Association mapping in plants in the post-GWAS genomics era. Elsevier; 2019. pp. 75-154. [DOI: 10.1016/bs.adgen.2018.12.001]

New perspectives of post-GWAS analyses: From markers to causal genes for more precise crop breeding

Crop breeding advancement is hindered by the imperfection of methods to reveal genes underlying key traits. Genome-wide Association Study (GWAS) is one such method, identifying genomic regions linked to phenotypes. Post-GWAS analyses predict candidate genes and assist in causative mutation (CM) recognition. Here, we assess post-GWAS approaches, address limitations in omics data integration and stress the importance of evaluating associated variants within a broader context of publicly available datasets. Recent advances in bioinformatics tools and genomic strategies for CM identification and allelic variation exploration are reviewed. We discuss the role of markers and marker panel development for more precise breeding. Finally, we highlight the perspectives and challenges of GWAS-based CM prediction for complex quantitative traits.

Elemental profiling and genome-wide association studies reveal genomic variants modulating ionomic composition in Populus trichocarpa leaves

The ionome represents elemental composition in plant tissues and can be an indicator of nutrient status as well as overall plant performance. Thus, identifying genetic determinants governing elemental uptake and storage is an important goal for breeding and engineering biomass feedstocks with improved performance. In this study, we coupled high-throughput ionome characterization of leaf tissues with high-resolution genome-wide association studies (GWAS) to uncover genetic loci that modulate ionomic composition in leaves of poplar (Populus trichocarpa). Significant agreement was observed across the three ionomic profiling platforms tested: inductively coupled plasma-mass spectrometry (ICP-MS), neutron activation analysis (NAA) and laser-induced breakdown spectroscopy (LIBS). Relative quantification of 20 elements using ICP-MS across a population of 584 genotypes, revealed larger variation in micro-nutrients and trace elements content than for macro-nutrients across genotypes. The GWAS performed using a set of high-density (>8.2 million) single nucleotide polymorphisms, identified over 600 loci significantly associated with variations in these mineral elements, pointing to numerous uncharacterized candidate genes. A significant enrichment for genes related to ion homeostasis and transport was observed, including several members of the cation-proton antiporters (CPA) family and MATE efflux transporters, previously reported to be critical for plant growth and fitness in other species. Our results also included a polymorphic copy of the high-affinity molybdenum transporter MOT1 found directly associated to molybdenum content. For the first time in a perennial plant, our results provide evidence of genetic control of mineral content in a model tree species.

Empowering rice breeding with NextGen genomics tools for rapid enhancement nitrogen use efficiency

As rice has no physiological capacity of fixing nitrogen in the soil, its production had always been reliant on the external application of nitrogen (N) to ensure enhanced productivity. In the light of improving nitrogen use efficiency (NUE) in rice, several advanced agronomic strategies have been proposed. However, the soared increase of the prices of N fertilizers and subsequent environmental downfalls caused by the excessive use of N fertilizers, reinforces the prerequisite adaptation of other sustainable, affordable, and globally acceptable strategies. An appropriate alternative approach would be to develop rice cultivars with better NUE. Conventional breeding techniques, however, have had only sporadic success in improving NUE, and hence, this paper proposes a new schema that employs the wholesome benefits of the recent advancements in omics technologies. The suggested approach promotes multidisciplinary research, since such cooperation enables the synthesis of many viewpoints, approaches, and data that result in a comprehensive understanding of NUE in rice. Such collaboration also encourages innovation that leads to developing rice varieties that use nitrogen more effectively, facilitate smart technology transfer, and promotes the adoption of NUE practices by farmers and stakeholders to minimize ecological impact and contribute to a sustainable agricultural future.

Breeding and biotechnology approaches to enhance the nutritional quality of rapeseed byproducts for sustainable alternative protein sources- a critical review

Global protein consumption is increasing exponentially, which requires efficient identification of potential, healthy, and simple protein sources to fulfil the demands. The existing sources of animal proteins are high in fat and low in fiber composition, which might cause serious health risks when consumed regularly. Moreover, protein production from animal sources can negatively affect the environment, as it often requires more energy and natural resources and contributes to greenhouse gas emissions. Thus, finding alternative plant-based protein sources becomes indispensable. Rapeseed is an important oilseed crop and the world's third leading oil source. Rapeseed byproducts, such as seed cakes or meals, are considered the best alternative protein source after soybean owing to their promising protein profile (30%-60% crude protein) to supplement dietary requirements. After oil extraction, these rapeseed byproducts can be utilized as food for human consumption and animal feed. However, anti-nutritional factors (ANFs) like glucosinolates, phytic acid, tannins, and sinapines make them unsuitable for direct consumption. Techniques like microbial fermentation, advanced breeding, and genome editing can improve protein quality, reduce ANFs in rapeseed byproducts, and facilitate their usage in the food and feed industry. This review summarizes these approaches and offers the best bio-nutrition breakthroughs to develop nutrient-rich rapeseed byproducts as plant-based protein sources.

Regulatory balance between ear rot resistance and grain yield and their breeding applications in maize and other crops

BACKGROUND

Fungi are prevalent pathogens that cause substantial yield losses of major crops. Ear rot (ER), which is primarily induced by Fusarium or Aspergillus species, poses a significant challenge to maize production worldwide. ER resistance is regulated by several small effect quantitative trait loci (QTLs). To date, only a few ER-related genes have been identified that impede molecular breeding efforts to breed ER-resistant maize varieties.

AIM OF REVIEW

Our aim here is to explore the research progress and mine genic resources related to ER resistance, and to propose a regulatory model elucidating the ER-resistant mechanism in maize as well as a trade-off model illustrating how crops balance fungal resistance and grain yield. Key Scientific Concepts of Review: This review presents a comprehensive bibliometric analysis of the research history and current trends in the genetic and molecular regulation underlying ER resistance in maize. Moreover, we analyzed and discovered the genic resources by identifying 162 environmentally stable loci (ESLs) from various independent forward genetics studies as well as 1391 conservatively differentially expressed genes (DEGs) that respond to Fusarium or Aspergillus infection through multi-omics data analysis. Additionally, this review discusses the syntenies found among maize ER, wheat Fusariumhead blight (FHB), and rice Bakanaedisease (RBD) resistance-related loci, along with the significant overlap between fungal resistance loci and reported yield-related loci, thus providing valuable insights into the regulatory mechanisms underlying the trade-offs between yield and defense in crops.

Identifying the physiological traits associated with DNA marker using genome wide association in wheat under heat stress

Heat stress poses a significant environmental challenge that profoundly impacts wheat productivity. It disrupts vital physiological processes such as photosynthesis, by impeding the functionality of the photosynthetic apparatus and compromising plasma membrane stability, thereby detrimentally affecting grain development in wheat. The scarcity of identified marker trait associations pertinent to thermotolerance presents a formidable obstacle in the development of marker-assisted selection strategies against heat stress. To address this, wheat accessions were systematically exposed to both normal and heat stress conditions and phenotypic data were collected on physiological traits including proline content, canopy temperature depression, cell membrane injury, photosynthetic rate, transpiration rate (at vegetative and reproductive stage and 'stay-green'. Principal component analysis elucidated the most significant contributors being proline content, transpiration rate, and canopy temperature depression, which exhibited a synergistic relationship with grain yield. Remarkably, cluster analysis delineated the wheat accessions into four discrete groups based on physiological attributes. Moreover, to explore the relationship between physiological traits and DNA markers, 158 wheat accessions were genotyped with 186 SSRs. Allelic frequency and polymorphic information content value were found to be highest on genome A (4.94 and 0.688), chromosome 1A (5.00 and 0.712), and marker Xgwm44 (13.0 and 0.916). Population structure, principal coordinate analysis and cluster analysis also partitioned the wheat accessions into four subpopulations based on genotypic data, highlighting their genetic homogeneity. Population diversity and presence of linkage disequilibrium established the suitability of population for association mapping. Additionally, linkage disequilibrium decay was most pronounced within a 15-20 cM region on chromosome 1A. Association mapping revealed highly significant marker trait associations at Bonferroni correction P < 0.00027. Markers Xwmc418 (located on chromosome 3D) and Xgwm233 (chromosome 7A) demonstrated associations with transpiration rate, while marker Xgwm494 (chromosome 3A) exhibited an association with photosynthetic rates at both vegetative and reproductive stages under heat stress conditions. Additionally, markers Xwmc201 (chromosome 6A) and Xcfa2129 (chromosome 1A) displayed robust associations with canopy temperature depression, while markers Xbarc163 (chromosome 4B) and Xbarc49 (chromosome 5A) were strongly associated with cell membrane injury at both stages. Notably, marker Xbarc49 (chromosome 5A) exhibited a significant association with the 'stay-green' trait under heat stress conditions. These results offers the potential utility in marker-assisted selection, gene pyramiding and genomic selection models to predict performance of wheat accession under heat stress conditions.

Comparison of machine learning methods for genomic prediction of selected Arabidopsis thaliana traits

We present a comparison of machine learning methods for the prediction of four quantitative traits in Arabidopsis thaliana. High prediction accuracies were achieved on individuals grown under standardized laboratory conditions from the 1001 Arabidopsis Genomes Project. An existing body of evidence suggests that linear models may be impeded by their inability to make use of non-additive effects to explain phenotypic variation at the population level. The results presented here use a nested cross-validation approach to confirm that some machine learning methods have the ability to statistically outperform linear prediction models, with the optimal model dependent on availability of training data and genetic architecture of the trait in question. Linear models were competitive in their performance as per previous work, though the neural network class of predictors was observed to be the most accurate and robust for traits with high heritability. The extent to which non-linear models exploit interaction effects will require further investigation of the causal pathways that lay behind their predictions. Future work utilizing more traits and larger sample sizes, combined with an improved understanding of their respective genetic architectures, may lead to improvements in prediction accuracy.

Discovering novel genomic regions explaining adaptation of bread wheat to conservation agriculture through GWAS

To sustainably increase wheat yield to meet the growing world population's food demand in the face of climate change, Conservation Agriculture (CA) is a promising approach. Still, there is a lack of genomic studies investigating the genetic basis of crop adaptation to CA. To dissect the genetic architecture of 19 morpho-physiological traits that could be involved in the enhanced adaptation and performance of genotypes under CA, we performed GWAS to identify MTAs under four contrasting production regimes viz., conventional tillage timely sown (CTTS), conservation agriculture timely sown (CATS), conventional tillage late sown (CTLS) and conservation agriculture late sown (CALS) using an association panel of 183 advanced wheat breeding lines along with 5 checks. Traits like Phi2 (Quantum yield of photosystem II; CATS:0.37, CALS: 0.31), RC (Relative chlorophyll content; CATS:55.51, CALS: 54.47) and PS1 (Active photosystem I centers; CATS:2.45, CALS: 2.23) have higher mean values in CA compared to CT under both sowing times. GWAS identified 80 MTAs for the studied traits across four production environments. The phenotypic variation explained (PVE) by these QTNs ranged from 2.15 to 40.22%. Gene annotation provided highly informative SNPs associated with Phi2, NPQ (Quantum yield of non-photochemical quenching), PS1, and RC which were linked with genes that play crucial roles in the physiological adaptation under both CA and CT. A highly significant SNP AX94651261 (9.43% PVE) was identified to be associated with Phi2, while two SNP markers AX94730536 (30.90% PVE) and AX94683305 (16.99% PVE) were associated with NPQ. Identified QTNs upon validation can be used in marker-assisted breeding programs to develop CA adaptive genotypes.

The OsCLV2s-OsCRN1 co-receptor regulates grain shape in rice

The highly conserved CLV-WUS negative feedback pathway plays a decisive role in regulating stem cell maintenance in shoot and floral meristems in higher plants, including Arabidopsis, rice, maize, and tomato. Here, we find significant natural variations in the OsCLV2c, OsCLV2d, and OsCRN1 loci in a genome-wide association study of grain shape in rice. OsCLV2a, OsCLV2c, OsCLV2d, and OsCRN1 negatively regulate grain length-width ratio and show distinctive geographical distribution, indica-japonica differentiation, and artificial selection signatures. Notably, OsCLV2a and OsCRN1 interact biochemically and genetically, suggesting that the two components function in a complex to regulate grain shape of rice. Furthermore, the genetic contributions of the haplotypes combining OsCLV2a, OsCLV2c, and OsCRN1 are significantly higher than those of each single gene alone in controlling key yield traits. These findings identify two groups of receptor-like kinases that may function as distinct co-receptors to control grain size in rice, thereby revealing a previously unrecognized role of the CLV class genes in regulating seed development and proposing a framework to understand the molecular mechanisms of the CLV-WUS pathway in rice and other crops.

Genomic determinants, architecture, and constraints in drought-related traits in Corymbia calophylla

BACKGROUND

Drought adaptation is critical to many tree species persisting under climate change, however our knowledge of the genetic basis for trees to adapt to drought is limited. This knowledge gap impedes our fundamental understanding of drought response and application to forest production and conservation. To improve our understanding of the genomic determinants, architecture, and trait constraints, we assembled a reference genome and detected ~ 6.5 M variants in 432 phenotyped individuals for the foundational tree Corymbia calophylla.

RESULTS

We found 273 genomic variants determining traits with moderate heritability (h2SNP = 0.26-0.64). Significant variants were predominantly in gene regulatory elements distributed among several haplotype blocks across all chromosomes. Furthermore, traits were constrained by frequent epistatic and pleiotropic interactions.

CONCLUSIONS

Our results on the genetic basis for drought traits in Corymbia calophylla have several implications for the ability to adapt to climate change: (1) drought related traits are controlled by complex genomic architectures with large haplotypes, epistatic, and pleiotropic interactions; (2) the most significant variants determining drought related traits occurred in regulatory regions; and (3) models incorporating epistatic interactions increase trait predictions. Our findings indicate that despite moderate heritability drought traits are likely constrained by complex genomic architecture potentially limiting trees response to climate change.

Mapping QTL associated with resistance to Pseudomonas syringae pv. actinidiae in kiwifruit (Actinidia chinensis var. chinensis)

Pseudomonas syringae pv. actinidiae (Psa) is a bacterial pathogen of kiwifruit. This pathogen causes leaf-spotting, cane dieback, wilting, cankers (lesions), and in severe cases, plant death. Families of diploid A. chinensis seedlings grown in the field show a range of susceptibilities to the disease with up to 100% of seedlings in some families succumbing to Psa. But the effect of selection for field resistance to Psa on the alleles that remain in surviving seedlings has not been assessed. The objective of this work was to analyse, the effect of plant removal from Psa on the allele frequency of an incomplete-factorial-cross population. This population was founded using a range of genotypically distinct diploid A. chinensis var. chinensis parents to make 28 F1 families. However, because of the diversity of these families, low numbers of surviving individuals, and a lack of samples from dead individuals, standard QTL mapping approaches were unlikely to yield good results. Instead, a modified bulk segregant analysis (BSA) overcame these drawbacks while reducing the costs of sampling and sample processing, and the complexity of data analysis. Because the method was modified, part one of this work was used to determine the signal strength required for a QTL to be detected with BSA. Once QTL detection accuracy was known, part two of this work analysed the 28 families from the incomplete-factorial-cross population that had multiple individuals removed due to Psa infection. Each family was assigned to one of eight bulks based on a single parent that contributed to the families. DNA was extracted in bulk by grinding sampled leaf discs together before DNA extraction. Each sample bulk was compared against a bulk made up of WGS data from the parents contributing to the sample bulk. The deviation in allele frequency from the expected allele frequency within surviving populations using the modified BSA method was able to identify 11 QTLs for Psa that were present in at least two analyses. The identification of these Psa resistance QTL will enable marker development to selectively breed for resistance to Psa in future kiwifruit breeding programs.

Natural variation in Brachypodium distachyon responses to combined abiotic stresses

The demand for agricultural production is becoming more challenging as climate change increases global temperature and the frequency of extreme weather events. This study examines the phenotypic variation of 149 accessions of Brachypodium distachyon under drought, heat, and the combination of stresses. Heat alone causes the largest amounts of tissue damage while the combination of stresses causes the largest decrease in biomass compared to other treatments. Notably, Bd21-0, the reference line for B. distachyon, did not have robust growth under stress conditions, especially the heat and combined drought and heat treatments. The climate of origin was significantly associated with B. distachyon responses to the assessed stress conditions. Additionally, a GWAS found loci associated with changes in plant height and the amount of damaged tissue under stress. Some of these SNPs were closely located to genes known to be involved in responses to abiotic stresses and point to potential causative loci in plant stress response. However, SNPs found to be significantly associated with a response to heat or drought individually are not also significantly associated with the combination of stresses. This, with the phenotypic data, suggests that the effects of these abiotic stresses are not simply additive, and the responses to the combined stresses differ from drought and heat alone.

Comparative quantitative trait loci analysis framework reveals relationships between salt stress responsive phenotypes and pathways

Soil salinity is a complex abiotic stress that involves several biological pathways. Hence, focusing on a specific or a few salt-tolerant phenotypes is unlikely to provide comprehensive insights into the intricate and interwinding mechanisms that regulate salt responsiveness. In this study, we develop a heuristic framework for systematically integrating and comprehensively evaluating quantitative trait loci (QTL) analyses from multiple stress-related traits obtained by different studies. Making use of a combined set of 46 salinity-related traits from three independent studies that were based on the same chromosome segment substitution line (CSSL) population of rice (Oryza sativa), we demonstrate how our approach can address technical biases and limitations from different QTL studies and calling methods. This allows us to compile a comprehensive list of trait-specific and multi-trait QTLs, as well as salinity-related candidate genes. In doing so, we discover several novel relationships between traits that demonstrate similar trends of phenotype scores across the CSSLs, as well as the similarities between genomic locations that the traits were mapped to. Finally, we experimentally validate our findings by expression analyses and functional validations of several selected candidate genes from multiple pathways in rice and Arabidopsis orthologous genes, including OsKS7 (ENT-KAURENE SYNTHASE 7), OsNUC1 (NUCLEOLIN 1) and OsFRO1 (FERRIC REDUCTASE OXIDASE 1) to name a few. This work not only introduces a novel approach for conducting comparative analyses of multiple QTLs, but also provides a list of candidate genes and testable hypotheses for salinity-related mechanisms across several biological pathways.

Advancements and Prospects of Genome-Wide Association Studies (GWAS) in Maize

Genome-wide association studies (GWAS) have emerged as a powerful tool for unraveling intricate genotype-phenotype association across various species. Maize (Zea mays L.), renowned for its extensive genetic diversity and rapid linkage disequilibrium (LD), stands as an exemplary candidate for GWAS. In maize, GWAS has made significant advancements by pinpointing numerous genetic loci and potential genes associated with complex traits, including responses to both abiotic and biotic stress. These discoveries hold the promise of enhancing adaptability and yield through effective breeding strategies. Nevertheless, the impact of environmental stress on crop growth and yield is evident in various agronomic traits. Therefore, understanding the complex genetic basis of these traits becomes paramount. This review delves into current and future prospectives aimed at yield, quality, and environmental stress resilience in maize and also addresses the challenges encountered during genomic selection and molecular breeding, all facilitated by the utilization of GWAS. Furthermore, the integration of omics, including genomics, transcriptomics, proteomics, metabolomics, epigenomics, and phenomics has enriched our understanding of intricate traits in maize, thereby enhancing environmental stress tolerance and boosting maize production. Collectively, these insights not only advance our understanding of the genetic mechanism regulating complex traits but also propel the utilization of marker-assisted selection in maize molecular breeding programs, where GWAS plays a pivotal role. Therefore, GWAS provides robust support for delving into the genetic mechanism underlying complex traits in maize and enhancing breeding strategies.

Structure-based prediction of protein-protein interaction network in rice

Comprehensive protein-protein interaction (PPI) maps are critical for understanding the functional organization of the proteome, but challenging to produce experimentally. Here, we developed a computational method for predicting PPIs based on protein docking. Evaluation of performance on benchmark sets demonstrated the ability of the docking-based method to accurately identify PPIs using predicted protein structures. By employing the docking-based method, we constructed a structurally resolved PPI network consisting of 24,653 interactions between 2,131 proteins, which greatly extends the current knowledge on the rice protein-protein interactome. Moreover, we mapped the trait-associated single nucleotide polymorphisms (SNPs) to the structural interactome, and computationally identified 14 SNPs that had significant consequences on PPI network. The protein structural interactome map provided a resource to facilitate functional investigation of PPI-perturbing alleles associated with agronomically important traits in rice.

Mining genic resources regulating nitrogen-use efficiency based on integrative biological analyses and their breeding applications in maize and other crops

Nitrogen (N) is an essential factor for limiting crop yields, and cultivation of crops with low nitrogen-use efficiency (NUE) exhibits increasing environmental and ecological risks. Hence, it is crucial to mine valuable NUE improvement genes, which is very important to develop and breed new crop varieties with high NUE in sustainable agriculture system. Quantitative trait locus (QTL) and genome-wide association study (GWAS) analysis are the most common methods for dissecting genetic variations underlying complex traits. In addition, with the advancement of biotechnology, multi-omics technologies can be used to accelerate the process of exploring genetic variations. In this study, we integrate the substantial data of QTLs, quantitative trait nucleotides (QTNs) from GWAS, and multi-omics data including transcriptome, proteome, and metabolome and further analyze their interactions to predict some NUE-related candidate genes. We also provide the genic resources for NUE improvement among maize, rice, wheat, and sorghum by homologous alignment and collinearity analysis. Furthermore, we propose to utilize the knowledge gained from classical cases to provide the frameworks for improving NUE and breeding N-efficient varieties through integrated genomics, systems biology, and modern breeding technologies.

Efficient identification of QTL for agronomic traits in foxtail millet (Setaria italica) using RTM- and MLM-GWAS

KEY MESSAGE

Eleven QTLs for agronomic traits were identified by RTM- and MLM-GWAS, putative candidate genes were predicted and two markers for grain weight were developed and validated. Foxtail millet (Setaria italica), the second most cultivated millet crop after pearl millet, is an important grain crop in arid regions. Seven agronomic traits of 408 diverse foxtail millet accessions from 15 provinces in China were evaluated in three environments. They were clustered into two divergent groups based on genotypic data using ADMIXTURE, which was highly consistent with their geographical distribution. Two models for genome-wide association studies (GWAS), namely restricted two-stage multi-locus multi-allele (RTM)-GWAS and mixed linear model (MLM)-GWAS, were used to dissect the genetic architecture of the agronomic traits based on 13,723 SNPs. Eleven quantitative trait loci (QTLs) for seven traits were identified using two models (RTM- and MLM-GWAS). Among them, five were considered stable QTLs that were identified in at least two environments using MLM-GWAS. One putative candidate gene (SETIT_006045mg, Chr4: 744,701-746,852) that can enhance grain weight per panicle was identified based on homologous gene comparison and gene expression analysis and was validated by haplotype analysis of 330 accessions with high-depth (10×) resequencing data (unpublished). In addition, homologous gene comparison and haplotype analysis identified one putative foxtail millet ortholog (SETIT_032906mg, Chr2: 5,020,600-5,029,771) with rice affecting the target traits. Two markers (cGWP6045 and kTGW2906) were developed and validated and can be used for marker-assisted selection of foxtail millet with high grain weight. The results provide a fundamental resource for foxtail millet genetic research and breeding and demonstrate the power of integrating RTM- and MLM-GWAS approaches as a complementary strategy for investigating complex traits in foxtail millet.

kGWASflow: a modular, flexible, and reproducible Snakemake workflow for k-mers-based GWAS

Genome-wide association studies (GWAS) have been widely used to identify genetic variation associated with complex traits. Despite its success and popularity, the traditional GWAS approach comes with a variety of limitations. For this reason, newer methods for GWAS have been developed, including the use of pan-genomes instead of a reference genome and the utilization of markers beyond single-nucleotide polymorphisms, such as structural variations and k-mers. The k-mers-based GWAS approach has especially gained attention from researchers in recent years. However, these new methodologies can be complicated and challenging to implement. Here, we present kGWASflow, a modular, user-friendly, and scalable workflow to perform GWAS using k-mers. We adopted an existing kmersGWAS method into an easier and more accessible workflow using management tools like Snakemake and Conda and eliminated the challenges caused by missing dependencies and version conflicts. kGWASflow increases the reproducibility of the kmersGWAS method by automating each step with Snakemake and using containerization tools like Docker. The workflow encompasses supplemental components such as quality control, read-trimming procedures, and generating summary statistics. kGWASflow also offers post-GWAS analysis options to identify the genomic location and context of trait-associated k-mers. kGWASflow can be applied to any organism and requires minimal programming skills. kGWASflow is freely available on GitHub (https://github.com/akcorut/kGWASflow) and Bioconda (https://anaconda.org/bioconda/kgwasflow).

Multi-locus genome-wide association study and genomic prediction for flowering time in chrysanthemum

MAIN CONCLUSION

Multi-locus GWAS detected several known and candidate genes responsible for flowering time in chrysanthemum. The associations could greatly increase the predictive ability of genome selection that accelerates the possible application of GS in chrysanthemum breeding. Timely flowering is critical for successful reproduction and determines the economic value for ornamental plants. To investigate the genetic architecture of flowering time in chrysanthemum, a multi-locus genome-wide association study (GWAS) was performed using a collection of 200 accessions and 330,710 single-nucleotide polymorphisms (SNPs) via 3VmrMLM method. Five flowering time traits including budding (FBD), visible colouring (VC), early opening (EO), full-bloom (OF) and senescing (SF) stages, plus five derived conditional traits were recorded in two environments. Extensive phenotypic variations were observed for these flowering time traits with coefficients of variation ranging from 6.42 to 38.27%, and their broad-sense heritability ranged from 71.47 to 96.78%. GWAS revealed 88 stable quantitative trait nucleotides (QTNs) and 93 QTN-by-environment interactions (QEIs) associated with flowering time traits, accounting for 0.50-8.01% and 0.30-10.42% of the phenotypic variation, respectively. Amongst the genes around these stable QTNs and QEIs, 21 and 10 were homologous to known flowering genes in Arabidopsis; 20 and 11 candidate genes were mined by combining the functional annotation and transcriptomics data, respectively, such as MYB55, FRIGIDA-like, WRKY75 and ANT. Furthermore, genomic selection (GS) was assessed using three models and seven unique marker datasets. We found the prediction accuracy (PA) using significant SNPs identified by GWAS under SVM model exhibited the best performance with PA ranging from 0.90 to 0.95. Our findings provide new insights into the dynamic genetic architecture of flowering time and the identified significant SNPs and candidate genes will accelerate the future molecular improvement of chrysanthemum.

Genome resources for the elite bread wheat cultivar Aikang 58 and mining of elite homeologous haplotypes for accelerating wheat improvement

Despite recent progress in crop genomics studies, the genomic changes brought about by modern breeding selection are still poorly understood, thus hampering genomics-assisted breeding, especially in polyploid crops with compound genomes such as common wheat (Triticum aestivum). In this work, we constructed genome resources for the modern elite common wheat variety Aikang 58 (AK58). Comparative genomics between AK58 and the landrace cultivar Chinese Spring (CS) shed light on genomic changes that occurred through recent varietal improvement. We also explored subgenome diploidization and divergence in common wheat and developed a homoeologous locus-based genome-wide association study (HGWAS) approach, which was more effective than single homoeolog-based GWAS in unraveling agronomic trait-associated loci. A total of 123 major HGWAS loci were detected using a genetic population derived from AK58 and CS. Elite homoeologous haplotypes (HHs), formed by combinations of subgenomic homoeologs of the associated loci, were found in both parents and progeny, and many could substantially improve wheat yield and related traits. We built a website where users can download genome assembly sequence and annotation data for AK58, perform blast analysis, and run JBrowse. Our work enriches genome resources for wheat, provides new insights into genomic changes during modern wheat improvement, and suggests that efficient mining of elite HHs can make a substantial contribution to genomics-assisted breeding in common wheat and other polyploid crops.

Identification of Multiple Genetic Loci Related to Low-Temperature Tolerance during Germination in Maize (Zea maize L.) through a Genome-Wide Association Study

Low-temperature stress during the germination stage is an important abiotic stress that affects the growth and development of northern spring maize and seriously restricts maize yield and quality. Although some quantitative trait locis (QTLs) related to low-temperature tolerance in maize have been detected, only a few can be commonly detected, and the QTL intervals are large, indicating that low-temperature tolerance is a complex trait that requires more in-depth research. In this study, 296 excellent inbred lines from domestic and foreign origins (America and Europe) were used as the study materials, and a low-coverage resequencing method was employed for genome sequencing. Five phenotypic traits related to low-temperature tolerance were used to assess the genetic diversity of maize through a genome-wide association study (GWAS). A total of 14 SNPs significantly associated with low-temperature tolerance were detected (-log10(P) > 4), and an SNP consistently linked to low-temperature tolerance in the field and indoors during germination was utilized as a marker. This SNP, 14,070, was located on chromosome 5 at position 2,205,723, which explained 4.84-9.68% of the phenotypic variation. The aim of this study was to enrich the genetic theory of low-temperature tolerance in maize and provide support for the innovation of low-temperature tolerance resources and the breeding of new varieties.

Deciphering spike architecture formation towards yield improvement in wheat

Wheat is the most widely grown crop globally, providing 20% of the daily consumed calories and protein content around the world. With the growing global population and frequent occurrence of extreme weather caused by climate change, ensuring adequate wheat production is essential for food security. The architecture of the inflorescence plays a crucial role in determining the grain number and size, which is a key trait for improving yield. Recent advances in wheat genomics and gene cloning techniques have improved our understanding of wheat spike development and its applications in breeding practices. Here, we summarize the genetic regulation network governing wheat spike formation, the strategies used for identifying and studying the key factors affecting spike architecture, and the progress made in breeding applications. Additionally, we highlight future directions that will aid in the regulatory mechanistic study of wheat spike determination and targeted breeding for grain yield improvement.

Green revolution to genome revolution: driving better resilient crops against environmental instability

Crop improvement programmes began with traditional breeding practices since the inception of agriculture. Farmers and plant breeders continue to use these strategies for crop improvement due to their broad application in modifying crop genetic compositions. Nonetheless, conventional breeding has significant downsides in regard to effort and time. Crop productivity seems to be hitting a plateau as a consequence of environmental issues and the scarcity of agricultural land. Therefore, continuous pursuit of advancement in crop improvement is essential. Recent technical innovations have resulted in a revolutionary shift in the pattern of breeding methods, leaning further towards molecular approaches. Among the promising approaches, marker-assisted selection, QTL mapping, omics-assisted breeding, genome-wide association studies and genome editing have lately gained prominence. Several governments have progressively relaxed their restrictions relating to genome editing. The present review highlights the evolutionary and revolutionary approaches that have been utilized for crop improvement in a bid to produce climate-resilient crops observing the consequence of climate change. Additionally, it will contribute to the comprehension of plant breeding succession so far. Investing in advanced sequencing technologies and bioinformatics will deepen our understanding of genetic variations and their functional implications, contributing to breakthroughs in crop improvement and biodiversity conservation.

Combined genome-wide association studies and expression quantitative trait locus analysis uncovers a genetic regulatory network of floral organ number in a tree peony (Paeonia suffruticosa Andrews) breeding population

Great progress has been made in our understanding of floral organ identity determination and its regulatory network in many species; however, the quantitative genetic basis of floral organ number variation is far less well understood for species-specific traits from the perspective of population variation. Here, using a tree peony (Paeonia suffruticosa Andrews, Paeoniaceae) cultivar population as a model, the phenotypic polymorphism and genetic variation based on genome-wide association studies (GWAS) and expression quantitative trait locus (eQTL) analysis were analyzed. Based on 24 phenotypic traits of 271 representative cultivars, the transcript profiles of 119 cultivars were obtained, which indicated abundant genetic variation in tree peony. In total, 86 GWAS-related cis-eQTLs and 3188 trans-eQTL gene pairs were found to be associated with the numbers of petals, stamens, and carpels. In addition, 19 floral organ number-related hub genes with 121 cis-eQTLs were obtained by weighted gene co-expression network analysis, among which five hub genes belonging to the ABCE genes of the MADS-box family and their spatial-temporal co-expression and regulatory network were constructed. These results not only help our understanding of the genetic basis of floral organ number variation during domestication, but also pave the way to studying the quantitative genetics and evolution of flower organ number and their regulatory network within populations.

A Genome-Wide Association Study to Identify Novel Candidate Genes Related to Low-Nitrogen Tolerance in Cucumber (Cucumis sativus L.)

Cucumber is one of the most important vegetables, and nitrogen is essential for the growth and fruit production of cucumbers. It is crucial to develop cultivars with nitrogen limitation tolerance or high nitrogen efficiency for green and efficient development in cucumber industry. To reveal the genetic basis of cucumber response to nitrogen starvation, a genome-wide association study (GWAS) was conducted on a collection of a genetically diverse population of cucumber (Cucumis sativus L.) comprising 88 inbred and DH accessions including the North China type, the Eurasian type, the Japanese and South China type mixed subtype, and the South China type subtype. Phenotypic evaluation of six traits under control (14 mM) and treatment (3.5 mM) N conditions depicted the presence of broad natural variation in the studied population. The GWAS results showed that there were significant differences in the population for nitrogen limitation treatment. Nine significant loci were identified corresponding to six LD blocks, three of which overlapped. Sixteen genes were selected by GO annotation associated with nitrogen. Five low-nitrogen stress tolerance genes were finally identified by gene haplotype analysis: CsaV3_3G003630 (CsNRPD1), CsaV3_3G002970 (CsNRT1.1), CsaV3_4G030260 (CsSnRK2.5), CsaV3_4G026940, and CsaV3_3G011820 (CsNPF5.2). Taken together, the experimental data and identification of candidate genes presented in this study offer valuable insights and serve as a useful reference for the genetic enhancement of nitrogen limitation tolerance in cucumbers.

GWAS and genomic prediction for pre-harvest sprouting tolerance involving sprouting score and two other related traits in spring wheat

In wheat, a genome-wide association study (GWAS) and genomic prediction (GP) analysis were conducted for pre-harvest sprouting (PHS) tolerance and two of its related traits. For this purpose, an association panel of 190 accessions was phenotyped for PHS (using sprouting score), falling number, and grain color over two years and genotyped with 9904 DArTseq based SNP markers. GWAS for main-effect quantitative trait nucleotides (M-QTNs) using three different models (CMLM, SUPER, and FarmCPU) and epistatic QTNs (E-QTNs) using PLINK were performed. A total of 171 M-QTNs (CMLM, 47; SUPER, 70; FarmCPU, 54) for all three traits, and 15 E-QTNs involved in 20 first-order epistatic interactions were identified. Some of the above QTNs overlapped the previously reported QTLs, MTAs, and cloned genes, allowing delineating 26 PHS-responsive genomic regions that spread over 16 wheat chromosomes. As many as 20 definitive and stable QTNs were considered important for use in marker-assisted recurrent selection (MARS). The gene, TaPHS1, for PHS tolerance (PHST) associated with one of the QTNs was also validated using the KASP assay. Some of the M-QTNs were shown to have a key role in the abscisic acid pathway involved in PHST. Genomic prediction accuracies (based on the cross-validation approach) using three different models ranged from 0.41 to 0.55, which are comparable to the results of previous studies. In summary, the results of the present study improved our understanding of the genetic architecture of PHST and its related traits in wheat and provided novel genomic resources for wheat breeding based on MARS and GP.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-023-01357-5.

Mapping Genetic Variation in Arabidopsis in Response to Plant Growth-Promoting Bacterium Azoarcus olearius DQS-4T

Plant growth-promoting bacteria (PGPB) can enhance plant health by facilitating nutrient uptake, nitrogen fixation, protection from pathogens, stress tolerance and/or boosting plant productivity. The genetic determinants that drive the plant-bacteria association remain understudied. To identify genetic loci highly correlated with traits responsive to PGPB, we performed a genome-wide association study (GWAS) using an Arabidopsis thaliana population treated with Azoarcus olearius DQS-4^T. Phenotypically, the 305 Arabidopsis accessions tested responded differently to bacterial treatment by improving, inhibiting, or not affecting root system or shoot traits. GWA mapping analysis identified several predicted loci associated with primary root length or root fresh weight. Two statistical analyses were performed to narrow down potential gene candidates followed by haplotype block analysis, resulting in the identification of 11 loci associated with the responsiveness of Arabidopsis root fresh weight to bacterial inoculation. Our results showed considerable variation in the ability of plants to respond to inoculation by A. olearius DQS-4^T while revealing considerable complexity regarding statistically associated loci with the growth traits measured. This investigation is a promising starting point for sustainable breeding strategies for future cropping practices that may employ beneficial microbes and/or modifications of the root microbiome.

Genetics of spot blotch resistance in bread wheat (Triticum aestivum L.) using five models for GWAS

Genetic architecture of resistance to spot blotch in wheat was examined using a Genome-Wide Association Study (GWAS) involving an association panel comprising 303 diverse genotypes. The association panel was evaluated at two different locations in India including Banaras Hindu University (BHU), Varanasi (Uttar Pradesh), and Borlaug Institute for South Asia (BISA), Pusa, Samastipur (Bihar) for two consecutive years (2017-2018 and 2018-2019), thus making four environments (E1, BHU 2017-18; E2, BHU 2018-19; E3, PUSA, 2017-18; E4, PUSA, 2018-19). The panel was genotyped for 12,196 SNPs based on DArT-seq (outsourced to DArT Ltd by CIMMYT); these SNPs included 5,400 SNPs, which could not be assigned to individual chromosomes and were therefore, described as unassigned by the vendor. Phenotypic data was recorded on the following three disease-related traits: (i) Area Under Disease Progress Curve (AUDPC), (ii) Incubation Period (IP), and (iii) Lesion Number (LN). GWAS was conducted using each of five different models, which included two single-locus models (CMLM and SUPER) and three multi-locus models (MLMM, FarmCPU, and BLINK). This exercise gave 306 MTAs, but only 89 MTAs (33 for AUDPC, 30 for IP and 26 for LN) including a solitary MTA detected using all the five models and 88 identified using four of the five models (barring SUPER) were considered to be important. These were used for further analysis, which included identification of candidate genes (CGs) and their annotation. A majority of these MTAs were novel. Only 70 of the 89 MTAs were assigned to individual chromosomes; the remaining 19 MTAs belonged to unassigned SNPs, for which chromosomes were not known. Seven MTAs were selected on the basis of minimum P value, number of models, number of environments and location on chromosomes with respect to QTLs reported earlier. These 7 MTAs, which included five main effect MTAs and two for epistatic interactions, were considered to be important for marker-assisted selection (MAS). The present study thus improved our understanding of the genetics of resistance against spot blotch in wheat and provided seven MTAs, which may be used for MAS after due validation.

Genetic improvement toward nitrogen-use efficiency in rice: Lessons and perspectives

The indispensable role of nitrogen fertilizer in ensuring world food security together with the severe threats it poses to the ecosystem makes the usage of nitrogen fertilizer a major challenge for sustainable agriculture. Genetic improvement of crops with high nitrogen-use efficiency (NUE) is one of the most feasible solutions for tackling this challenge. In the last two decades, extensive efforts toward dissecting the variation of NUE-related traits and the underlying genetic basis in different germplasms have been made, and a series of achievements have been obtained in crops, especially in rice. Here, we summarize the approaches used for genetic dissection of NUE and the functions of the causal genes in modulating NUE as well as their applications in NUE improvement in rice. Strategies for exploring the variants controlling NUE and breeding future crops with "less-input-more-output" for sustainable agriculture are also proposed.

Genome-wide association study of traits in sacred lotus uncovers MITE-associated variants underlying stamen petaloid and petal number variations

Understanding the genetic variants responsible for floral trait diversity is important for the molecular breeding of ornamental flowers. Widely used in water gardening for thousands of years, the sacred lotus exhibits a wide range of diversity in floral organs. Nevertheless, the genetic variations underlying various morphological characteristics in lotus remain largely unclear. Here, we performed a genome-wide association study of sacred lotus for 12 well-recorded ornamental traits. Given a moderate linkage disequilibrium level of 32.9 kb, we successfully identified 149 candidate genes responsible for seven flower traits and plant size variations, including many pleiotropic genes affecting multiple floral-organ-related traits, such as NnKUP2. Notably, we found a 2.75-kb presence-and-absence genomic fragment significantly associated with stamen petaloid and petal number variations, which was further confirmed by re-examining another independent population dataset with petal number records. Intriguingly, this fragment carries MITE transposons bound by siRNAs and is related to the expression differentiation of a nearby candidate gene between few-petalled and double-petalled lotuses. Overall, these genetic variations and candidate genes responsible for diverse lotus traits revealed by our GWAS highlight the role of transposon variations, particularly MITEs, in shaping floral trait diversity.

Genome-wide association analysis to delineate high-quality SNPs for seed micronutrient density in chickpea (Cicer arietinum L.)

Chickpea is the most important nutrient-rich grain legume crop in the world. A diverse core set of 147 chickpea genotypes was genotyped with a Axiom(®)50K CicerSNP array and trait phenotyped in two different environments for four seed micronutrients (Zn, Cu, Fe and Mn). The trait data and high-throughput 50K SNP genotypic data were used for the genome-wide association study (GWAS). The study led to the discovery of genes/QTLs for seed Zn, Cu, Fe and Mn, concentrations in chickpea. The analysis of seed micronutrient data revealed significant differences for all four micronutrient concentrations (P ≤ 0.05). The mean concentrations of seed Zn, Cu, Fe and Mn pooled over the 2 years were 45.9 ppm, 63.8 ppm 146.1 ppm, and 27.0 ppm, respectively. The analysis of results led to the identification of 35 SNPs significantly associated with seed Zn, Cu, Fe and Mn concentrations. Among these 35 marker-trait associations (MTAs), 5 were stable (consistently identified in different environments), 6 were major (explaining more than 15% of the phenotypic variation for an individual trait) and 3 were both major and stable MTAs. A set of 6 MTAs, MTAs (3 for Mn, 2 for Fe, and 1 for Cu) reported by us during the present study have been also reported in the same/almost same genomic regions in earlier studies and therefore declared as validated MTAs. The stable, major and validated MTAs identified during the present study will prove useful in future chickpea molecular breeding programs aimed at enhancing the seed nutrient density of chickpea.

Genomic regions associate with major axes of variation driven by gas exchange and leaf construction traits in cultivated sunflower (Helianthus annuus L.)

Stomata and leaf veins play an essential role in transpiration and the movement of water throughout leaves. These traits are thus thought to play a key role in the adaptation of plants to drought and a better understanding of the genetic basis of their variation and coordination could inform efforts to improve drought tolerance. Here, we explore patterns of variation and covariation in leaf anatomical traits and analyze their genetic architecture via genome-wide association (GWA) analyses in cultivated sunflower (Helianthus annuus L.). Traits related to stomatal density and morphology as well as lower-order veins were manually measured from digital images while the density of minor veins was estimated using a novel deep learning approach. Leaf, stomatal, and vein traits exhibited numerous significant correlations that generally followed expectations based on functional relationships. Correlated suites of traits could further be separated along three major principal component (PC) axes that were heavily influenced by variation in traits related to gas exchange, leaf hydraulics, and leaf construction. While there was limited evidence of colocalization when individual traits were subjected to GWA analyses, major multivariate PC axes that were most strongly influenced by several traits related to gas exchange or leaf construction did exhibit significant genomic associations. These results provide insight into the genetic basis of leaf trait covariation and showcase potential targets for future efforts aimed at modifying leaf anatomical traits in sunflower.

Integrating speed breeding with artificial intelligence for developing climate-smart crops

INTRODUCTION

In climate change, breeding crop plants with improved productivity, sustainability, and adaptability has become a daunting challenge to ensure global food security for the ever-growing global population. Correspondingly, climate-smart crops are also the need to regulate biomass production, which is imperative for the maintenance of ecosystem services worldwide. Since conventional breeding technologies for crop improvement are limited, time-consuming, and involve laborious selection processes to foster new and improved crop varieties. An urgent need is to accelerate the plant breeding cycle using artificial intelligence (AI) to depict plant responses to environmental perturbations in real-time.

MATERIALS AND METHODS

The review is a collection of authorized information from various sources such as journals, books, book chapters, technical bulletins, conference papers, and verified online contents.

CONCLUSIONS

Speed breeding has emerged as an essential strategy for accelerating the breeding cycles of crop plants by growing them under artificial light and temperature conditions. Furthermore, speed breeding can also integrate marker-assisted selection and cutting-edged gene-editing tools for early selection and manipulation of essential crops with superior agronomic traits. Scientists have recently applied next-generation AI to delve deeper into the complex biological and molecular mechanisms that govern plant functions under environmental cues. In addition, AIs can integrate, assimilate, and analyze complex OMICS data sets, an essential prerequisite for successful speed breeding protocol implementation to breed crop plants with superior yield and adaptability.

Cytogenetics to functional genomics: six decades journey of Professor P.K. Gupta

We had the fortune of starting our scientific/research careers in the Molecular Biology and Crop Biotechnology Laboratory of Professor P.K. Gupta at Ch. Charan Singh University, Meerut, UP, India. Here, we describe the most important scientific contributions of our beloved mentor in the area of cytotaxonomy, cytogenetics, mutation breeding, quantitative genetics, molecular biology, crop biotechnology and plant genomics, on his 85th birthday. Important contributions made in the development and use of genomics resources including the development and use of different kinds of molecular markers, genetic and physical mapping, quantitative trait locus (QTL) interval mapping, genome-wide association mapping and molecular breeding including marker-assisted selection have been briefly summarized. Efforts have been also made to give readers a glimpse of important contributions in the study of cytology/apomixis of grasses, cytotaxonomic studies in asteraceae/fabaceae, nuclear/repetitive DNA content in Lolium, interspecific/intergeneric relationships involving the genus Hordeum and re-examining taxonomy of the tribe Triticeae.

Genetic Structure and Molecular Mechanisms Underlying the Formation of Tassel, Anther, and Pollen in the Male Inflorescence of Maize (Zea mays L.)

Maize tassel is the male reproductive organ which is located at the plant's apex; both its morphological structure and fertility have a profound impact on maize grain yield. More than 40 functional genes regulating the complex tassel traits have been cloned up to now. However, the detailed molecular mechanisms underlying the whole process, from male inflorescence meristem initiation to tassel morphogenesis, are seldom discussed. Here, we summarize the male inflorescence developmental genes and construct a molecular regulatory network to further reveal the molecular mechanisms underlying tassel-trait formation in maize. Meanwhile, as one of the most frequently studied quantitative traits, hundreds of quantitative trait loci (QTLs) and thousands of quantitative trait nucleotides (QTNs) related to tassel morphology have been identified so far. To reveal the genetic structure of tassel traits, we constructed a consensus physical map for tassel traits by summarizing the genetic studies conducted over the past 20 years, and identified 97 hotspot intervals (HSIs) that can be repeatedly mapped in different labs, which will be helpful for marker-assisted selection (MAS) in improving maize yield as well as for providing theoretical guidance in the subsequent identification of the functional genes modulating tassel morphology. In addition, maize is one of the most successful crops in utilizing heterosis; mining of the genic male sterility (GMS) genes is crucial in developing biotechnology-based male-sterility (BMS) systems for seed production and hybrid breeding. In maize, more than 30 GMS genes have been isolated and characterized, and at least 15 GMS genes have been promptly validated by CRISPR/Cas9 mutagenesis within the past two years. We thus summarize the maize GMS genes and further update the molecular regulatory networks underlying male fertility in maize. Taken together, the identified HSIs, genes and molecular mechanisms underlying tassel morphological structure and male fertility are useful for guiding the subsequent cloning of functional genes and for molecular design breeding in maize. Finally, the strategies concerning efficient and rapid isolation of genes controlling tassel morphological structure and male fertility and their application in maize molecular breeding are also discussed.

Genetics as a key to improving crop photosynthesis

Since the basic biochemical mechanisms of photosynthesis are remarkably conserved among plant species, genetic modification approaches have so far been the main route to improve the photosynthetic performance of crops. Yet, phenotypic variation observed in wild species and between varieties of crop species implies there is standing natural genetic variation for photosynthesis, offering a largely unexplored resource to use for breeding crops with improved photosynthesis and higher yields. The reason this has not yet been explored is that the variation probably involves thousands of genes, each contributing only a little to photosynthesis, making them hard to identify without proper phenotyping and genetic tools. This is changing, though, and increasingly studies report on quantitative trait loci for photosynthetic phenotypes. So far, hardly any of these quantitative trait loci have been used in marker assisted breeding or genomic selection approaches to improve crop photosynthesis and yield, and hardly ever have the underlying causal genes been identified. We propose to take the genetics of photosynthesis to a higher level, and identify the genes and alleles nature has used for millions of years to tune photosynthesis to be in line with local environmental conditions. We will need to determine the physiological function of the genes and alleles, and design novel strategies to use this knowledge to improve crop photosynthesis through conventional plant breeding, based on readily available crop plant germplasm. In this work, we present and discuss the genetic methods needed to reveal natural genetic variation, and elaborate on how to apply this to improve crop photosynthesis.

Mining the Vavilov wheat diversity panel for new sources of adult plant resistance to stripe rust

Multi-year evaluation of the Vavilov wheat diversity panel identified new sources of adult plant resistance to stripe rust. Genome-wide association studies revealed the key genomic regions influencing resistance, including seven novel loci. Wheat stripe rust (YR) caused by Puccinia striiformis f. sp. tritici (Pst) poses a significant threat to global food security. Resistance genes commonly found in many wheat varieties have been rendered ineffective due to the rapid evolution of the pathogen. To identify novel sources of adult plant resistance (APR), 292 accessions from the N.I. Vavilov Institute of Plant Genetic Resources, Saint Petersburg, Russia, were screened for known APR genes (i.e. Yr18, Yr29, Yr46, Yr33, Yr39 and Yr59) using linked polymerase chain reaction (PCR) molecular markers. Accessions were evaluated against Pst (pathotype 134 E16 A + Yr17 + Yr27) at seedling and adult plant stages across multiple years (2014, 2015 and 2016) in Australia. Phenotypic analyses identified 132 lines that potentially carry novel sources of APR to YR. Genome-wide association studies (GWAS) identified 68 significant marker-trait associations (P < 0.001) for YR resistance, representing 47 independent quantitative trait loci (QTL) regions. Fourteen genomic regions overlapped with previously reported Yr genes, including Yr29, Yr56, Yr5, Yr43, Yr57, Yr30, Yr46, Yr47, Yr35, Yr36, Yrxy1, Yr59, Yr52 and YrYL. In total, seven QTL (positioned on chromosomes 1D, 2A, 3A, 3D, 5D, 7B and 7D) did not collocate with previously reported genes or QTL, indicating the presence of promising novel resistance factors. Overall, the Vavilov diversity panel provides a rich source of new alleles which could be used to broaden the genetic bases of YR resistance in modern wheat varieties.

Genomes within genomes: nested symbiosis and its implications for plant evolution

Many important plant traits are products of nested symbiosis: mobile genetic elements (MGEs) are nested within microbes, which in turn, are nested within plants. Plant trait variation is therefore not only determined by the plant's genome, but also by loci within microbes and MGEs. Yet it remains unclear how interactions and coevolution within nested symbiosis impacts the evolution of plant traits. Despite the complexities of nested symbiosis, including nonadditive interactions, understanding the evolution of plant traits is facilitated by combining quantitative genetic and functional genomic approaches that explicitly consider sources of nested genetic variation (from loci in MGEs to microbiomes). Additionally, understanding coevolution within nested symbiosis enables us to design or select for MGEs that promote plant health.

GWAS for main effects and epistatic interactions for grain morphology traits in wheat

In the present study in wheat, GWAS was conducted for identification of marker trait associations (MTAs) for the following six grain morphology traits: (1) grain cross-sectional area (GCSA), (2) grain perimeter (GP), (3) grain length (GL), (4) grain width (GWid), (5) grain length-width ratio (GLWR) and (6) grain form-density (GFD). The data were recorded on a subset of spring wheat reference set (SWRS) comprising 225 diverse genotypes, which were genotyped using 10,904 SNPs and phenotyped for two consecutive years (2017-2018, 2018-2019). GWAS was conducted using five different models including two single-locus models (CMLM, SUPER), one multi-locus model (FarmCPU), one multi-trait model (mvLMM) and a model for Q x Q epistatic interactions. False discovery rate (FDR) [P value -log10(p) ≥ 5] and Bonferroni correction [P value -log10(p) ≥ 6] (corrected p value < 0.05) were applied to eliminate false positives due to multiple testing. This exercise gave 88 main effect and 29 epistatic MTAs after FDR and 13 main effect and 6 epistatic MTAs after Bonferroni corrections. MTAs obtained after Bonferroni corrections were further utilized for identification of 55 candidate genes (CGs). In silico expression analysis of CGs in different tissues at different parts of the seed at different developmental stages was also carried out. MTAs and CGs identified during the present study are useful addition to available resources for MAS to supplement wheat breeding programmes after due validation and also for future strategic basic research.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12298-022-01164-w.

Genetic architecture of variation in Arabidopsis thaliana rosettes

Rosette morphology across Arabidopsis accessions exhibits considerable variation. Here we report a high-throughput phenotyping approach based on automatic image analysis to quantify rosette shape and dissect the underlying genetic architecture. Shape measurements of the rosettes in a core set of Recombinant Inbred Lines from an advanced mapping population (Multiparent Advanced Generation Inter-Cross or MAGIC) derived from inter-crossing 19 natural accessions. Image acquisition and analysis was scaled to extract geometric descriptors from time stamped images of growing rosettes. Shape analyses revealed heritable morphological variation at early juvenile stages and QTL mapping resulted in over 116 chromosomal regions associated with trait variation within the population. Many QTL linked to variation in shape were located near genes related to hormonal signalling and signal transduction pathways while others are involved in shade avoidance and transition to flowering. Our results suggest rosette shape arises from modular integration of sub-organ morphologies and can be considered a functional trait subjected to selective pressures of subsequent morphological traits. On an applied aspect, QTLs found will be candidates for further research on plant architecture.

Multiple haplotype-based analyses provide genetic and evolutionary insights into tomato fruit weight and composition

Improving fruit quality traits such as metabolic composition remains a challenge for tomato breeders. To better understand the genetic architecture of these traits and decipher the demographic history of the loci controlling tomato quality traits, we applied an innovative approach using multiple haplotype-based analyses, aiming to test the potentials of haplotype based study in association and genomic prediction studies. We performed and compared haplotype vs SNP-based associations (hapQTL) with multi-locus mixed model (MLMM), focusing on tomato fruit weight and metabolite contents (i.e. sugars, organic acids and amino acids). Using a panel of 163 tomato accessions genotyped with 5995 SNPs, we detected a total of 784 haplotype blocks, with an average size of haplotype blocks ~58 kb. A total of 108 significant associations for 26 traits were detected thanks to Haplotype/SNP-based Bayes models. Haplotype-based Bayes model (97 associations) outperformed SNP-based Bayes model (50 associations) and MLMM (53 associations) in identifying marker-trait associations as well as in genomic prediction (especially for those traits with moderate to low heritability). To decipher the demographic history, we identified 24 positive selective sweeps using the integrated haplotype score (iHS). Most of the significant associations for tomato quality traits were located within selective sweeps (54.63% and 71.7% in hapQTL and MLMM models, respectively). Promising candidate genes were identified controlling tomato fruit weight and metabolite contents. We thus demonstrated the benefits of using haplotypes for evolutionary and genetic studies, providing novel insights into tomato quality improvement and breeding history.

Biparental Crossing and QTL Mapping for Validation of Genome-Wide Association Studies

Association mapping (AM), also known as genome-wide association studies (GWAS), is increasingly being employed in crop plants for the identification of QTL/genes and marker-trait associations (MTAs) in natural populations. Large numbers of such associations have been identified for variety of traits in different crop plants. However, not many of these associations have been used practically in the crop improvement program due to lack of validation. Although there are different ways through which the results of AM/GWAS could be validated, the best approach is to develop a biparental population for the trait of interest. An overview of the steps involved in the validation of results of AM using biparental mapping population in plants is provided in this chapter.

Comprehensive evaluation of mapping complex traits in wheat using genome-wide association studies

Genome-wide association studies (GWAS) are effectively applied to detect the marker trait associations (MTAs) using whole genome-wide variants for complex quantitative traits in different crop species. GWAS has been applied in wheat for different quality, biotic and abiotic stresses, and agronomic and yield-related traits. Predictions for marker-trait associations are controlled with the development of better statistical models taking population structure and familial relatedness into account. In this review, we have provided a detailed overview of the importance of association mapping, population design, high-throughput genotyping and phenotyping platforms, advancements in statistical models and multiple threshold comparisons, and recent GWA studies conducted in wheat. The information about MTAs utilized for gene characterization and adopted in breeding programs is also provided. In the literature that we surveyed, as many as 86,122 wheat lines have been studied under various GWA studies reporting 46,940 loci. However, further utilization of these is largely limited. The future breakthroughs in area of genomic selection, multi-omics-based approaches, machine, and deep learning models in wheat breeding after exploring the complex genetic structure with the GWAS are also discussed. This is a most comprehensive study of a large number of reports on wheat GWAS and gives a comparison and timeline of technological developments in this area. This will be useful to new researchers or groups who wish to invest in GWAS.

Genome-Wide Association Study Statistical Models: A Review

Statistical models are at the core of the genome-wide association study (GWAS). In this chapter, we provide an overview of single- and multilocus statistical models, Bayesian, and machine learning approaches for association studies in plants. These models are discussed based on their basic methodology, cofactors adjustment accounted for, statistical power and computational efficiency. New statistical models and machine learning algorithms are both showing improved performance in detecting missed signals, rare mutations and prioritizing causal genetic variants; nevertheless, further optimization and validation studies are required to maximize the power of GWAS.

Genomic Regions Associated with Fusarium Wilt Resistance in Flax

Modern flax cultivars are susceptible to many diseases; arguably, the most economically damaging of these is the Fusarium wilt fungal disease. Over the past decades international flax breeding initiatives resulted in the development of resistant cultivars. However, much remains to be learned about the mechanisms of resistance to Fusarium infection in flax. As a first step to uncover the genetic factors associated with resistance to Fusarium wilt disease, we performed a genome-wide association study (GWAS) using 297 accessions from the collection of the Federal Research Centre of the Bast Fiber Crops, Torzhok, Russia. These genotypes were infected with a highly pathogenic Fusarium oxysporum f.sp. lini MI39 strain; the wilt symptoms were documented in the course of three successive years. Six different single-locus models implemented in GAPIT3 R package were applied to a selected subset of 72,526 SNPs. A total of 15 QTNs (Quantitative Trait Nucleotides) were detected during at least two years of observation, while eight QTNs were found during all three years of the experiment. Of these, ten QTNs occupied a region of 640 Kb at the start of chromosome 1, while the remaining QTNs mapped to chromosomes 8, 11 and 13. All stable QTNs demonstrate a statistically significant allelic effect across 3 years of the experiment. Importantly, several QTNs spanned regions that harbored genes involved in the pathogen recognition and plant immunity response, including the KIP1-like protein (Lus10025717) and NBS-LRR protein (Lus10025852). Our results provide novel insights into the genetic architecture of flax resistance to Fusarium wilt and pinpoint potential candidate genes for further in-depth studies.

Deciphering the Genetic Architecture of Plant Virus Resistance by GWAS, State of the Art and Potential Advances

Growing virus resistant varieties is a highly effective means to avoid yield loss due to infection by many types of virus. The challenge is to be able to detect resistance donors within plant species diversity and then quickly introduce alleles conferring resistance into elite genetic backgrounds. Until now, mainly monogenic forms of resistance with major effects have been introduced in crops. Polygenic resistance is harder to map and introduce in susceptible genetic backgrounds, but it is likely more durable. Genome wide association studies (GWAS) offer an opportunity to accelerate mapping of both monogenic and polygenic resistance, but have seldom been implemented and described in the plant-virus interaction context. Yet, all of the 48 plant-virus GWAS published so far have successfully mapped QTLs involved in plant virus resistance. In this review, we analyzed general and specific GWAS issues regarding plant virus resistance. We have identified and described several key steps throughout the GWAS pipeline, from diversity panel assembly to GWAS result analyses. Based on the 48 published articles, we analyzed the impact of each key step on the GWAS power and showcase several GWAS methods tailored to all types of viruses.

Metabolomics for Crop Breeding: General Considerations

The development of new, more productive varieties of agricultural crops is becoming an increasingly difficult task. Modern approaches for the identification of beneficial alleles and their use in elite cultivars, such as quantitative trait loci (QTL) mapping and marker-assisted selection (MAS), are effective but insufficient for keeping pace with the improvement of wheat or other crops. Metabolomics is a powerful but underutilized approach that can assist crop breeding. In this review, basic methodological information is summarized, and the current strategies of applications of metabolomics related to crop breeding are explored using recent examples. We briefly describe classes of plant metabolites, cellular localization of metabolic pathways, and the strengths and weaknesses of the main metabolomics technique. Among the commercialized genetically modified crops, about 50 with altered metabolic enzyme activities have been identified in the International Service for the Acquisition of Agri-biotech Applications (ISAAA) database. These plants are reviewed as encouraging examples of the application of knowledge of biochemical pathways. Based on the recent examples of metabolomic studies, we discuss the performance of metabolic markers, the integration of metabolic and genomic data in metabolic QTLs (mQTLs) and metabolic genome-wide association studies (mGWAS). The elucidation of metabolic pathways and involved genes will help in crop breeding and the introgression of alleles of wild relatives in a more targeted manner.

GWAS for genetics of complex quantitative traits: Genome to pangenome and SNPs to SVs and k-mers

The development of improved methods for genome-wide association studies (GWAS) for genetics of quantitative traits has been an active area of research during the last 25 years. This activity initially started with the use of mixed linear model (MLM), which was variously modified. During the last decade, however, with the availability of high throughput next generation sequencing (NGS) technology, development and use of pangenomes and novel markers including structural variations (SVs) and k-mers for GWAS has taken over as a new thrust area of research. Pangenomes and SVs are now available in humans, livestock, and a number of plant species, so that these resources along with k-mers are being used in GWAS for exploring additional genetic variation that was hitherto not available for analysis. These developments have resulted in significant improvement in GWAS methodology for detection of marker-trait associations (MTAs) that are relevant to human healthcare and crop improvement.