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Doubled haploid (DH) lines developed by two wheat breeding programs (Kansas State University and the University of Saskatchewan) were evaluated to determine the extent of chromosomal aberrations induced during the DH development process.This study utilized the digital karyotyping method, based on sequencing depth and read mapping, to identify chromosomal aberration in individual wheat plants.The supplementary materials generated from this study are available here.

The A-genome diploid wheats represent the earliest domesticated and cultivated wheat species in the Fertile Crescent and the donor of the wheat A sub-genome. The A-genome species encompass the cultivated einkorn (Triticum. monococcum L. subsp. monococcum), wild einkorn (T. monococcum L. subsp. aegilopoides (Link) Thell.) and T. urartu. We evaluated the collection of 930 accessions in the Wheat Genetics Resource Center (WGRC), using genotyping-by-sequencing (GBS) and identified 13,089 curated SNPs. Genomic analysis detected misclassified and duplicated accessions with most duplicates originated from the same or a nearby locations. About 56% (n = 520) of the WGRC A-genome species collections were duplicates supporting the need for genomic characterization for effective curation and maintenance of these collections. Population structure analysis confirmed the morphology-based classifications of the accessions and reflected the species geographic distributions. We also showed that the T. urartu as the closest A-genome diploid to wheat through phylogenetic analysis. Population analysis within the wild einkorn group showed three genetically distinct clusters, which corresponded with wild einkorn races α, β, and γ described previously. The T. monococcum genome-wide FST scan identified candidate genomic regions harboring domestication selection signature (Btr1) on the short arm of chromosome 3Am at ~ 70 Mb. We established A-genome core set (79 accessions) based on allelic diversity, geographical distribution, and available phenotypic data. The individual species core set maintained at least 80% of allelic variants in the A-genome collection and constitute a valuable genetic resource to improve wheat and domesticated einkorn in breeding programs.One-sentence summaryGenotyping of gene bank collections of diploid A-genome relatives of wheat uncovered relatively higher genetic diversity and unique evolutionary relationships which gives insight to the effective use of these germplasm for wheat improvement.

In hard‐winter wheat (Triticum aestivum L.) breeding, the evaluation of end‐use quality is expensive and time‐consuming, being relegated to the final stages of the breeding program after selection for many traits including disease resistance, agronomic performance, and grain yield. In this study, our objectives were to identify genetic variants underlying baking quality traits through genome‐wide association study (GWAS) and develop improved genomic selection (GS) models for the quality traits in hard‐winter wheat. Advanced breeding lines (n = 462) from 2015–2017 were genotyped using genotyping‐by‐sequencing (GBS) and evaluated for baking quality. Significant associations were detected for mixograph mixing time and bake mixing time, most of which were within or in tight linkage to glutenin and gliadin loci and could be suitable for marker‐assisted breeding. Candidate genes for newly associated loci are phosphate‐dependent decarboxylase and lipid transfer protein genes, which are believed to affect nitrogen metabolism and dough development, respectively. The use of GS can both shorten the breeding cycle time and significantly increase the number of lines that could be selected for quality traits, thus we evaluated various GS models for end‐use quality traits. As a baseline, univariate GS models had 0.25–0.55 prediction accuracy in cross‐validation and from 0 to 0.41 in forward prediction. By including secondary traits as additional predictor variables (univariate GS with covariates) or correlated response variables (multivariate GS), the prediction accuracies were increased relative to the univariate model using only genomic information. The improved genomic prediction models have great potential to further accelerate wheat breeding for end‐use quality.

The development of next generation sequencing (NGS) enabled a shift from array-based genotyping to high-throughput genotyping by directly sequencing genomic libraries. Even though whole genome sequencing was initially too costly for routine analysis in large populations, such as those utilized for breeding or genetic studies, continued advancements in genome sequencing and bioinformatics have provided the opportunity to utilize whole-genome information. As new sequencing platforms can routinely provide high-quality sequencing data for sufficient genome coverage, a limitation comes in the time and high 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 amount of data required and extended to 3,072 samples or more. Panels of double 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 1x down to 0.01x per sample. Using reference genomes, we detected chromosome dosage, identified aneuploidy, and karyotyped introgression lines from the low coverage 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.

Developing methodologies in the fields of phenomics and genomic prediction have the potential to increase the production of crop species by accelerating germplasm improvement. The integration of these technologies into germplasm improvement and breeding programs requires evidence that there will be a direct economic benefit to the program. We determined a basic set of parameters, such as prediction accuracy greater than 0.3, the ability to genotype over 7 lines for the cost of one phenotypic evaluation, and heritability levels below 0.4, at which the use of genomic selection would be of economic benefit in terms of genetic gain and operational costs to the Kansas State University (KSU) winter wheat breeding program. The breeding program was then examined to determine whether the parameters benefitting genomic selection were observed or achievable in a practical sense. Our results show that the KSU winter wheat breeding program is at a decision point with regards to their primary means of selection. A few operational changes to increase prediction accuracy would place the program in the parameter space where genomic selection is expected to outpace the current phenotypic selection methodology at a parity of the operation cost and would be of greatest benefit to the program.

BackgroundPrecise measurement of plant traits with precision and speed on large populations has emerged as a critical bottleneck in connecting genotype to phenotype in genetics and breeding. This bottleneck limits advancements in understanding plant genomes and the development of improved, high-yielding crop varieties.ResultsHere we demonstrate the application of deep learning on proximal imaging from a mobile field vehicle to directly score plant morphology and developmental stages in wheat under field conditions. We developed and trained a convolutional neural network with image datasets labeled from expert visual scores and used this ‘breeder-trained’ network to directly score wheat morphology and developmental stages. For both morphological (awned) and phenological (flowering time) traits, we demonstrate high heritability and extremely high accuracy against the ‘ground-truth’ values from visual scoring. Using the traits scored by the network, we tested genotype-to-phenotype association using the deep learning phenotypes and uncovered novel epistatic interactions for flowering time. Enabled by the time-series high-throughput phenotyping, we describe a new phenotype as the rate of flowering and show heritable genetic control.ConclusionsWe demonstrated a field-based high-throughput phenotyping approach using deep learning that can directly score morphological and developmental phenotypes in genetic populations. Most powerfully, the deep learning approach presented here gives a conceptual advancement in high-throughput plant phenotyping as it can potentially score any trait in any plant species through leveraging expert knowledge from breeders, geneticist, pathologists and physiologists.

Published in Agron. J. 105:1271–1276 (2013) doi:10.2134/agronj2013.0072 Copyright © 2013 by the American Society of Agronomy, 5585 Guilford Road, Madison, WI 53711. All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. D of environmentally sustainable dedicated energy crops may address concerns about soil and environmental degradation. Dedicated energy crops such as perennial WSGs can be a potential alternative to crop residue removal to provide cellulosic biomass for renewable energy production while improving soil and environmental quality (Blanco-Canqui, 2010). Excessive crop residue removal can adversely affect soil structural stability, SOC pools, water transmission characteristics, soil microbial activity, and other soil properties (Wilhelm et al., 2004; Blanco-Canqui and Lal, 2009). In contrast, perennial WSGs due to their year-round surface cover may protect soil from erosion, improve soil properties, soil productivity, and wildlife habitat and diversity. In addition to their potential as biofuel, perennial WSGs may also serve as a valuable animal feedstock, which is particularly important in years of drought (Craine et al., 2010). In the Great Plains, wind erosion is a major environmental concern. This region witnessed the worst dust storms in United States history during the 1930s (Colacicco et al., 1989). It is well recognized that herbaceous wind barriers can reduce wind erosion, improve crop yield, prevent sandblast damage to crops and trap snow to improve soil moisture (Bilbro and Fryrear, 1988). Similar to wind barriers, plantations of WSGs when grown for forage and biofuel may be an effective management practice to reduce wind erosion. Perennial WSGs provide permanent vegetative cover which can adsorb wind energy, reducing wind velocity (Bilbro and Fryrear, 1997). Extensive and deep root systems under perennial WSGs may also stabilize and anchor soil, increasing soil aggregate size and stability. In the Great Plains, wind erosion is usually the greatest between February and May when winds are strong and crops are sparse or not present to protect the soil surface. Presence of dormant WSGs in early spring may reduce wind erosion compared with row crops with limited surface residue cover. Bilbro and Fryrear (1997) concluded that tall and lodge-resistant plants, such as switchgrass, increased the effective distance of wind barriers. Grasses are able to absorb blowing soil particles and reduce the loss of windblown materials (Bilbro and Fryrear, 1997). Current research on dedicated bioenergy crops mostly focuses on increasing production of biomass (Propheter et al., 2010). As a result, data on dedicated bioenergy crop impacts on soil and water conservation, soil physical properties, SOC dynamics, and other soil and environmental factors are limited, particularly in Kansas. This information is, however, needed to assess the potential benefits of growing dedicated energy crops under different regions. Benefits for WSGs for improving soil properties may be inconsistent, depending on the length of management, grass species, soil type, and climate (Schwartz et al., 2003). Most dedicated bioenergy crops are expected to be grown in marginal lands to reduce concerns over competition for land with prime agricultural production (Kort et al., 1998; Cai et al., 2011). Throughout the central Great Plains in general and ABSTRACT

The Global Wheat Head Detection (GWHD) dataset was created in 2020 and has assembled 193,634 labelled wheat heads from 4700 RGB images acquired from various acquisition platforms and 7 countries/institutions. With an associated competition hosted in Kaggle, GWHD_2020 has successfully attracted attention from both the computer vision and agricultural science communities. From this first experience, a few avenues for improvements have been identified regarding data size, head diversity, and label reliability. To address these issues, the 2020 dataset has been reexamined, relabeled, and complemented by adding 1722 images from 5 additional countries, allowing for 81,553 additional wheat heads. We now release in 2021 a new version of the Global Wheat Head Detection dataset, which is bigger, more diverse, and less noisy than the GWHD_2020 version., Plant Phenomics, 2021