Characterization of a new greenbug resistance gene Gb9 in a synthetic hexaploid wheat.

Greenbug [Schizaphis graminum (Rondani)] is a serious insect pest that not only damages cereal crops, but also transmits several destructive viruses. The emergence of new greenbug biotypes in the field makes it urgent to identify novel greenbug resistance genes in wheat. CWI 76364 (PI 703397), a synthetic hexaploid wheat (SHW) line, exhibits greenbug resistance. Evaluation of an F2:3 population from cross OK 14319 × CWI 76364 indicated that a dominant gene, designated Gb9, conditions greenbug resistance in CWI 76364. Selective genotyping of a subset of F2 plants with contrasting phenotypes by genotyping-by-sequencing identified 25 SNPs closely linked to Gb9 on chromosome arm 7DL. Ten of these SNPs were converted to Kompetitive allele-specific polymerase chain reaction (KASP) markers for genotyping the entire F2 population. Genetic analysis delimited Gb9 to a 0.6-Mb interval flanked by KASP markers located at 599,835,668 bp (Stars-KASP872) and 600,471,081 bp (Stars-KASP881) on 7DL. Gb9 was 0.5 cM distal to Stars-KASP872 and 0.5 cM proximal to Stars-KASP881. Allelism tests indicated that Gb9 is a new greenbug resistance gene which confers resistance to greenbug biotypes C, E, H, I, and TX1. TX1 is one of the most widely virulent biotypes and has overcome most known wheat greenbug resistance genes. The introgression of Gb9 into locally adapted wheat cultivars is of economic importance, and the KASP markers developed in this study can be used to tag Gb9 in cultivar development.

Characterization of flag leaf morphology identifies a major genomic region controlling flag leaf angle in the US winter wheat (Triticum aestivum L.).

KEY MESSAGE: Multi-environmental characterization of flag leaf morphology traits in the US winter wheat revealed nine stable genomic regions for different flag leaf-related traits including a major region governing flag leaf angle. Flag leaf in wheat is the primary contributor to accumulating photosynthetic assimilates. Flag leaf morphology (FLM) traits determine the overall canopy structure and capacity to intercept the light, thus influencing photosynthetic efficiency. Hence, understanding the genetic control of these traits could be useful for breeding desirable ideotypes in wheat. We used a panel of 272 accessions from the hard winter wheat (HWW) region of the USA to investigate the genetic architecture of five FLM traits including flag leaf length (FLL), width (FLW), angle (FLANG), length-width ratio, and area using multilocation field experiments. Multi-environment GWAS using 14,537 single-nucleotide polymorphisms identified 36 marker-trait associations for different traits, with nine being stable across environments. A novel and major stable region for FLANG (qFLANG.1A) was identified on chromosome 1A accounting for 9-13% variation. Analysis of spatial distribution for qFLANG.1A in a set of 2354 breeding lines from the HWW region showed a higher frequency of allele associated with narrow leaf angle. A KASP assay was developed for allelic discrimination of qFLANG.1A and was used for its independent validation in a diverse set of spring wheat accessions. Furthermore, candidate gene analysis for two regions associated with FLANG identified seven putative genes of interest for each of the two regions. The present study enhances our understanding of the genetic control of FLM in wheat, particularly FLANG, and these results will be useful for dissecting the genes underlying canopy architecture in wheat facilitating the development of climate-resilient wheat varieties.

Characterization of Quantitative Trait Loci for Leaf Rust Resistance in the Uzbekistani Wheat Landrace Teremai Bugdai.

Leaf rust, caused by Puccinia triticina, is a major cause of wheat yield losses globally, and novel leaf rust resistance genes are needed to enhance wheat leaf rust resistance. Teremai Bugdai is a landrace from Uzebekistan that is highly resistant to many races of P. triticina in the United States. To unravel leaf rust resistance loci in Teremai Bugdai, a recombinant inbred line (RIL) population of Teremai Bugdai × TAM 110 was evaluated for response to P. triticina race Pt54-1 (TNBGJ) and genotyped using single nucleotide polymorphism (SNP) markers generated by genotyping-by-sequencing (GBS). Quantitative trait loci (QTL) analysis using 5,130 high-quality GBS-SNPs revealed three QTLs, QLr-Stars-2DS, QLr-Stars-6BL, and QLr.Stars-7BL, for leaf rust resistance in two experiments. QLr-Stars-2DS, which is either a new Lr2 allele or a new resistance locus, was delimited to an ∼19.47-Mb interval between 46.4 and 65.9 Mb on 2DS and explained 31.3 and 33.2% of the phenotypic variance in the two experiments. QLr-Stars-6BL was mapped in an ∼84.0-kb interval between 719.48 and 719.56 Mb on 6BL, accounting for 33 to 36.8% of the phenotypic variance in two experiments. QLr.Stars-7BL was placed in a 350-kb interval between 762.41 and 762.76 Mb on 7BL and explained 4.4 to 5.3% of the phenotypic variance. Nine GBS-SNPs flanking these QTLs were converted to kompetitive allele specific PCR (KASP) markers, and these markers can be used to facilitate their introgression into locally adapted wheat lines.

A Glutathione S-Transferase from Thinopyrum ponticum Confers Fhb7 Resistance to Fusarium Head Blight in Wheat.

Fusarium head blight (FHB), mainly incited by Fusarium graminearum, has caused great losses in grain yield and quality of wheat globally. Fhb7, a major gene from 7E chromosome of Thinopyrum ponticum, confers broad resistance to multiple Fusarium species in wheat and has recently been cloned and identified as encoding a glutathione S-transferase (GST). However, some recent reports raised doubt about whether GST is the causal gene of Fhb7. To resolve the discrepancy and validate the gene function of GST in wheat, we phenotyped Fhb7 near-isogenic lines (Jimai22-Fhb7 versus Jimai22) and GST overexpressed lines for FHB resistance. Jimai22-Fhb7 showed significantly higher FHB resistance with a lower percentage of symptomatic spikelets, Fusarium-damaged kernels, and deoxynivalenol content than susceptible Jimai22 in three experiments. All the positive GST transgenic lines driven by either the maize ubiquitin promoter or its native promoter with high gene expression in the wheat cultivar 'Fielder' showed high FHB resistance. Only one maize ubiquitin promoter-driven transgenic line showed low GST expression and similar susceptibility to Fielder, suggesting that high GST expression confers Fhb7 resistance to FHB. Knockout of GST in the Jimai22-Fhb7 line using CRISPR-Cas9-based gene editing showed significantly higher FHB susceptibility compared with the nonedited control plants. Therefore, we confirmed GST as the causal gene of Fhb7 for FHB resistance. Considering its major effect on FHB resistance, pyramiding Fhb7 with other quantitative trait loci has a great potential to create highly FHB-resistant wheat cultivars.

Quantitative trait loci for rolled leaf in a wheat EMS mutant from Jagger.

KEY MESSAGE: Two QTLs with major effects on rolled leaf trait were consistently detected on chromosomes 1A (QRl.hwwg-1AS) and 5A (QRl.hwwg-5AL) in the field experiments. Rolled leaf (RL) is a morphological strategy to protect plants from dehydration under stressed field conditions. Identification of quantitative trait loci (QTLs) underlining RL is essential to breed drought-tolerant wheat cultivars. A mapping population of 154 recombinant inbred lines was developed from the cross between JagMut1095, a mutant of Jagger, and Jagger to identify quantitative trait loci (QTLs) for the RL trait. A linkage map of 3106 cM was constructed with 1003 unique SNPs from 21 wheat chromosomes. Two consistent QTLs were identified for RL on chromosomes 1A (QRl.hwwg-1AS) and 5A (QRl.hwwg-5AL) in all field experiments. QRl.hwwg-1AS explained 24-56% of the phenotypic variation and QRl.hwwg-5AL explained up to 20% of the phenotypic variation. The combined percent phenotypic variation associated with the two QTLs was up to 61%. Analyses of phenotypic and genotypic data of recombinants generated from heterogeneous inbred families of JagMut1095 × Jagger delimited QRl.hwwg-1AS to a 6.04 Mb physical interval. This work lays solid foundation for further fine mapping and map-based cloning of QRl.hwwg-1AS.

Identification of a Novel Pm65 Allele Conferring a Wide Spectrum of Resistance to Powdery Mildew in Wheat Accession PI 351817.

Powdery mildew is caused by the highly adaptive biotrophic fungus Blumeria graminis f. sp. tritici infecting wheat worldwide. Novel powdery mildew resistance genes are urgently needed that can be used rapidly in wheat cultivar development with minimal disruption of trait advances elsewhere. PI 351817 is a German cultivar exhibiting a wide spectrum of resistance to B. graminis f. sp. tritici isolates collected from different wheat-growing regions of the United States. Evaluation of an F2 population and 237 F2:3 lines derived from OK1059060-2C14 × PI 351817 for responses to B. graminis f. sp. tritici isolate OKS(14)-B-3-1 identified a single dominant gene, designated Pm351817, for powdery mildew resistance in PI 351817. Using bulked segregant analysis (BSA) and simple sequence repeat (SSR) markers, Pm351817 was mapped in the terminal region of the long arm of chromosome 2A. Deep sequencing of the genotyping-by-sequencing libraries of the two parental lines identified a set of single-nucleotide polymorphism (SNP) markers in the 2AL candidate gene region. Those SNP markers was subsequently converted to Kompetitive allele-specific PCR (KASP) markers for genotyping the mapping population. Linkage analysis delimited Pm351817 to a 634-kb interval between Stars-KASP656 (771,207,512 bp) and Stars-KASP662 (771,841,609 bp) on 2AL, based on the Chinese Spring reference sequence IWGSC RefSeq v 2.1. Tests of allelism indicated that Pm351817 is located at the Pm65 locus. Pm351817 shows resistance to all B. graminis f. sp. tritici isolates used in this study and can be used to enhance powdery mildew resistance in the United States. KASP markers flanking Pm351817 can be used to select Pm351817 in wheat breeding programs after further tests for polymorphism.

Identification and characterization of the novel leaf rust resistance gene Lr81 in wheat.

KEY MESSAGE: The novel, leaf rust seedling resistance gene, Lr81, was identified in a Croatian breeding line and mapped to a genomic region of less than 100 Kb on chromosome 2AS. Leaf rust, caused by Puccinia triticina, is the most common and widespread rust disease in wheat. Races of Puccinia triticina evolve rapidly in the southern Great Plains of the USA, and leaf rust resistance genes often lose effectiveness shortly after deployment in wheat production. PI 470121, a wheat breeding line developed by the University of Zagreb in Croatia, showed high resistance to Puccinia triticina races collected from Oklahoma, suggesting that PI 470121 could be a leaf rust resistance source for the southern Great Plains of the USA. Genetic analysis based on an F2 population and F2:3 families derived from the cross PI 470121 × Stardust indicated that PI 470121 carries a dominant seedling resistance gene, designated as Lr81. Linkage mapping delimited Lr81 to a genomic region of 96,148 bp flanked by newly developed KASP markers Xstars-KASP320 and Xstars-KASP323 on the short arm of chromosome 2A, spanning 67,030,206-67,132,354 bp in the Chinese Spring reference assembly (IWGSC RefSeq v1.0). Deletion bin mapping assigned Lr81 to the terminal bin 2AS-0.78-1.00. Allelism tests indicated that Lr81 is a distinctive leaf rust resistance locus with the physical order Lr65-Lr17-Lr81. Marker-assisted selection based on a set of markers closely linked to leaf rust resistance genes in PI 470121 and Stardust enabled identification of a recombinant inbred line RIL92 carrying Lr81 only. Lr81 is a valuable leaf rust resistance source that can be rapidly introgressed into locally adapted cultivars using KASP markers Xstars-KASP320 and Xstars-KASP323.

Whole-genome analysis of hard winter wheat germplasm identifies genomic regions associated with spike and kernel traits.

Genetic dissection of yield component traits including spike and kernel characteristics is essential for the continuous improvement in wheat yield. Genome-wide association studies (GWAS) have been frequently used to identify genetic determinants for spike and kernel-related traits in wheat, though none have been employed in hard winter wheat (HWW) which represents a major class in US wheat acreage. Further, most of these studies relied on assembled diversity panels instead of adapted breeding lines, limiting the transferability of results to practical wheat breeding. Here we assembled a population of advanced/elite breeding lines and well-adapted cultivars and evaluated over four environments for phenotypic analysis of spike and kernel traits. GWAS identified 17 significant multi-environment marker-trait associations (MTAs) for various traits, representing 12 putative quantitative trait loci (QTLs), with five QTLs affecting multiple traits. Four of these QTLs mapped on three chromosomes 1A, 5B, and 7A for spike length, number of spikelets per spike (NSPS), and kernel length are likely novel. Further, a highly significant QTL was detected on chromosome 7AS that has not been previously associated with NSPS and putative candidate genes were identified in this region. The allelic frequencies of important quantitative trait nucleotides (QTNs) were deduced in a larger set of 1,124 accessions which revealed the importance of identified MTAs in the US HWW breeding programs. The results from this study could be directly used by the breeders to select the lines with favorable alleles for making crosses, and reported markers will facilitate marker-assisted selection of stable QTLs for yield components in wheat breeding.

Identification of a novel major QTL from Chinese wheat cultivar Ji5265 for Fusarium head blight resistance in greenhouse.

KEY MESSAGE: A novel major QTL for FHB resistance was mapped to a 6.8 Mb region on chromosome 2D in a Chinese wheat cultivar Ji5265, and diagnostic KASP markers were developed for detecting it in a worldwide wheat collection. Fusarium head blight (FHB) is a serious disease in wheat (Triticum aestivum L.) and causes significant reductions in grain yield and quality worldwide. Breeding for FHB resistance is the most effective strategy to minimize the losses caused by FHB; therefore, identification of major quantitative trait loci (QTLs) conferring FHB resistance and development of diagnostic markers for the QTLs are prerequisites for marker-assisted selection (MAS). Ji5265 is a Chinese wheat cultivar resistant to FHB in multiple environments. An F6 population of 179 recombinant inbred lines (RILs) was developed from Ji5265 × Wheaton. The population was genotyped by genotyping-by-sequencing (GBS) and phenotyped for FHB Type II resistance in greenhouses. A major QTL, designated as QFhb-2DL, was mapped in a 6.8 Mb region between the markers GBS10238 and GBS12056 on the long arm of chromosome 2D in Ji5265 and explained ~ 30% of the phenotypic variation for FHB resistance. The effect of QFhb-2DL on FHB resistance was validated using near-isogenic lines (NILs) derived from residual heterozygotes from an F6 RIL of Ji5265 × Wheaton. The two flanking markers were converted into Kompetitive allele-specific PCR (KASP) markers (KASP10238 and KASP12056) and validated to be diagnostic in a collection of 2,065 wheat accessions. These results indicate that QFhb-2DL is a novel major QTL for resistance to FHB spread within a spike (Type II) and the two KASP markers can be used for MAS to improve wheat FHB resistance in wheat breeding programs.

High-resolution genome-wide association study and genomic prediction for disease resistance and cold tolerance in wheat.

KEY MESSAGE: High-resolution genome-wide association study (GWAS) facilitated QTL fine mapping and candidate gene identification, and the GWAS based genomic prediction models were highly predictive and valuable in wheat genomic breeding. Wheat is a major staple food crop and provides more than one-fifth of the daily calories and dietary proteins for humans. Genome-wide association study (GWAS) and genomic selection (GS) for wheat stress resistance and tolerance related traits are critical to understanding their genetic architecture for improvement of breeding selection efficiency. However, the insufficient marker density in previous studies limited the utility of GWAS and GS in wheat genomic breeding. Here, we conducted a high-resolution GWAS for wheat leaf rust (LR), yellow rust (YR), powdery mildew (PM), and cold tolerance (CT) by genotyping a panel of 768 wheat cultivars using genotyping-by-sequencing. Among 153 quantitative trait loci (QTLs) identified, 81 QTLs were delimited to ≤ 1.0 Mb intervals with three validated using bi-parental populations. Furthermore, 837 stress resistance-related genes were identified in the QTL regions with 12 showing induced expression by YR and PM pathogens. Genomic prediction using 2608, 4064, 3907, and 2136 pre-selected SNPs based on GWAS and genotypic correlations between the SNPs showed high prediction accuracies of 0.76, 0.73, and 0.78 for resistance to LR, YR, and PM, respectively, and 0.83 for resistance to cold damage. Our study laid a solid foundation for large-scale QTL fine mapping, candidate gene validation and GS in wheat.

Characterization of an Incomplete Leaf Rust Resistance Gene on Chromosome 1RS and Development of KASP Markers for Lr47 in Wheat.

Leaf rust, caused by Puccinia triticina, is one of the most common wheat (Triticum aestivum) diseases in the Great Plains of the United States. A population of recombinant inbred lines from CI 17884 × 'Bainong 418' was evaluated for responses to leaf rust race Pt52-2 and genotyped using single nucleotide polymorphism (SNP) markers. Quantitative trait locus analysis identified a minor gene for resistance to leaf rust, designated QLr.stars-1RS, on the 1BL.1RS translocation segment in 'Bainong 418', and another leaf rust resistance gene, Lr47, on chromosome 7A of CI 17884. Lr47, originally identified in CI 17884 and located in a wheat-T. speltoides translocation segment 7S#1S, remains one of only a few race-specific resistance genes still effective in the Great Plains. A set of 7A-specific simple sequence repeat markers were developed and used to genotype CI 17884 and a pair of near-isogenic lines differing in the presence or absence of 7S#1S, PI 603918, and 'Pavon F76'. Haplotype analysis indicated that the estimated length of 7S#1S was 157.23 to 174.42 Megabases, accounting for ∼23% of the 7A chromosome. Two SNPs on 7S#1S and four SNPs on the 1RS chromosome arm were converted to Kompetitive allele-specific PCR (KASP) markers, which were subsequently validated in a panel of cultivars and elite breeding lines released within the last decade. Of these, one- and two-KASP markers are specific to the 1RS chromosome arm and 7S#1S, respectively, indicating that they can facilitate the introgression of Lr47 and QLr.stars-1RS into locally adapted wheat cultivars and breeding lines.

Genomic diversity in pearl millet inbred lines derived from landraces and improved varieties.

BACKGROUND: Genetic improvement of pearl millet is lagging behind most of the major crops. Development of genomic resources is expected to expedite breeding for improved agronomic traits, stress tolerance, yield, and nutritional quality. Genotyping a breeding population with high throughput markers enables exploration of genetic diversity, population structure, and linkage disequilibrium (LD) which are important preludes for marker-trait association studies and application of genomic-assisted breeding. RESULTS: Genotyping-by-sequencing (GBS) libraries of 309 inbred lines derived from landraces and improved varieties from Africa and India generated 54,770 high quality single nucleotide polymorphism (SNP) markers. On average one SNP per 29 Kb was mapped in the reference genome, with the telomeric regions more densely mapped than the pericentromeric regions of the chromosomes. Population structure analysis using 30,208 SNPs evenly distributed in the genome divided 309 accessions into five subpopulations with different levels of admixture. Pairwise genetic distance (GD) between accessions varied from 0.09 to 0.33 with the average distance of 0.28. Rapid LD decay implied low tendency of markers inherited together. Genetic differentiation estimates were the highest between subgroups 4 and 5, and the lowest between subgroups 1 and 2. CONCLUSIONS: Population genomic analysis of pearl millet inbred lines derived from diverse geographic and agroecological features identified five subgroups mostly following pedigree differences with different levels of admixture. It also revealed the prevalence of high genetic diversity in pearl millet, which is very useful in defining heterotic groups for hybrid breeding, trait mapping, and holds promise for improving pearl millet for yield and nutritional quality. The short LD decay observed suggests an absence of persistent haplotype blocks in pearl millet. The diverse genetic background of these lines and their low LD make this set of germplasm useful for traits mapping.

Multiplex restriction amplicon sequencing: a novel next-generation sequencing-based marker platform for high-throughput genotyping.

To enable rapid selection of traits in marker-assisted breeding, markers must be technically simple, low-cost, high-throughput and randomly distributed in a genome. We developed such a technology, designated as Multiplex Restriction Amplicon Sequencing (MRASeq), which reduces genome complexity by polymerase chain reaction (PCR) amplification of amplicons flanked by restriction sites. The first PCR primers contain restriction site sequences at 3'-ends, preceded by 6-10 bases of specific or degenerate nucleotide sequences and then by a unique M13-tail sequence which serves as a binding site for a second PCR that adds sequencing primers and barcodes to allow sample multiplexing for sequencing. The sequences of restriction sites and adjacent nucleotides can be altered to suit different species. Physical mapping of MRASeq SNPs from a biparental population of allohexaploid wheat (Triticum aestivum L.) showed a random distribution of SNPs across the genome. MRASeq generated thousands of SNPs from a wheat biparental population and natural populations of wheat and barley (Hordeum vulgare L.). This novel, next-generation sequencing-based genotyping platform can be used for linkage mapping to screen quantitative trait loci (QTL), background selection in breeding and many other genetics and breeding applications of various species.

The Hessian fly recessive resistance gene h4 mapped to chromosome 1A of the wheat cultivar 'Java' using genotyping-by-sequencing.

KEY MESSAGE: The recessive Hessian fly resistance gene h4 and flanking SNP markers were located to a 642 kb region in chromosome 1A of the wheat cultivar 'Java.' Hessian fly (HF), Mayetiola destructor, is one of the most destructive insect pests in wheat worldwide. The wheat cultivar 'Java' was reported to carry a recessive gene (h4) for HF resistance; however, its chromosome location has not been determined. To map the HF resistance gene in Java, two populations of recombinant inbred lines (RILs) were developed from 'Bobwhite' × Java and 'Overley' × Java, respectively, and were phenotyped for responses to infestation of HF Great Plains biotype. Analysis of phenotypic data from the F1 and the RIL populations confirmed that one recessive gene conditioned HF resistance in Java. Two linkage maps were constructed using single-nucleotide polymorphism (SNP) markers generated by genotyping-by-sequencing (GBS). The h4 gene was mapped to the distal end of the short arm of chromosome 1A, which explained 60.4 to 70.5% of the phenotypic variation for HF resistance in the two populations. The GBS-SNPs in the h4 candidate interval were converted into Kompetitive Allele-Specific Polymerase Chain Reaction (KASP) markers to eliminate the missing data points in GBS-SNPs. Using the revised maps with KASP markers, h4 was further located to a 642 kb interval (6,635,984-7,277,935 bp). The two flanking KASP markers, KASP3299 and KASP1871, as well as four other closely linked KASP markers, may be useful for pyramiding h4 with other HF resistance genes in breeding.

Mapping and characterization of the new adult plant leaf rust resistance gene Lr77 derived from Santa Fe winter wheat.

KEY MESSAGE: A new gene for adult plant leaf rust resistance in wheat was mapped to chromosome 3BL. This gene was designated as Lr77. 'Santa Fe' is a hard red winter cultivar that has had long-lasting resistance to the leaf rust fungus, Puccinia triticina. The objective of this study was to determine the chromosome location of the adult plant leaf rust resistance in Santa Fe wheat. A partial backcross line of 'Thatcher' (Tc) wheat with adult plant leaf rust resistance derived from Santa Fe was crossed with Thatcher to develop a Thatcher//Tc*2/Santa Fe F6 recombinant inbred line (RIL) population. The RIL population and parental lines were evaluated for segregation of leaf rust resistance in three field plot tests and in an adult plant greenhouse test. A genetic map of the RIL population was constructed using 90,000 single-nucleotide polymorphism (SNP) markers with the Illumina Infinium iSelect 90K wheat bead array. A significant quantitative trait locus for reduction of leaf rust severity in all four tests was found on chromosome 3BL that segregated as a single adult plant resistance gene. The RILs with the allele from the resistant parent for SNP marker IWB10344 had lower leaf rust severity and a moderately resistant to moderately susceptible response compared to the susceptible RILs and Thatcher. The gene derived from Santa Fe on chromosome 3BL was designated as Lr77. Kompetitive allele-specific polymerase chain reaction assay markers linked to Lr77 on 3BL should be useful for selection of wheat germplasm with this gene.

Identification of a new Rsg1 allele conferring resistance to multiple greenbug biotypes from barley accessions PI 499276 and PI 566459.

Greenbug [Schizaphis graminum (Rondani)] is a major insect pest that significantly affects barley production worldwide. The identification of novel greenbug resistance genes is crucial for sustainable barley production and global food security. To identify greenbug resistance genes from a US breeding line PI 499276 and a Chinese cultivar PI 566459, two F6:7 recombinant inbred line (RIL) populations developed from crosses Weskan × PI 499276 and Weskan × PI 566459 were phenotyped for responses to greenbug biotype E and genotyped using genotyping-by-sequencing (GBS). Linkage analysis using single nucleotide polymorphism and kompetitive allele-specific polymorphism (KASP) markers delimited the greenbug resistance genes from PI 499276 and PI 566459 to a 1.2 Mb genomic region between 666.5 and 667.7 Mb on the long arm of chromosome 3H in the Morex Hordeum vulgare r1 reference sequence. Allelism tests based on responses of four F2 populations to greenbug biotype E indicated that the greenbug resistance gene in PI 499276 and PI 566459 is either allelic or very close to Rsg1. Given that PI 499276 and PI 566459 shared the same unique resistance pattern to a set of 14 greenbug biotypes, which is different from those of other Rsg1 alleles, they carry a new Rsg1 allele. The greenbug resistance genes in Post 90, PI 499276/PI 566459, and WBDC 336 were designated as Rsg1.a1, Rsg1.a2, and Rsg1.a3, respectively. KASP markers KASP-Rsg1a3-1, KASP-Rsg1a3-2, and KASP160 can be used to tag Rsg1.a2 in barley breeding.

Integrating genomics, phenomics, and deep learning improves the predictive ability for Fusarium head blight-related traits in winter wheat.

Fusarium head blight (FHB) remains one of the most destructive diseases of wheat (Triticum aestivum L.), causing considerable losses in yield and end-use quality. Phenotyping of FHB resistance traits, Fusarium-damaged kernels (FDK), and deoxynivalenol (DON), is either prone to human biases or resource expensive, hindering the progress in breeding for FHB-resistant cultivars. Though genomic selection (GS) can be an effective way to select these traits, inaccurate phenotyping remains a hurdle in exploiting this approach. Here, we used an artificial intelligence (AI)-based precise FDK estimation that exhibits high heritability and correlation with DON. Further, GS using AI-based FDK (FDK_QVIS/FDK_QNIR) showed a two-fold increase in predictive ability (PA) compared to GS for traditionally estimated FDK (FDK_V). Next, the AI-based FDK was evaluated along with other traits in multi-trait (MT) GS models to predict DON. The inclusion of FDK_QNIR and FDK_QVIS with days to heading as covariates improved the PA for DON by 58% over the baseline single-trait GS model. We next used hyperspectral imaging of FHB-infected wheat kernels as a novel avenue to improve the MT GS for DON. The PA for DON using selected wavebands derived from hyperspectral imaging in MT GS models surpassed the single-trait GS model by around 40%. Finally, we evaluated phenomic prediction for DON by integrating hyperspectral imaging with deep learning to directly predict DON in FHB-infected wheat kernels and observed an accuracy (R2 = 0.45) comparable to best-performing MT GS models. This study demonstrates the potential application of AI and vision-based platforms to improve PA for FHB-related traits using genomic and phenomic selection.

Enhancing the potential of phenomic and genomic prediction in winter wheat breeding using high-throughput phenotyping and deep learning.

Integrating high-throughput phenotyping (HTP) based traits into phenomic and genomic selection (GS) can accelerate the breeding of high-yielding and climate-resilient wheat cultivars. In this study, we explored the applicability of Unmanned Aerial Vehicles (UAV)-assisted HTP combined with deep learning (DL) for the phenomic or multi-trait (MT) genomic prediction of grain yield (GY), test weight (TW), and grain protein content (GPC) in winter wheat. Significant correlations were observed between agronomic traits and HTP-based traits across different growth stages of winter wheat. Using a deep neural network (DNN) model, HTP-based phenomic predictions showed robust prediction accuracies for GY, TW, and GPC for a single location with R2 of 0.71, 0.62, and 0.49, respectively. Further prediction accuracies increased (R2 of 0.76, 0.64, and 0.75) for GY, TW, and GPC, respectively when advanced breeding lines from multi-locations were used in the DNN model. Prediction accuracies for GY varied across growth stages, with the highest accuracy at the Feekes 11 (Milky ripe) stage. Furthermore, forward prediction of GY in preliminary breeding lines using DNN trained on multi-location data from advanced breeding lines improved the prediction accuracy by 32% compared to single-location data. Next, we evaluated the potential of incorporating HTP-based traits in multi-trait genomic selection (MT-GS) models in the prediction of GY, TW, and GPC. MT-GS, models including UAV data-based anthocyanin reflectance index (ARI), green chlorophyll index (GCI), and ratio vegetation index 2 (RVI_2) as covariates demonstrated higher predictive ability (0.40, 0.40, and 0.37, respectively) as compared to single-trait model (0.23) for GY. Overall, this study demonstrates the potential of integrating HTP traits into DL-based phenomic or MT-GS models for enhancing breeding efficiency.

Characterization of Rsg3, a novel greenbug resistance gene from the Chinese barley landrace PI 565676.

Greenbug (Schizaphis graminum Rondani) is a pest that poses a serious threat to cereal production worldwide. Yield losses caused by greenbug are predicted to increase because of global warming. To date, only a few barley (Hordeum vulgare L.) greenbug resistance genes have been reported and new genes are urgently needed because of the continuous occurrence of novel greenbug biotypes. PI 565676, a landrace collected from Henan province of China, exhibits high resistance to several predominant greenbug biotypes. An F6:7 recombinant inbred line (RIL) population derived from the cross PI 565676 × 'Weskan' was evaluated for response to greenbug biotypes E and F using a standard aphid assay protocol, and a randomized complete block design with two replicates was adopted. The RIL population was genotyped using single-nucleotide polymorphisms (SNPs) markers generated by genotyping-by-sequencing (GBS). Gene mapping placed the greenbug resistance gene in PI 565676, designated Rsg3, to an interval of 93,140 bp between 667,558,306 and 667,651,446 bp on the long arm of chromosome 3H. Four high-confidence genes were annotated in this region with one encoding a leucine-rich repeat-containing protein. An allelism test indicated that Rsg3 is independent of the Rsg1 locus, with estimated recombination frequency of 12.85 ± 0.20% and genetic distance of 13.14 ± 0.21 cM between the two loci. Therefore, Rsg3 represents a new locus for greenbug resistance. Two SNPs flanking Rsg3 were converted to Kompetitive Allele Specific PCR (KASP) markers, which can be used to tag Rsg3 in barley breeding.

Skim exome capture genotyping in wheat.

Next-generation sequencing (NGS) technology advancements continue to reduce the cost of high-throughput genome-wide genotyping for breeding and genetics research. Skim sequencing, which surveys the entire genome at low coverage, has become feasible for quantitative trait locus (QTL) mapping and genomic selection in various crops. However, the genome complexity of allopolyploid crops such as wheat (Triticum aestivum L.) still poses a significant challenge for genome-wide genotyping. Targeted sequencing of the protein-coding regions (i.e., exome) reduces sequencing costs compared to whole genome re-sequencing and can be used for marker discovery and genotyping. We developed a method called skim exome capture (SEC) that combines the strengths of these existing technologies and produces targeted genotyping data while decreasing the cost on a per-sample basis compared to traditional exome capture. Specifically, we fragmented genomic DNA using a tagmentation approach, then enriched those fragments for the low-copy genic portion of the genome using commercial wheat exome baits and multiplexed the sequencing at different levels to achieve desired coverage. We demonstrated that for a library of 48 samples, ∼7-8× target coverage was sufficient for high-quality variant detection. For higher multiplexing levels of 528 and 1056 samples per library, we achieved an average coverage of 0.76× and 0.32×, respectively. Combining these lower coverage SEC sequencing data with genotype imputation using a customized wheat practical haplotype graph database that we developed, we identified hundreds of thousands of high-quality genic variants across the genome. The SEC method can be used for high-resolution QTL mapping, genome-wide association studies, genomic selection, and other downstream applications.