Establishment and Optimization of Genomic Selection to Accelerate the Domestication and Improvement of Intermediate Wheatgrass

Diversity of cereal rye (Secale cereale) germplasm in the Southeast United States

Cereal rye (Secale cereale) is a grain, forage, and cover crop, with specific regional production practices. Maintaining regional varieties is challenged by rye reproductive biology, as wind pollination may dilute varietal distinction. Similarly, breeding new population varieties for regional needs lacks efficiency gains seen in other grains. We sought to address the challenges in maintaining and breeding rye population varieties in the Southeastern United States, where rye is a forage and cover crop, with emerging grain markets. Genetic and phenotypic variation within and between populations was characterized to determine varietal differentiation and test the efficacy of genomic selection for population improvement. Using 15 Southeastern rye accessions and eight breeding populations, we genotyped and phenotyped more than 500 individuals and found that most phenotypic and genetic variation is within rather than among accessions. Latitude of variety source, but not end use, contributed to intervarietal variation for heading date, but not traits associated with seed yield. Genomic prediction accuracy was moderately high (r > 0.3) for most traits, but within-population prediction accuracy was more variable, with only some populations having nonzero within-population prediction accuracy. This work establishes the inter- and intravarietal differentiation in Southeastern rye accessions, and demonstrates viability of genomics-enabled population improvement for regional varieties.

Improving complex agronomic and domestication traits in the perennial grain crop intermediate wheatgrass with genetic mapping and genomic prediction

The perennial grass Thinopyrum intermedium (intermediate wheatgrass [IWG]) is being domesticated as a food crop. With a deep root system and high biomass, IWG can help reduce soil and water erosion and limit nutrient runoff. As a novel grain crop undergoing domestication, IWG lags in yield, seed size, and other agronomic traits compared to annual grains. Better characterization of trait variation and identification of genetic markers associated with loci controlling the traits could help in further improving this crop. The University of Minnesota's Cycle 5 IWG breeding population of 595 spaced plants was evaluated at two locations in 2021 and 2022 for agronomic traits plant height, grain yield, and spike weight, and domestication traits shatter resistance, free grain threshing, and seed size. Pairwise trait correlations were weak to moderate with the highest correlation observed between seed size and height (0.41). Broad-sense trait heritabilities were high (0.68-0.77) except for spike weight (0.49) and yield (0.44). Association mapping using 24,284 genome-wide single nucleotide polymorphism markers identified 30 main quantitative trait loci (QTLs) across all environments and 32 QTL-by-environment interactions (QTE) at each environment. The genomic prediction model significantly improved predictions when parents were used in the training set and significant QTLs and QTEs used as covariates. Seed size was the best predicted trait with model predictive ability (r) of 0.72; yield was predicted moderately well (r = 0.45). We expect this discovery of significant genomic loci and mostly high trait predictions from genomic prediction models to help improve future IWG breeding populations.

Genetic constitution and variability in synthetic populations of intermediate wheatgrass, an outcrossing perennial grain crop

Intermediate wheatgrass (IWG) is a perennial grass that produces nutritious grain while offering substantial ecosystem services. Commercial varieties of this crop are mostly synthetic panmictic populations that are developed by intermating a few selected individuals. As development and generation advancement of these synthetic populations is a multiyear process, earlier synthetic generations are tested by the breeders and subsequent generations are released to the growers. A comparison of generations within IWG synthetic cultivars is currently lacking. In this study, we used simulation models and genomic prediction to analyze population differences and trends of genetic variance in 4 synthetic generations of MN-Clearwater, a commercial cultivar released by the University of Minnesota. Little to no differences were observed among the 4 generations for population genetic, genetic kinship, and genome-wide marker relationships measured via linkage disequilibrium. A reduction in genetic variance was observed when 7 parents were used to generate synthetic populations while using 20 led to the best possible outcome in determining population variance. Genomic prediction of plant height, free threshing ability, seed mass, and grain yield among the 4 synthetic generations showed a few significant differences among the generations, yet the differences in values were negligible. Based on these observations, we make 2 major conclusions: (1) the earlier and latter synthetic generations of IWG are mostly similar to each other with minimal differences and (2) using 20 genotypes to create synthetic populations is recommended to sustain ample genetic variance and trait expression among all synthetic generations.

Review of applications of artificial intelligence (AI) methods in crop research

Sophisticated and modern crop improvement techniques can bridge the gap for feeding the ever-increasing population. Artificial intelligence (AI) refers to the simulation of human intelligence in machines, which refers to the application of computational algorithms, machine learning (ML) and deep learning (DL) techniques. This is aimed to generalise patterns and relationships from historical data, employing various mathematical optimisation techniques thus making prediction models for facilitating selection of superior genotypes. These techniques are less resource intensive and can solve the problem based on the analysis of large-scale phenotypic datasets. ML for genomic selection (GS) uses high-throughput genotyping technologies to gather genetic information on a large number of markers across the genome. The prediction of GS models is based on the mathematical relation between genotypic and phenotypic data from the training population. ML techniques have emerged as powerful tools for genome editing through analysing large-scale genomic data and facilitating the development of accurate prediction models. Precise phenotyping is a prerequisite to advance crop breeding for solving agricultural production-related issues. ML algorithms can solve this problem through generating predictive models, based on the analysis of large-scale phenotypic datasets. DL models also have the potential reliability of precise phenotyping. This review provides a comprehensive overview on various ML and DL models, their applications, potential to enhance the efficiency, specificity and safety towards advanced crop improvement protocols such as genomic selection, genome editing, along with phenotypic prediction to promote accelerated breeding.

Plant Biotechnology-An Indispensable Tool for Crop Improvement

Traditional plant breeding has helped to increase food production dramatically over the past five decades, and many countries have managed to produce enough food for the growing population, particularly in the developing world [...].

The improvement of agronomic performances in the cold weather conditions for perennial wheatgrass by crossing Thinopyrum intermedium with wheat-Th. intermedium partial amphiploids

Thinopyrum intermedium (2n=6x=42, StStJrJrJvsJvs) is resistant or tolerant to biotic and abiotic stresses, making it suitable for developing perennial crops and forage. Through five cycles of selection, we developed 24 perennial wheatgrass lines, designated 19HSC-Q and 20HSC-Z, by crossing wheat-Th. intermedium partial amphiploids with Th. intermedium. The cold resistance, morphological performance, chromosome composition, and yield components of these perennial lines were investigated from 2019 to 2022. Six lines of 19HSC-Q had higher 1,000-kernel weight, grains per spike, and tiller number than Th. intermedium, as well as surviving -30°C in winter. Lines 19HSC-Q14, 19HSC-Q18, and 19HSC-Q20 had the best performances for grain number per spike and 1,000-kernel weight. The 20HSC-Z lines, 20HSC-Z1, 20HSC-Z2, and 20HSC-Z3, were able to survive in the cold winter in Harbin and had been grown for two years. Sequential multicolor GISH analysis revealed that the Jvs subgenome of Th. intermedium were divided into two karyotypes, three pairs of type-I Jvs chromosomes and four pairs of type-II Jvs chromosomes. Both Th. intermedium and the 24 advanced perennial wheatgrass lines had similar chromosome compositions, but the translocations among subgenome chromosomes were detected in some lines with prominent agronomic traits, such as 19HSC-Q11, 19HSC-Q14, 19HSC-Q18, 19HSC-Q20, and the three 20HSC-Z lines. The chromosome aberrations were distinguished into two types: the large fragment translocation with St-Jr, Jvs-St, Jr-IIJvs, and Jvs-Jr and the small fragment introgression of Jr-St, St-IJvs, and Jvs-Jr. These chromosomal variations can be used to further analyze the relationship between the subgenomes and phenotypes of Th. intermedium. The results of this study provide valuable materials for the next selection cycle of cold-resistant perennial wheatgrass.

Origin of current intermediate wheatgrass germplasm being developed for Kernza grain production

Intermediate wheatgrass (IWG, Thinopyrum intermedium [Host] Barkworth & D. R. Dewey) has been developed as a perennial grain crop to provide ecosystem services, environmental benefits, and human food. Grain and products derived from IWG varieties improved for food production have been marketed under the registered trademark, Kernza. In the 1980s, a joint breeding effort between the Rodale Institute (RI) and the Big Flats Plant Material Center used IWG plant introductions (PI) from the National Plant Germplasm System (NPGS) and recurrent phenotypic selection to improve populations of IWG with the goal of developing a perennial grain. Initial selections were provided to The Land Institute where they were subsequently improved for grain production, yet the identity of the founder material of improved, food-grade IWG has not been publicly documented. Recently recovered original documents have been used to reconstruct the early breeding program to identify the most likely 20 PIs that form the founders of modern food-grade IWG. Molecular data using genotyping-by-sequencing in current elite breeding material, remnant seed from the initial RI selections, and preserved sample material have provided supporting evidence for the historical records. The genetic origin for food-grade IWG is focused between the Black Sea and Caspian Sea in the Stavropol region of Russia, with smaller contributions likely from collections as distant as Kazakhstan in the east to Turkey in the west. This work connects the flow of germplasm and utility of NPGS PIs to present day IWG grain cultivars being developed in multiple breeding programs around the world.

Crop adaptation to climate change: An evolutionary perspective

The disciplines of evolutionary biology and plant and animal breeding have been intertwined throughout their development, with responses to artificial selection yielding insights into the action of natural selection and evolutionary biology providing statistical and conceptual guidance for modern breeding. Here we offer an evolutionary perspective on a grand challenge of the 21st century: feeding humanity in the face of climate change. We first highlight promising strategies currently under way to adapt crops to current and future climate change. These include methods to match crop varieties with current and predicted environments and to optimize breeding goals, management practices, and crop microbiomes to enhance yield and sustainable production. We also describe the promise of crop wild relatives and recent technological innovations such as speed breeding, genomic selection, and genome editing for improving environmental resilience of existing crop varieties or for developing new crops. Next, we discuss how methods and theory from evolutionary biology can enhance these existing strategies and suggest novel approaches. We focus initially on methods for reconstructing the evolutionary history of crops and their pests and symbionts, because such historical information provides an overall framework for crop-improvement efforts. We then describe how evolutionary approaches can be used to detect and mitigate the accumulation of deleterious mutations in crop genomes, identify alleles and mutations that underlie adaptation (and maladaptation) to agricultural environments, mitigate evolutionary trade-offs, and improve critical proteins. Continuing feedback between the evolution and crop biology communities will ensure optimal design of strategies for adapting crops to climate change.

A high-throughput skim-sequencing approach for genotyping, dosage estimation and identifying translocations

The development of next-generation sequencing (NGS) enabled a shift from array-based genotyping to directly sequencing genomic libraries for high-throughput genotyping. Even though whole-genome sequencing was initially too costly for routine analysis in large populations such as breeding or genetic studies, continued advancements in genome sequencing and bioinformatics have provided the opportunity to capitalize on whole-genome information. As new sequencing platforms can routinely provide high-quality sequencing data for sufficient genome coverage to genotype various breeding populations, a limitation comes in the time and cost of library construction when multiplexing a large number of samples. Here we describe a high-throughput whole-genome skim-sequencing (skim-seq) approach that can be utilized for a broad range of genotyping and genomic characterization. Using optimized low-volume Illumina Nextera chemistry, we developed a skim-seq method and combined up to 960 samples in one multiplex library using dual index barcoding. With the dual-index barcoding, the number of samples for multiplexing can be adjusted depending on the amount of data required, and could be extended to 3,072 samples or more. Panels of doubled haploid wheat lines (Triticum aestivum, CDC Stanley x CDC Landmark), wheat-barley (T. aestivum x Hordeum vulgare) and wheat-wheatgrass (Triticum durum x Thinopyrum intermedium) introgression lines as well as known monosomic wheat stocks were genotyped using the skim-seq approach. Bioinformatics pipelines were developed for various applications where sequencing coverage ranged from 1 × down to 0.01 × per sample. Using reference genomes, we detected chromosome dosage, identified aneuploidy, and karyotyped introgression lines from the skim-seq data. Leveraging the recent advancements in genome sequencing, skim-seq provides an effective and low-cost tool for routine genotyping and genetic analysis, which can track and identify introgressions and genomic regions of interest in genetics research and applied breeding programs.

Genomic-environmental associations in wild cranberry (Vaccinium macrocarpon Ait.)

Understanding the genetic basis of local adaptation in natural plant populations, particularly crop wild relatives, may be highly useful for plant breeding. By characterizing genetic variation for adaptation to potentially stressful environmental conditions, breeders can make targeted use of crop wild relatives to develop cultivars for novel or changing environments. This is especially appealing for improving long-lived woody perennial crops such as the American cranberry (Vaccinium macrocarpon Ait.), the cultivation of which is challenged by biotic and abiotic stresses. In this study, we used environmental association analyses in a collection of 111 wild cranberry accessions to identify potentially adaptive genomic regions for a range of bioclimatic and soil conditions. We detected 126 significant associations between SNP marker loci and environmental variables describing temperature, precipitation, and soil attributes. Many of these markers tagged genes with functional annotations strongly suggesting a role in adaptation to biotic or abiotic conditions. Despite relatively low genetic variation in cranberry, our results suggest that local adaptation to divergent environments is indeed present, and the identification of potentially adaptive genetic variation may enable a selective use of this germplasm for breeding more stress-tolerant cultivars.

Advances in integrated genomic selection for rapid genetic gain in crop improvement: a review

Genomic selection and its importance in crop breeding. Integration of GS with new breeding tools and developing SOP for GS to achieve maximum genetic gain with low cost and time. The success of conventional breeding approaches is not sufficient to meet the demand of a growing population for nutritious food and other plant-based products. Whereas, marker assisted selection (MAS) is not efficient in capturing all the favorable alleles responsible for economic traits in the process of crop improvement. Genomic selection (GS) developed in livestock breeding and then adapted to plant breeding promised to overcome the drawbacks of MAS and significantly improve complicated traits controlled by gene/QTL with small effects. Large-scale deployment of GS in important crops, as well as simulation studies in a variety of contexts, addressed G × E interaction effects and non-additive effects, as well as lowering breeding costs and time. The current study provides a complete overview of genomic selection, its process, and importance in modern plant breeding, along with insights into its application. GS has been implemented in the improvement of complex traits including tolerance to biotic and abiotic stresses. Furthermore, this review hypothesises that using GS in conjunction with other crop improvement platforms accelerates the breeding process to increase genetic gain. The objective of this review is to highlight the development of an appropriate GS model, the global open source network for GS, and trans-disciplinary approaches for effective accelerated crop improvement. The current study focused on the application of data science, including machine learning and deep learning tools, to enhance the accuracy of prediction models. Present study emphasizes on developing plant breeding strategies centered on GS combined with routine conventional breeding principles by developing GS-SOP to achieve enhanced genetic gain.

Genetic architecture and QTL selection response for Kernza perennial grain domestication traits

Analysis of multi-year breeding program data revealed that the genetic architecture of an intermediate wheatgrass population was highly polygenic for both domestication and agronomic traits, supporting the use of genomic selection for new crop domestication. Perennial grains have the potential to provide food for humans and decrease the negative impacts of annual agriculture. Intermediate wheatgrass (IWG, Thinopyrum intermedium, Kernza®) is a promising perennial grain candidate that The Land Institute has been breeding since 2003. We evaluated four consecutive breeding cycles of IWG from 2016 to 2020 with each cycle containing approximately 1100 unique genets. Using genotyping-by-sequencing markers, quantitative trait loci (QTL) were mapped for 34 different traits using genome-wide association analysis. Combining data across cycles and years, we found 93 marker-trait associations for 16 different traits, with each association explaining 0.8-5.2% of the observed phenotypic variance. Across the four cycles, only three QTL showed an F_ST differentiation > 0.15 with two corresponding to a decrease in floret shattering. Additionally, one marker associated with brittle rachis was 216 bp from an ortholog of the btr2 gene. Power analysis and quantitative genetic theory were used to estimate the effective number of QTL, which ranged from a minimum of 33 up to 558 QTL for individual traits. This study suggests that key agronomic and domestication traits are under polygenic control and that molecular methods like genomic selection are needed to accelerate domestication and improvement of this new crop.

Perennials as Future Grain Crops: Opportunities and Challenges

Perennial grain crops could make a valuable addition to sustainable agriculture, potentially even as an alternative to their annual counterparts. The ability of perennials to grow year after year significantly reduces the number of agricultural inputs required, in terms of both planting and weed control, while reduced tillage improves soil health and on-farm biodiversity. Presently, perennial grain crops are not grown at large scale, mainly due to their early stages of domestication and current low yields. Narrowing the yield gap between perennial and annual grain crops will depend on characterizing differences in their life cycles, resource allocation, and reproductive strategies and understanding the trade-offs between annualism, perennialism, and yield. The genetic and biochemical pathways controlling plant growth, physiology, and senescence should be analyzed in perennial crop plants. This information could then be used to facilitate tailored genetic improvement of selected perennial grain crops to improve agronomic traits and enhance yield, while maintaining the benefits associated with perennialism.

Optimizing the extrusion conditions for the production of expanded intermediate wheatgrass (Thinopyrum intermedium) products

Abstract

In this study, the effects of extrusion conditions such as feed moisture content (20%, 24%, and 28%), screw speed (200, 300, and 400 rpm), and extrusion temperature (130, 150, and 170°C) on the physical and functional properties (moisture content, expansion ratio, bulk density, hardness, water absorption index [WAI], water solubility index [WSI]) of intermediate wheatgrass (IWG) were investigated for the first time. Response surface methodology was used to model and optimize the extrusion conditions to produce expanded IWG. The model coefficient of determination (R²) was high for all the responses (0.87–0.98). All the models were found to be significant (p < 0.05) and were validated with independent experiments. Generally, all the extrusion conditions were found to have significant effects on the IWG properties measured. Increasing the screw speed and decreasing the extrusion temperature resulted in IWG extrudates with a high expansion ratio. This also resulted in IWG extrudates with generally low hardness and bulk density. Screw speed was found to have the most significant effect on the WAI and WSI, with increasing screw speed resulting in a significant (p < 0.05) decrease in WAI and a significant (p < 0.05) increase in WSI. The optimum conditions for obtaining an IWG extrudate with a high expansion ratio and WAI were found to be 20% feed moisture, 200 –356 rpm screw speed, and 130–154°C extrusion temperature.

Practical Application

Extrusion cooking was employed in the production of expanded IWG. This research could provide a foundation to produce expanded IWG, which can potentially be used as breakfast cereals and snacks. This is critical in the efforts to commercialize IWG for mainstream food applications.

Genome-wide association mapping and genomic prediction for kernel color traits in intermediate wheatgrass (Thinopyrum intermedium)

BACKGROUND

Intermediate wheatgrass (IWG) is a novel perennial grain crop currently undergoing domestication. It offers important ecosystem benefits while producing grain suitable for human consumption. Several aspects of plant biology and genetic control are yet to be studied in this new crop. To understand trait behavior and genetic characterization of kernel color in IWG breeding germplasm from the University of Minnesota was evaluated for the CIELAB components (L*, a*, b*) and visual differences. Trait values were used in a genome-wide association scan to reveal genomic regions controlling IWG's kernel color. The usability of genomic prediction in predicting kernel color traits was also evaluated using a four-fold cross validation method.

RESULTS

A wide phenotypic variation was observed for all four kernel color traits with pairwise trait correlations ranging from - 0.85 to 0.27. Medium to high estimates of broad sense trait heritabilities were observed and ranged from 0.41 to 0.78. A genome-wide association scan with single SNP markers detected 20 significant marker-trait associations in 9 chromosomes and 23 associations in 10 chromosomes using multi-allelic haplotype blocks. Four of the 20 significant SNP markers and six of the 23 significant haplotype blocks were common between two or more traits. Evaluation of genomic prediction of kernel color traits revealed the visual score to have highest mean predictive ability (r² = 0.53); r² for the CIELAB traits ranged from 0.29-0.33. A search for candidate genes led to detection of seven IWG genes in strong alignment with MYB36 transcription factors from other cereal crops of the Triticeae tribe. Three of these seven IWG genes had moderate similarities with R-A1, R-B1, and R-D1, the three genes that control grain color in wheat.

CONCLUSIONS

We characterized the distribution of kernel color in IWG for the first time, which revealed a broad phenotypic diversity in an elite breeding germplasm. Identification of genetic loci controlling the trait and a proof-of-concept that genomic selection might be useful in selecting genotypes of interest could help accelerate the breeding of this novel crop towards specific end-use.

Influence of Pollen Dispersal and Mating Pattern in Domestication of Intermediate Wheatgrass, a Novel Perennial Food Crop

Intermediate wheatgrass (IWG) is a perennial forage grass that is currently being domesticated as a grain crop. It is a primarily wind-pollinated outcrossing species and expresses severe inbreeding depression when self-pollinated. Characterization of pollen dispersal, mating parameters, and change in genetic diversity due to pollen movement is currently lacking in IWG. In this study, we examined pollen dispersal in an IWG selection nursery by evaluating 846 progeny from 15 mother plants and traced their parentage to 374 fathers. A set of 2,500 genomic loci was used to characterize the population. We assigned paternity to 769 (91%) progeny and the average number of fathers per mother plant was 37, from an average of 56 progeny examined per mother. An extensive number (80%) of pollination events occurred within 10 m of the mother plants. Pollination success was not correlated with trait attributes of the paternal genotypes. Mating system analysis confirmed that IWG is highly outcrossing and inbreeding was virtually absent. Neither genetic diversity nor the genome-estimated trait values of progeny were significantly affected by pollinator distance. The distance of pollinator in an IWG breeding nursery therefore was not found to be a major contributor in maintaining genetic diversity. These findings reveal the pollen dispersal model in IWG for the first time and its effect on genetic diversity, which will be valuable in designing future IWG breeding populations. Information generated and discussed in this study could be applied in understanding gene flow and genetic diversity of other open-pollinated species.

Supporting in situ conservation of the genetic diversity of crop wild relatives using genomic technologies

The last decade has witnessed huge technological advances in genomics, particularly in DNA sequencing. Here, we review the actual and potential application of genomics in supporting in situ conservation of crop wild relatives (CWRs). In addition to helping in prioritization of protection of CWR taxa and in situ conservation sites, genome analysis is allowing the identification of novel alleles that need to be prioritized for conservation. Genomics is enabling the identification of potential sources of important adaptive traits that can guide the establishment or enrichment of in situ genetic reserves. Genomic tools also have the potential for developing a robust framework for monitoring and reporting genome‐based indicators of genetic diversity changes associated with factors such as land use or climate change. These tools have been demonstrated to have an important role in managing the conservation of populations, supporting sustainable access and utilization of CWR diversity, enhancing accelerated domestication of new crops and forensic genomics thus preventing misappropriation of genetic resources. Despite this great potential, many policy makers and conservation managers have failed to recognize and appreciate the need to accelerate the application of genomics to support the conservation and management of biodiversity in CWRs to underpin global food security. Funding and inadequate genomic expertise among conservation practitioners also remain major hindrances to the widespread application of genomics in conservation.

Re-imagining crop domestication in the era of high throughput phenomics

De novo domestication is an exciting option for increasing species diversity and ecosystem service functionality of agricultural landscapes. Genomic selection (GS), the application of genomic markers to predict phenotypic traits in a breeding population, offers the possibility of rapid genetic improvement, making GS especially attractive for modifying traits of long-lived species. However, for some wild species just entering the domestication pipeline, especially those with large and complex genomes, a lack of funding and/or prior genome characterization, GS is often out of reach. High throughput phenomics has the potential to augment traditional pedigree selection, reduce costs and amplify impacts of genomic selection, and even create new predictive selection approaches independent of sequencing or pedigrees.

QTL for seed shattering and threshability in intermediate wheatgrass align closely with well-studied orthologs from wheat, barley, and rice

Perennial grain crops have the potential to improve agricultural sustainability but few existing species produce sufficient grain yield to be economically viable. The outcrossing, allohexaploid, and perennial forage species intermediate wheatgrass (IWG) [Thinopyrum intermedium (Host) Barkworth & D. R. Dewey] has shown promise in undergoing direct domestication as a perennial grain crop using phenotypic and genomic selection. However, decades of selection will be required to achieve yields on par with annual small-grain crops. Marker-aided selection could accelerate progress if important genomic regions associated with domestication were identified. Here we use the IWG nested association mapping (NAM) population, with 1,168 F₁ progeny across 10 families to dissect the genetic control of brittle rachis, floret shattering, and threshability. We used a genome-wide association study (GWAS) with 8,003 single nucleotide polymorphism (SNP) markers and linkage mapping-both within-family and combined across families-with a robust phenotypic dataset collected from four unique year-by-location combinations. A total of 29 quantitative trait loci (QTL) using GWAS and 20 using the combined linkage analysis were detected, and most large-effect QTL were in common across the two analysis methods. We reveal that the genetic control of these traits in IWG is complex, with significant QTL across multiple chromosomes, sometimes within and across homoeologous groups and effects that vary depending on the family. In some cases, these QTL align within 216 bp to 31 Mbp of BLAST hits for known domestication genes in related species and may serve as precise targets of selection and directions for further study to advance the domestication of IWG.

Nested association mapping reveals the genetic architecture of spike emergence and anthesis timing in intermediate wheatgrass

Intermediate wheatgrass (Thinopyrum intermedium) is an outcrossing, cool season grass species currently undergoing direct domestication as a perennial grain crop. Though many traits are selection targets, understanding the genetic architecture of those important for local adaptation may accelerate the domestication process. Nested association mapping (NAM) has proven useful in dissecting the genetic control of agronomic traits many crop species, but its utility in primarily outcrossing, perennial species has yet to be demonstrated. Here, we introduce an intermediate wheatgrass NAM population developed by crossing ten phenotypically divergent donor parents to an adapted common parent in a reciprocal manner, yielding 1,168 F₁ progeny from 10 families. Using genotyping by sequencing, we identified 8,003 SNP markers and developed a population-specific consensus genetic map with 3,144 markers across 21 linkage groups. Using both genomewide association mapping and linkage mapping combined across and within families, we characterized the genetic control of flowering time. In the analysis of two measures of maturity across four separate environments, we detected as many as 75 significant QTL, many of which correspond to the same regions in both analysis methods across 11 chromosomes. The results demonstrate a complex genetic control that is variable across years, locations, traits, and within families. The methods were effective at detecting previously identified QTL, as well as new QTL that align closely to the well-characterized flowering time orthologs from barley, including Ppd-H1 and Constans. Our results demonstrate the utility of the NAM population for understanding the genetic control of flowering time and its potential for application to other traits of interest.

Development of whole-genome prediction models to increase the rate of genetic gain in intermediate wheatgrass (Thinopyrum intermedium) breeding

The development of perennial grain crops is driven by the vision of simultaneous food production and enhanced ecosystem services. Typically, perennial crops like intermediate wheatgrass (IWG)[Thinopyrum intermedium (Host) Barkworth & D.R Dewey] have low seed yield and other detrimental traits. Next-generation sequencing has made genomic selection (GS) a tractable and viable breeding method. To investigate how an IWG breeding program may use GS, we evaluated 3,658 genets over 2 yr for 46 traits to build a training population. Six statistical models were used to evaluate the non-replicated data, and a model using autoregressive order 1 (AR1) spatial correction for rows and columns combined with the genomic relationship matrix provided the highest estimates of heritability. Genomic selection models were built from 18,357 single nucleotide polymorphism markers via genotyping-by-sequencing, and a 20-fold cross-validation showed high predictive ability for all traits (r > .80). Predictive abilities improved with increased training population size and marker numbers, even with larger amounts of missing data per marker. On the basis of these results, we propose a GS breeding method that is capable of completing one cycle per year compared with a minimum of 2 yr per cycle with phenotypic selection. We estimate that this breeding approach can increase the rate of genetic gain up to 2.6× above phenotypic selection for spike yield in IWG, allowing GS to enable rapid domestication and improvement of this crop. These breeding methods should be transferable to other species with similar long breeding cycles or limited capacity for replicated observations.

Machine learning approaches for crop improvement: Leveraging phenotypic and genotypic big data

Highly efficient and accurate selection of elite genotypes can lead to dramatic shortening of the breeding cycle in major crops relevant for sustaining present demands for food, feed, and fuel. In contrast to classical approaches that emphasize the need for resource-intensive phenotyping at all stages of artificial selection, genomic selection dramatically reduces the need for phenotyping. Genomic selection relies on advances in machine learning and the availability of genotyping data to predict agronomically relevant phenotypic traits. Here we provide a systematic review of machine learning approaches applied for genomic selection of single and multiple traits in major crops in the past decade. We emphasize the need to gather data on intermediate phenotypes, e.g. metabolite, protein, and gene expression levels, along with developments of modeling techniques that can lead to further improvements of genomic selection. In addition, we provide a critical view of factors that affect genomic selection, with attention to transferability of models between different environments. Finally, we highlight the future aspects of integrating high-throughput molecular phenotypic data from omics technologies with biological networks for crop improvement.

Sequenced-based paternity analysis to improve breeding and identify self-incompatibility loci in intermediate wheatgrass (Thinopyrum intermedium)

KEY MESSAGE

Paternity assignment and genome-wide association analyses for fertility were applied to a Thinopyrum intermedium breeding program. A lack of progeny between combinations of parents was associated with loci near self-incompatibility genes. In outcrossing species such as intermediate wheatgrass (IWG, Thinopyrum intermedium), polycrossing is often used to generate novel recombinants through each cycle of selection, but it cannot track pollen-parent pedigrees and it is unknown how self-incompatibility (SI) genes may limit the number of unique crosses obtained. This study investigated the potential of using next-generation sequencing to assign paternity and identify putative SI loci in IWG. Using a reference population of 380 individuals made from controlled crosses of 64 parents, paternity was assigned with 92% agreement using Cervus software. Using this approach, 80% of 4158 progeny (n = 3342) from a polycross of 89 parents were assigned paternity. Of the 89 pollen parents, 82 (92%) were represented with 1633 unique full-sib families representing 42% of all potential crosses. The number of progeny per successful pollen parent ranged from 1 to 123, with number of inflorescences per pollen parent significantly correlated to the number of progeny (r = 0.54, p < 0.001). Shannon's diversity index, assessing the total number and representation of families, was 7.33 compared to a theoretical maximum of 8.98. To test our hypothesis on the impact of SI genes, a genome-wide association study of the number of progeny observed from the 89 parents identified genetic effects related to non-random mating, including marker loci located near putative SI genes. Paternity testing of polycross progeny can impact future breeding gains by being incorporated in breeding programs to optimize polycross methodology, maintain genetic diversity, and reveal genetic architecture of mating patterns.

Advancing crested wheatgrass [Agropyron cristatum (L.) Gaertn.] breeding through genotyping-by-sequencing and genomic selection

Crested wheatgrass [Agropyron cristatum (L.) Gaertn.] provides high quality, highly palatable forage for early season grazing. Genetic improvement of crested wheatgrass has been challenged by its complex genome, outcrossing nature, long breeding cycle, and lack of informative molecular markers. Genomic selection (GS) has potential for improving traits of perennial forage species, and genotyping-by-sequencing (GBS) has enabled the development of genome-wide markers in non-model polyploid plants. An attempt was made to explore the utility of GBS and GS in crested wheatgrass breeding. Sequencing and phenotyping 325 genotypes representing 10 diverse breeding lines were performed. Bioinformatics analysis identified 827, 3,616, 14,090 and 46,136 single nucleotide polymorphism markers at 20%, 30%, 40% and 50% missing marker levels, respectively. Four GS models (BayesA, BayesB, BayesCπ, and rrBLUP) were examined for the accuracy of predicting nine agro-morphological and three nutritive value traits. Moderate accuracy (0.20 to 0.32) was obtained for the prediction of heading days, leaf width, plant height, clump diameter, tillers per plant and early spring vigor for genotypes evaluated at Saskatoon, Canada. Similar accuracy (0.29 to 0.35) was obtained for predicting fall regrowth and plant height for genotypes evaluated at Swift Current, Canada. The Bayesian models displayed similar or higher accuracy than rrBLUP. These findings show the feasibility of GS application for a non-model species to advance plant breeding.

Improving Prediction Accuracy Using Multi-allelic Haplotype Prediction and Training Population Optimization in Wheat

The use of haplotypes may improve the accuracy of genomic prediction over single SNPs because haplotypes can better capture linkage disequilibrium and genomic similarity in different lines and may capture local high-order allelic interactions. Additionally, prediction accuracy could be improved by portraying population structure in the calibration set. A set of 383 advanced lines and cultivars that represent the diversity of the University of Minnesota wheat breeding program was phenotyped for yield, test weight, and protein content and genotyped using the Illumina 90K SNP Assay. Population structure was confirmed using single SNPs. Haplotype blocks of 5, 10, 15, and 20 adjacent markers were constructed for all chromosomes. A multi-allelic haplotype prediction algorithm was implemented and compared with single SNPs using both k-fold cross validation and stratified sampling optimization. After confirming population structure, the stratified sampling improved the predictive ability compared with k-fold cross validation for yield and protein content, but reduced the predictive ability for test weight. In all cases, haplotype predictions outperformed single SNPs. Haplotypes of 15 adjacent markers showed the best improvement in accuracy for all traits; however, this was more pronounced in yield and protein content. The combined use of haplotypes of 15 adjacent markers and training population optimization significantly improved the predictive ability for yield and protein content by 14.3 (four percentage points) and 16.8% (seven percentage points), respectively, compared with using single SNPs and k-fold cross validation. These results emphasize the effectiveness of using haplotypes in genomic selection to increase genetic gain in self-fertilized crops.

New Food Crop Domestication in the Age of Gene Editing: Genetic, Agronomic and Cultural Change Remain Co-evolutionarily Entangled

The classic domestication scenario for grains and fruits has been portrayed as the lucky fixation of major-effect "domestication genes." Characterization of these genes plus recent improvements in generating novel alleles (e.g., by gene editing) have created great interest in de novo domestication of new crops from wild species. While new gene editing technologies may accelerate some genetic aspects of domestication, we caution that de novo domestication should be understood as an iterative process rather than a singular event. Changes in human social preferences and relationships and ongoing agronomic innovation, along with broad genetic changes, may be foundational. Allele frequency changes at many loci controlling quantitative traits not normally included in the domestication syndrome may be required to achieve sufficient yield, quality, defense, and broad adaptation. The environments, practices and tools developed and maintained by farmers and researchers over generations contribute to crop yield and success, yet those may not be appropriate for new crops without a history of agronomy. New crops must compete with crops that benefit from long-standing participation in human cultural evolution; adoption of new crops may require accelerating the evolution of new crops' culinary and cultural significance, the emergence of markets and trade, and the formation and support of agricultural and scholarly institutions. We provide a practical framework that highlights and integrates these genetic, agronomic, and cultural drivers of change to conceptualize de novo domestication for communities of new crop domesticators, growers and consumers. Major gene-focused domestication may be valuable in creating allele variants that are critical to domestication but will not alone result in widespread and ongoing cultivation of new crops. Gene editing does not bypass or diminish the need for classical breeding, ethnobotanical and horticultural knowledge, local agronomy and crop protection research and extension, farmer participation, and social and cultural research and outreach. To realize the ecological and social benefits that a new era of de novo domestication could offer, we call on funding agencies, proposal reviewers and authors, and research communities to value and support these disciplines and approaches as essential to the success of the breakthroughs that are expected from gene editing techniques.

DNA sequence-based mapping and comparative genomics of the genome of Pseudoroegneria spicata (Pursh) Á. Löve versus wheat (Triticum aestivum L.) and barley (Hordeum vulgare L.)

Bluebunch wheatgrass (referred to as BBWG) [Pseudoroegneria spicata (Pursh) Á. Löve] is an important rangeland Triticeae grass used for forage, conservation, and restoration. This diploid has the basic St genome that occurs also in many polyploid Triticeae species, which serve as a gene reservoir for wheat improvement. Until now, the St genome in diploid species of Pseudoroegneria has not been mapped. Using a double-cross mapping populations, we mapped 230 expressed sequence tag derived simple sequence repeat (EST-SSR) and 3468 genotyping-by-sequencing (GBS) markers to 14 linkage groups (LGs), two each for the seven homologous groups of the St genome. The 227 GBS markers of BBWG that matched those in a previous study helped identify the unclassified seven LGs of the St sub-genome among 21 LGs of Thinopyrum intermedium (Host) Barkworth & D.R. Dewey. Comparisons of GBS sequences in BBWG to whole-genome sequences in bread wheat (Triticum aestivum L.) and barley (Hordeum vulgare L.) revealed that the St genome shared a homology of 35% and 24%, a synteny of 86% and 84%, and a collinearity of 0.85 and 0.86, with ABD and H, respectively. This first-draft molecular map of the St genome will be useful in breeding cereal and forage crops.

Enhancing Crop Domestication Through Genomic Selection, a Case Study of Intermediate Wheatgrass

Perennial grains could simultaneously provide food for humans and a host of ecosystem services, including reduced erosion, minimized nitrate leaching, and increased carbon capture. Yet most of the world's food and feed is supplied by annual grains. Efforts to domesticate intermediate wheatgrass (Thinopyrumn intermedium, IWG) as a perennial grain crop have been ongoing since the 1980's. Currently, there are several breeding programs within North America and Europe working toward developing IWG into a viable crop. As new breeding efforts are established to provide a widely adapted crop, questions of how genomic and phenotypic data can be used among sites and breeding programs have emerged. Utilizing five cycles of breeding data that span 8 years and two breeding programs, University of Minnesota, St. Paul, MN, and The Land Institute, Salina, KS, we developed genomic selection (GS) models to predict IWG traits. Seven traits were evaluated with free-threshing seed, seed mass, and non-shattering being considered domestication traits while agronomic traits included spike yield, spikelets per inflorescence, plant height, and spike length. We used 6,199 genets - unique, heterozygous, individual plants - that had been profiled with genotyping-by-sequencing, resulting in 23,495 SNP markers to develop GS models. Within cycles, the predictive ability of GS was high, ranging from 0.11 to 0.97. Across-cycle predictions were generally much lower, ranging from -0.22 to 0.76. The prediction ability for domestication traits was higher than agronomic traits, with non-shattering and free threshing prediction abilities ranging from 0.27 to 0.75 whereas spike yield had prediction abilities ranging from -0.22 to 0.26. These results suggest that progress to reduce shattering and increase the percent free-threshing grain can be made irrespective of the location and breeding program. While site-specific programs may be required for agronomic traits, synergies can be achieved in rapidly improving key domestication traits for IWG. As other species are targeted for domestication, these results will aid in rapidly domesticating new crops.

Dominance and G×E interaction effects improve genomic prediction and genetic gain in intermediate wheatgrass (Thinopyrum intermedium)

Genomic selection (GS) based recurrent selection methods were developed to accelerate the domestication of intermediate wheatgrass [IWG, Thinopyrum intermedium (Host) Barkworth & D.R. Dewey]. A subset of the breeding population phenotyped at multiple environments is used to train GS models and then predict trait values of the breeding population. In this study, we implemented several GS models that investigated the use of additive and dominance effects and G×E interaction effects to understand how they affected trait predictions in intermediate wheatgrass. We evaluated 451 genotypes from the University of Minnesota IWG breeding program for nine agronomic and domestication traits at two Minnesota locations during 2017-2018. Genet-mean based heritabilities for these traits ranged from 0.34 to 0.77. Using four-fold cross validation, we observed the highest predictive abilities (correlation of 0.67) in models that considered G×E effects. When G×E effects were fitted in GS models, trait predictions improved by 18%, 15%, 20%, and 23% for yield, spike weight, spike length, and free threshing, respectively. Genomic selection models with dominance effects showed only modest increases of up to 3% and were trait-dependent. Cross-environment predictions were better for high heritability traits such as spike length, shatter resistance, free threshing, grain weight, and seed length than traits with low heritability and large environmental variance such as spike weight, grain yield, and seed width. Our results confirm that GS can accelerate IWG domestication by increasing genetic gain per breeding cycle and assist in selection of genotypes with promise of better performance in diverse environments.

Genome Wide Association Study and Genomic Selection of Amino Acid Concentrations in Soybean Seeds

Soybean is a major source of protein for human consumption and animal feed. Releasing new cultivars with high nutritional value is one of the major goals in soybean breeding. To achieve this goal, genome-wide association studies of seed amino acid contents were conducted based on 249 soybean accessions from China, US, Japan, and South Korea. The accessions were evaluated for 15 amino acids and genotyped by sequencing. Significant genetic variation was observed for amino acids among the accessions. Among the 231 single nucleotide polymorphisms (SNPs) significantly associated with variations in amino acid contents, fifteen SNPs localized near 14 candidate genes involving in amino acid metabolism. The amino acids were classified into two groups with five in one group and seven amino acids in the other. Correlation coefficients among the amino acids within each group were high and positive, but the correlation coefficients of amino acids between the two groups were negative. Twenty-five SNP markers associated with multiple amino acids can be used to simultaneously improve multi-amino acid concentration in soybean. Genomic selection analysis of amino acid concentration showed that selection efficiency of amino acids based on the markers significantly associated with all 15 amino acids was higher than that based on random markers or markers only associated with individual amino acid. The identified markers could facilitate selection of soybean varieties with improved seed quality.

Genome-Wide Association Study of Yield Component Traits in Intermediate Wheatgrass and Implications in Genomic Selection and Breeding

Intermediate wheatgrass (Thinopyrum intermedium, IWG) is a perennial grain crop with high biomass and grain yield, long seeds, and resistance to pests and diseases. It also reduces soil erosion, nitrate and mineral leaching into underground water tables, and sequesters carbon in its roots. The domestication timeline of IWG as a grain crop spans only 3 decades, hence it lags annual grain crops in yield and seed characteristics. One approach to improve its agronomic traits is by using molecular markers to uncover marker-trait associations. In this study, we performed association mapping on IWG breeding germplasm from the third recurrent selection cycle at the University of Minnesota. The IWG population was phenotyped in St Paul, MN in 2017 and 2018, and in Crookston, MN in 2018 for grain yield, seed length, width and weight, spike length and weight, and number of spikelets per spike. Strong positive correlations were observed among most trait pairs, with correlations as high as 0.76. Genotyping using high throughput sequencing identified 8,899 high-quality genome-wide SNPs which were combined with phenotypic data in association mapping to discover regions associated with the yield component traits. We detected 154 genetic loci associated with these traits of which 19 were shared between at least two traits. Prediction of breeding values using significant loci as fixed effects in genomic selection model improved predictive abilities by up to 14%. Genetic mapping of agronomic traits followed by using genomic selection to predict breeding values can assist breeders in selecting superior genotypes to accelerate IWG domestication.

Genome mapping of quantitative trait loci (QTL) controlling domestication traits of intermediate wheatgrass (Thinopyrum intermedium)

Allohexaploid (2n = 6x = 42) intermediate wheatgrass (Thinopyrum intermedium), abbreviated IWG, is an outcrossing perennial grass belonging to the tertiary gene pool of wheat. Perenniality would be valuable option for grain production, but attempts to introgress this complex trait from wheat-Thinopyrum hybrids have not been commercially successful. Efforts to breed IWG itself as a dual-purpose forage and grain crop have demonstrated useful progress and applications, but grain yields are significantly less than wheat. Therefore, genetic and physical maps have been developed to accelerate domestication of IWG. Herein, these maps were used to identify quantitative trait loci (QTLs) and candidate genes associated with IWG grain production traits in a family of 266 full-sib progenies derived from two heterozygous parents, M26 and M35. Transgressive segregation was observed for 17 traits related to seed size, shattering, threshing, inflorescence capacity, fertility, stem size, and flowering time. A total of 111 QTLs were detected in 36 different regions using 3826 genotype-by-sequence markers in 21 linkage groups. The most prominent QTL had a LOD score of 15 with synergistic effects of 29% and 22% over the family means for seed retention and percentage of naked seeds, respectively. Many QTLs aligned with one or more IWG gene models corresponding to 42 possible domestication orthogenes including the wheat Q and RHT genes. A cluster of seed-size and fertility QTLs showed possible alignment to a putative Z self-incompatibility gene, which could have detrimental grain-yield effects when genetic variability is low. These findings elucidate pathways and possible hurdles in the domestication of IWG.

Genomic approaches for studying crop evolution

Understanding how crop plants evolved from their wild relatives and spread around the world can inform about the origins of agriculture. Here, we review how the rapid development of genomic resources and tools has made it possible to conduct genetic mapping and population genetic studies to unravel the molecular underpinnings of domestication and crop evolution in diverse crop species. We propose three future avenues for the study of crop evolution: establishment of high-quality reference genomes for crops and their wild relatives; genomic characterization of germplasm collections; and the adoption of novel methodologies such as archaeogenetics, epigenomics, and genome editing.

Wheat genetic resources in the post-genomics era: promise and challenges

Background

Wheat genetic resources have been used for genetic improvement since 1876, when Stephen Wilson (Transactions and Proceedings of the Botanical Society of Edinburgh 12: 286) consciously made the first wide hybrid involving wheat and rye in Scotland. Wide crossing continued with sporadic attempts in the first half of 19th century and became a sophisticated scientific discipline during the last few decades with considerable impact in farmers' fields. However, a large diversity of untapped genetic resources could contribute in meeting future wheat production challenges.

Perspectives and Conclusion

Recently the complete reference genome of hexaploid (Chinese Spring) and tetraploid (Triticum turgidum ssp. dicoccoides) wheat became publicly available coupled with on-going international efforts on wheat pan-genome sequencing. We anticipate that an objective appraisal is required in the post-genomics era to prioritize genetic resources for use in the improvement of wheat production if the goal of doubling yield by 2050 is to be met. Advances in genomics have resulted in the development of high-throughput genotyping arrays, improved and efficient methods of gene discovery, genomics-assisted selection and gene editing using endonucleases. Likewise, ongoing advances in rapid generation turnover, improved phenotyping, envirotyping and analytical methods will significantly accelerate exploitation of exotic genes and increase the rate of genetic gain in breeding. We argue that the integration of these advances will significantly improve the precision and targeted identification of potentially useful variation in the wild relatives of wheat, providing new opportunities to contribute to yield and quality improvement, tolerance to abiotic stresses, resistance to emerging biotic stresses and resilience to weather extremes.

Has Selection for Grain Yield Altered Intermediate Wheatgrass?

Perennial grains are demonstrating a greater probability of occupying land currently dedicated to other agricultural production. Arable land that is currently in use for forage or annual crop production becomes utilized. Breeding materials for the introduction of perennial grains directly into the human food chain has required utilizing existing plant materials in the domestication of species or manufacturing diverse crosses to introduce perenniality into existing crops. In the domestication of intermediate wheatgrass (Thinopyrum intermedium (Host), Barkworth and Dewey), existing forage cultivars or plant accessions were used to develop populations selected for grain production. A comparison of Cycle 3 materials from The Land Institute (TLI), Salina, KS, USA to USDA-Germplasm Resources Information Network (GRIN) accessions took place under space-planted field conditions at Carman, MB, Canada from 2011 to 2014. One hundred plants (75 TLI and 25 GRIN identified in May 2012) were followed through three seed harvests cycles with phenological, morphological and agronomic traits measured throughout. Selection for seed productivity (TLI materials) reduced the importance of biomass plant−1 on seed yield plant−1, leading to an increase in harvest index. Principal component analysis demonstrated the separation of the germplasm sources and the differential impact of years on the performance of all accessions. Path coefficient analysis also indicated that plant biomass production was of less importance on seed yield plant−1 in the TLI materials. Analysis removing area plant−1 as a factor increased both the importance of biomass and heads on seed yield cm−2 in the TLI materials, especially in the first two seed production years. Plant differences due to selection appear to have reduced overall plant area and increased harvest index in the TLI materials, indicating progress for grain yield under selection. However, a greater understanding of the dynamics within a seed production field is needed to provide insight into the development of more effective selection criteria for long-term field level production.

Uncovering the Genetic Architecture of Seed Weight and Size in Intermediate Wheatgrass through Linkage and Association Mapping

Intermediate wheatgrass [IWG; (Host) Barkworth & D.R. Dewey subsp. ] is being developed as a new perennial grain crop that has a large allohexaploid genome similar to that of wheat ( L.). Breeding for increased seed weight is one of the primary goals for improving grain yield of IWG. As a new crop, however, the genetic architecture of seed weight and size has not been characterized, and selective breeding of IWG may be more intricate than wheat because of its self-incompatible mating system and perennial growth habit. Here, seed weight, seed area size, seed width, and seed length were evaluated across multiple years, in a heterogeneous breeding population comprised of 1126 genets and two clonally replicated biparental populations comprised of 172 and 265 genets. Among 10,171 DNA markers discovered using genotyping-by-sequencing (GBS) in the breeding population, 4731 markers were present in a consensus genetic map previously constructed using seven full-sib populations. Thirty-three quantitative trait loci (QTL) associated with seed weight and size were identified using association mapping (AM), of which 23 were verified using linkage mapping in the biparental populations. About 37.6% of seed weight variation in the breeding population was explained by 15 QTL, 12 of which also contributed to either seed length or seed width. When performing either phenotypic selection or genomic selection for seed weight, we observed the frequency of favorable QTL alleles were increased to >46%. Thus, by combining AM and genomic selection, we can effectively select the favorable QTL alleles for seed weight and size in IWG breeding populations.

Evaluating Methods of Updating Training Data in Long-Term Genomewide Selection

Genomewide selection is hailed for its ability to facilitate greater genetic gains per unit time. Over breeding cycles, the requisite linkage disequilibrium (LD) between quantitative trait loci and markers is expected to change as a result of recombination, selection, and drift, leading to a decay in prediction accuracy. Previous research has identified the need to update the training population using data that may capture new LD generated over breeding cycles; however, optimal methods of updating have not been explored. In a barley (Hordeum vulgare L.) breeding simulation experiment, we examined prediction accuracy and response to selection when updating the training population each cycle with the best predicted lines, the worst predicted lines, both the best and worst predicted lines, random lines, criterion-selected lines, or no lines. In the short term, we found that updating with the best predicted lines or the best and worst predicted lines resulted in high prediction accuracy and genetic gain, but in the long term, all methods (besides not updating) performed similarly. We also examined the impact of including all data in the training population or only the most recent data. Though patterns among update methods were similar, using a smaller but more recent training population provided a slight advantage in prediction accuracy and genetic gain. In an actual breeding program, a breeder might desire to gather phenotypic data on lines predicted to be the best, perhaps to evaluate possible cultivars. Therefore, our results suggest that an optimal method of updating the training population is also very practical.

Development of the first consensus genetic map of intermediate wheatgrass (Thinopyrum intermedium) using genotyping-by-sequencing

Development of the first consensus genetic map of intermediate wheatgrass gives insight into the genome and tools for molecular breeding. Intermediate wheatgrass (Thinopyrum intermedium) has been identified as a candidate for domestication and improvement as a perennial grain, forage, and biofuel crop and is actively being improved by several breeding programs. To accelerate this process using genomics-assisted breeding, efficient genotyping methods and genetic marker reference maps are needed. We present here the first consensus genetic map for intermediate wheatgrass (IWG), which confirms the species' allohexaploid nature (2n = 6x = 42) and homology to Triticeae genomes. Genotyping-by-sequencing was used to identify markers that fit expected segregation ratios and construct genetic maps for 13 heterogeneous parents of seven full-sib families. These maps were then integrated using a linear programming method to produce a consensus map with 21 linkage groups containing 10,029 markers, 3601 of which were present in at least two populations. Each of the 21 linkage groups contained between 237 and 683 markers, cumulatively covering 5061 cM (2891 cM--Kosambi) with an average distance of 0.5 cM between each pair of markers. Through mapping the sequence tags to the diploid (2n = 2x = 14) barley reference genome, we observed high colinearity and synteny between these genomes, with three homoeologous IWG chromosomes corresponding to each of the seven barley chromosomes, and mapped translocations that are known in the Triticeae. The consensus map is a valuable tool for wheat breeders to map important disease-resistance genes within intermediate wheatgrass. These genomic tools can help lead to rapid improvement of IWG and development of high-yielding cultivars of this perennial grain that would facilitate the sustainable intensification of agricultural systems.

Genomic Selection in the Era of Next Generation Sequencing for Complex Traits in Plant Breeding

Genomic selection (GS) is a promising approach exploiting molecular genetic markers to design novel breeding programs and to develop new markers-based models for genetic evaluation. In plant breeding, it provides opportunities to increase genetic gain of complex traits per unit time and cost. The cost-benefit balance was an important consideration for GS to work in crop plants. Availability of genome-wide high-throughput, cost-effective and flexible markers, having low ascertainment bias, suitable for large population size as well for both model and non-model crop species with or without the reference genome sequence was the most important factor for its successful and effective implementation in crop species. These factors were the major limitations to earlier marker systems viz., SSR and array-based, and was unimaginable before the availability of next-generation sequencing (NGS) technologies which have provided novel SNP genotyping platforms especially the genotyping by sequencing. These marker technologies have changed the entire scenario of marker applications and made the use of GS a routine work for crop improvement in both model and non-model crop species. The NGS-based genotyping have increased genomic-estimated breeding value prediction accuracies over other established marker platform in cereals and other crop species, and made the dream of GS true in crop breeding. But to harness the true benefits from GS, these marker technologies will be combined with high-throughput phenotyping for achieving the valuable genetic gain from complex traits. Moreover, the continuous decline in sequencing cost will make the WGS feasible and cost effective for GS in near future. Till that time matures the targeted sequencing seems to be more cost-effective option for large scale marker discovery and GS, particularly in case of large and un-decoded genomes.