A targeted genotyping-by-sequencing tool (Rapture) for genomics-assisted breeding in oat.

We adapted and tested a Rapture assay as an enhancement of genotyping-by-sequencing (GBS) in oat (Avena sativa). This assay was based on an additional bait-based capture of specific DNA fragments representing approximately 10,000 loci within the enzyme-based complexity reduction provided by GBS. By increasing the specificity of GBS to include only those fragments that provided effective polymorphic markers, it was possible to achieve deeper sequence coverage of target markers, while simultaneously sequencing a greater number of samples on a single unit of next-generation sequencing. The Rapture assay consistently out-performed the GBS assay when filtering markers at 80% completeness or greater, even though the total number of reads per sample was only 25% that of GBS. The reduced sequencing cost per sample for Rapture more than compensated for the increased cost of the capture reaction. Thus, Rapture generated a more repeatable set of marker data at a cost per sample that was approximately 40% less than GBS. Additional advantages of Rapture included accurate identification of heterozygotes, and the possibility to increase the depth or length of sequence reads with less impact on the cost per sample. We tested Rapture for genomic selection and diversity analysis and concluded that it is an effective alternative to GBS or other SNP assays. We recommend the use of Rapture in oat and the development of similar assays in other crops with large complex genomes.

Plant architecture, plasticity, and adaptation strategies of two oat genotypes under different competition intensities.

BACKGROUND: The hypothesis that positive and negative interactions account for adaptive strategies was tested in a controlled study with two oat (Avena sativa) genotypes: 'Manotick' with erect leaves and 'Oa1316-1' with prostrate leaves. An increasing competition pattern was designed by varying the number of seeds planted in each container and the space between containers, thus creating different planting density regimes (i.e. alternative and solid treatments). RESULTS: Total biomass of individual plants tended to decrease exponentially with increasing density in both genotypes. Under high density stress, Manotick allocated more biomass to the roots and produced 50% more tillers, leading to more non-productive tillers and lower harvest index in the alternative than in the solid treatment. In contrast, Oa1316-1 allocated more biomass to panicles and stems, and less to the roots, with fewer tillers. CONCLUSIONS: With increasing density and strengthening intraspecific competition, Manotick reduced aboveground biomass allocation, leading to lower yield, while Oa1316-1 decreased allocation to the roots, but increased allocation to the panicles under an increasingly competitive environment. These adjustments were mechanically derived from negative and positive interactions, ensuring greater yield in the prostrate type. Our findings provided a novel rationale for a planting strategy based on plant type selections.

Genome analysis in Avena sativa reveals hidden breeding barriers and opportunities for oat improvement.

Oat (Avena sativa L.) is an important and nutritious cereal crop, and there is a growing need to identify genes that contribute to improved oat varieties. Here we utilize a newly sequenced and annotated oat reference genome to locate and characterize quantitative trait loci (QTLs) affecting agronomic and grain-quality traits in five oat populations. We find strong and significant associations between the positions of candidate genes and QTL that affect heading date, as well as those that influence the concentrations of oil and ß-glucan in the grain. We examine genome-wide recombination profiles to confirm the presence of a large, unbalanced translocation from chromosome 1 C to 1 A, and a possible inversion on chromosome 7D. Such chromosome rearrangements appear to be common in oat, where they cause pseudo-linkage and recombination suppression, affecting the segregation, localization, and deployment of QTLs in breeding programs.

A Systematic Narration of Some Key Concepts and Procedures in Plant Breeding.

The goal of a plant breeding program is to develop new cultivars of a crop kind with improved yield and quality for a target region and end-use. Improved yield across locations and years means better adaptation to the climatic, soil, and management conditions in the target region. Improved or maintained quality renders and adds value to the improved yield. Both yield and quality must be considered simultaneously, which constitutes the greatest challenge to successful cultivar development. Cultivar development consists of two stages: the development of a promising breeding population and the selection of the best genotypes out of it. A complete breeder's equation was presented to cover both stages, which consists of three key parameters for a trait of interest: the population mean (µ), the population variability (σ G ), and the achieved heritability (h 2 or H), under the multi-location, multi-year framework. Population development is to maximize µσ G and progeny selection is to improve H. Approaches to improve H include identifying and utilizing repeatable genotype by environment interaction (GE) through mega-environment analysis, accommodating unrepeatable GE through adequate testing, and reducing experimental error via replication and spatial analysis. Related concepts and procedures were critically reviewed, including GGE (genotypic main effect plus genotype by environment interaction) biplot analysis, GGE + GGL (genotypic main effect plus genotype by location interaction) biplot analysis, LG (location-grouping) biplot analysis, stability analysis, spatial analysis, adequate testing, and optimum replication. Selection on multiple traits includes independent culling and index selection, for the latter GYT (genotype by yield*trait) biplot analysis was recommended. Genomic selection may provide an alternative and potentially more effective approach in all these aspects. Efforts were made to organize and comment on these concepts and procedures in a systematic manner.

The Genetic Architecture of Milling Quality in Spring Oat Lines of the Collaborative Oat Research Enterprise.

Most oat grains destined for human consumption must possess the ability to pass through an industrial de-hulling process with minimal breakage and waste. Uniform grain size and a high groat to hull ratio are desirable traits related to milling performance. The purpose of this study was to characterize the genetic architecture of traits related to milling quality by identifying quantitative trait loci (QTL) contributing to variation among a diverse collection of elite and foundational spring oat lines important to North American oat breeding programs. A total of 501 lines from the Collaborative Oat Research Enterprise (CORE) panel were evaluated for genome-wide association with 6 key milling traits. Traits were evaluated in 13 location years. Associations for 36,315 markers were evaluated for trait means across and within location years, as well as trait variance across location years, which was used to assess trait stability. Fifty-seven QTL influencing one or more of the milling quality related traits were identified, with fourteen QTL mapped influencing mean and variance across location years. The most prominent QTL was Qkernel.CORE.4D on chromosome 4D at approximately 212 cM, which influenced the mean levels of all traits. QTL were identified that influenced trait variance but not mean, trait mean only and both.

Association of asparagine concentration in wheat with cultivar, location, fertilizer, and their interaction.

The need to produce wheat with low asparagine concentration is of great importance as a measure to mitigate acrylamide concentration in wheat-based products. The association of asparagine concentration in Canadian bread wheat with cultivar, growing location, fertilizer and their interaction were investigated. Wheat cultivars (8) were grown in 2 locations under 4 fertilizer treatments in triplicate (which consisted of two nitrogen rates (90 or 120 lbs/acre) with or without 15 lbs sulphur per acre). The asparagine concentration ranged from 168.9 to 1050 µg/g and was significantly affected by cultivar, location, and their interaction but not fertilizer treatment. Location and cultivar were responsible for 80% and 14% of the variation, respectively. Some cultivars were not affected by location and maintained their low asparagine accumulation trait. Thus, breeding strategies should aim to identify cultivars that are low asparagine accumulating and are stable across different growing environments.

Estimation of the Optimal Number of Replicates in Crop Variety Trials.

Replicated multi-location yield trials are conducted every year in all regions throughout the world for all regionally important crops. Heritability, i.e., selection accuracy based on variety trials, improves with increased number of replicates. However, each replicate is associated with considerable cost. Therefore, it is important for crop variety trials to be optimally replicated. Based on the theory of quantitative genetics, functions that quantitatively define optimal replication on the single-trial basis and on multi-location trial basis were derived. The function on the single-trial basis often over-estimates the optimum number of replicates; it is the function on multi-location trial basis that is recommended for determining the optimal number of replicates. Applying the latter function to the yield data from the 2015-2019 Ottawa oat registration trials conducted both in Ontario and in other provinces of Canada led to the conclusion that a single replicate or two replicates would have sufficed under the current multi-location trial setup. This conclusion was empirically confirmed by comparing genotypic rankings based on all replicates with that on any two replicates. Use of two replicates can save 33-50% of field plots without affecting the selection efficacy.

LG biplot: a graphical method for mega-environment investigation using existing crop variety trial data.

Due to the presence of genotype by environment interaction (GE), no crop cultivar performed the best in all regions. Therefore, the growing regions of a crop must be divided into sub-regions or mega-environments, and specifically adapted cultivars must be bred and deployed in each mega-environment. Meaningful mega-environment delineation must be based on repeatable GE patterns, which can be extracted from multi-year, multi-location crop variety trials. In regional crop variety trials, usually the same set of genotypes are tested across locations within a year, but different sets of genotypes are tested in different years, leading to highly unbalanced multi-year data. Such data are abundant for all crops and regions; but there has been no way to fully utilize them for mega-environment delineation. This paper presents a new method that allows utilization of existing variety trial data to identify repeatable GE patterns, to delineate mega-environments, and to understand the scope of unrepeatable GE at a location and within a mega-environment.

Genotype by Yield*Trait (GYT) Biplot: a Novel Approach for Genotype Selection based on Multiple Traits.

Genotype selection based on multiple traits is a key issue in plant breeding; it has been dependent on setting a subjective weight for each trait in index selection and a subjective truncation point for each trait in independent culling, and the weights and truncation points can be highly subjective. In this paper we proposed and demonstrated a novel approach for genotype selection based on multiple traits, the genotype by yield*trait (GYT) biplot, where "trait" can be any breeding objective other than yield; it may be an agronomic trait, a grain quality, processing quality, or nutritional quality trait, or a disease resistance. The GYT biplot ranks genotypes based on their levels in combining yield with other target traits and at the same time shows their trait profiles, i.e., their strengths and weaknesses. Compared to existing methods, this approach is graphical, objective, effective, and straightforward. Underlying the GYT biplot approach is the paradigm shift that genotypes should be evaluated by their levels in combining yield with other traits as opposed to by their levels in individual traits. An oat dataset from multi-year multi-locations trials was used to demonstrate the GYT biplot approach.

Screening Oat Genotypes for Tolerance to Salinity and Alkalinity.

A set of four experiments was conducted to develop methods for screening oat tolerance to salt and alkali and the following results were obtained. (1) In experiment 1, 68.5 mmol L-1 salt and 22.5 mmol L-1 alkali were identified as appropriate concentrations for determining oat tolerance to salinity and alkalinity during germination. (2) These concentrations were used in experiment 2 to screen 248 oat genotypes and 21 were identified to be tolerant to salinity and alkalinity in germination. (3) In experiment 3, one salt treatment, 40 L of Na2SO4:NaCl (1:1), 150 mmol L-1, was found to be optimal for screening oat tolerance to salinity during growth and development. For alkalinity tolerance, the optimal treatment was 40 L of Na2CO3:NaHCO3 (1:1) at 75 mmol L-1. (4) No significant correlation was found between tolerances at the germination and adult stages or between tolerances to salt and alkali. Three lines were found to be tolerant to both salt and alkali in both germination and adult stages. (5) In experiment 4, 25 out of 262 oat genotypes were found to be tolerant to both salinity and alkalinity. (6) GGE biplot analysis was found to be effective in interpreting the multivariate data and the plastic cone-container system was found to be cost-effective system for screening adult plant tolerance to salt and alkali. (7) The symptoms of salt stress and alkali stress were found to be different; alkali stress mainly reduces the chlorophyll content, while salinity mainly disrupts water absorption.

Optimization of cotton variety registration criteria aided with a genotype-by-trait biplot analysis.

China is one of the largest cotton producing countries in the world thanks to high yields, on which a variety registration system has mainly focused, so that a lack of quality is nowadays acknowledged as a weak point of the cotton industry in that country. The objective of this study was to check the hypothesis that bias in cultivar selection in favor of yield has been maintained through the application of an imperfect selection index (SI), but that a better outcome is possible. Our demonstration is based on an analysis of the data from ten years of cotton variety trials using genotype-by-trait biplots, implemented both for the cultivar selection index (SI) currently applied in China and for an adjusted selection index (ASI) that more effectively took into account the antagonism between yield and quality traits. The main findings were: 1) significant negative associations between yield and fiber quality hindered their simultaneous improvement; 2) registered genotypes were mainly determined by the SI which was primarily yield-oriented; 3) no progress in fiber quality was recorded unlike yield; 4) balanced progress in yield and quality is possible through an adjusted selection index (ASI) guided by genotype-by-trait biplot analysis.

Population Genomics Related to Adaptation in Elite Oat Germplasm.

Six hundred thirty five oat ( L.) lines and 4561 single-nucleotide polymorphism (SNP) loci were used to evaluate population structure, linkage disequilibrium (LD), and genotype-phenotype association with heading date. The first five principal components (PCs) accounted for 25.3% of genetic variation. Neither the eigenvalues of the first 25 PCs nor the cross-validation errors from = 1 to 20 model-based analyses suggested a structured population. However, the PC and = 2 model-based analyses supported clustering of lines on spring oat vs. southern United States origin, accounting for 16% of genetic variation ( < 0.0001). Single-locus -statistic () in the highest 1% of the distribution suggested linkage groups that may be differentiated between the two population subgroups. Population structure and kinship-corrected LD of = 0.10 was observed at an average pairwise distance of 0.44 cM (0.71 and 2.64 cM within spring and southern oat, respectively). On most linkage groups LD decay was slower within southern lines than within the spring lines. A notable exception was found on linkage group Mrg28, where LD decay was substantially slower in the spring subpopulation. It is speculated that this may be caused by a heterogeneous translocation event on this chromosome. Association with heading date was most consistent across location-years on linkage groups Mrg02, Mrg12, Mrg13, and Mrg24.

Biplot Analysis of Host-by-Pathogen Data.

Effective breeding for disease resistance relies on a thorough understanding of host-by-pathogen relations. Achieving such understanding can be difficult and challenging, particularly for large data sets with complex host genotype-by-pathogen strain interactions. This paper presents a biplot approach that facilitates visual analysis of host-by-pathogen data. A biplot displays both host genotypes and pathogen isolates in a single scatter plot; each genotype or isolate is displayed as a point defined by its scores on the first two principal components derived from subjecting genotype- or strain-centered data to singular value decomposition. From a biplot, clusters of host genotypes and clusters of pathogen strains can be simultaneously visualized. Moreover, the basis for genotype and strain classifications, i.e., interactions between individual genotypes and strains, can be visualized at the same time. A biplot based on genotype-centered data and that based on strain-centered data are appropriate for visual evaluation of susceptibility/resistance of genotypes and virulence/avirulence of strains, respectively. Biplot analysis of genotype-by-strain is illustrated with published response scores of 13 barley line groups to 8 net blotch isolate groups.

SNP discovery and chromosome anchoring provide the first physically-anchored hexaploid oat map and reveal synteny with model species.

A physically anchored consensus map is foundational to modern genomics research; however, construction of such a map in oat (Avena sativa L., 2n = 6x = 42) has been hindered by the size and complexity of the genome, the scarcity of robust molecular markers, and the lack of aneuploid stocks. Resources developed in this study include a modified SNP discovery method for complex genomes, a diverse set of oat SNP markers, and a novel chromosome-deficient SNP anchoring strategy. These resources were applied to build the first complete, physically-anchored consensus map of hexaploid oat. Approximately 11,000 high-confidence in silico SNPs were discovered based on nine million inter-varietal sequence reads of genomic and cDNA origin. GoldenGate genotyping of 3,072 SNP assays yielded 1,311 robust markers, of which 985 were mapped in 390 recombinant-inbred lines from six bi-parental mapping populations ranging in size from 49 to 97 progeny. The consensus map included 985 SNPs and 68 previously-published markers, resolving 21 linkage groups with a total map distance of 1,838.8 cM. Consensus linkage groups were assigned to 21 chromosomes using SNP deletion analysis of chromosome-deficient monosomic hybrid stocks. Alignments with sequenced genomes of rice and Brachypodium provide evidence for extensive conservation of genomic regions, and renewed encouragement for orthology-based genomic discovery in this important hexaploid species. These results also provide a framework for high-resolution genetic analysis in oat, and a model for marker development and map construction in other species with complex genomes and limited resources.

Biplot Analysis of Test Sites and Trait Relations of Soybean in Ontario.

Superior crop cultivars must be identified through multi-environment trials (MET) and on the basis of multiple traits. The objectives of this paper were to describe two types of biplots, the GGE biplot and the GT biplot, which graphically display genotype by environment data and genotype by trait data, respectively, and hence facilitate cultivar evaluation on the basis of MET data and multiple traits. Genotype main effect plus genotype by environment interaction effect (GGE) biplot analysis of the soybean [Glycine max (L.) Merr.] yield data for the 2800 crop heat unit area of Ontario for MET in the period 1994-1999 revealed yearly crossover genotype by site interactions. The eastern Ontario site Winchester showed a different genotype response pattern from the three southwestern Ontario sites in four of the six years. The interactions were not large enough to divide the area into different mega-environments as when analyzed over years, a single cultivar yielded the best in all four sites. The southwestern site, St. Pauls, was found to always group together with at least one of the other three sites; it did not provide unique information on genotype performance. Therefore, in future cultivar evaluations, Winchester should always be used but St. Pauls can be dismissed. Applying GT biplot to the 1994-1999 multiple trait data illustrated that GT biplots graphically displayed the interrelationships among seed yield, oil content, protein content, plant height, and days to maturity, among other traits, and facilitated visual cultivar comparisons and selection. It was found that selection for seed yield alone was not only the simplest, but also the most effective strategy in the early stages of soybean breeding.

Biplot Analysis of Diallel Data.

Diallel crosses have been used in genetic research to determine the inheritance of important traits among a set of genotypes and to identify superior parents for hybrid or cultivar development. Conventional diallel analysis is limited to partitioning the total variation of the data into general combining ability (GCA) of each genotype and specific combining ability (SCA) of each cross. In this paper we formulate a biplot approach for graphical diallel analysis. The biplot is constructed by the first two principal components (PCs) derived from subjecting the tester-centered diallel data to singular value decomposition. It displays the most important entry by tester patterns of the data and allows the following information to be extracted visually: (i) GCA of each genotype; (ii) SCA of each genotype; (iii) groups of parents with similar genetics; and (iv) superior hybrids. In addition, the biplot allows hypotheses to be formulated concerning the genetics of the genotypes. Three published diallel data sets of wheat (Triticum aestivum L.) and maize (Zea mays L.) were used to demonstrate the biplot approach and detailed procedures were provided for constructing and interpreting a biplot.