Explore the vast range of titles available.

Fusarium head blight (FHB), which is mainly caused by Fusarium graminearum , is a destructive wheat disease that threatens global wheat production. Fhb1 , a quantitative trait locus discovered in Chinese germplasm, provides the most stable and the largest effect on FHB resistance in wheat. Here we show that TaHRC , a gene that encodes a putative histidine-rich calcium-binding protein, is the key determinant of Fhb1 -mediated resistance to FHB. We demonstrate that TaHRC encodes a nuclear protein conferring FHB susceptibility and that a deletion spanning the start codon of this gene results in FHB resistance. Identical sequences of the TaHRC-R allele in diverse accessions indicate that Fhb1 had a single origin, and phylogenetic and haplotype analyses suggest that the TaHRC-R allele most likely originated from a line carrying the Dahongpao haplotype. This discovery opens a new avenue to improve FHB resistance in wheat, and possibly in other cereal crops, by manipulating TaHRC sequence through bioengineering approaches. Genetic studies using map-based cloning, gene editing, RNA interference, haplotyping and association analyses identify a deletion in TaHRC as a key determinant of Fhb1 -mediated resistance to Fusarium head blight in 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.

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 R 2 of 0.71, 0.62, and 0.49, respectively. Further prediction accuracies increased (R 2 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.

With the advent of next generation sequencing (NGS) technologies, single nucleotide polymorphisms (SNPs) have become the major type of marker for genotyping in many crops. However, the availability of SNP markers for important traits of bread wheat (Triticum aestivum L.) that can be effectively used in marker-assisted selection (MAS) is still limited and SNP assays for MAS are usually uniplex. A shift from uniplex to multiplex assays will allow the simultaneous analysis of multiple markers and increase MAS efficiency. We designed 33 locus-specific markers from SNP or indel-based marker sequences that linked to 20 different quantitative trait loci (QTL) or genes of agronomic importance in wheat and analyzed the amplicon sequences using an Ion Torrent Proton Sequencer and a custom allele detection pipeline to determine the genotypes of 24 selected germplasm accessions. Among the 33 markers, 27 were successfully multiplexed and 23 had 100% SNP call rates. Results from analysis of \"kompetitive allele-specific PCR\" (KASP) and sequence tagged site (STS) markers developed from the same loci fully verified the genotype calls of 23 markers. The NGS-based multiplexed assay developed in this study is suitable for rapid and high-throughput screening of SNPs and some indel-based markers in wheat.

Fusarium head blight (FHB) is a destructive disease that reduces wheat grain yield and quality. To date, the quantitative trait locus on 3BS (Fhb1) from Sumai 3 has shown the largest effect on FHB resistance. Single nucleotide polymorphism (SNP) is the most common form of genetic variation and is suitable for high-throughput marker-assisted selection (MAS). We analyzed SNPs derived from 23 wheat expressed sequence tags (ESTs) that previously mapped near Fhb1 on chromosome 3BS. Using 71 Ning 7840/Clark BC7F7 recombinant inbred lines and the single-base extension method, we mapped seven SNP markers between Xgwm533 and Xgwm493, flanking markers for Fhb1. Five of the SNPs explained 45–54% of the phenotypic variation for FHB resistance. Haplotype analysis of 63 wheat accessions from eight countries based on SNPs in EST sequences, simple sequence repeats, and sequence tagged sites in the Fhb1 region identified four major groups: (1) US-Clark, (2) Asian, (3) US-Ernie, and (4) Chinese Spring. The Asian group consisted of Chinese and Japanese accessions that carry Fhb1 and could be differentiated from other groups by marker Xsnp3BS-11. All Sumai 3-related accessions formed a subgroup within the Asian group and could be sorted out by Xsnp3BS-8. The SNP markers identified in this study should be useful for MAS of Fhb1 and fine mapping to facilitate cloning of the Fhb1 resistance gene.

Moderate heat stress accompanied by short episodes of extreme heat during the post-anthesis stage is common in most US wheat growing areas and causes substantial yield losses. Sink strength (grain number) is a key yield limiting factor in modern wheat varieties. Increasing spike fertility (SF) and improving the partitioning of assimilates can optimize sink strength which is essential to improve wheat yield potential under a hot and humid environment. A genome-wide association study (GWAS) allows identification of novel quantitative trait loci (QTLs) associated with SF and other partitioning traits that can assist in marker assisted breeding. In this study, GWAS was performed on a soft wheat association mapping panel (SWAMP) comprised of 236 elite lines using 27,466 single nucleotide polymorphisms (SNPs). The panel was phenotyped in two heat stress locations over 3 years. GWAS identified 109 significant marker-trait associations (MTAs) (p ≤ 9.99 x 10-5) related to eight phenotypic traits including SF (a major component of grain number) and spike harvest index (SHI, a major component of grain weight). MTAs detected on chromosomes 1B, 3A, 3B, and 5A were associated with multiple traits and are potentially important targets for selection. More than half of the significant MTAs (60 out of 109) were found in genes encoding different types of proteins related to metabolism, disease, and abiotic stress including heat stress. These MTAs could be potential targets for further validation study and may be used in marker-assisted breeding for improving wheat grain yield under post-anthesis heat stress conditions. This is the first study to identify novel QTLs associated with SF and SHI which represent the major components of grain number and grain weight, respectively, in wheat.

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.

Barley (Hordeum vulgare) is a climate‐resilient crop widely cultivated in both highly productive and suboptimal agricultural systems, and its ability to adapt to multiple biotic and abiotic stresses has contributed significantly to food security. Greenbug is a destructive insect pest for global barley production, and new greenbug resistance genes are needed to overcome the challenges posed by diverse greenbug biotypes in fields. CI 2458 is a Chinese landrace exhibiting a unique resistance profile to a set of 14 greenbug biotypes, which suggests the presence of a new greenbug resistance gene in CI 2458. A recombinant inbred line population from the cross Weskan × CI 2458 was developed, evaluated for responses to greenbug biotype F, and genotyped using single nucleotide polymorphism (SNP) markers generated by genotyping‐by‐sequencing. Linkage analysis revealed a single gene, designated Rsg4, conditioning greenbug resistance in CI 2458. Rsg4 was delimited to a 1.14 Mb interval between SNP markers S3H_666512114 and S3H_667651446 in the terminal region of chromosome arm 3HL, with genetic distances of 1.2 cM proximal to S3H_667651446 and 1.1 cM distal to S3H_666512114. Allelism tests confirmed that Rsg4 is a new greenbug resistance gene independent of Rsg1 and Rsg3, which reside in the same chromosome arm. Rsg4 differs from Rsg1 alleles and Rsg3 in its resistance to greenbug biotype TX1, one of the most widely virulent biotypes. The introgression of Rsg4 into locally adapted barley cultivars is of agronomic importance, and kompetitive allele‐specific polymerase chain reaction (KASP) markers flanking Rsg4, KASP‐Rsg336‐1 and KASP‐Rsg336‐2, enable rapid pyramiding of Rsg4 with other resistance genes to develop durable greenbug‐resistant cultivars. Core Ideas A new greenbug resistance gene, Rsg4, was identified in the Chinese landrace CI 2458. Rsg4 was delimited to a 1.14 Mb interval in the terminal region of chromosome arm 3HL. Rsg4 confers resistance to economically important US greenbug biotypes. Plain Language Summary Barley is a climate‐resilient crop widely cultivated in both highly productive and suboptimal agricultural systems. Annual yield loss of barley to insect pests is 8.8% worldwide. Greenbug is a major destructive insect pest in barley. Although resistance to greenbug is available for currently prevalent biotypes, the shifts of greenbug biotypes in fields necessitate novel greenbug resistance genes to overcome the challenges posed by newly emerging greenbug biotypes. CI 2458 is a landrace exhibiting a unique resistance profile to a set of 14 greenbug biotypes, suggesting the presence of a new greenbug resistance gene in CI 2458. Genetic analysis indicated that the greenbug resistance gene in CI 2458, designated Rsg4, resides in the terminal region of chromosome arm 3HL. Rsg4 differs from other greenbug resistance genes located in 3HL in its resistance to greenbug biotype TX1, which is virulent to other resistance genes in 3HL. Rsg4 is a valuable greenbug resistance source.

Background Grain number (GN) is one of the key yield contributing factors in modern wheat ( Triticum aestivum ) varieties. Fruiting efficiency (FE) is a key trait for increasing GN by making more spike assimilates available to reproductive structures. Thousand grain weight (TGW) is also an important component of grain yield. To understand the genetic architecture of FE and TGW, we performed a genome-wide association study (GWAS) in a panel of 236 US soft facultative wheats that were phenotyped in three experiments at two locations in Florida and genotyped with 20,706 single nucleotide polymorphisms (SNPs) generated from genotyping-by-sequencing (GBS). Results FE showed significant positive associations with GN, grain yield (GY), and harvest index (HI). Likewise, TGW mostly had a positive correlation with GY and HI, but a negative correlation with GN. Eighteen marker-trait associations (MTAs) for FE and TGW were identified on 11 chromosomes, with nine MTAs within genes. Several MTAs associated with other traits were found within genes with different biological and metabolic functions including nuclear pore complex protein, F-box protein, oligopeptide transporter, and glycoside vacuolar protein. Two KASP markers showed significant mean differences for FE and TGW traits in a validation population. Conclusions KASP marker development and validation demonstrated the utility of these markers for improving FE and TGW in breeding programs. The results suggest that optimizing intra-spike partitioning and utilizing marker-assisted selection (MAS) can enhance GY and HI.

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.