistance (APR) to STB, SNB, and TS, we compared the predictive ability of least-squares (LS) approach with genomic-enabled prediction models including genomic best linear unbiased predictor (GBLUP), Bayesian ridge regression (BRR), Bayes A (BA), Bayes B (BB), Bayes Cπ (BC), Bayesian least absolute shrinkage and selection operator (BL), and reproducing kernel Hilbert spaces markers (RKHS-M), a pedigree-based model (RKHS-P) and RKHS markers and pedigree (RKHS-MP).

Single-Step Genomic and Pedigree Genotype × Environment Interaction Models for Predicting Wheat Lines in International Environments.

Genomic prediction models have been commonly used in plant breeding but only in reduced datasets comprising a few hundred genotyped individuals. However, pedigree information for an entire breeding population is frequently available, as are historical data on the performance of a large number of selection candidates. The single-step method extends the genomic relationship information from genotyped individuals to pedigree information from a larger number of phenotyped individuals in order to combine relationship information on all members of the breeding population.

Multitrait, Random Regression, or Simple Repeatability Model in High-Throughput Phenotyping Data Improve Genomic Prediction for Wheat Grain Yield.

High-throughput phenotyping (HTP) platforms can be used to measure traits that are genetically correlated with wheat ( L.) grain yield across time. Incorporating such secondary traits in the multivariate pedigree and genomic prediction models would be desirable to improve indirect selection for grain yield.

Genomic and pedigree-based prediction for leaf, stem, and stripe rust resistance in wheat.

Genomic prediction for seedling and adult plant resistance to wheat rusts was compared to prediction using few markers as fixed effects in a least-squares approach and pedigree-based prediction. The unceasing plant-pathogen arms race and ephemeral nature of some rust resistance genes have been challenging for wheat (Triticum aestivum L.) breeding programs and farmers.

Predicting grain yield using canopy hyperspectral reflectance in wheat breeding data.

Background: Modern agriculture uses hyperspectral cameras to obtain hundreds of reflectance data measured at discrete narrow bands to cover the whole visible light spectrum and part of the infrared and ultraviolet light spectra, depending on the camera. This information is used to construct vegetation indices (VI) (e.g.

Wheat quality improvement at CIMMYT and the use of genomic selection on it.

The International Center for Maize and Wheat Improvement (CIMMYT) leads the Global Wheat Program, whose main objective is to increase the productivity of wheat cropping systems to reduce poverty in developing countries. The priorities of the program are high grain yield, disease resistance, tolerance to abiotic stresses (drought and heat), and desirable quality. The Wheat Chemistry and Quality Laboratory has been continuously evolving to be able to analyze the largest number of samples possible, in the shortest time, at lowest cost, in order to deliver data on diverse quality traits on time to the breeders for making selections for advancement in the breeding pipeline.

Survival Following Gastro-Left Ventricular Fistula in a Patient Post Roux-en-Y Gastric Bypass.

We report a case of a 55-year old woman with a prior roux-en-Y gastric bypass who survived after surgical repair of a gastro-left ventricular fistula.

Advances and Challenges in Genomic Selection for Disease Resistance.

Breeding for disease resistance is a central focus of plant breeding programs, as any successful variety must have the complete package of high yield, disease resistance, agronomic performance, and end-use quality. With the need to accelerate the development of improved varieties, genomics-assisted breeding is becoming an important tool in breeding programs. With marker-assisted selection, there has been success in breeding for disease resistance; however, much of this work and research has focused on identifying, mapping, and selecting for major resistance genes that tend to be highly effective but vulnerable to breakdown with rapid changes in pathogen races.