Large portions of the world's arable acreage experience water stress on a regular basis. Improving crop productivity in such drought‐prone environments is a critical breeding objective. The goal of this study was to detect quantitative trait loci (QTL) associated with alfalfa (Medicago sativa L.) forage productivity during drought stress. Two first‐generation backcross (BC1) mapping populations (n = 253) derived from a cross between M. sativa subsp. sativa and M. sativa subsp. falcata were used to develop an updated tetraploid (2n = 4x = 32) genetic linkage map constructed from 600 single‐dose allele molecular markers. Map lengths associated with the two populations were 1293 and 1049 cM, with an average marker density of 3.8 and 3.9 cM, respectively. Half‐sib families derived from 206 BC1 individuals were evaluated for forage yield in seeded plots in seven water‐stressed environments in New Mexico and Oklahoma, USA. Significant genotype effects were detected within each population and environment. Interval mapping analysis identified 10 and 15 QTL that, respectively, improved or reduced forage yield during drought. Average phenotypic effects of each QTL on biomass yield ranged from 3 to 6% and the direction of these effects were generally consistent over environments. Desirable alleles identified in these parents may be suitable for marker‐aided introgression into elite populations to incrementally improve their forage productivity in water‐limited environments.

Much of the world's population growth will occur in regions where food insecurity is prevalent, with large increases in food demand projected in regions of Africa and South Asia. While improving food security in these regions will require a multi-faceted approach, improved performance of crop varieties in these regions will play a critical role. Current rates of genetic gain in breeding programs serving Africa and South Asia fall below rates achieved in other regions of the world. Given resource constraints, increased genetic gain in these regions cannot be achieved by simply expanding the size of breeding programs. New approaches to breeding are required. The Genomic Open-source Breeding informatics initiative (GOBii) and Excellence in Breeding Platform (EiB) are working with public sector breeding programs to build capacity, develop breeding strategies, and build breeding informatics capabilities to enable routine use of new technologies that can improve the efficiency of breeding programs and increase genetic gains. Simulations evaluating breeding strategies indicate cost-effective implementations of genomic selection (GS) are feasible using relatively small training sets, and proof-of-concept implementations have been validated in the International Maize and Wheat Improvement Center (CIMMYT) maize breeding program. Progress on GOBii, EiB, and implementation of GS in CIMMYT and International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) breeding programs are discussed, as well as strategies for routine implementation of GS in breeding programs serving Africa and South Asia.

High-throughput image-phenotyping promises to accelerate the rate of genetic improvement in plant breeding through varietal selections informed by longitudinal growth models. To facilitate routine analyses and to drive breeding decisions, data integration is critical for effective management of germplasm, field experiment design, phenotyping, tissue sampling, genotyping, aerial-phenotyping campaigns, image files, and geo-spatial information. To this end, ImageBreed provides a software solution for end-to-end image-based phenotyping integrated into the Breedbase plant breeding system. ImageBreed provides open-source orthophotomosaic construction for raw image captures from standard color cameras and from the MicaSense Red-Edge multispectral camera. Additionally, previously assembled orthophotomosaic raster images can be uploaded. Orthophotomosaic images allow for streamlined extraction of plot-polygon images; however, ImageBreed plot-polygon images can also be extracted directly from raw aerial image captures. A web-database interface streamlines assignment of plot-polygon images from the orthophotomosaic or raw aerial-captures to the field experiment design. Image processes spanning Fourier-transform filtering, thresholding, and vegetation index masking are applied to reduce noise in extracted phenotypes. Summary-statistic phenotypic values are extracted for every observed plot-polygon image using a structured ontology. Plot-polygon images are queryable against genotypic, phenotypic, and experimental design information for training of machine learning models and Abbreviations: API, application programming interface; CNN, convolutional neural network; FT-HPF, Fourier transform high-pass filter; FT-LPF, Fourier transform low-pass filter; ND, Chado Natural Diversity Database Schema; NDRE, normalized difference red-edge vegetation index; NDVI, normalized difference vegetation index; TGI, triangular greenness index; UAV, unoccupied aerial vehicle; VARI, visible atmospherically resistant index. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

1AbstractWhole genome duplications have played an important role in the evolution of angiosperms. These events often occur through hybridization between closely related species, resulting in an allopolyploid with multiple subgenomes. With the availability of affordable genotyping and a reference genome to locate markers, breeders of allopolyploids now have the opportunity to manipulate subgenomes independently. This also presents a unique opportunity to investigate epistatic interactions between homeologous orthologs across subgenomes. We present a statistical framework for partitioning genetic variance to the subgenomes of an allopolyploid, predicting breeding values for each subgenome, and determining the importance of inter-genomic epistasis. We demonstrate using an allohexaploid wheat breeding population evaluated in Ithaca, NY and an important wheat dataset previously shown to demonstrate non-additive genetic variance. Subgenome covariance matrices were constructed and used to calculate subgenome interaction covariance matrices across subgenomes for variance component estimation and genomic prediction. We propose a method to extract population structure from all subgenomes at once before covariances are calculated to reduce collinearity between subgenome estimates. Variance parameter estimation was shown to be reliable for additive subgenome effects, but was less reliable for subgenome interaction components. Predictive ability was equivalent to current genomic prediction methods. Including only inter-genomic interactions resulted in the same increase in accuracy as modeling all pairwise marker interactions. Thus, we provide a new tool for breeders of allopolyploid crops to characterize the genetic architecture of existing populations, determine breeding goals, and develop new strategies for selection of additive effects and fixation of inter-genomic epistasis.

Hybridization between related species results in the formation of an allopolyploid with multiple subgenomes. These subgenomes will each contain complete, yet evolutionarily divergent, sets of genes. Like a diploid hybrid, allopolyploids will have two versions, or homeoalleles, for every gene. Partial functional redundancy between homeologous genes should result in a deviation from additivity. These epistatic interactions between homeoalleles are analogous to dominance effects, but are fixed across subgenomes through self pollination. An allopolyploid can be viewed as an immortalized hybrid, with the opportunity to select and fix favorable homeoallelic interactions within inbred varieties. We present a subfunctionalization epistasis model to estimate the degree of functional redundancy between homeoallelic loci and a statistical framework to determine their importance within a population. We provide an example using the homeologous dwarfing genes of allohexaploid wheat, Rht-1, and search for genome-wide patterns indicative of homeoallelic subfunctionalization in a breeding population. Using the IWGSC RefSeq vl.0 sequence, 23,796 homeoallelic gene sets were identified and anchored to the nearest DNA marker to form 10,172 homeologous marker sets. Interaction predictors constructed from products of marker scores were used to fit the homeologous main and interaction effects, as well as estimate whole genome genetic values. Some traits displayed a pattern indicative of homeoallelic subfunctionalization, while other traits showed a less clear pattern or were not affected. Using genomic prediction accuracy to evaluate importance of marker interactions, we show that homeologous interactions explain a portion of the non-additive genetic signal, but are less important than other epistatic interactions.

Whole genome duplications have played an important role in the evolution of angiosperms. These events often occur through hybridization between closely related species, resulting in an allopolyploid with multiple subgenomes. With the availability of affordable genotyping and a reference genome to locate markers, breeders of allopolyploids now have the opportunity to manipulate subgenomes independently. This also presents a unique opportunity to investigate epistatic interactions between homeologous orthologs across subgenomes. We present a statistical framework for partitioning genetic variance to the subgenomes of an allopolyploid, predicting breeding values for each subgenome, and determining the importance of inter-genomic epistasis. We demonstrate using an allohexaploid wheat breeding population evaluated in Ithaca, NY and an important wheat dataset from CIMMYT previously shown to demonstrate non-additive genetic variance. Subgenome covariance matrices were constructed and used to calculate subgenome interaction covariance matrices for variance component estimation and genomic prediction. We propose a method to extract population structure from all subgenomes at once before covariances are calculated to reduce collinearity between subgenome estimates. Variance parameter estimation was shown to be reliable for additive subgenome effects, but was less reliable for subgenome interaction components. Predictive ability was equivalent to current genomic prediction methods. Including only inter-genomic interactions resulted in the same increase in accuracy as modeling all pairwise marker interactions. Thus, we provide a new tool for breeders of allopolyploid crops to characterize the genetic architecture of existing populations, determine breeding goals, and develop new strategies for selection of additive effects and fixation of inter-genomic epistasis.

Fructans are naturally occurring plant polymers composed of fructose molecules. Approximately 15% of flowering plant species contain fructans, including wheat (Triticum aestivum L.). Fructans serve as carbon stores in plants and exhibit potentially beneficial effects on human health. The main objectives of this study were to examine the effects of genotype and environment on winter wheat grain fructan content and to assess the feasibility of using genomic selection for grain fructan content. Total grain fructan content was determined for 288 winter wheat genotypes grown across 2 yr at three locations each year. Observed variation in wheat grain fructan content was significantly influenced by genotype, environment, and genotype × environment interactions. The high genetic correlation, small impact of genotype × environment interactions on genomic predictability, and lack of significant hits in a genome‐wide association study suggest that genomic selection is a suitable tool in breeding for wheat grain fructan content. The results of this study will be useful for implementing recurrent genomic selection in winter wheat and guiding future decisions regarding breeding methodologies for total fructan content in wheat. This study provides a deeper understanding of the effects of genotype, environment, and genotype × environment interaction on fructan content, which will have implications for breeders seeking to develop nutritionally improved, climate‐resilient wheat cultivars.

Hybridization between related species results in the formation of an allopolyploid with multiple subgenomes. These subgenomes will each contain complete, yet evolutionarily divergent, sets of genes. Like a diploid hybrid, allopolyploids will have two versions, or homeoalleles, for every gene. Partial functional redundancy between homeologous genes should result in a deviation from additivity. These epistatic interactions between homeoalleles are analogous to dominance effects, but are fixed across subgenomes through self pollination. An allopolyploid can be viewed as an immortalized hybrid, with the opportunity to select and fix favorable homeoallelic interactions within inbred varieties. We present a subfunctionalization epistasis model to estimate the degree of functional redundancy between homeoallelic loci and a statistical framework to determine their importance within a population. We provide an example using the homeologous dwarfing genes of allohexaploid wheat, Rht-1, and search for genome-wide patterns indicative of homeoallelic subfunctionalization in a breeding population. Using the IWGSC RefSeq v1.0 sequence, 23,796 homeoallelic gene sets were identified and anchored to the nearest DNA marker to form 10,172 homeologous marker sets. Interaction predictors constructed from products of marker scores were used to fit the homeologous main and interaction effects, as well as estimate whole genome genetic values. Some traits displayed a pattern indicative of homeoallelic subfunctionalization, while other traits showed a less clear pattern or were not affected. Using genomic prediction accuracy to evaluate importance of marker interactions, we show that homeologous interactions explain a portion of the nonadditive genetic signal, but are less important than other epistatic interactions.