Implementing multi-trait genomic selection to improve grain milling quality in oats (Avena sativa L.).

Oats (Avena sativa L.) provide unique nutritional benefits and contribute to sustainable agricultural systems. Breeding high-value oat varieties that meet milling industry standards is crucial for satisfying the demand for oat-based food products. Test weight, thins, and groat percentage are primary traits that define oat milling quality and the final price of food-grade oats. Conventional selection for milling quality is costly and burdensome. Multi-trait genomic selection (MTGS) combines information from genome-wide markers and secondary traits genetically correlated with primary traits to predict breeding values of primary traits on candidate breeding lines. MTGS can improve prediction accuracy and significantly accelerate the rate of genetic gain. In this study, we evaluated different MTGS models that used morphometric grain traits to improve prediction accuracy for primary grain quality traits within the constraints of a breeding program. We evaluated 558 breeding lines from the University of Illinois Oat Breeding Program across 2 years for primary milling traits, test weight, thins, and groat percentage, and secondary grain morphometric traits derived from kernel and groat images. Kernel morphometric traits were genetically correlated with test weight and thins percentage but were uncorrelated with groat percentage. For test weight and thins percentage, the MTGS model that included the kernel morphometric traits in both training and candidate sets outperformed single-trait models by 52% and 59%, respectively. In contrast, MTGS models for groat percentage were not significantly better than the single-trait model. We found that incorporating kernel morphometric traits can improve the genomic selection for test weight and thins percentage.

Identifying novel sources of resistance to wheat stem sawfly in five wild wheat species.

BACKGROUND: The wheat stem sawfly (WSS, Cephus cinctus) is a major pest of wheat (Triticum aestivum) and can cause significant yield losses. WSS damage results from stem boring and/or cutting, leading to the lodging of wheat plants. Although solid-stem wheat genotypes can effectively reduce larval survival, they may have lower yields than hollow-stem genotypes and show inconsistent solidness expression. Because of limited resistance sources to WSS, evaluating diverse wheat germplasm for novel resistance genes is crucial. We evaluated 91 accessions across five wild wheat species (Triticum monococcum, T. urartu, T. turgidum, T. timopheevii, and Aegilops tauschii) and common wheat cultivars (T. aestivum) for antixenosis (host selection) and antibiosis (host suitability) to WSS. Host selection was measured as the number of eggs after adult oviposition, and host suitability was determined by examining the presence or absence of larval infestation within the stem. The plants were grown in the greenhouse and brought to the field for WSS infestation. In addition, a phylogenetic analysis was performed to determine the relationship between the WSS traits and phylogenetic clustering. RESULTS: Overall, Ae. tauschii, T. turgidum and T. urartu had lower egg counts and larval infestation than T. monococcum, and T. timopheevii. T. monococcum, T. timopheevii, T. turgidum, and T. urartu had lower larval weights compared with T. aestivum. CONCLUSION: This study shows that wild relatives of wheat could be a valuable source of alleles for enhancing resistance to WSS and identifies specific germplasm resources that may be useful for breeding. © 2024 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

An online database for einkorn wheat to aid in gene discovery and functional genomics studies.

Diploid A-genome wheat (einkorn wheat) presents a nutrition-rich option as an ancient grain crop and a resource for the improvement of bread wheat against abiotic and biotic stresses. Realizing the importance of this wheat species, reference-level assemblies of two einkorn wheat accessions were generated (wild and domesticated). This work reports an einkorn genome database that provides an interface to the cereals research community to perform comparative genomics, applied genetics and breeding research. It features queries for annotated genes, the use of a recent genome browser release, and the ability to search for sequence alignments using a modern BLAST interface. Other features include a comparison of reference einkorn assemblies with other wheat cultivars through genomic synteny visualization and an alignment visualization tool for BLAST results. Altogether, this resource will help wheat research and breeding. Database URL  https://wheat.pw.usda.gov/GG3/pangenome.

Genomic characterization and gene bank curation of Aegilops: the wild relatives of wheat.

Genetic diversity found in crop wild relatives is critical to preserve and utilize for crop improvement to achieve sustainable food production amid climate change and increased demand. We genetically characterized a large collection of 1,041 Aegilops accessions distributed among 23 different species using more than 45K single nucleotide polymorphisms identified by genotyping-by-sequencing. The Wheat Genetics Resource Center (WGRC) Aegilops germplasm collection was curated through the identification of misclassified and redundant accessions. There were 49 misclassified and 28 sets of redundant accessions within the four diploid species. The curated germplasm sets now have improved utility for genetic studies and wheat improvement. We constructed a phylogenetic tree and principal component analysis cluster for all Aegilops species together, giving one of the most comprehensive views of Aegilops. The Sitopsis section and the U genome Aegilops clade were further scrutinized with in-depth population analysis. The genetic relatedness among the pair of Aegilops species provided strong evidence for the species evolution, speciation, and diversification. We inferred genome symbols for two species Ae. neglecta and Ae. columnaris based on the sequence read mapping and the presence of segregating loci on the pertinent genomes as well as genetic clustering. The high genetic diversity observed among Aegilops species indicated that the genus could play an even greater role in providing the critical need for untapped genetic diversity for future wheat breeding and improvement. To fully characterize these Aegilops species, there is an urgent need to generate reference assemblies for these wild wheats, especially for the polyploid Aegilops.

Einkorn genomics sheds light on history of the oldest domesticated wheat.

Einkorn (Triticum monococcum) was the first domesticated wheat species, and was central to the birth of agriculture and the Neolithic Revolution in the Fertile Crescent around 10,000 years ago1,2. Here we generate and analyse 5.2-Gb genome assemblies for wild and domesticated einkorn, including completely assembled centromeres. Einkorn centromeres are highly dynamic, showing evidence of ancient and recent centromere shifts caused by structural rearrangements. Whole-genome sequencing analysis of a diversity panel uncovered the population structure and evolutionary history of einkorn, revealing complex patterns of hybridizations and introgressions after the dispersal of domesticated einkorn from the Fertile Crescent. We also show that around 1% of the modern bread wheat (Triticum aestivum) A subgenome originates from einkorn. These resources and findings highlight the history of einkorn evolution and provide a basis to accelerate the genomics-assisted improvement of einkorn and bread wheat.

Integration of genetic and genomics resources in einkorn wheat enables precision mapping of important traits.

Einkorn wheat (Triticum monococcum) is an ancient grain crop and a close relative of the diploid progenitor (T. urartu) of polyploid wheat. It is the only diploid wheat species having both domesticated and wild forms and therefore provides an excellent system to identify domestication genes and genes for traits of interest to utilize in wheat improvement. Here, we leverage genomic advancements for einkorn wheat using an einkorn reference genome assembly combined with skim-sequencing of a large genetic population of 812 recombinant inbred lines (RILs) developed from a cross between a wild and a domesticated T. monococcum accession. We identify 15,919 crossover breakpoints delimited to a median and average interval of 114 Kbp and 219 Kbp, respectively. This high-resolution mapping resource enables us to perform fine-scale mapping of one qualitative (red coleoptile) and one quantitative (spikelet number per spike) trait, resulting in the identification of small physical intervals (400 Kb to 700 Kb) with a limited number of candidate genes. Furthermore, an important domestication locus for brittle rachis is also identified on chromosome 7A. This resource presents an exciting route to perform trait discovery in diploid wheat for agronomically important traits and their further deployment in einkorn as well as tetraploid pasta wheat and hexaploid bread wheat cultivars.

Genome-wide association mapping of rust resistance in Aegilops longissima.

The rust diseases, including leaf rust caused by Puccinia triticina (Pt), stem rust caused by P. graminis f. sp. tritici (Pgt), and stripe rust caused by P. striiformis f. sp. tritici (Pst), are major limiting factors in wheat production worldwide. Identification of novel sources of rust resistance genes is key to developing cultivars resistant to rapidly evolving pathogen populations. Aegilops longissima is a diploid wild grass native to the Levant and closely related to the modern bread wheat D subgenome. To explore resistance genes in the species, we evaluated a large panel of Ae. longissima for resistance to several races of Pt, Pgt, and Pst, and conducted a genome-wide association study (GWAS) to map rust resistance loci in the species. A panel of 404 Ae. longissima accessions, mostly collected from Israel, were screened for seedling-stage resistance to four races of Pt, four races of Pgt, and three races of Pst. Out of the 404 accessions screened, two were found that were resistant to all 11 races of the three rust pathogens screened. The percentage of all accessions screened that were resistant to a given rust pathogen race ranged from 18.5% to 99.7%. Genotyping-by-sequencing (GBS) was performed on 381 accessions of the Ae. longissima panel, wherein 125,343 single nucleotide polymorphisms (SNPs) were obtained after alignment to the Ae. longissima reference genome assembly and quality control filtering. Genetic diversity analysis revealed the presence of two distinct subpopulations, which followed a geographic pattern of a northern and a southern subpopulation. Association mapping was performed in the genotyped portion of the collection (n = 381) and in each subpopulation (n = 204 and 174) independently via a single-locus mixed-linear model, and two multi-locus models, FarmCPU, and BLINK. A large number (195) of markers were significantly associated with resistance to at least one of 10 rust pathogen races evaluated, nine of which are key candidate markers for further investigation due to their detection via multiple models and/or their association with resistance to more than one pathogen race. The novel resistance loci identified will provide additional diversity available for use in wheat breeding.

Discussion: Prioritize perennial grain development for sustainable food production and environmental benefits.

Perennial grains have potential to contribute to ecological intensification of food production by enabling the direct harvest of human-edible crops without requiring annual cycles of disturbance and replanting. Studies of prototype perennial grains and other herbaceous perennials point to the ability of agroecosystems including these crops to protect water quality, enhance wildlife habitat, build soil quality, and sequester soil carbon. However, genetic improvement of perennial grain candidates has been hindered by limited investment due to uncertainty about whether the approach is viable. As efforts to develop perennial grain crops have expanded in past decades, critiques of the approach have arisen. With a recent report of perennial rice producing yields equivalent to those of annual rice over eight consecutive harvests, many theoretical concerns have been alleviated. Some valid questions remain over the timeline for new crop development, but we argue these may be mitigated by implementation of recent technological advances in crop breeding and genetics such as low-cost genotyping, genomic selection, and genome editing. With aggressive research investment in the development of new perennial grain crops, they can be developed and deployed to provide atmospheric greenhouse gas reductions.

Physical mapping of the wheat genes in low-recombination regions: radiation hybrid mapping of the C-locus.

KEY MESSAGE: This work reports the physical mapping of an important gene affecting spike compactness located in a low-recombination region of hexaploid wheat. This work paves the way for the eventual isolation and characterization of the factor involved but also opens up possibilities to use this approach to precisely map other wheat genes located on proximal parts of wheat chromosomes that show highly reduced recombination. Mapping wheat genes, in the centromeric and pericentromeric regions (~ 2/3rd of a given chromosome), poses a formidable challenge due to highly suppressed recombination. Using an example of compact spike locus (C-locus), this study provides an approach to precisely map wheat genes in the pericentromeric and centromeric regions that house ~ 30% of wheat genes. In club-wheat, spike compactness is controlled by the dominant C-locus, but previous efforts have failed to localize it, on a particular arm of chromosome 2D. We integrated radiation hybrid (RH) and high-resolution genetic mapping to locate C-locus on the short arm of chromosome 2D. Flanking markers of the C-locus span a physical distance of 11.0 Mb (231.0-242 Mb interval) and contain only 11 high-confidence annotated genes. This work demonstrates the value of this integrated strategy in mapping dominant genes in the low-recombination regions of the wheat genome. A comparison of the mapping resolutions of the RH and genetic maps using common anchored markers indicated that the RH map provides ~ 9 times better resolution that the genetic map even with much smaller population size. This study provides a broadly applicable approach to fine map wheat genes in regions of suppressed recombination.

Wheat doubled haploids have a marked prevalence of chromosomal aberrations.

Double haploid (DH) population development is widely used in many crops, including wheat (Triticum aestivum L.), to rapidly produce fixed germplasm for breeding and genetic studies. The genome shock that takes place during DH induction could induce chromosomal aberrations that can impact genome integrity and subsequently plant fitness and agronomic performance. To evaluate the extent of chromosomal aberrations that exist as a result of the DH process, we studied two wheat DH populations: CDC Stanley×CDC Landmark and KS13H9×SYMonument. We utilized high-throughput skim sequencing to construct digital karyotypes of these populations to quantify deletions and aneuploidy with high resolution and accuracy, which was confirmed in selected plants by cytological analysis. The two populations studied showed high proportion of abnormal primary DH lines, 55 and 45%, respectively, based on at least one abnormality per progeny. The chromosomal abnormalities are genetically unstable and were observed segregating in the subsequent generations. These observations have important implications for the use of DH lines in genetics and breeding.

Genomics and phenomics enabled prebreeding improved early-season chilling tolerance in Sorghum.

In temperate climates, earlier planting of tropical-origin crops can provide longer growing seasons, reduce water loss, suppress weeds, and escape post-flowering drought stress. However, chilling sensitivity of sorghum, a tropical-origin cereal crop, limits early planting, and over 50 years of conventional breeding has been stymied by coinheritance of chilling tolerance (CT) loci with undesirable tannin and dwarfing alleles. In this study, phenomics and genomics-enabled approaches were used for prebreeding of sorghum early-season CT. Uncrewed aircraft systems (UAS) high-throughput phenotyping platform tested for improving scalability showed moderate correlation between manual and UAS phenotyping. UAS normalized difference vegetation index values from the chilling nested association mapping population detected CT quantitative trait locus (QTL) that colocalized with manual phenotyping CT QTL. Two of the 4 first-generation Kompetitive Allele Specific PCR (KASP) molecular markers, generated using the peak QTL single nucleotide polymorphisms (SNPs), failed to function in an independent breeding program as the CT allele was common in diverse breeding lines. Population genomic fixation index analysis identified SNP CT alleles that were globally rare but common to the CT donors. Second-generation markers, generated using population genomics, were successful in tracking the donor CT allele in diverse breeding lines from 2 independent sorghum breeding programs. Marker-assisted breeding, effective in introgressing CT allele from Chinese sorghums into chilling-sensitive US elite sorghums, improved early-planted seedling performance ratings in lines with CT alleles by up to 13-24% compared to the negative control under natural chilling stress. These findings directly demonstrate the effectiveness of high-throughput phenotyping and population genomics in molecular breeding of complex adaptive traits.

Multi-modal deep learning improves grain yield prediction in wheat breeding by fusing genomics and phenomics.

MOTIVATION: Developing new crop varieties with superior performance is highly important to ensure robust and sustainable global food security. The speed of variety development is limited by long field cycles and advanced generation selections in plant breeding programs. While methods to predict yield from genotype or phenotype data have been proposed, improved performance and integrated models are needed. RESULTS: We propose a machine learning model that leverages both genotype and phenotype measurements by fusing genetic variants with multiple data sources collected by unmanned aerial systems. We use a deep multiple instance learning framework with an attention mechanism that sheds light on the importance given to each input during prediction, enhancing interpretability. Our model reaches 0.754 ± 0.024 Pearson correlation coefficient when predicting yield in similar environmental conditions; a 34.8% improvement over the genotype-only linear baseline (0.559 ± 0.050). We further predict yield on new lines in an unseen environment using only genotypes, obtaining a prediction accuracy of 0.386 ± 0.010, a 13.5% improvement over the linear baseline. Our multi-modal deep learning architecture efficiently accounts for plant health and environment, distilling the genetic contribution and providing excellent predictions. Yield prediction algorithms leveraging phenotypic observations during training therefore promise to improve breeding programs, ultimately speeding up delivery of improved varieties. AVAILABILITY AND IMPLEMENTATION: Available at https://github.com/BorgwardtLab/PheGeMIL (code) and https://doi.org/doi:10.5061/dryad.kprr4xh5p (data).

An unusual tandem kinase fusion protein confers leaf rust resistance in wheat.

The introgression of chromosome segments from wild relatives is an established strategy to enrich crop germplasm with disease-resistance genes1. Here we use mutagenesis and transcriptome sequencing to clone the leaf rust resistance gene Lr9, which was introduced into bread wheat from the wild grass species Aegilops umbellulata2. We established that Lr9 encodes an unusual tandem kinase fusion protein. Long-read sequencing of a wheat Lr9 introgression line and the putative Ae. umbellulata Lr9 donor enabled us to assemble the ~28.4-Mb Lr9 translocation and to identify the translocation breakpoint. We likewise cloned Lr58, which was reportedly introgressed from Aegilops triuncialis3, but has an identical coding sequence compared to Lr9. Cytogenetic and haplotype analyses corroborate that the two genes originate from the same translocation event. Our work sheds light on the emerging role of kinase fusion proteins in wheat disease resistance, expanding the repertoire of disease-resistance genes for breeding.

The wheat stem rust resistance gene Sr43 encodes an unusual protein kinase.

To safeguard bread wheat against pests and diseases, breeders have introduced over 200 resistance genes into its genome, thus nearly doubling the number of designated resistance genes in the wheat gene pool1. Isolating these genes facilitates their fast-tracking in breeding programs and incorporation into polygene stacks for more durable resistance. We cloned the stem rust resistance gene Sr43, which was crossed into bread wheat from the wild grass Thinopyrum elongatum2,3. Sr43 encodes an active protein kinase fused to two domains of unknown function. The gene, which is unique to the Triticeae, appears to have arisen through a gene fusion event 6.7 to 11.6 million years ago. Transgenic expression of Sr43 in wheat conferred high levels of resistance to a wide range of isolates of the pathogen causing stem rust, highlighting the potential value of Sr43 in resistance breeding and engineering.

Identification and high-resolution mapping of a novel tiller number gene (tin6) by combining forward genetics screen and MutMap approach in bread wheat.

Wheat (Triticum aestivum) is one of the most important food crops worldwide, providing up to 20% of the caloric intake per day. Developing high-yielding wheat cultivars with tolerance against abiotic and biotic stresses is important to keep up with the increasing human population. Tiller number is one of the major yield-related traits, directly affecting the number of grains produced per plant; however, only a small number of QTL and underlining genes have been identified for this important factor. Identification of novel genetic variation underlying contrasting traits and their precise genetic mapping in wheat is considered difficult due to the complexity and size of the genome; however, advancements in genomic resources have made efficient gene localization more possible. In this study, we report the characterization of a novel tillering number gene using a mutant identified in the forward genetic screen of an ethyl methane sulfonate (EMS)-treated population of cv. "Jagger." By crossing the low tillering mutant with the Jagger wild-type plant, we generated an F2 population and used the MutMap approach to identify a novel physical interval on 11 Mb on chromosome 2DS. Using an F2 population of 442 gametes and polymorphic SNP markers, we were able to delineate the tin6 locus to a 2.1 Mb region containing 22 candidate genes.

Genomics-driven breeding for local adaptation of durum wheat is enhanced by farmers' traditional knowledge.

In the smallholder, low-input farming systems widespread in sub-Saharan Africa, farmers select and propagate crop varieties based on their traditional knowledge and experience. A data-driven integration of their knowledge into breeding pipelines may support the sustainable intensification of local farming. Here, we combine genomics with participatory research to tap into traditional knowledge in smallholder farming systems, using durum wheat (Triticum durum Desf.) in Ethiopia as a case study. We developed and genotyped a large multiparental population, called the Ethiopian NAM (EtNAM), that recombines an elite international breeding line with Ethiopian traditional varieties maintained by local farmers. A total of 1,200 EtNAM lines were evaluated for agronomic performance and farmers' appreciation in three locations in Ethiopia, finding that women and men farmers could skillfully identify the worth of wheat genotypes and their potential for local adaptation. We then trained a genomic selection (GS) model using farmer appreciation scores and found that its prediction accuracy over grain yield (GY) was higher than that of a benchmark GS model trained on GY. Finally, we used forward genetics approaches to identify marker-trait associations for agronomic traits and farmer appreciation scores. We produced genetic maps for individual EtNAM families and used them to support the characterization of genomic loci of breeding relevance with pleiotropic effects on phenology, yield, and farmer preference. Our data show that farmers' traditional knowledge can be integrated in genomics-driven breeding to support the selection of best allelic combinations for local adaptation.

Exploring genetic diversity of wild and related tetraploid wheat species Triticum turgidum and Triticum timopheevii.

INTRODUCTION: The domestication bottleneck has reduced genetic diversity inwheat, necessitating the use of wild relatives in breeding programs. Wild tetraploid wheat are widely used in the breeding programs but with morphological characters, it is difficult to distinguish these, resulting in misclassification/mislabeling or duplication of accessions in the Gene bank. OBJECTIVES: The study aims to exploreGenotyping by sequencing (GBS) to characterize wild and domesticated tetraploid wheat accessions to generate a core set of accessions to be used in the breeding program. METHODS: TASSEL-GBS pipeline was used for SNP discovery, fastStructure was used to determine the population structure and PowerCore was used to generate a core sets. Nucleotide diversity matrices of Nie's and F-statistics (FST) index were used to determine the center of genetic diversity. RESULTS: We found 65 % and 47 % duplicated accessions in Triticum timopheevii and T. turgidum respectively. Genome-wide nucleotide diversity and FST scan uncovered a lower intra and higher inter-species differentiation. Distinct FST regions were identified in genomic regions belonging to domestication genes: non-brittle rachis (Btr1) and vernalization (VRN-1).Our results suggest that Israel, Jordan, Syria, and Lebanonas the hub of genetic diversity of wild emmer;Turkey, and Georgia for T. durum; and Iraq, Azerbaijan, and Armenia for theT. timopheevii. Identified core set accessions preserved more than 93 % of the available genetic diversity. Genome wide association study (GWAS) indicated the potential chromosomal segment for resistance to leaf rust in T. timopheevii. CONCLUSION: The present study explored the potential of GBS technology in data reduction while maintaining the significant genetic diversity of the species. Wild germplasm showed more differentiation than domesticated accessions, indicating the availability of sufficient diversity for crop improvement. With reduced complexity, the core set preserves the genetic diversity of the gene bank collections and will aid in a more robust characterization of wild germplasm.

A high-throughput skim-sequencing approach for genotyping, dosage estimation and identifying translocations.

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

Genetic networks underlying salinity tolerance in wheat uncovered with genome-wide analyses and selective sweeps.

KEY MESSAGE: A genetic framework underpinning salinity tolerance at reproductive stage was revealed by genome-wide SNP markers and major adaptability genes in synthetic-derived wheats, and trait-associated loci were used to predict phenotypes. Using wild relatives of crops to identify genes related to improved productivity and resilience to climate extremes is a prioritized area of crop genetic improvement. High salinity is a widespread crop production constraint, and development of salt-tolerant cultivars is a sustainable solution. We evaluated a panel of 294 wheat accessions comprising synthetic-derived wheat lines (SYN-DERs) and modern bread wheat advanced lines under control and high salinity conditions at two locations. The GWAS analysis revealed a quantitative genetic framework of more than 200 loci with minor effect underlying salinity tolerance at reproductive stage. The significant trait-associated SNPs were used to predict phenotypes using a GBLUP model, and the prediction accuracy (r2) ranged between 0.57 and 0.74. The r2 values for flag leaf weight, days to flowering, biomass, and number of spikes per plant were all above 0.70, validating the phenotypic effects of the loci discovered in this study. Furthermore, the germplasm sets were compared to identify selection sweeps associated with salt tolerance loci in SYN-DERs. Six loci associated with salinity tolerance were found to be differentially selected in the SYN-DERs (12.4 Mb on chromosome (chr)1B, 7.1 Mb on chr2A, 11.2 Mb on chr2D, 200 Mb on chr3D, 600 Mb on chr6B, and 700.9 Mb on chr7B). A total of 228 reported markers and genes, including 17 well-characterized genes, were uncovered using GWAS and EigenGWAS. A linkage disequilibrium (LD) block on chr5A, including the Vrn-A1 gene at 575 Mb and its homeologs on chr5D, were strongly associated with multiple yield-related traits and flowering time under salinity stress conditions. The diversity panel was screened with more than 68 kompetitive allele-specific PCR (KASP) markers of functional genes in wheat, and the pleiotropic effects of superior alleles of Rht-1, TaGASR-A1, and TaCwi-A1 were revealed under salinity stress. To effectively utilize the extensive genetic information obtained from the GWAS analysis, a genetic interaction network was constructed to reveal correlations among the investigated traits. The genetic network data combined with GWAS, selective sweeps, and the functional gene survey provided a quantitative genetic framework for identifying differentially retained loci associated with salinity tolerance in wheat.