Modeling epistasis in genomic selection is impeded by a high computational load. The extended genomic best linear unbiased prediction (EG-BLUP) with an epistatic relationship matrix and the ...reproducing kernel Hilbert space regression (RKHS) are two attractive approaches that reduce the computational load. In this study, we proved the equivalence of EG-BLUP and genomic selection approaches, explicitly modeling epistatic effects. Moreover, we have shown why the RKHS model based on a Gaussian kernel captures epistatic effects among markers. Using experimental data sets in wheat and maize, we compared different genomic selection approaches and concluded that prediction accuracy can be improved by modeling epistasis for selfing species but may not for outcrossing species.
Increasing wheat yield is a key global challenge to producing sufficient food for a growing human population. Wheat grain yield can be boosted by exploiting heterosis, the superior performance of ...hybrids compared with midparents. Here we present a tailored quantitative genetic framework to study the genetic basis of midparent heterosis in hybrid populations derived from crosses among diverse parents. We applied this framework to an extensive data set assembled for winter wheat. Grain yield was assessed for 1,604 hybrids and their 135 parental elite breeding lines in 11 environments. The hybrids outperformed the midparents by 10% on average, representing approximately 15 years of breeding progress in wheat, thus further substantiating the remarkable potential of hybrid-wheat breeding. Genome-wide prediction and association mapping implemented through the developed quantitative genetic framework showed that dominance effects played a less prominent role than epistatic effects in grain-yield heterosis in wheat.
Genebanks have the long-term mission of preserving plant genetic resources as an agricultural legacy for future crop improvement. Operating procedures for seed storage and plant propagation have been ...in place for decades, but there is a lack of effective means for the discovery and transfer of beneficial alleles from landraces and wild relatives into modern varieties. Here, we review the prospects of using molecular passport data derived from genomic sequence information as a universal monitoring tool at the single-plant level within and between genebanks. Together with recent advances in breeding methodologies, the transformation of genebanks into bio-digital resource centers will facilitate the selection of useful genetic variation and its use in breeding programs, thus providing easy access to past crop diversity. We propose linking catalogs of natural genetic variation and enquiries into biological mechanisms of plant performance as a long-term joint research goal of genebanks, plant geneticists and breeders.
To achieve the food and energy security of an increasing World population likely to exceed nine billion by 2050 represents a major challenge for plant breeding. Our ability to measure traits under ...field conditions has improved little over the last decades and currently constitutes a major bottleneck in crop improvement. This work describes the development of a tractor-pulled multi-sensor phenotyping platform for small grain cereals with a focus on the technological development of the system. Various optical sensors like light curtain imaging, 3D Time-of-Flight cameras, laser distance sensors, hyperspectral imaging as well as color imaging are integrated into the system to collect spectral and morphological information of the plants. The study specifies: the mechanical design, the system architecture for data collection and data processing, the phenotyping procedure of the integrated system, results from field trials for data quality evaluation, as well as calibration results for plant height determination as a quantified example for a platform application. Repeated measurements were taken at three developmental stages of the plants in the years 2011 and 2012 employing triticale (×Triticosecale Wittmack L.) as a model species. The technical repeatability of measurement results was high for nearly all different types of sensors which confirmed the high suitability of the platform under field conditions. The developed platform constitutes a robust basis for the development and calibration of further sensor and multi-sensor fusion models to measure various agronomic traits like plant moisture content, lodging, tiller density or biomass yield, and thus, represents a major step towards widening the bottleneck of non-destructive phenotyping for crop improvement and plant genetic studies.
Bread-making quality traits are central targets for wheat breeding. The objectives of our study were to (1) examine the presence of major effect QTLs for quality traits in a Central European elite ...wheat population, (2) explore the optimal strategy for predicting the hybrid performance for wheat quality traits, and (3) investigate the effects of marker density and the composition and size of the training population on the accuracy of prediction of hybrid performance. In total 135 inbred lines of Central European bread wheat (Triticum aestivum L.) and 1,604 hybrids derived from them were evaluated for seven quality traits in up to six environments. The 135 parental lines were genotyped using a 90k single-nucleotide polymorphism array. Genome-wide association mapping initially suggested presence of several quantitative trait loci (QTLs), but cross-validation rather indicated the absence of major effect QTLs for all quality traits except of 1000-kernel weight. Genomic selection substantially outperformed marker-assisted selection in predicting hybrid performance. A resampling study revealed that increasing the effective population size in the estimation set of hybrids is relevant to boost the accuracy of prediction for an unrelated test population.
Global food security demands the development and delivery of new technologies to increase and secure cereal production on finite arable land without increasing water and fertilizer use. There are ...several options for boosting wheat yields, but most offer only small yield increases. Wheat is an inbred plant, and hybrids hold the potential to deliver a major lift in yield and will open a wide range of new breeding opportunities. A series of technological advances are needed as a base for hybrid wheat programmes. These start with major changes in floral development and architecture to separate the sexes and force outcrossing. Male sterility provides the best method to block self-fertilization, and modifying the flower structure will enhance pollen access. The recent explosion in genomic resources and technologies provides new opportunities to overcome these limitations. This review outlines the problems with existing hybrid wheat breeding systems and explores molecular-based technologies that could improve the hybrid production system to reduce hybrid seed production costs, a prerequisite for a commercial hybrid wheat system.
The genomic relationship matrix plays a key role in the analysis of genetic diversity, genomic prediction, and genome-wide association studies. The epistatic genomic relationship matrix is a natural ...generalization of the classic genomic relationship matrix in the sense that it implicitly models the epistatic effects among all markers. Calculating the exact form of the epistatic relationship matrix requires high computational load, and is hence not feasible when the number of markers is large, or when high-degree of epistasis is in consideration. Currently, many studies use the Hadamard product of the classic genomic relationship matrix as an approximation. However, the quality of the approximation is difficult to investigate in the strict mathematical sense. In this study, we derived iterative formulas for the precise form of the epistatic genomic relationship matrix for arbitrary degree of epistasis including both additive and dominance interactions. The key to our theoretical results is the observation of an interesting link between the elements in the genomic relationship matrix and symmetric polynomials, which motivated the application of the corresponding mathematical theory. Based on the iterative formulas, efficient recursive algorithms were implemented. Compared with the approximation by the Hadamard product, our algorithms provided a complete solution to the problem of calculating the exact epistatic genomic relationship matrix. As an application, we showed that our new algorithms easily relieved the computational burden in a previous study on the approximation behavior of two limit models.
Hybrid breeding promises to boost yield and stability. The single most important element in implementing hybrid breeding is the recognition of a high-yielding heterotic pattern.We have developed a ...three-step strategy for identifying heterotic patterns for hybrid breeding comprising the following elements. First, the full hybrid performance matrix is compiled using genomic prediction. Second, a high-yielding heterotic pattern is searched based on a developed simulated annealing algorithm. Third, the long-term success of the identified heterotic pattern is assessed by estimating the usefulness, selection limit, and representativeness of the heterotic pattern with respect to a defined base population. This three-step approach was successfully implemented and evaluated using a phenotypic and genomic wheat dataset comprising 1,604 hybrids and their 135 parents. Integration of metabolomic-based prediction was not as powerful as genomic prediction. We show that hybrid wheat breeding based on the identified heterotic pattern can boost grain yield through the exploitation of heterosis and enhance recurrent selection gain. Our strategy represents a key step forward in hybrid breeding and is relevant for self-pollinating crops, which are currently shifting from pure-line to high-yielding and resilient hybrid varieties.
ABSTRACT
Accurate prediction of single cross performance is of fundamental importance to increase the selection gain in wheat (Triticum aestivum L.) hybrid breeding programs. We used experimental ...data from a commercial wheat breeding program as well as simulated data sets and evaluated the prospects of predicting grain yield performance of untested hybrids applying different cross‐validation scenarios. We used ridge regression best linear unbiased prediction (RR‐BLUP), BayesA, BayesB, BayesC, and BayesCπ facilitating genomic selection for additive and dominance effects. In total 90 hybrids were evaluated for grain yield in unreplicated trials at four locations. The parental lines were fingerprinted with a 9000 (9k) single nucleotide polymorphism array. We observed in the cross‐validation study high prediction accuracies for all five genomic models with a slight superiority of RR‐BLUP and BayesB. Interestingly, ignoring dominance effects resulted in equal or even higher prediction accuracies. This lack of improvement points toward the need for further refine the genomic selection models to more precisely estimate dominance effects.
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