Haplotype-Based, Genome-Wide Association Study Reveals Stable Genomic Regions for Grain Yield in CIMMYT Spring Bread Wheat

Accelerating wheat improvement through trait characterization: advances and perspectives

Wheat (Triticum spp.) is a primary dietary staple food for humanity. Many wheat genetic resources with variable genomes have a record of domestication history and are widespread throughout the world. To develop elite wheat varieties, agronomical and stress-responsive trait characterization is foremost for evaluating existing germplasm to promote breeding. However, genomic complexity is one of the primary impediments to trait mining and characterization. Multiple reference genomes and cutting-edge technologies like haplotype mapping, genomic selection, precise gene editing tools, high-throughput phenotyping platforms, high-efficiency genetic transformation systems, and speed-breeding facilities are transforming wheat functional genomics research to understand the genomic diversity of polyploidy. This review focuses on the research achievements in wheat genomics, the available omics approaches, and bioinformatic resources developed in the past decades. Advances in genomics and system biology approaches are highlighted to circumvent bottlenecks in genomic and phenotypic selection, as well as gene transfer. In addition, we propose conducting precise functional genomic studies and developing sustainable breeding strategies for wheat. These developments in understanding wheat traits have speed up the creation of high-yielding, stress-resistant, and nutritionally enhanced wheat varieties, which will help in addressing global food security and agricultural sustainability in the era of climate change.

Genome-wide association study and expression of candidate genes for Fe and Zn concentration in sorghum grains

Sorghum germplasm showed grain Fe and Zn genetic variability, but a few varieties were biofortified with these minerals. This work contributes to narrowing this gap. Fe and Zn concentrations along with 55,068 high-quality GBS SNP data from 140 sorghum accessions were used in this study. Both micronutrients exhibited good variability with respective ranges of 22.09-52.55 ppm and 17.92-43.16 ppm. Significant marker-trait associations were identified on chromosomes 1, 3, and 5. Two major effect SNPs (S01_72265728 and S05_58213541) explained 35% and 32% of Fe and Zn phenotypic variance, respectively. The SNP S01_72265728 was identified in the cytochrome P450 gene and showed a positive effect on Fe accumulation in the kernel, while S05_58213541 was intergenic near Sobic.005G134800 (zinc-binding ribosomal protein) and showed negative effect on Zn. Tissue-specific in silico expression analysis resulted in higher levels of Sobic.003G350800 gene product in several tissues such as leaf, root, flower, panicle, and stem. Sobic.005G188300 and Sobic.001G463800 were expressed moderately at grain maturity and anthesis in leaf, root, panicle, and seed tissues. The candidate genes expressed in leaves, stems, and grains will be targeted to improve grain and stover quality. The haplotypes identified will be useful in forward genetics breeding.

Genome-wide scanning to identify and validate single nucleotide polymorphism markers associated with drought tolerance in spring wheat seedlings

Unlike other growth stages of wheat, very few studies on drought tolerance have been done at the seedling stage, and this is due to the complexity and sensitivity of this stage to drought stress resulting from climate change. As a result, the drought tolerance of wheat seedlings is poorly understood and very few genes associated with drought tolerance at this stage were identified. To address this challenge, a set of 172 spring wheat genotypes representing 20 different countries was evaluated under drought stress at the seedling stage. Drought stress was applied on all tested genotypes by water withholding for 13 days. Two types of traits, namely morphological and physiological traits were scored on the leaves of all tested genotypes. Genome-wide association study (GWAS) is one of the effective genetic analysis methods that was used to identify target single nucleotide polymorphism (SNP) markers and candidate genes for later use in marker-assisted selection. The tested plant materials were genotyped using 25k Infinium iSelect array (25K) (herein after it will be identified as 25K) (for 172 genotypes) and genotyping-by-sequencing (GBS) (for 103 genotypes), respectively. The results of genotyping revealed 21,093 25K and 11,362 GBS-SNPs, which were used to perform GWAS analysis for all scored traits. The results of GWAS revealed that 131 and 55 significant SNPs were controlling morphological and physiological traits, respectively. Moreover, a total of eight and seven SNP markers were found to be associated with more than one morphological and physiological trait under drought stress, respectively. Remarkably, 10 significant SNPs found in this study were previously reported for their association with drought tolerance in wheat. Out of the 10 validated SNP markers, four SNPs were associated with drought at the seedling stage, while the remaining six SNPs were associated with drought stress at the reproductive stage. Moreover, the results of gene enrichment revealed 18 and six pathways as highly significant biological and molecular pathways, respectively. The selection based on drought-tolerant alleles revealed 15 genotypes with the highest number of different drought-tolerant alleles. These genotypes can be used as candidate parents in future breeding programs to produce highly drought-tolerant genotypes with high genetic diversity. Our findings in this study provide novel markers and useful information on the genetic basis of drought tolerance at early growth stages.

Genomic wide association study and selective sweep analysis identify genes associated with improved yield under drought in Turkish winter wheat germplasm

A panel comprising of 84 Turkish winter wheat landraces (LR) and 73 modern varieties (MV) was analyzed with genome wide association study (GWAS) to identify genes/genomic regions associated with increased yield under favorable and drought conditions. In addition, selective sweep analysis was conducted to detect signatures of selection in the winter wheat genome driving the differentiation between LR and MV, to gather an understanding of genomic regions linked to adaptation and yield improvement. The panel was genotyped with 25 K wheat SNP array and phenotyped for agronomic traits for two growing seasons (2018 and 2019) in Konya, Turkey. Year 2018 was treated as drought environment due to very low precipitation prior to heading whereas year 2019 was considered as a favorable season. GWAS conducted with SNPs and haplotype blocks using mixed linear model identified 18 genomic regions in the vicinities of known genes i.e., TaERF3-3A, TaERF3-3B, DEP1-5A, FRIZZY PANICLE-2D, TaSnRK23-1A, TaAGL6-A, TaARF12-2A, TaARF12-2B, WAPO1, TaSPL16-7D, TaTGW6-A1, KAT-2B, TaOGT1, TaSPL21-6B, TaSBEIb, trs1/WFZP-A, TaCwi-A1-2A and TaPIN1-7A associated with grain yield (GY) and yield related traits. Haplotype-based GWAS identified five haplotype blocks (H1A-42, H2A-71, H4A-48, H7B-123 and H7B-124), with the favorable haplotypes showing a yield increase of > 700 kg/ha in the drought season. SNP-based GWAS, detected only one larger effect genomic region on chromosome 7B, in common with haplotype-based GWAS. On an average, the percentage variation (PV) explained by haplotypes was 8.0% higher than PV explained by SNPs for all the investigated traits. Selective sweep analysis detected 39 signatures of selection between LR and MV of which 15 were within proximity of known functional genes controlling flowering (PRR-A1, PPR-D1, TaHd1-6B), GY and GY components (TaSus2-2B, TaGS2-B1, AG1-1A/WAG1-1A, DUO-A1, DUO-B1, AG2-3A/WAG2-3A, TaLAX1, TaSnRK210-4A, FBP, TaLAX1, TaPIL1 and AP3-1-7A/WPA3-7A) and 10 regions underlying various transcription factors and regulatory genes. The study outcomes contribute to utilization of LR in breeding winter wheat.

Genome-wide association study of drought tolerance in wheat (Triticum aestivum L.) identifies SNP markers and candidate genes

Drought stress poses a severe threat to global wheat production, necessitating an in-depth exploration of the genetic basis for drought tolerance associated traits. This study employed a 90 K SNP array to conduct a genome-wide association analysis, unravelling genetic determinants of key traits related to drought tolerance in wheat, namely plant height, root length, and root and shoot dry weight. Using the mixed linear model (MLM) method on 125 wheat accessions subjected to both well-watered and drought stress treatments, we identified 53 SNPs significantly associated with stress susceptibility (SSI) and tolerance indices (STI) for the targeted traits. Notably, chromosomes 2A and 3B stood out with ten and nine associated markers, respectively. Across 17 chromosomes, 44 unique candidate genes were pinpointed, predominantly located on the distal ends of 1A, 1B, 1D, 2A, 3A, 3B, 4A, 6A, 6B, 7A, 7B, and 7D chromosomes. These genes, implicated in diverse functions related to plant growth, development, and stress responses, offer a rich resource for future investigation. A clustering pattern emerged, notably with seven genes associated with SSI for plant height and four genes linked to both STI of plant height and shoot dry weight, converging on specific regions of chromosome arms of 2AS and 3BL. Additionally, shared genes encoding polygalacturonase, auxilin-related protein 1, peptide deformylase, and receptor-like kinase underscored the interconnectedness between plant height and shoot dry weight. In conclusion, our findings provide insights into the molecular mechanisms governing wheat drought tolerance, identifying promising genomic loci for further exploration and crop improvement strategies.

Wheat adaptation to environmental stresses under climate change: Molecular basis and genetic improvement

Wheat (Triticum aestivum) is a staple food for about 40% of the world's population. As the global population has grown and living standards improved, high yield and improved nutritional quality have become the main targets for wheat breeding. However, wheat production has been compromised by global warming through the more frequent occurrence of extreme temperature events, which have increased water scarcity, aggravated soil salinization, caused plants to be more vulnerable to diseases, and directly reduced plant fertility and suppressed yield. One promising option to address these challenges is the genetic improvement of wheat for enhanced resistance to environmental stress. Several decades of progress in genomics and genetic engineering has tremendously advanced our understanding of the molecular and genetic mechanisms underlying abiotic and biotic stress responses in wheat. These advances have heralded what might be considered a "golden age" of functional genomics for the genetic improvement of wheat. Here, we summarize the current knowledge on the molecular and genetic basis of wheat resistance to abiotic and biotic stresses, including the QTLs/genes involved, their functional and regulatory mechanisms, and strategies for genetic modification of wheat for improved stress resistance. In addition, we also provide perspectives on some key challenges that need to be addressed.

Breeding effects on durum wheat traits detected using GWAS and haplotype block analysis

Introduction

The recent boosting of genomic data in durum wheat (Triticum turgidum subsp. durum) offers the opportunity to better understand the effects of breeding on the genetic structures that regulate the expression of traits of agronomic interest. Furthermore, the identification of DNA markers useful for marker-assisted selection could also improve the reliability of technical protocols used for variety protection and registration.

Methods

Within this motivation context, 123 durum wheat accessions, classified into three groups: landraces (LR), ancient (OC) and modern cultivars (MC), were evaluated in two locations, for 34 agronomic traits, including UPOV descriptors, to assess the impact of changes that occurred during modern breeding.

Results

The association mapping analysis, performed with 4,241 SNP markers and six multi-locus-GWAS models, revealed 28 reliable Quantitative Trait Nucleotides (QTNs) related to plant morphology and kernel-related traits. Some important genes controlling flowering time and plant height were in linkage disequilibrium (LD) decay with QTNs identified in this study. A strong association for yellow berry was found on chromosome 6A (Q.Yb-6A) in a region containing the nadh-ubiquinone oxidoreductase subunit, a gene involved in starch metabolism. The Q.Kcp-2A harbored the PPO locus, with the associated marker (Ku_c13700_1196) in LD decay with Ppo-A1 and Ppo-A2. Interestingly, the Q.FGSGls-2B.1, identified by RAC875_c34512_685 for flag leaf glaucosity, mapped less than 1 Mb from the Epistatic inhibitors of glaucousness (Iw1), thus representing a good candidate for supporting the morphological DUS traits also with molecular markers. LD haplotype block approach revealed a higher diversity, richness and length of haploblocks in MC than OC and LR (580 in LR, 585 in OC and 612 in MC), suggesting a possible effect exerted by breeding programs on genomic regions associated with the agronomic traits.

Discussion

Our findings pave new ways to support the phenotypic characterization necessary for variety registration by using a panel of cost-effectiveness SNP markers associated also to the UPOV descriptors. Moreover, the panel of associated SNPs might represent a reservoir of favourable alleles to use in durum wheat breeding and genetics.

Genetic Enhancement of Cereals Using Genomic Resources for Nutritional Food Security

Advances in genomics resources have facilitated the evolution of cereal crops with enhanced yield, improved nutritional values, and heightened resistance to various biotic and abiotic stresses. Genomic approaches present a promising avenue for the development of high-yielding varieties, thereby ensuring food and nutritional security. Significant improvements have been made within the omics domain, specifically in genomics, transcriptomics, and proteomics. The advent of Next-Generation Sequencing (NGS) techniques has yielded an immense volume of data, accompanied by substantial progress in bioinformatic tools for proficient analysis. The synergy between genomics and computational tools has been acknowledged as pivotal for unravelling the intricate mechanisms governing genome-wide gene regulation. Within this review, the essential genomic resources are delineated, and their harmonization in the enhancement of cereal crop varieties is expounded upon, with a paramount focus on fulfilling the nutritional requisites of humankind. Furthermore, an encompassing compendium of the available genomic resources for cereal crops is presented, accompanied by an elucidation of their judicious utilization in the advancement of crop attributes.

Evolutionarily recent retrotransposons contribute to schizophrenia

Transposable elements (TEs) are mobile genetic elements that constitute half of the human genome. Recent studies suggest that polymorphic non-reference TEs (nrTEs) may contribute to cognitive diseases, such as schizophrenia, through a cis-regulatory effect. The aim of this work is to identify sets of nrTEs putatively linked to an increased risk of developing schizophrenia. To do so, we inspected the nrTE content of genomes from the dorsolateral prefrontal cortex of schizophrenic and control individuals and identified 38 nrTEs that possibly contribute to the emergence of this psychiatric disorder, two of them further confirmed with haplotype-based methods. We then performed in silico functional inferences and found that 9 of the 38 nrTEs act as expression/alternative splicing quantitative trait loci (eQTLs/sQTLs) in the brain, suggesting a possible role in shaping the human cognitive genome structure. To our knowledge, this is the first attempt at identifying polymorphic nrTEs that can contribute to the functionality of the brain. Finally, we suggest that a neurodevelopmental genetic mechanism, which involves evolutionarily young nrTEs, can be key to understanding the ethio-pathogenesis of this complex disorder.

Identifying genomic regions determining shoot and root traits related to nitrogen uptake efficiency in a multiparent advanced generation intercross (MAGIC) winter wheat population in a high-throughput phenotyping facility

In the context of a continuously increasing human population that needs to be fed, with environmental protection in mind, nitrogen use efficiency (NUE) improvement is becoming very important. To understand the natural variation of traits linked to nitrogen uptake efficiency (UPE), one component of NUE, the multiparent advanced generation intercross (MAGIC) winter wheat population WM-800 was phenotyped under two contrasting nitrogen (N) levels in a high-throughput phenotyping facility for six weeks. Three biomass-related, three root-related, and two reflectance-related traits were measured weekly under each treatment. Subsequently, the population was genetically analysed using a total of 13,060 polymorphic haplotypes and singular SNPs for a genome-wide association study (GWAS). In total, we detected 543 quantitative trait loci (QTL) across all time points and traits, which were pooled into 42 stable QTL (sQTL; present in at least three of the six weeks). Besides Rht-B1 and Rht-D1, candidate genes playing a role in gibberellic acid-regulated growth and nitrate transporter genes from the NPF gene family, like NRT 1.1, were linked to sQTL. Two novel sQTL on chromosomes 5 A and 6D showed pleiotropic effects on several traits. The high number of N-specific sQTL indicates that selection for UPE is useful specifically under N-limited conditions.

Haplotype-tagged SNPs improve genomic prediction accuracy for Fusarium head blight resistance and yield-related traits in wheat

Linkage disequilibrium (LD)-based haplotyping with subsequent SNP tagging improved the genomic prediction accuracy up to 0.07 and 0.092 for Fusarium head blight resistance and spike width, respectively, across six different models. Genomic prediction is a powerful tool to enhance genetic gain in plant breeding. However, the method is accompanied by various complications leading to low prediction accuracy. One of the major challenges arises from the complex dimensionality of marker data. To overcome this issue, we applied two pre-selection methods for SNP markers viz. LD-based haplotype-tagging and GWAS-based trait-linked marker identification. Six different models were tested with preselected SNPs to predict the genomic estimated breeding values (GEBVs) of four traits measured in 419 winter wheat genotypes. Ten different sets of haplotype-tagged SNPs were selected by adjusting the level of LD thresholds. In addition, various sets of trait-linked SNPs were identified with different scenarios from the training-test combined and only from the training populations. The BRR and RR-BLUP models developed from haplotype-tagged SNPs had a higher prediction accuracy for FHB and SPW by 0.07 and 0.092, respectively, compared to the corresponding models developed without marker pre-selection. The highest prediction accuracy for SPW and FHB was achieved with tagged SNPs pruned at weak LD thresholds (r² < 0.5), while stringent LD was required for spike length (SPL) and flag leaf area (FLA). Trait-linked SNPs identified only from training populations failed to improve the prediction accuracy of the four studied traits. Pre-selection of SNPs via LD-based haplotype-tagging could play a vital role in optimizing genomic selection and reducing genotyping costs. Furthermore, the method could pave the way for developing low-cost genotyping methods through customized genotyping platforms targeting key SNP markers tagged to essential haplotype blocks.

Genome-wide association study of agronomical and root-related traits in spring barley collection grown under field conditions

The root system is a key component for plant survival and productivity. In particular, under stress conditions, developing plants with a better root architecture can ensure productivity. The objectives of this study were to investigate the phenotypic variation of selected root- and yield-related traits in a diverse panel of spring barley genotypes. By performing a genome-wide association study (GWAS), we identified several associations underlying the variations occurring in root- and yield-related traits in response to natural variations in soil moisture. Here, we report the results of the GWAS based on both individual single-nucleotide polymorphism markers and linkage disequilibrium (LD) blocks of markers for 11 phenotypic traits related to plant morphology, grain quality, and root system in a group of spring barley accessions grown under field conditions. We also evaluated the root structure of these accessions by using a nondestructive method based on electrical capacitance. The results showed the importance of two LD-based blocks on chromosomes 2H and 7H in the expression of root architecture and yield-related traits. Our results revealed the importance of the region on the short arm of chromosome 2H in the expression of root- and yield-related traits. This study emphasized the pleiotropic effect of this region with respect to heading time and other important agronomic traits, including root architecture. Furthermore, this investigation provides new insights into the roles played by root traits in the yield performance of barley plants grown under natural conditions with daily variations in soil moisture content.

Wheat Omics: Advancements and Opportunities

Plant omics, which includes genomics, transcriptomics, metabolomics and proteomics, has played a remarkable role in the discovery of new genes and biomolecules that can be deployed for crop improvement. In wheat, great insights have been gleaned from the utilization of diverse omics approaches for both qualitative and quantitative traits. Especially, a combination of omics approaches has led to significant advances in gene discovery and pathway investigations and in deciphering the essential components of stress responses and yields. Recently, a Wheat Omics database has been developed for wheat which could be used by scientists for further accelerating functional genomics studies. In this review, we have discussed various omics technologies and platforms that have been used in wheat to enhance the understanding of the stress biology of the crop and the molecular mechanisms underlying stress tolerance.

Crop germplasm: Current challenges, physiological-molecular perspective, and advance strategies towards development of climate-resilient crops

Germplasm is a long-term resource management mission and investment for civilization. An estimated ∼7.4 million accessions are held in 1750 plant germplasm centres around the world; yet, only 2% of these assets have been utilized as plant genetic resources (PGRs). According to recent studies, the current food yield trajectory will be insufficient to feed the world's population in 2050. Additionally, possible negative effects in terms of crop failure because of climate change are already being experienced across the world. Therefore, it is necessary to reconciliation of research advancement and innovation of practices for further exploration of the potential of crop germplasm especially for the complex traits associated with yield such as water- and nitrogen use efficiency. In this review, we tried to address current challenges, research gaps, physiological and molecular aspects of two broad spectrum complex traits such as water- and nitrogen-use efficiency, and advanced integrated strategies that could provide a platform for combined stress management for climate-smart crop development. Additionally, recent development in technologies that are directly related to germplasm characterization was highlighted for further molecular utilization towards the development of elite varieties.

Unraveling Genomic Regions Controlling Root Traits as a Function of Nitrogen Availability in the MAGIC Wheat Population WM-800

An ever-growing world population demands to be fed in the future and environmental protection and climate change need to be taken into account. An important factor here is nitrogen uptake efficiency (NUpE), which is influenced by the root system (the interface between plant and soil). To understand the natural variation of root system architecture (RSA) as a function of nitrogen (N) availability, a subset of the multiparent advanced generation intercross (MAGIC) winter wheat population WM-800 was phenotyped under two contrasting N treatments in a high-throughput phenotyping system at the seedling stage. Fourteen root and shoot traits were measured. Subsequently, these traits were genetically analyzed using 13,060 polymorphic haplotypes and SNPs in a genome-wide association study (GWAS). In total, 64 quantitative trait loci (QTL) were detected; 60 of them were N treatment specific. Candidate genes for the detected QTL included NRT1.1 and genes involved in stress signaling under N-, whereas candidate genes under N+ were more associated with general growth, such as mei2 and TaWOX11b. This finding may indicate (i) a disparity of the genetic control of root development under low and high N supply and, furthermore, (ii) the need for an N specific selection of genes and genotypes in breeding new wheat cultivars with improved NUpE.

Two decades of association mapping: Insights on disease resistance in major crops

Climate change across the globe has an impact on the occurrence, prevalence, and severity of plant diseases. About 30% of yield losses in major crops are due to plant diseases; emerging diseases are likely to worsen the sustainable production in the coming years. Plant diseases have led to increased hunger and mass migration of human populations in the past, thus a serious threat to global food security. Equipping the modern varieties/hybrids with enhanced genetic resistance is the most economic, sustainable and environmentally friendly solution. Plant geneticists have done tremendous work in identifying stable resistance in primary genepools and many times other than primary genepools to breed resistant varieties in different major crops. Over the last two decades, the availability of crop and pathogen genomes due to advances in next generation sequencing technologies improved our understanding of trait genetics using different approaches. Genome-wide association studies have been effectively used to identify candidate genes and map loci associated with different diseases in crop plants. In this review, we highlight successful examples for the discovery of resistance genes to many important diseases. In addition, major developments in association studies, statistical models and bioinformatic tools that improve the power, resolution and the efficiency of identifying marker-trait associations. Overall this review provides comprehensive insights into the two decades of advances in GWAS studies and discusses the challenges and opportunities this research area provides for breeding resistant varieties.

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 architecture and QTL selection response for Kernza perennial grain domestication traits

Analysis of multi-year breeding program data revealed that the genetic architecture of an intermediate wheatgrass population was highly polygenic for both domestication and agronomic traits, supporting the use of genomic selection for new crop domestication. Perennial grains have the potential to provide food for humans and decrease the negative impacts of annual agriculture. Intermediate wheatgrass (IWG, Thinopyrum intermedium, Kernza®) is a promising perennial grain candidate that The Land Institute has been breeding since 2003. We evaluated four consecutive breeding cycles of IWG from 2016 to 2020 with each cycle containing approximately 1100 unique genets. Using genotyping-by-sequencing markers, quantitative trait loci (QTL) were mapped for 34 different traits using genome-wide association analysis. Combining data across cycles and years, we found 93 marker-trait associations for 16 different traits, with each association explaining 0.8-5.2% of the observed phenotypic variance. Across the four cycles, only three QTL showed an F_ST differentiation > 0.15 with two corresponding to a decrease in floret shattering. Additionally, one marker associated with brittle rachis was 216 bp from an ortholog of the btr2 gene. Power analysis and quantitative genetic theory were used to estimate the effective number of QTL, which ranged from a minimum of 33 up to 558 QTL for individual traits. This study suggests that key agronomic and domestication traits are under polygenic control and that molecular methods like genomic selection are needed to accelerate domestication and improvement of this new crop.

Capturing Wheat Phenotypes at the Genome Level

Recent technological advances in next-generation sequencing (NGS) technologies have dramatically reduced the cost of DNA sequencing, allowing species with large and complex genomes to be sequenced. Although bread wheat (Triticum aestivum L.) is one of the world's most important food crops, efficient exploitation of molecular marker-assisted breeding approaches has lagged behind that achieved in other crop species, due to its large polyploid genome. However, an international public-private effort spanning 9 years reported over 65% draft genome of bread wheat in 2014, and finally, after more than a decade culminated in the release of a gold-standard, fully annotated reference wheat-genome assembly in 2018. Shortly thereafter, in 2020, the genome of assemblies of additional 15 global wheat accessions was released. As a result, wheat has now entered into the pan-genomic era, where basic resources can be efficiently exploited. Wheat genotyping with a few hundred markers has been replaced by genotyping arrays, capable of characterizing hundreds of wheat lines, using thousands of markers, providing fast, relatively inexpensive, and reliable data for exploitation in wheat breeding. These advances have opened up new opportunities for marker-assisted selection (MAS) and genomic selection (GS) in wheat. Herein, we review the advances and perspectives in wheat genetics and genomics, with a focus on key traits, including grain yield, yield-related traits, end-use quality, and resistance to biotic and abiotic stresses. We also focus on reported candidate genes cloned and linked to traits of interest. Furthermore, we report on the improvement in the aforementioned quantitative traits, through the use of (i) clustered regularly interspaced short-palindromic repeats/CRISPR-associated protein 9 (CRISPR/Cas9)-mediated gene-editing and (ii) positional cloning methods, and of genomic selection. Finally, we examine the utilization of genomics for the next-generation wheat breeding, providing a practical example of using in silico bioinformatics tools that are based on the wheat reference-genome sequence.

Genome-wide association study identifies loci and candidate genes for grain micronutrients and quality traits in wheat (Triticum aestivum L.)

Malnutrition due to micronutrients and protein deficiency is recognized among the major global health issues. Genetic biofortification of wheat is a cost-effective and sustainable strategy to mitigate the global micronutrient and protein malnutrition. Genomic regions governing grain zinc concentration (GZnC), grain iron concentration (GFeC), grain protein content (GPC), test weight (TW), and thousand kernel weight (TKW) were investigated in a set of 184 diverse bread wheat genotypes through genome-wide association study (GWAS). The GWAS panel was genotyped using Breeders' 35 K Axiom Array and phenotyped in three different environments during 2019-2020. A total of 55 marker-trait associations (MTAs) were identified representing all three sub-genomes of wheat. The highest number of MTAs were identified for GPC (23), followed by TKW (15), TW (11), GFeC (4), and GZnC (2). Further, a stable SNP was identified for TKW, and also pleiotropic regions were identified for GPC and TKW. In silico analysis revealed important putative candidate genes underlying the identified genomic regions such as F-box-like domain superfamily, Zinc finger CCCH-type proteins, Serine-threonine/tyrosine-protein kinase, Histone deacetylase domain superfamily, and SANT/Myb domain superfamily proteins, etc. The identified novel MTAs will be validated to estimate their effects in different genetic backgrounds for subsequent use in marker-assisted selection.

Selection signatures in the CIMMYT International Elite Spring and Semi-arid Wheat Yield Trials

The International Maize and Wheat Improvement Center (CIMMYT) annually distributes advanced wheat (Triticum aestivum L.) breeding lines to collaborators worldwide through the International Wheat Improvement Network. Lines are disseminated through international nurseries, including the Elite Spring Wheat Yield Trial (ESWYT) targeted to optimal (irrigated and high production) wheat production areas and the Semi-arid Wheat Yield Trial (SAWYT) targeted to low rainfall environments. A total of 2,184 wheat lines that formed the ESWYT and SAWYT since 1979 and 1992, respectively, were genotyped using genotyping-by-sequencing to explore trends of genetic diversity and selection footprints associated with continuous crop improvement and adaptation. Due to a small population size of each trial, adjacent year trials were pooled into subpopulations. Population structure was evaluated using discriminant analysis of principal components and fixation index. High levels of admixture within and across the ESWYT and SAWYT subpopulations were revealed, indicating that the entire genetic diversity in the overall CIMMYT germplasm pool is harnessed to target core traits to individual mega-environments. Genome wide scans of deviations of minor allele frequencies at each marker identified large linkage blocks in several chromosomes. The scans also revealed that 9.8 and 2.0% of the SNP markers could be associated to selection signatures over time and to environmental adaptation (significant deviations between ESWYT and SAWYT), respectively. Several known genes and previously identified haplotypes associated with grain yield in more recent CIMMYT elite germplasm did fall into genomic regions with directional selection.

GWAS Case Studies in Wheat

With the advancements in next-generation sequencing technologies, leading to millions of single nucleotide polymorphisms in all crop species including wheat, genome-wide association study (GWAS) has become a leading approach for trait dissection. In wheat, GWAS has been conducted for a plethora of traits and more and more studies are being conducted and reported in journals. While application of GWAS has become a routine in wheat using the standardized approaches, there has been a great leap forward using newer models and combination of GWAS with other sets of data. This chapter has reviewed all these latest advancements in GWAS in wheat by citing the most important studies and their outputs. Specially, we have focused on studies that conducted meta-GWAS, multilocus GWAS, haplotype-based GWAS, Environmental- and Eigen-GWAS, and/or GWAS combined with gene regulatory network and pathway analyses or epistatic interactions analyses; all these have taken the association mapping approach to new heights in wheat.

Features and applications of haplotypes in crop breeding

Climate change with altered pest-disease dynamics and rising abiotic stresses threatens resource-constrained agricultural production systems worldwide. Genomics-assisted breeding (GAB) approaches have greatly contributed to enhancing crop breeding efficiency and delivering better varieties. Fast-growing capacity and affordability of DNA sequencing has motivated large-scale germplasm sequencing projects, thus opening exciting avenues for mining haplotypes for breeding applications. This review article highlights ways to mine haplotypes and apply them for complex trait dissection and in GAB approaches including haplotype-GWAS, haplotype-based breeding, haplotype-assisted genomic selection. Improvement strategies that efficiently deploy superior haplotypes to hasten breeding progress will be key to safeguarding global food security.

Genetic dissection of grain architecture-related traits in a winter wheat population

BACKGROUND

The future productivity of wheat (T. aestivum L.) as the most grown crop worldwide is of utmost importance for global food security. Thousand kernel weight (TKW) in wheat is closely associated with grain architecture-related traits, e.g. kernel length (KL), kernel width (KW), kernel area (KA), kernel diameter ratio (KDR), and factor form density (FFD). Discovering the genetic architecture of natural variation in these traits, identifying QTL and candidate genes are the main aims of this study. Therefore, grain architecture-related traits in 261 worldwide winter accessions over three field-year experiments were evaluated.

RESULTS

Genome-wide association analysis using 90K SNP array in FarmCPU model revealed several interesting genomic regions including 17 significant SNPs passing false discovery rate threshold and strongly associated with the studied traits. Four of associated SNPs were physically located inside candidate genes within LD interval e.g. BobWhite_c5872_589 (602,710,399 bp) found to be inside TraesCS6A01G383800 (602,699,767-602,711,726 bp). Further analysis reveals the four novel candidate genes potentially involved in more than one grain architecture-related traits with a pleiotropic effects e.g. TraesCS6A01G383800 gene on 6A encoding oxidoreductase activity was associated with TKW and KA. The allelic variation at the associated SNPs showed significant differences betweeen the accessions carying the wild and mutated alleles e.g. accessions carying C allele of BobWhite_c5872_589, TraesCS6A01G383800 had significantly higher TKW than the accessions carying T allele. Interestingly, these genes were highly expressed in the grain-tissues, demonstrating their pivotal role in controlling the grain architecture.

CONCLUSIONS

These results are valuable for identifying regions associated with kernel weight and dimensions and potentially help breeders in improving kernel weight and architecture-related traits in order to increase wheat yield potential and end-use quality.