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.

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.

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 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.

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.

Genetic characterization of root architectural traits in barley (Hordeum vulgare L.) using SNP markers.

Increasing attention is paid to providing new tools to breeders for targeted breeding for specific root traits that are beneficial in low-fertility, drying soils; however, such information is not available for barley (Hordeum vulgare L.). A panel of 191 barley accessions (originating from Australia, Europe, and Africa) was phenotyped for 26 root and shoot traits using the semi-hydroponic system and genotyped using 21 062 high-quality single nucleotide polymorphism (SNP) markers generated by genotyping-by-sequencing (GBS). The population structure analysis of the barley panel identified six distinct groups. We detected 1199 significant (P<0.001) marker-trait associations (MTAs) with r2 values up to 0.41. The strongest MTAs were found for root diameter in the top 20 cm and the longest root length. Based on the physical locations of these MTAs in the barley reference genome, we identified 37 putative QTLs for the root traits, and three QTLs for shoot traits, with nine QTLs located in the same physical regions. The genomic region 640-653 Mb on chromosome 7H was significant for five root length-related traits, where 440 annotated genes were located. The putative QTLs for various root traits identified in this study may be useful for genetic improvement regarding the adaptation of new barley cultivars to suboptimal environments and abiotic stresses.

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.

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.

Multi-trait genomic selection improves the prediction accuracy of end-use quality traits in hard winter wheat.

Improvement of end-use quality remains one of the most important goals in hard winter wheat (HWW) breeding. Nevertheless, the evaluation of end-use quality traits is confined to later development generations owing to resource-intensive phenotyping. Genomic selection (GS) has shown promise in facilitating selection for end-use quality; however, lower prediction accuracy (PA) for complex traits remains a challenge in GS implementation. Multi-trait genomic prediction (MTGP) models can improve PA for complex traits by incorporating information on correlated secondary traits, but these models remain to be optimized in HWW. A set of advanced breeding lines from 2015 to 2021 were genotyped with 8725 single-nucleotide polymorphisms and was used to evaluate MTGP to predict various end-use quality traits that are otherwise difficult to phenotype in earlier generations. The MTGP model outperformed the ST model with up to a twofold increase in PA. For instance, PA was improved from 0.38 to 0.75 for bake absorption and from 0.32 to 0.52 for loaf volume. Further, we compared MTGP models by including different combinations of easy-to-score traits as covariates to predict end-use quality traits. Incorporation of simple traits, such as flour protein (FLRPRO) and sedimentation weight value (FLRSDS), substantially improved the PA of MT models. Thus, the rapid low-cost measurement of traits like FLRPRO and FLRSDS can facilitate the use of GP to predict mixograph and baking traits in earlier generations and provide breeders an opportunity for selection on end-use quality traits by culling inferior lines to increase selection accuracy and genetic gains.

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.

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.

QTL mapping of yield components and kernel traits in wheat cultivars TAM 112 and Duster.

In the Southern Great Plains, wheat cultivars have been selected for a combination of outstanding yield and drought tolerance as a long-term breeding goal. To understand the underlying genetic mechanisms, this study aimed to dissect the quantitative trait loci (QTL) associated with yield components and kernel traits in two wheat cultivars `TAM 112' and `Duster' under both irrigated and dryland environments. A set of 182 recombined inbred lines (RIL) derived from the cross of TAM 112/Duster were planted in 13 diverse environments for evaluation of 18 yield and kernel related traits. High-density genetic linkage map was constructed using 5,081 single nucleotide polymorphisms (SNPs) from genotyping-by-sequencing (GBS). QTL mapping analysis detected 134 QTL regions on all 21 wheat chromosomes, including 30 pleiotropic QTL regions and 21 consistent QTL regions, with 10 QTL regions in common. Three major pleiotropic QTL on the short arms of chromosomes 2B (57.5 - 61.6 Mbps), 2D (37.1 - 38.7 Mbps), and 7D (66.0 - 69.2 Mbps) colocalized with genes Ppd-B1, Ppd-D1, and FT-D1, respectively. And four consistent QTL associated with kernel length (KLEN), thousand kernel weight (TKW), plot grain yield (YLD), and kernel spike-1 (KPS) (Qklen.tamu.1A.325, Qtkw.tamu.2B.137, Qyld.tamu.2D.3, and Qkps.tamu.6A.113) explained more than 5% of the phenotypic variation. QTL Qklen.tamu.1A.325 is a novel QTL with consistent effects under all tested environments. Marker haplotype analysis indicated the QTL combinations significantly increased yield and kernel traits. QTL and the linked markers identified in this study will facilitate future marker-assisted selection (MAS) for pyramiding the favorable alleles and QTL map-based cloning.

Multi-Locus Genome-Wide Association Studies to Characterize Fusarium Head Blight (FHB) Resistance in Hard Winter Wheat.

Fusarium head blight (FHB), caused by the fungus Fusarium graminearum Schwabe is an important disease of wheat that causes severe yield losses along with serious quality concerns. Incorporating the host resistance from either wild relatives, landraces, or exotic materials remains challenging and has shown limited success. Therefore, a better understanding of the genetic basis of native FHB resistance in hard winter wheat (HWW) and combining it with major quantitative trait loci (QTLs) can facilitate the development of FHB-resistant cultivars. In this study, we evaluated a set of 257 breeding lines from the South Dakota State University (SDSU) breeding program to uncover the genetic basis of native FHB resistance in the US hard winter wheat. We conducted a multi-locus genome-wide association study (ML-GWAS) with 9,321 high-quality single-nucleotide polymorphisms (SNPs). A total of six distinct marker-trait associations (MTAs) were identified for the FHB disease index (DIS) on five different chromosomes including 2A, 2B, 3B, 4B, and 7A. Further, eight MTAs were identified for Fusarium-damaged kernels (FDK) on six chromosomes including 3B, 5A, 6B, 6D, 7A, and 7B. Out of the 14 significant MTAs, 10 were found in the proximity of previously reported regions for FHB resistance in different wheat classes and were validated in HWW, while four MTAs represent likely novel loci for FHB resistance. Accumulation of favorable alleles of reported MTAs resulted in significantly lower mean DIS and FDK score, demonstrating the additive effect of FHB resistance alleles. Candidate gene analysis for two important MTAs identified several genes with putative proteins of interest; however, further investigation of these regions is needed to identify genes conferring FHB resistance. The current study sheds light on the genetic basis of native FHB resistance in the US HWW germplasm and the resistant lines and MTAs identified in this study will be useful resources for FHB resistance breeding via marker-assisted selection.

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 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.

Genotyping-by-Sequencing Based Molecular Genetic Diversity of Pakistani Bread Wheat (Triticum aestivum L.) Accessions.

Spring wheat (Triticum aestivum L.) is one of the most imperative staple food crops, with an annual production of 765 million tons globally to feed ∼40% world population. Genetic diversity in available germplasm is crucial for sustainable wheat improvement to ensure global food security. A diversity panel of 184 Pakistani wheat accessions was genotyped using 123,596 high-quality single nucleotide polymorphism (SNP) markers generated by genotyping-by-sequencing with 42% of the SNPs mapped on B, 36% on A, and 22% on D sub-genomes of wheat. Chromosome 2B contains the most SNPs (9,126), whereas 4D has the least (2,660) markers. The mean polymorphic information content, genetic diversity, and major allele frequency of the population were 0.157, 0.1844, and 0.87, respectively. Analysis of molecular variance revealed a higher genetic diversity (80%) within the sub-population than among the sub-populations (20%). The genome-wide linkage disequilibrium was 0.34 Mbp for the whole wheat genome. Among the three subgenomes, A has the highest LD decay value (0.29 Mbp), followed by B (0.2 Mbp) and D (0.07 Mbp) genomes, respectively. The results of population structure, principal coordinate analysis, phylogenetic tree, and kinship analysis also divided the whole population into three clusters comprising 31, 33, and 120 accessions in group 1, group 2, and group 3, respectively. All groups were dominated by the local wheat accessions. Estimation of genetic diversity will be a baseline for the selection of breeding parents for mutations and the genome-wide association and marker-assisted selection studies.

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.

Multi-Trait Multi-Environment Genomic Prediction of Agronomic Traits in Advanced Breeding Lines of Winter Wheat.

Genomic prediction is a promising approach for accelerating the genetic gain of complex traits in wheat breeding. However, increasing the prediction accuracy (PA) of genomic prediction (GP) models remains a challenge in the successful implementation of this approach. Multivariate models have shown promise when evaluated using diverse panels of unrelated accessions; however, limited information is available on their performance in advanced breeding trials. Here, we used multivariate GP models to predict multiple agronomic traits using 314 advanced and elite breeding lines of winter wheat evaluated in 10 site-year environments. We evaluated a multi-trait (MT) model with two cross-validation schemes representing different breeding scenarios (CV1, prediction of completely unphenotyped lines; and CV2, prediction of partially phenotyped lines for correlated traits). Moreover, extensive data from multi-environment trials (METs) were used to cross-validate a Bayesian multi-trait multi-environment (MTME) model that integrates the analysis of multiple-traits, such as G × E interaction. The MT-CV2 model outperformed all the other models for predicting grain yield with significant improvement in PA over the single-trait (ST-CV1) model. The MTME model performed better for all traits, with average improvement over the ST-CV1 reaching up to 19, 71, 17, 48, and 51% for grain yield, grain protein content, test weight, plant height, and days to heading, respectively. Overall, the empirical analyses elucidate the potential of both the MT-CV2 and MTME models when advanced breeding lines are used as a training population to predict related preliminary breeding lines. Further, we evaluated the practical application of the MTME model in the breeding program to reduce phenotyping cost using a sparse testing design. This showed that complementing METs with GP can substantially enhance resource efficiency. Our results demonstrate that multivariate GS models have a great potential in implementing GS in 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.