Genome-wide association mapping reveals a rich genetic architecture of complex traits in Oryza sativa

Identification of OsPK5 involved in rice glycolytic metabolism and GA/ABA balance for improving seed germination via genome-wide association study

Seed germination plays a pivotal role in the plant life cycle, and its precise regulatory mechanisms are not clear. In this study, 19 quantitative trait loci (QTLs) associated with rice seed germination were identified through genome-wide association studies (GWAS) of the following traits in 2016 and 2017: germination rate (GR) at 3, 5, and 7 days after imbibition (DAI) and germination index (GI). Two major stable QTLs, qSG4 and qSG11.1, were found to be associated with GR and GI over 2 continuous years. Furthermore, OsPK5, encoding a pyruvate kinase, was shown to be a crucial regulator of seed germination in rice, and might be a causal gene of the key QTL qSG11.1, on chromosome 11. Natural variation in OsPK5 function altered the activity of pyruvate kinase. The disruption of OsPK5 function resulted in slow germination and seedling growth during seed germination, blocked glycolytic metabolism, caused glucose accumulation, decreased energy levels, and affected the GA/ABA balance. Taken together, our results provide novel insights into the roles of OsPK5 in seed germination, and facilitate its application in rice breeding to improve seed vigour.

Genome-Wide Association Study of Six Forage Traits in Ramie (Boehmeria nivea L. Gaud)

Genome-wide association study (GWAS) of six forage traits using whole-genome sequencing data generated from 301 ramie accessions found that traits were continuously distributed; the maximum variant coefficient was fresh weight per clump (FWPC) (2019) and individual plant height (IPH) (2019) minimum. Correlation analysis demonstrated that 2019 and 2020 results were similar; all traits were correlated. GWAS analysis demonstrated that six traits exhibited consistent and precise association signals. Of the latter, 104 were significant and detected in 43 genomic regions. By screening forage trait-associated single nucleotide polymorphisms and combining Manhattan map with genome annotation, signals were categorized according to functional annotations. One loci associated with fresh weight per plant (FWP) (chromosome 5; Bnt05G007759), two associated with FWPC (chromosome 13; Bnt13G018582, and Bnt13G018583), and two associated with leaf dry weight per plant (LDWP) and dry weight per plant (DWP) (chromosome 4; Bnt04G005779 and Bnt04G005780), were identified. We describe forage trait candidate genes that are highly correlated with FWP and FWPC; Bnt05G007759 may be involved in nitrogen metabolism, while Bnt13G018582 and Bnt13G018583 may encode TEOSINTE branch 1/CYCLOIDEA/proliferating cytokine 1 (TCP) domains. Bnt04G005779 and Bnt04G005780, which may regulate growth and development, are highly related to LDWP and DWP. These genomic resources will provide a basis for breeding varieties.

Genome-Wide Associations with Resistance to Bipolaris Leaf Spot (Bipolaris oryzae (Breda de Haan) Shoemaker) in a Northern Switchgrass Population (Panicum virgatum L.)

Switchgrass (Panicum virgatum L.), a northern native perennial grass, suffers from yield reduction from Bipolaris leaf spot caused by Bipolaris oryzae (Breda de Haan) Shoemaker. This study aimed to determine the resistant populations via multiple phenotyping approaches and identify potential resistance genes from genome-wide association studies (GWAS) in the switchgrass northern association panel. The disease resistance was evaluated from both natural (field evaluations in Ithaca, New York and Phillipsburg, Philadelphia) and artificial inoculations (detached leaf and leaf disk assays). The most resistant populations based on a combination of three phenotyping approaches—detached leaf, leaf disk, and mean from two locations—were ‘SW788’, ‘SW806’, ‘SW802’, ‘SW793’, ‘SW781’, ‘SW797’, ‘SW798’, ‘SW803’, ‘SW795’, ‘SW805’. The GWAS from the association panel showed 27 significant SNPs on 12 chromosomes: 1K, 2K, 2N, 3K, 3N, 4N, 5K, 5N, 6N, 7K, 7N, and 9N. These markers accumulatively explained the phenotypic variance of the resistance ranging from 3.28 to 26.52%. Within linkage disequilibrium of 20 kb, these SNP markers linked with the potential resistance genes included the genes encoding for NBS-LRR, PPR, cell-wall related proteins, homeostatic proteins, anti-apoptotic proteins, and ABC transporter.

Genome-Wide Association Study Identifies a Rice Panicle Blast Resistance Gene, Pb2, Encoding NLR Protein

Rice blast is one of the main diseases in rice and can occur in different rice growth stages. Due to the complicated procedure of panicle blast identification and instability of panicle blast infection influenced by the environment, most cloned rice resistance genes are associated with leaf blast. In this study, a rice panicle blast resistance gene, Pb2, was identified by genome-wide association mapping based on the panicle blast resistance phenotypes of 230 Rice Diversity Panel 1 (RDP1) accessions with 700,000 single-nucleotide polymorphism (SNP) markers. A genome-wide association study identified 18 panicle blast resistance loci (PBRL) within two years, including 9 reported loci and 2 repeated loci (PBRL2 and PBRL13, PBRL10 and PBRL18). Among them, the repeated locus (PBRL10 and PBRL18) was located in chromosome 11. By haplotype and expression analysis, one of the Nucleotide-binding domain and Leucine-rich Repeat (NLR) Pb2 genes was highly conserved in multiple resistant rice cultivars, and its expression was significantly upregulated after rice blast infection. Pb2 encodes a typical NBS-LRR protein with NB-ARC domain and LRR domain. Compared with wild type plants, the transgenic rice of Pb2 showed enhanced resistance to panicle and leaf blast with reduced lesion number. Subcellular localization of Pb2 showed that it is located on plasma membrane, and GUS tissue-staining observation found that Pb2 is highly expressed in grains, leaf tips and stem nodes. The Pb2 transgenic plants showed no difference in agronomic traits with wild type plants. It indicated that Pb2 could be useful for breeding of rice blast resistance.

The influence of genetic architecture on responses to selection under drought in rice

Accurately predicting responses to selection is a major goal in biology and important for successful crop breeding in changing environments. However, evolutionary responses to selection can be constrained by such factors as genetic and cross‐environment correlations, linkage, and pleiotropy, and our understanding of the extent and impact of such constraints is still developing. Here, we conducted a field experiment to investigate potential constraints to selection for drought resistance in rice (Oryza sativa) using phenotypic selection analysis and quantitative genetics. We found that traits related to drought response were heritable, and some were under selection, including selection for earlier flowering, which could allow drought escape. However, patterns of selection generally were not opposite under wet and dry conditions, and we did not find individual or closely linked genes that influenced multiple traits, indicating a lack of evidence that antagonistic pleiotropy, linkage, or cross‐environment correlations would constrain selection for drought resistance. In most cases, genetic correlations had little influence on responses to selection, with direct and indirect selection largely congruent. The exception to this was seed mass under drought, which was predicted to evolve in the opposite direction of direct selection due to correlations. Because of this indirect effect on selection on seed mass, selection for drought resistance was not accompanied by a decrease in seed mass, and yield increased with fecundity. Furthermore, breeding lines with high fitness and yield under drought also had high fitness and yield under wet conditions, indicating that there was no evidence for a yield penalty on drought resistance. We found multiple genes in which expression influenced both water use efficiency (WUE) and days to first flowering, supporting a genetic basis for the trade‐off between drought escape and avoidance strategies. Together, these results can provide helpful guidance for understanding and managing evolutionary constraints and breeding stress‐resistant crops.

Dissection of the Genetic Basis of Yield Traits in Line per se and Testcross Populations and Identification of Candidate Genes for Hybrid Performance in Maize

Dissecting the genetic basis of yield traits in hybrid populations and identifying the candidate genes are important for molecular crop breeding. In this study, a BC1F3:4 population, the line per se (LPS) population, was constructed by using elite inbred lines Zheng58 and PH4CV as the parental lines. The population was genotyped with 55,000 SNPs and testcrossed to Chang7-2 and PH6WC (two testers) to construct two testcross (TC) populations. The three populations were evaluated for hundred kernel weight (HKW) and yield per plant (YPP) in multiple environments. Marker-trait association analysis (MTA) identified 24 to 151 significant SNPs in the three populations. Comparison of the significant SNPs identified common and specific quantitative trait locus/loci (QTL) in the LPS and TC populations. Genetic feature analysis of these significant SNPs proved that these SNPs were associated with the tested traits and could be used to predict trait performance of both LPS and TC populations. RNA-seq analysis was performed using maize hybrid varieties and their parental lines, and differentially expressed genes (DEGs) between hybrid varieties and parental lines were identified. Comparison of the chromosome positions of DEGs with those of significant SNPs detected in the TC population identified potential candidate genes that might be related to hybrid performance. Combining RNA-seq analysis and MTA results identified candidate genes for hybrid performance, providing information that could be useful for maize hybrid breeding.

PhieABEs: a PAM-less/free high-efficiency adenine base editor toolbox with wide target scope in plants

Adenine base editors (ABEs), which are generally engineered adenosine deaminases and Cas variants, introduce site-specific A-to-G mutations for agronomic trait improvement. However, notably varying editing efficiencies, restrictive requirements for protospacer-adjacent motifs (PAMs) and a narrow editing window greatly limit their application. Here, we developed a robust high-efficiency ABE (PhieABE) toolbox for plants by fusing an evolved, highly active form of the adenosine deaminase TadA8e and a single-stranded DNA-binding domain (DBD), based on PAM-less/free Streptococcus pyogenes Cas9 (SpCas9) nickase variants that recognize the PAM NGN (for SpCas9n-NG and SpGn) or NNN (for SpRYn). By targeting 29 representative targets in rice and assessing the results, we demonstrate that PhieABEs have significantly improved base-editing activity, expanded target range and broader editing windows compared to the ABE7.10 and general ABE8e systems. Among these PhieABEs, hyper ABE8e-DBD-SpRYn (hyABE8e-SpRY) showed nearly 100% editing efficiency at some tested sites, with a high proportion of homozygous base substitutions in the editing windows and no single guide RNA (sgRNA)-dependent off-target changes. The original sgRNA was more compatible with PhieABEs than the evolved sgRNA. In conclusion, the DBD fusion effectively promotes base-editing efficiency, and this novel PhieABE toolbox should have wide applications in plant functional genomics and crop improvement.

NeuralLasso: Neural Networks Meet Lasso in Genomic Prediction

Prediction of complex traits based on genome-wide marker information is of central importance for both animal and plant breeding. Numerous models have been proposed for the prediction of complex traits and still considerable effort has been given to improve the prediction accuracy of these models, because various genetics factors like additive, dominance and epistasis effects can influence of the prediction accuracy of such models. Recently machine learning (ML) methods have been widely applied for prediction in both animal and plant breeding programs. In this study, we propose a new algorithm for genomic prediction which is based on neural networks, but incorporates classical elements of LASSO. Our new method is able to account for the local epistasis (higher order interaction between the neighboring markers) in the prediction. We compare the prediction accuracy of our new method with the most commonly used prediction methods, such as BayesA, BayesB, Bayesian Lasso (BL), genomic BLUP and Elastic Net (EN) using the heterogenous stock mouse and rice field data sets.

Development and Application of a Target Capture Sequencing SNP-Genotyping Platform in Rice

The development of efficient, robust, and high-throughput SNP genotyping platforms is pivotal for crop genetics and breeding. Recently, SNP genotyping platforms based on target capture sequencing, which is very flexible in terms of the number of SNP markers, have been developed for maize, cassava, and fava bean. We aimed to develop a target capture sequencing SNP genotyping platform for rice. A target capture sequencing panel containing 2565 SNPs, including 1225 SNPs informative for japonica and 1339 SNPs informative for indica, was developed. This platform was used in diversity analysis of 50 rice varieties. Of the 2565 SNP markers, 2341 (91.3%) produced useful polymorphic genotype data, enabling the production of a phylogenetic tree of the 50 varieties. The mean number of markers polymorphic between any two varieties was 854. The platform was used for QTL mapping of preharvest sprouting (PHS) resistance in an F₈ recombinant inbred line population derived from the cross Odae × Joun. A genetic map comprising 475 markers was constructed, and two QTLs for PHS resistance were identified on chromosomes 4 and 11. This system is a powerful tool for rice genetics and breeding and will facilitate QTL studies and gene mapping, germplasm diversity analysis, and marker-assisted selection.

Multi-locus genome-wide association studies (ML-GWAS) reveal novel genomic regions associated with seedling and adult plant stage leaf rust resistance in bread wheat (Triticum aestivum L.)

Leaf rust is one of the important diseases limiting global wheat production and productivity. To identify quantitative trait nucleotides (QTNs) or genomic regions associated with seedling and adult plant leaf rust resistance, multilocus genome-wide association studies (ML-GWAS) were performed on a panel of 400 diverse wheat genotypes using 35 K single-nucleotide polymorphism (SNP) genotyping assays and trait data of leaf rust resistance. Association analyses using six multi-locus GWAS models revealed a set of 201 significantly associated QTNs for seedling and 65 QTNs for adult plant resistance (APR), explaining 1.98-31.72% of the phenotypic variation for leaf rust. Among these QTNs, 51 reliable QTNs for seedling and 15 QTNs for APR were consistently detected in at least two GWAS models and were considered reliable QTNs. Three genomic regions were pleiotropic, each controlling two to three pathotype-specific seedling resistances to leaf rust. We also identified candidate genes, such as leucine-rich repeat receptor-like (LRR) protein kinases, P-loop containing nucleoside triphosphate hydrolase and serine-threonine/tyrosine-protein kinases (STPK), which have a role in pathogen recognition and disease resistance linked to the significantly associated genomic regions. The QTNs identified in this study can prove useful in wheat molecular breeding programs aimed at enhancing resistance to leaf rust and developing next-generation leaf rust-resistant varieties.

Gene Region Association Analysis of Longitudinal Quantitative Traits Based on a Function-On-Function Regression Model

In the process of growth and development in life, gene expressions that control quantitative traits will turn on or off with time. Studies of longitudinal traits are of great significance in revealing the genetic mechanism of biological development. With the development of ultra-high-density sequencing technology, the associated analysis has tremendous challenges to statistical methods. In this paper, a longitudinal functional data association test (LFDAT) method is proposed based on the function-on-function regression model. LFDAT can simultaneously treat phenotypic traits and marker information as continuum variables and analyze the association of longitudinal quantitative traits and gene regions. Simulation studies showed that: 1) LFDAT performs well for both linkage equilibrium simulation and linkage disequilibrium simulation, 2) LFDAT has better performance for gene regions (include common variants, low-frequency variants, rare variants and mixture), and 3) LFDAT can accurately identify gene switching in the growth and development stage. The longitudinal data of the Oryza sativa projected shoot area is analyzed by LFDAT. It showed that there is the advantage of quick calculations. Further, an association analysis was conducted between longitudinal traits and gene regions by integrating the micro effects of multiple related variants and using the information of the entire gene region. LFDAT provides a feasible method for studying the formation and expression of longitudinal traits.

Principles of Nanoparticle Design for Genome Editing in Plants

Precise plant genome editing technologies have provided new opportunities to accelerate crop improvement and develop more sustainable agricultural systems. In particular, the prokaryote-derived CRISPR platforms allow precise manipulation of the crop genome, enabling the generation of high-yielding and stress-tolerant crop varieties. Nanotechnology has the potential to catalyze the development of a novel molecular toolbox even further by introducing the possibility of a rapid, universal delivery method to edit the plant genome in a species-independent manner. In this Perspective, we highlight how nanoparticles can help unlock the full potential of CRISPR/Cas technology in targeted manipulation of the plant genome to improve agricultural output. We discuss current challenges hampering progress in nanoparticle-enabled plant gene-editing research and application in the field, and highlight how rational nanoparticle design can overcome them. Finally, we examine the implications of the regulatory frameworks and social acceptance for the future of nano-enabled precision breeding in the developing world.

Genome-wide association study for lignocellulosic compounds and fermentable sugar in rice straw

Cellulose and lignin are the two main components of secondary plant cell walls with substantial impact on stalk in the field and on straw during industrial processing. The amount of fermentable sugar that can be accessed is another important parameter affecting various industrial applications. In the present study, genetic variability of rice (Oryza sativa L.) genotypes for cellulose, lignin, and fermentable sugars contents was analyzed in rice straw. A genome-wide association study of 33,484 single nucleotide polymorphisms (SNPs) with a minor allele frequency (MAF) >0.05 was performed. The genome-wide association study identified seven, three, and three genomic regions to be significantly associated with cellulose, lignin, and fermentable sugar contents, respectively. Candidate genes in the associated genomic regions were enzymes mainly involved in cell wall metabolism. Novel SNP markers associated with cellulose were tagged to GH16, peroxidase, GT6, GT8, and CSLD2. For lignin content, Villin protein, OsWAK1/50/52/53, and GH16 were identified. For fermentable sugar content, UTP-glucose-1-phosphate uridylyltransferase, BRASSINOSTEROID INSENSITIVE 1, and receptor-like protein kinase 5 were found. The results of this study should improve our understanding of the genetic basis of the factors that might be involved in biosynthesis, turnover, and modification of major cell wall components and saccharides in rice straw.

Genome-wide association study reveals that the cupin domain protein OsCDP3.10 regulates seed vigour in rice

Seed vigour is an imperative trait for the direct seeding of rice. In this study, we examined the genetic regulation of seedling percentage at the early germination using a genome-wide association study in rice. One major quantitative trait loci qSP3 for seedling percentage was identified, and the candidate gene was validated as qSP3, encoding a cupin domain protein OsCDP3.10 for the synthesis of 52 kDa globulin. Disruption of this gene in Oscdp3.10 mutants reduced the seed vigour, including the germination potential and seedling percentage, at the early germination in rice. The lacking accumulation of 52 kDa globulin was observed in the mature grains of the Oscdp3.10 mutants. The significantly lower amino acid contents were observed in the mature grains and the early germinating seeds of the Oscdp3.10 mutants compared with those of wild-type. Rice OsCDP3.10 regulated seed vigour mainly via modulating the amino acids e.g. Met, Glu, His, and Tyr that contribute to hydrogen peroxide (H₂ O₂ ) accumulation in the germinating seeds. These results provide important insights into the application of seed priming with the amino acids and the selection of OsCDP3.10 to improve seed vigour in rice.

Genomic prediction models for traits differing in heritability for soybean, rice, and maize

BACKGROUND

Genomic selection is a powerful tool in plant breeding. By building a prediction model using a training set with markers and phenotypes, genomic estimated breeding values (GEBVs) can be used as predictions of breeding values in a target set with only genotype data. There is, however, limited information on how prediction accuracy of genomic prediction can be optimized. The objective of this study was to evaluate the performance of 11 genomic prediction models across species in terms of prediction accuracy for two traits with different heritabilities using several subsets of markers and training population proportions. Species studied were maize (Zea mays, L.), soybean (Glycine max, L.), and rice (Oryza sativa, L.), which vary in linkage disequilibrium (LD) decay rates and have contrasting genetic architectures.

RESULTS

Correlations between observed and predicted GEBVs were determined via cross validation for three training-to-testing proportions (90:10, 70:30, and 50:50). Maize, which has the shortest extent of LD, showed the highest prediction accuracy. Amongst all the models tested, Bayes B performed better than or equal to all other models for each trait in all the three crops. Traits with higher broad-sense and narrow-sense heritabilities were associated with higher prediction accuracy. When subsets of markers were selected based on LD, the accuracy was similar to that observed from the complete set of markers. However, prediction accuracies were significantly improved when using a subset of total markers that were significant at P ≤ 0.05 or P ≤ 0.10. As expected, exclusion of QTL-associated markers in the model reduced prediction accuracy. Prediction accuracy varied among different training population proportions.

CONCLUSIONS

We conclude that prediction accuracy for genomic selection can be improved by using the Bayes B model with a subset of significant markers and by selecting the training population based on narrow sense heritability.

Genome-Wide Association Analysis for Salt-Induced Phenotypic and Physiologic Responses in Rice at Seedling and Reproductive Stages

Salinity is one of the main adverse environmental factors severely inhibiting rice growth and decreasing grain productivity. Developing rice varieties with salt tolerance (ST) is one of the most economical approaches to cope with salinity stress. In this study, the salt tolerance of 220 rice accessions from rice diversity panel l (RDP1), representing five subpopulations, were evaluated based on 16 ST indices at both seedling and reproductive stages under salt stress. An apparent inconsistency was found for ST between the two stages. Through a gene-based/tightly linked genome-wide association study with 201,332 single nucleotide polymorphisms (SNPs) located within genes and their flanking regions were used, a total of 214 SNPs related to 251 genes, significantly associated with 16 ST-related indices, were detected at both stages. Eighty-two SNPs with low frequency favorable (LFF) alleles in the population were proposed to hold high breeding potential in improving rice ST. Fifty-four rice accessions collectively containing all these LFF alleles were identified as donors of these alleles. Through the integration of meta-quantitative trait locus (QTL) for ST and the response patterns of differential expression genes to salt stress, thirty-eight candidate genes were suggested to be involved in the regulation of rice ST. In total, the present study provides valuable information for further characterizing ST-related genes and for breeding ST varieties across whole developmental stages through marker-assisted selection (MAS).

A transcriptome-based association study of growth, wood quality, and oleoresin traits in a slash pine breeding population

Slash pine (Pinus elliottii Engelm.) is an important timber and resin species in the United States, China, Brazil and other countries. Understanding the genetic basis of these traits will accelerate its breeding progress. We carried out a genome-wide association study (GWAS), transcriptome-wide association study (TWAS) and weighted gene co-expression network analysis (WGCNA) for growth, wood quality, and oleoresin traits using 240 unrelated individuals from a Chinese slash pine breeding population. We developed high quality 53,229 single nucleotide polymorphisms (SNPs). Our analysis reveals three main results: (1) the Chinese breeding population can be divided into three genetic groups with a mean inbreeding coefficient of 0.137; (2) 32 SNPs significantly were associated with growth and oleoresin traits, accounting for the phenotypic variance ranging from 12.3% to 21.8% and from 10.6% to 16.7%, respectively; and (3) six genes encoding PeTLP, PeAP2/ERF, PePUP9, PeSLP, PeHSP, and PeOCT1 proteins were identified and validated by quantitative real time polymerase chain reaction for their association with growth and oleoresin traits. These results could be useful for tree breeding and functional studies in advanced slash pine breeding program.

Slash pine is an important source of original timber and resin production on commercial forest plantations. It is necessary to implement precise breeding strategies to improve timber quality and resin yield. However, little is known about the species’ molecular genetic basis. Using a transcriptome dataset with sequencing from 240 individuals in the slash pine population, we combined multiple approaches (based on gene variation, expression variation and co-expression network) to dissect the genetic structure for slash pine major breeding traits. We found that the research population could be divided into three genetic groups with a mean heterozygosity of 0.2246. We also found that six genes with important functions in slash pine resin synthesis and timber formation through association studies. Four new SNPs associatation with the average ring width were also discovered. Our results provide new insights into the molecular genetic basis of important traits in slash pine and provide a comprehensive method for association analyses of conifer tree species with large genome.

Identification of Heterotic Groups and Patterns Based on Genotypic and Phenotypic Characteristics Among Rice Accessions of Diverse Origins

Identification of the right parental combinations to maximize heterosis is the major goal of hybrid breeding, which could be achieved through identification of heterotic groups. The main objective of this study was to identify promising heterotic groups for future rice breeding programs. A collection of 359 rice genotypes of diverse origins of China and abroad, composed of inbreds, maintainers, restorers, and temperature-sensitive genic male sterile (TGMS) lines were genotyped using 10K SNP chips. The SNP data set was subjected to genomic analyses for estimation of genetic divergence and diversity. Significant variations were observed in the germplasm with the identification of six different genetic groups. These lines were assigned to the genetic groups independent of their origin. Taking an account of commercially used heterotic groups present in each cluster, three cytoplasmic male sterile (CMS) lines and 14 inbred and restorer lines with moderate to high genetic distances selected from five heterotic patterns were crossed and obtained 42 F1 hybrids. A total of 14 hybrids were found with significant maximum mid- and better-parent heterosis, namely, TaifengA × Guang122, TaifengA × Wushansimiao, and TaifengA × Minghui63 for earliness; Guang8A × Huazhan for dwarf stature; and Guang8A × Huanghuzhan-1, TaifengA × Yuexiangzhan, Guang8A × Minhui3301, TianfengA × Guang122, Guang8A × Yahui2115, TianfengA × Huanghuazhan, TianfengA × Minghui63, TianfengA × Minhui3301, TaifengA × Gui99, and Guang8A × Yuenongsimiao for yield and yield-related traits. Mid-parent and better-parent heterotic F1 hybrids were in positive correlation with the genetic distances as that manifested by commercially used heterotic groups, encouraging the use of genotypic data for identification of heterotic groups. Our study provides an informative strategy for the development of early maturing, lodging resistant and high-yielding commercial hybrids and cultivars in future heterosis breeding programs.

Identification of Candidate Forage Yield Genes in Sorghum (Sorghum bicolor L.) Using Integrated Genome-Wide Association Studies and RNA-Seq

Genetic dissection of forage yield traits is critical to the development of sorghum as a forage crop. In the present study, association mapping was performed with 85,585 SNP markers on four forage yield traits, namely plant height (PH), tiller number (TN), stem diameter (SD), and fresh weight per plant (FW) among 245 sorghum accessions evaluated in four environments. A total of 338 SNPs or quantitative trait nucleotides (QTNs) were associated with the four traits, and 21 of these QTNs were detected in at least two environments, including four QTNs for PH, ten for TN, six for SD, and one for FW. To identify candidate genes, dynamic transcriptome expression profiling was performed at four stages of sorghum development. One hundred and six differentially expressed genes (DEGs) that were enriched in hormone signal transduction pathways were found in all stages. Weighted gene correlation network analysis for PH and SD indicated that eight modules were significantly correlated with PH and that three modules were significantly correlated with SD. The blue module had the highest positive correlation with PH and SD, and the turquoise module had the highest negative correlation with PH and SD. Eight candidate genes were identified through the integration of genome-wide association studies (GWAS) and RNA sequencing. Sobic.004G143900, an indole-3-glycerol phosphate synthase gene that is involved in indoleacetic acid biosynthesis, was down-regulated as sorghum plants grew in height and was identified in the blue module, and Sobic.003G375100, an SD candidate gene, encoded a DNA repair RAD52-like protein 1 that plays a critical role in DNA repair-linked cell cycle progression. These findings demonstrate that the integrative analysis of omics data is a promising approach to identify candidate genes for complex traits.

Construction of transgenic detection system of Brassica napus L. based on single nucleotide polymorphism chip

Brassica napus L. is a vital oil crop in China. As auxiliary tools for rapeseed breeding, transgenic technologies play a considerable role in heterosis, variety improvement, and pest resistance. Research on transgenic detection technologies is of great significance for the introduction, supervision, and development of transgenic rapeseed in China. However, the transgenic detection methods currently in use are complex and time-consuming, with low output. A single nucleotide polymorphism (SNP) chip can effectively overcome such limitations. In the present study, we collected 40 transgenic elements and designed 291 probes. The probe sequences were submitted to Illumina Company, and the Infinium chip technology was used to prepare SNP chips. In the present Brassica napus transgenic detection experiment, 84 high-quality probes of 17 transgenic elements were preliminarily screened, and genotyping effect was optimised for the probe signal value. Ultimately, a transgenic detection system for B. napus was developed. The developed system has the advantages of simple operation, minimal technical errors, and stable detection outcomes. A transgenic detection sensitivity test revealed that the probe designed could accurately detect 1% of transgenic samples and had high detection sensitivity. In addition, in repeatability tests, the CaMV35S promoter coefficient of variation was approximately 3.58%. Therefore, the SNP chip had suitable repeatability in transgene detection. The SNP chip developed could be used to construct transgenic detection systems for B. napus.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s13205-021-03062-6.

Genomic Prediction: Progress and Perspectives for Rice Improvement

Genomic prediction can be a powerful tool to achieve greater rates of genetic gain for quantitative traits if thoroughly integrated into a breeding strategy. In rice as in other crops, the interest in genomic prediction is very strong with a number of studies addressing multiple aspects of its use, ranging from the more conceptual to the more practical. In this chapter, we review the literature on rice (Oryza sativa) and summarize important considerations for the integration of genomic prediction in breeding programs. The irrigated breeding program at the International Rice Research Institute is used as a concrete example on which we provide data and R scripts to reproduce the analysis but also to highlight practical challenges regarding the use of predictions. The adage "To someone with a hammer, everything looks like a nail" describes a common psychological pitfall that sometimes plagues the integration and application of new technologies to a discipline. We have designed this chapter to help rice breeders avoid that pitfall and appreciate the benefits and limitations of applying genomic prediction, as it is not always the best approach nor the first step to increasing the rate of genetic gain in every context.

Bayesian optimization of multivariate genomic prediction models based on secondary traits for improved accuracy gains and phenotyping costs

We propose a novel approach to the Bayesian optimization of multivariate genomic prediction models based on secondary traits to improve accuracy gains and phenotyping costs via efficient Pareto frontier estimation. Multivariate genomic prediction based on secondary traits, such as data from various omics technologies including high-throughput phenotyping (e.g., unmanned aerial vehicle-based remote sensing), has attracted much attention because it offers improved accuracy gains compared with genomic prediction based only on marker genotypes. Although there is a trade-off between accuracy gains and phenotyping costs of secondary traits, no attempt has been made to optimize these trade-offs. In this study, we propose a novel approach to optimize multivariate genomic prediction models for secondary traits measurable at early growth stages for improved accuracy gains and phenotyping costs. The proposed approach employs Bayesian optimization for efficient Pareto frontier estimation, representing the maximum accuracy at a given cost. The proposed approach successfully estimated the optimal secondary trait combinations across a range of costs while providing genomic predictions for only about [Formula: see text] of all possible combinations. The simulation results reflecting the characteristics of each scenario of the simulated target traits showed that the obtained optimal combinations were reasonable. Analysis of real-time target trait data showed that the proposed multivariate genomic prediction model had significantly superior accuracy compared to the univariate genomic prediction model.

A Genome Wide Association Study Revealed Key Single Nucleotide Polymorphisms/Genes Associated With Seed Germination in Gossypium hirsutum L

Fast and uniform seed germination is essential to stabilize crop yields in agricultural production. It is important to understand the genetic basis of seed germination for improving the vigor of crop seeds. However, little is known about the genetic basis of seed vigor in cotton. In this study, we evaluated four seed germination-related traits of a core collection consisting of 419 cotton accessions, and performed a genome-wide association study (GWAS) to explore important loci associated with seed vigor using 3.66 million high-quality single nucleotide polymorphisms (SNPs). The results showed that four traits, including germination potential, germination rate, germination index, and vigor index, exhibited broad variations and high correlations. A total of 92 significantly associated SNPs located within or near 723 genes were identified for these traits, of which 13 SNPs could be detected in multiple traits. Among these candidate genes, 294 genes were expressed at seed germination stage. Further function validation of the two genes of higher expression showed that Gh_A11G0176 encoding Hsp70-Hsp90 organizing protein negatively regulated Arabidopsis seed germination, while Gh_A09G1509 encoding glutathione transferase played a positive role in regulating tobacco seed germination and seedling growth. Furthermore, Gh_A09G1509 might promote seed germination and seedling establishment through regulating glutathione metabolism in the imbibitional seeds. Our findings provide unprecedented information for deciphering the genetic basis of seed germination and performing molecular breeding to improve field emergence through genomic selection in cotton.

Methionine oxidation of CLK4 promotes the metabolic switch and redox homeostasis in esophageal carcinoma via inhibiting MITF selective autophagy

BACKGROUND

Metabolic reprogramming and redox homeostasis contribute to esophageal squamous cell carcinoma (ESCC). CDC-like kinase 4 (CLK4) is a dual-specificity kinase that can phosphorylate substrates' tyrosine or serine/threonine residue. However, the role and mechanism of CLK4 in ESCC remain unknown.

METHODS

CLK4 expression was analysed using publicly available datasets and confirmed in ESCC tissues and cell lines. The biological roles of CLK4 were studied with gain and loss-of-function experiments. Mass spectrometry was employed to examine the effects of CLK4 on metabolic profiling. In vitro kinase assay, co-immunoprecipitation, glutathione S-transferase pulldown, chromatin immunoprecipitation and luciferase reporter were used to elucidate the relationship among CLK4, microphthalmia-associated transcription factor (MITF), COP1 and ZRANB1.

RESULTS

CLK4 down-regulation was observed in ESCC cell lines and clinical samples and associated with the methylation of its promoter. Low levels of CLK4 promoted ESCC development by affecting the purine synthesis pathway and nicotinamide adenine dinucleotide phosphate (NADPH)/nicotinamide adenine dinucleotide phosphate (NADP+ ) ratio. Interestingly, CLK4 inhibited ESCC development by blocking MITF-enhanced de novo purine synthesis and redox balance. Mechanistically, wild type CLK4 (WT-CLK4) but not kinase-dead CLK4-K189R mutant phosphorylated MITF at Y360. This modification promoted its interaction with E3 ligase COP1 and its K63-linked ubiquitination at K308/K372, leading to sequestosome 1 recognition and autophagic degradation. However, the deubiquitinase ZRANB1 rescued MITF ubiquitination and degradation. In turn, MITF bound to E- rather than M-boxes in CLK4 promoter and transcriptionally down-regulated its expression in ESCC. Clinically, the negative correlations were observed between CLK4, MITF, and purine metabolic markers, which predicts a poor clinical outcome of ESCC patients. Notably, CLK4 itself was a redox-sensitive kinase, and its methionine oxidation at M307 impaired kinase activity, enhanced mitochondria length and inhibited lipid peroxidation, contributing to ESCC.

CONCLUSIONS

Our data highlight the potential role of CLK4 in modulating redox status and nucleotide metabolism, suggesting potential therapeutic targets in ESCC treatment.

Artificial selection in the expansion of rice cultivation

Gene distributions and population genomics suggest artificial selection of ghd7 osprr37, for extremely early heading date of rice, in the Tohoku region of Japan. The ranges of cultivated crops expanded into various environmental conditions around the world after their domestication. Hokkaido, Japan, lies at the northern limit of cultivation of rice, which originated in the tropics. Novel genotypes for extremely early heading date in Hokkaido are controlled by loss-of-function of both Grain number, plant height and heading date 7 (Ghd7) and Oryza sativa Pseudo-Response Regulator 37 (OsPRR37). We traced genotypes for extremely early heading date and analyzed the phylogeny of rice varieties grown historically in Japan. The mutations in Ghd7 and OsPRR37 had distinct local distributions. Population genomics revealed that varieties collected from the Tohoku region of northern Japan formed three clusters. Mutant alleles of Ghd7 and OsPRR37 appear to have allowed rice cultivation to spread into Hokkaido. Our results show that the mutations of two genes might be occurred in the process of artificial selection during early rice cultivation in the Tohoku region.

Use of Allele-Specific Amplification for Rapid Identification of Aromatic and Non-aromatic Rice Germplasms

In the current context of global climate change trends, threat to food and nutrition security, collection, conservation and management, and characterization and evaluation of crop germplasms especially traditional landraces are gaining momentum more than ever before. Aromatic rice is an elite category of cultivated rice having huge sociocultural heritage value, fetching premium prices globally. Hence, its identification, in situ conservation, and appropriate characterization are likely to augment reliability of this distinctive category of rice, and rice commodity chain actors. badh2.1 is recognised as the major allele responsible for rice 2-acetyl-1-pyrroline aroma production in a vast number of aromatic rice globally. However, most of the previous works on the genetics and biochemical pathways of aroma expression in rice have encompassed mainly Basmati, Sadri, Della, Jasmine, and a few modern hybrids. But apart from these spotlighted varieties, a myriad of indigenous, aromatic rice germplasms exists. Allele-specific amplification, a low-cost, accurate method invented by Bradbury et al. 2005, can be utilized successfully for discriminating the rarely explored aromatic and nonaromatic rice as described.

Rice functional genomics: decades' efforts and roads ahead

Rice (Oryza sativa L.) is one of the most important crops in the world. Since the completion of rice reference genome sequences, tremendous progress has been achieved in understanding the molecular mechanisms on various rice traits and dissecting the underlying regulatory networks. In this review, we summarize the research progress of rice biology over past decades, including omics, genome-wide association study, phytohormone action, nutrient use, biotic and abiotic responses, photoperiodic flowering, and reproductive development (fertility and sterility). For the roads ahead, cutting-edge technologies such as new genomics methods, high-throughput phenotyping platforms, precise genome-editing tools, environmental microbiome optimization, and synthetic methods will further extend our understanding of unsolved molecular biology questions in rice, and facilitate integrations of the knowledge for agricultural applications.

Rice functional genomics: decades' efforts and roads ahead

Rice (Oryza sativa L.) is one of the most important crops in the world. Since the completion of rice reference genome sequences, tremendous progress has been achieved in understanding the molecular mechanisms on various rice traits and dissecting the underlying regulatory networks. In this review, we summarize the research progress of rice biology over past decades, including omics, genome-wide association study, phytohormone action, nutrient use, biotic and abiotic responses, photoperiodic flowering, and reproductive development (fertility and sterility). For the roads ahead, cutting-edge technologies such as new genomics methods, high-throughput phenotyping platforms, precise genome-editing tools, environmental microbiome optimization, and synthetic methods will further extend our understanding of unsolved molecular biology questions in rice, and facilitate integrations of the knowledge for agricultural applications.

FMixFN: A Fast Big Data-Oriented Genomic Selection Model Based on an Iterative Conditional Expectation algorithm

Genomic selection is an approach to select elite breeding stock based on the use of dense genetic markers and that has led to the development of various models to derive a predictive equation. However, the current genomic selection software faces several issues such as low prediction accuracy, low computational efficiency, or an inability to handle large-scale sample data. We report the development of a genomic prediction model named FMixFN with four zero-mean normal distributions as the prior distributions to optimize the predictive ability and computing efficiency. The variance of the prior distributions in our model is precisely determined based on an F2 population, and genomic estimated breeding values (GEBV) can be obtained accurately and quickly in combination with an iterative conditional expectation algorithm. We demonstrated that FMixFN improves computational efficiency and predictive ability compared to other methods, such as GBLUP, SSgblup, MIX, BayesR, BayesA, and BayesB. Most importantly, FMixFN may handle large-scale sample data, and thus should be able to meet the needs of large breeding companies or combined breeding schedules. Our study developed a Bayes genomic selection model called FMixFN, which combines stable predictive ability and high computational efficiency, and is a big data-oriented genomic selection model that has potential in the future. The FMixFN method can be freely accessed at https://zenodo.org/record/5560913 (DOI: 10.5281/zenodo.5560913).

Marker-Assisted Development and Evaluation of Monogenic Lines of Rice cv. Kaohsiung 145 Carrying Blast Resistance Genes

Rice blast is a serious threat to global rice production. Large-scale and long-term cultivation of rice varieties with a single blast resistance gene usually leads to breakdown of resistance. To effectively control rice blast in Taiwan, marker-assisted backcrossing was conducted to develop monogenic lines carrying different blast resistance genes in the genetic background of an elite japonica rice cultivar, Kaohsiung 145 (KH145). Eleven International Rice Research Institute (IRRI)-bred blast-resistant lines (IRBLs) showing broad-spectrum resistance to local Pyricularia oryzae isolates were used as resistance donors. Sequencing analysis revealed that the recurrent parent, KH145, does not carry known resistance alleles at the target Pi2/9, Pik, Pita, and Ptr loci. For each IRBL × KH145 cross, we screened 21 to 370 (average of 108) plants per generation from the BC₁F₁ to BC₃F₁/BC₄F₁ generation. A total of 1,499 BC₃F₂/BC₄F₂ lines carrying homozygous resistance alleles were selected and self-crossed for four to six successive generations. The derived lines were also evaluated for background genotype using genotyping by sequencing, for blast resistance under artificial inoculation and natural infection conditions, and for agronomic performance in multiple field trials. In Chiayi and Taitung blast nurseries in 2018 to 2020, Pi2, Pi9, and Ptr conferred high resistance, Pi20 and Pik-h moderate resistance, and Pi1, Pi7, Pik-p, and Pik susceptibility to leaf blast; only Pi2, Pi9, and Ptr conferred effective resistance against panicle blast. The monogenic lines showed agronomic traits, yield, and grain quality similar to those of KH145, suggesting the potential of growing a mixture of lines to achieve durable resistance in the field.

Transcription factor OsbZIP49 controls tiller angle and plant architecture through the induction of indole-3-acetic acid-amido synthetases in rice

Tiller angle is an important determinant of plant architecture in rice (Oryza sativa L.). Auxins play a critical role in determining plant architecture; however, the underlying metabolic and signaling mechanisms are still largely unknown. In this study, we have identified a member of the bZIP family of TGA class transcription factors, OsbZIP49, that participates in the regulation of plant architecture and is specifically expressed in gravity-sensing tissues, including the shoot base, nodes and lamina joints. Transgenic rice plants overexpressing OsbZIP49 displayed a tiller-spreading phenotype with reduced plant height and internode lengths. In contrast, CRISPR/Cas9-mediated knockout of OsbZIP49 resulted in a compact architecture. Follow-up studies indicated that the effects of OsbZIP49 on tiller angles are mediated through changes in shoot gravitropic responses. Additionally, we provide evidence that OsbZIP49 activates the expression of indole-3-acetic acid-amido synthetases OsGH3-2 and OsGH3-13 by directly binding to TGACG motifs located within the promoters of both genes. Increased GH3-catalyzed conjugation of indole-3-acetic acid (IAA) in rice transformants overexpressing OsbZIP49 resulted in the increased accumulation of IAA-Asp and IAA-Glu, and a reduction in local free auxin, tryptamine and IAA-Glc levels. Exogenous IAA or naphthylacetic acid (NAA) partially restored shoot gravitropic responses in OsbZIP49-overexpressing plants. Knockout of OsbZIP49 led to reduced expression of both OsGH3-2 and OsGH3-13 within the shoot base, and increased accumulation of IAA and increased OsIAA20 expression levels were observed in transformants following gravistimulation. Taken together, the present results reveal the role transcription factor OsbZIP49 plays in determining plant architecture, primarily due to its influence on local auxin homeostasis.

A genome-wide association study reveals that the cytochrome b5 involved in seed reserve mobilization during seed germination in rice

A candidate gene cytochrome b5 for the major QTL qSRMP9 for rice seed reserve mobilization was validated during seed germination using a genome-wide association study approach. Seed reserve mobilization plays important roles in the early seedling growth in rice. However, the genetic basis underlying this process is poorly understood. In this study, the genetic architecture of variation in seed reserve mobilization during seed germination was studied using a genome-wide association study approach in rice. Three quantitative trait loci (QTL) including qSRMP6, qSRMP9, and qSRMP12 for seed reserve mobilization percentage were identified. In which, the candidate gene cytochrome b5 (OsCyb5) for the major QTL qSRMP9 was validated. Disruption of this gene in Oscyb5 mutants reduced the seed reserve mobilization and seedling growth compared with wild-type (WT) in rice. There were no significant differences of grain size, starch, protein and total soluble sugar content in the mature grains between Oscyb5 mutants and WT. However, the α-amylase activity in the germinating seeds of Oscyb5 mutants was significantly decreased compared to that of WT, and then, the starch and sugar mobilization and the glucose accumulation during seed germination were significantly decreased in Oscyb5 mutants. Two elite haplotypes of OsCyb5 associated with the higher seed reserve mobilization percentage and its elite single nucleotide polymorphism variations were mainly existed in the INDICA and AUS accessions. The natural variation of OsCyb5 contributing to seed reserve mobilization might be useful for the future rice breeding.

Natural variation in the fast phase of chlorophyll a fluorescence induction curve (OJIP) in a global rice minicore panel

Photosynthesis can be probed through Chlorophyll a fluorescence induction (FI), which provides detailed insight into the electron transfer process in Photosystem II, and beyond. Here, we have systematically studied the natural variation of the fast phase of the FI, i.e. the OJIP phase, in rice. The OJIP phase of the Chl a fluorescence induction curve is referred to as "fast transient" lasting for less than a second; it is obtained after a dark-adapted sample is exposed to saturating light. In the OJIP curve, "O" stands for "origin" (minimal fluorescence), "P" for "peak" (maximum fluorescence), and J and I for inflection points between the O and P levels. Further, F_o is the fluorescence intensity at the "O" level, whereas F_m is the intensity at the P level, and F_v (= F_m - F_o) is the variable fluorescence. We surveyed a set of quantitative parameters derived from the FI curves of 199 rice accessions, grown under both field condition (FC) and growth room condition (GC). Our results show a significant variation between Japonica (JAP) and Indica (IND) subgroups, under both the growth conditions, in almost all the parameters derived from the OJIP curves. The ratio of the variable to the maximum (F_v/F_m) and of the variable to the minimum (F_v/F_o) fluorescence, the performance index (PI_abs), as well as the amplitude of the I-P phase (A_I-P) show higher values in JAP compared to that in the IND subpopulation. In contrast, the amplitude of the O-J phase (A_O-J) and the normalized area above the OJIP curve (S_m) show an opposite trend. The performed genetic analysis shows that plants grown under GC appear much more affected by environmental factors than those grown in the field. We further conducted a genome-wide association study (GWAS) using 11 parameters derived from plants grown in the field. In total, 596 non-unique significant loci based on these parameters were identified by GWAS. Several photosynthesis-related proteins were identified to be associated with different OJIP parameters. We found that traits with high correlation are usually associated with similar genomic regions. Specifically, the thermal phase of FI, which includes the amplitudes of the J-I and I-P subphases (A_J-I and A_I-P) of the OJIP curve, is, in turn, associated with certain common genomic regions. Our study is the first one dealing with the natural variations in rice, with the aim to characterize potential candidate genes controlling the magnitude and half-time of each of the phases in the OJIP FI curve.

Recent advances in CRISPR/Cas9 and applications for wheat functional genomics and breeding

Common wheat (Triticum aestivum L.) is one of the three major food crops in the world; thus, wheat breeding programs are important for world food security. Characterizing the genes that control important agronomic traits and finding new ways to alter them are necessary to improve wheat breeding. Functional genomics and breeding in polyploid wheat has been greatly accelerated by the advent of several powerful tools, especially CRISPR/Cas9 genome editing technology, which allows multiplex genome engineering. Here, we describe the development of CRISPR/Cas9, which has revolutionized the field of genome editing. In addition, we emphasize technological breakthroughs (e.g., base editing and prime editing) based on CRISPR/Cas9. We also summarize recent applications and advances in the functional annotation and breeding of wheat, and we introduce the production of CRISPR-edited DNA-free wheat. Combined with other achievements, CRISPR and CRISPR-based genome editing will speed progress in wheat biology and promote sustainable agriculture.

New drought-adaptive loci underlying candidate genes on wheat chromosome 4B with improved photosynthesis and yield responses

Flag leaf serves as an essential source of assimilates during grain filling, thereby contributing to grain yield up to 48%. Thus, high-throughput phenotyping of flag leaves is crucial to determine their physiological and genetic basis of yield formation and drought adaptation. Here, we utilized 200 wheat cultivars to identify drought-adaptive loci underlying candidate genes associated with flag leaf biomass and photosynthesis-related traits using a genome-wide association study (GWAS). GWAS revealed 21 significant marker-trait associations for key photosynthetic traits in response to drought stress. Analysis of linkage disequilibrium (LD) in these SNPs intervals discovered 103 significant SNPs that established distinct LD blocks containing a total of 382 candidate genes putatively involved in physiological processes, including photosynthesis and water responses. Further, in silico transcript analysis identified two candidate genes in locus AX-580365925 on chromosome 4B, those were found to be highly expressed under drought and associated with proton-transporting ATP synthase activity and stress response pathways. Accordingly, we identified significant allelic haplotype differences on this same locus. The tolerant haplotype (higher chlorophyll content under drought) representing major allele was more abundant and stably increased photosynthetic efficiency and yield under drought scenarios. Collectively, this study offers new adaptive loci and beneficial alleles to reshape the flag leaf physiological and associated photosynthetic components for better yield and sustainability to water-deficit stress.

Genetic architecture of photosynthesis energy partitioning as revealed by a genome-wide association approach

The photosynthesis process is determined by the intensity level and spectral quality of the light; therefore, leaves need to adapt to a changing environment. The incident energy absorbed can exceed the sink capability of the photosystems, and, in this context, photoinhibition may occur in both photosystem II (PSII) and photosystem I (PSI). Quantum yield parameters analyses reveal how the energy is managed. These parameters are genotype-dependent, and this genotypic variability is a good opportunity to apply mapping association strategies to identify genomic regions associated with photosynthesis energy partitioning. An experimental and mathematical approach is proposed for the determination of an index which estimates the energy per photon flux for each spectral bandwidth (Δ_λ) of the light incident (QI index). Based on the QI, the spectral quality of the plant growth, environmental lighting, and the actinic light of PAM were quantitatively very similar which allowed an accurate phenotyping strategy of a rice population. A total of 143 genomic single regions associated with at least one trait of chlorophyll fluorescence were identified. Moreover, chromosome 5 gathers most of these regions indicating the importance of this chromosome in the genetic regulation of the photochemistry process. Through a GWAS strategy, 32 genes of rice genome associated with the main parameters of the photochemistry process of photosynthesis in rice were identified. Association between light-harvesting complexes and the potential quantum yield of PSII, as well as the relationship between coding regions for PSI-linked proteins in energy distribution during the photochemical process of photosynthesis is analyzed.

Association Mapping and Functional Analysis of Rice Cold Tolerance QTLs at the Bud Burst Stage

Cold tolerance at the bud burst stage (CTB) is a key trait for direct-seeded rice. Although quantitative trait loci (QTL) affecting CTB in rice have been mapped using traditional linkage mapping and genome-wide association study (GWAS) methods, the underlying genes remain unknown. In this study, we evaluated the CTB phenotype of 339 cultivars in the Rice Diversity Panel II (RDP II) collection. GWAS identified four QTLs associated with CTB (qCTBs), distributed on chromosomes 1-3. Among them, qCTB-1-1 overlaps with Osa-miR319b, a known cold tolerance micro RNA gene. The other three qCTBs have not been reported. In addition, we characterised the candidate gene OsRab11C1 for qCTB-1-2 that encodes a Rab protein belonging to the small GTP-binding protein family. Overexpression of OsRab11C1 significantly reduced CTB, while gene knockout elevated CTB as well as cold tolerance at the seedling stage, suggesting that OsRab11C1 negatively regulates rice cold tolerance. Molecular analysis revealed that OsRab11C1 modulates cold tolerance by suppressing the abscisic acid signalling pathway and proline biosynthesis. Using RDP II and GWAS, we identified four qCTBs that are involved in CTB and determined the function of the candidate gene OsRab11C1 in cold tolerance. Our results demonstrate that OsRab11C1 is a negative regulator of cold tolerance and knocking out of the gene by genome-editing may provide enhanced cold tolerance in rice.

Genome-wide association analysis uncovers the genetic architecture of tradeoff between flowering date and yield components in sesame

BACKGROUND

Unrevealing the genetic makeup of crop morpho-agronomic traits is essential for improving yield quality and sustainability. Sesame (Sesamum indicum L.) is one of the oldest oil-crops in the world. Despite its economic and agricultural importance, it is an 'orphan crop-plant' that has undergone limited modern selection, and, as a consequence preserved wide genetic diversity. Here we established a new sesame panel (SCHUJI) that contains 184 genotypes representing wide phenotypic variation and is geographically distributed. We harnessed the natural variation of this panel to perform genome-wide association studies for morpho-agronomic traits under the Mediterranean climate conditions.

RESULTS

Field-based phenotyping of the SCHUJI panel across two seasons exposed wide phenotypic variation for all traits. Using 20,294 single-nucleotide polymorphism markers, we detected 50 genomic signals associated with these traits. Major genomic region on LG2 was associated with flowering date and yield-related traits, exemplified the key role of the flowering date on productivity.

CONCLUSIONS

Our results shed light on the genetic architecture of flowering date and its interaction with yield components in sesame and may serve as a basis for future sesame breeding programs in the Mediterranean basin.

Salinity tolerance mechanisms and their breeding implications

BACKGROUND

The era of first green revolution brought about by the application of chemical fertilizers surely led to the explosion of food grains, but left behind the notable problem of salinity. Continuous application of these fertilizers coupled with fertilizer-responsive crops make the country self-reliant, but continuous deposition of these led to altered the water potential and thus negatively affecting the proper plant functioning from germination to seed setting.

MAIN BODY

Increased concentration of anion and cations and their accumulation and distribution cause cellular toxicity and ionic imbalance. Plants respond to salinity stress by any one of two mechanisms, viz., escape or tolerate, by either limiting their entry via root system or controlling their distribution and storage. However, the understanding of tolerance mechanism at the physiological, biochemical, and molecular levels will provide an insight for the identification of related genes and their introgression to make the crop more resilient against salinity stress.

SHORT CONCLUSION

Novel emerging approaches of plant breeding and biotechnologies such as genome-wide association studies, mutational breeding, marker-assisted breeding, double haploid production, hyperspectral imaging, and CRISPR/Cas serve as engineering tools for dissecting the in-depth physiological mechanisms. These techniques have well-established implications to understand plants' adaptions to develop more tolerant varieties and lower the energy expenditure in response to stress and, constitutively fulfill the void that would have led to growth resistance and yield penalty.

Genome-wide association mapping and genomic prediction for adult stage sclerotinia stem rot resistance in Brassica napus (L) under field environments

Sclerotinia stem rot (SSR) is a fungal disease of rapeseed/canola that causes significant seed yield losses and reduces its oil content and quality. In the present study, the reaction of 187 diverse canola genotypes to SSR was characterized at full flowering stage using the agar plug to stem inoculation method in four environments. Genome-wide association study (GWAS) using three different algorithms identified 133 significant SNPs corresponding with 123 loci for disease traits like stem lesion length (LL), lesion width (LW), and plant mortality at 14 (PM_14D) and 21 (PM_21D) days. The explained phenotypic variation of these SNPs ranged from 3.6 to 12.1%. Nineteen significant SNPs were detected in two or more environments, disease traits with at least two GWAS algorithms. The strong correlations observed between LL and other three disease traits evaluated, suggest they could be used as proxies for SSR resistance phenotyping. Sixty-nine candidate genes associated with disease resistance mechanisms were identified. Genomic prediction (GP) analysis with all the four traits employing genome-wide markers resulted in 0.41-0.64 predictive ability depending on the model specifications. The highest predictive ability for PM_21D with three models was about 0.64. From our study, the identified resistant genotypes and stable significant SNP markers will serve as a valuable resource for future SSR resistance breeding. Our study also suggests that genomic selection holds promise for accelerating canola breeding progress by enabling breeders to select SSR resistance genotypes at the early stage by reducing the need to phenotype large numbers of genotypes.

Comprehensive analytical and empirical evaluation of genomic prediction across diverse accessions in maize

Efficiently exploiting natural genetic diversity captured by accessions stored in genebanks is crucial to genetic improvement of major crops. Selecting accessions of interest from genebanks has traditionally required information from extensive and expensive evaluation; however, low-cost genotyping combined with genomic prediction have enabled us to generate predicted genetic merits for the entire set with targeted phenotypic evaluation of representative subsets. To explore this general approach, analytical assessment and empirical validation of the maize (Zea mays L.) association population (MAP) as a training population were conducted in the present study. Cross-validation within the MAP revealed mostly modest to strong predictive ability for 36 traits, generally in parallel with the square root of heritability. The MAP was then used to train the prediction models to generate genomic estimated breeding values (GEBVs) for the Ames Diversity Panel. Empirical validation conducted for nine traits across two validation populations confirmed the accuracy level indicated by the cross-validation of the training population. An upper bound for reliability (U value) was calculated for the accessions in the prediction population using genotypic data. The group of accessions with high U values generally had high predictive ability, even though the range of observed trait values was similar to the group of accessions with low U values. Our comprehensive analysis validated the general approach of turbocharging genebanks with genomics and genomic prediction. In addition, breeders and researchers can consider both GEBVs and U values to balance the needs of improving specific traits and broadening genetic diversity when selecting accessions from genebanks.

Patterns of physiological parameters and nitrogen partitioning in flag leaf explain differential grain protein content in rice

The grain protein content (GPC) in rice is low, and more efforts with agronomic and molecular approaches were performed to increase them. However, the rice research focusing on the plant physiological behaviour that modulates the phenomenon of grain protein filling is very scarce. This work contains physiological parameters related to photosynthetic activity in the flag leaf in the grain filling period and N partitioning assays of high (Nutriar) and traditional (Camba) GPC cultivars. Results indicated a higher photosynthetic capacity, a better capacity to provide CO₂ to the chloroplast and a healthier PSII structure in Camba relative to Nutriar. Chlorophyll fluorescence parameters decreased more steeply over time in the high protein variety, and a strong negative correlation was observed between GPC and PSII structure parameters. N content in the flag leaf at anthesis showed lower values and higher remobilisation during the grain filling period in Nutriar compared to Camba. The results of this work suggested that the inactivation of some PSII structures in higher GPC cultivars is associated with N remobilisation and would contribute to an increase in the free N available to be translocated to the grain.

Single-Trait and Multiple-Trait Genomic Prediction From Multi-Class Bayesian Alphabet Models Using Biological Information

Genomic prediction has been widely used in multiple areas and various genomic prediction methods have been developed. The majority of these methods, however, focus on statistical properties and ignore the abundant useful biological information like genome annotation or previously discovered causal variants. Therefore, to improve prediction performance, several methods have been developed to incorporate biological information into genomic prediction, mostly in single-trait analysis. A commonly used method to incorporate biological information is allocating molecular markers into different classes based on the biological information and assigning separate priors to molecular markers in different classes. It has been shown that such methods can achieve higher prediction accuracy than conventional methods in some circumstances. However, these methods mainly focus on single-trait analysis, and available priors of these methods are limited. Thus, in both single-trait and multiple-trait analysis, we propose the multi-class Bayesian Alphabet methods, in which multiple Bayesian Alphabet priors, including RR-BLUP, BayesA, BayesB, BayesCΠ, and Bayesian LASSO, can be used for markers allocated to different classes. The superior performance of the multi-class Bayesian Alphabet in genomic prediction is demonstrated using both real and simulated data. The software tool JWAS offers open-source routines to perform these analyses.

Genome-wide association study uncovers major genetic loci associated with seed flooding tolerance in soybean

BACKGROUND

Seed flooding stress is one of the threatening environmental stressors that adversely limits soybean at the germination stage across the globe. The knowledge on the genetic basis underlying seed-flooding tolerance is limited. Therefore, we performed a genome-wide association study (GWAS) using 34,718 single nucleotide polymorphism (SNPs) in a panel of 243 worldwide soybean collections to identify genetic loci linked to soybean seed flooding tolerance at the germination stage.

RESULTS

In the present study, GWAS was performed with two contrasting models, Mixed Linear Model (MLM) and Multi-Locus Random-SNP-Effect Mixed Linear Model (mrMLM) to identify significant SNPs associated with electrical conductivity (EC), germination rate (GR), shoot length (ShL), and root length (RL) traits at germination stage in soybean. With MLM, a total of 20, 40, 4, and 9 SNPs associated with EC, GR, ShL and RL, respectively, whereas in the same order mrMLM detected 27, 17, 13, and 18 SNPs. Among these SNPs, two major SNPs, Gm_08_11971416, and Gm_08_46239716 were found to be consistently connected with seed-flooding tolerance related traits, namely EC and GR across two environments. We also detected two SNPs, Gm_05_1000479 and Gm_01_53535790 linked to ShL and RL, respectively. Based on Gene Ontology enrichment analysis, gene functional annotations, and protein-protein interaction network analysis, we predicted eight candidate genes and three hub genes within the regions of the four SNPs with Cis-elements in promoter regions which may be involved in seed-flooding tolerance in soybeans and these warrant further screening and functional validation.

CONCLUSIONS

Our findings demonstrate that GWAS based on high-density SNP markers is an efficient approach to dissect the genetic basis of complex traits and identify candidate genes in soybean. The trait associated SNPs could be used for genetic improvement in soybean breeding programs. The candidate genes could help researchers better understand the molecular mechanisms underlying seed-flooding stress tolerance in soybean.

Population Structure of Nation-Wide Rice in Thailand

BACKGROUND

Thailand is a country with large diversity in rice varieties due to its rich and diverse ecology. In this paper, 300 rice accessions from all across Thailand were sequenced to identify SNP variants allowing for the population structure to be explored.

RESULTS

The result of inferred population structure from admixture and clustering analysis illustrated strong evidence of substructure in each geographical region. The results of phylogenetic tree, PCA analysis, and machine learning on population identifying SNPs also supported the inferred population structure.

CONCLUSION

The population structure inferred in this study contains five subpopulations that tend to group individuals based on location. So, each subpopulation has unique genetic patterns, agronomic traits, as well as different environmental conditions. This study can serve as a reference point of the nation-wide population structure for supporting breeders and researchers who are interested in Thai rice.

Association mapping of drought tolerance and agronomic traits in rice (Oryza sativa L.) landraces

BACKGROUND

Asian cultivars were predominantly represented in global rice panel selected for sequencing and to identify novel alleles for drought tolerance. Diverse genetic resources adapted to Indian subcontinent were not represented much in spite harboring useful alleles that could improve agronomic traits, stress resilience and productivity. These rice accessions are valuable genetic resource in developing rice varieties suited to different rice ecosystem that experiences varying drought stress level, and at different crop stages. A core collection of rice germplasm adapted to Southwestern Indian peninsular genotyped using SSR markers and characterized by contrasting water regimes to associate genomic regions for physiological, root traits and yield related traits. Genotyping-By-Sequencing of selected accessions within the diverse panel revealed haplotype variation in genic content within genomic regions mapped for physiological, morphological and root traits.

RESULTS

Diverse rice panel (99 accessions) were evaluated in field and measurements on plant physiological, root traits and yield related traits were made over five different seasons experiencing varying drought stress intensity at different crop stages. Traits like chlorophyll stability index, leaf rolling, days to 50% flowering, chlorophyll content, root volume and root biomass were identified as best predictors of grain yield under stress. Association mapping revealed genetic variation among accessions and revealed 14 genomic targets associated with different physiological, root and plant production traits. Certain accessions were found to have beneficial allele to improve traits, plant height, root length and spikelet fertility, that contribute to the grain yield under stress. Genomic characterization of eleven accessions revealed haplotype variation within key genomic targets on chromosomes 1, 4, 6 and 11 for potential use as molecular markers to combine drought avoidance and tolerance traits. Genes mined within the genomic QTL intervals identified were prioritized based on tissue specific expression level in publicly available rice transcriptome data.

CONCLUSION

The genetic and genomic resources identified will enable combining traits with agronomic value to optimize yield under stress and hasten trait introgression into elite cultivars. Alleles associated with plant height, specific leaf area, root length from PTB8 and spikelet fertility and grain weight from PTB26 can be harnessed in future rice breeding program.

Identification of SNPs and Candidate Genes for Milk Production Ability in Yorkshire Pigs

Sow milk production ability is an important limiting factor impacting suboptimal growth and the survival of piglets. Through pig genetic improvement, litter sizes have been increased. Larger litters need more suckling mammary glands, which results in increased milk from the lactating sow. Hence, there is much significance to exploring sow lactation performance. For milk production ability, it is not practical to directly measure the milk yield, we used litter weight gain (LWG) throughout sow lactation as an indicator. In this study, we estimated the heritability of LWG, namely, 0.18 ± 0.07. We then performed a GWAS, and detected seven significant SNPs, namely, Sus scrofa Chromosome (SSC) 2: ASGA0010040 (p = 7.73E-11); SSC2:MARC0029355 (p = 1.30E-08), SSC6: WU_10.2_6_65751151 (p = 1.32E-10), SSC7: MARC0058875 (p = 4.99E-09), SSC10: WU_10.2_10_49571394 (p = 6.79E-08), SSC11: M1GA0014659 (p = 1.19E-07), and SSC15: MARC0042106 (p = 1.16E-07). We performed the distribution of phenotypes corresponding to the genotypes of seven significant SNPs and showed that ASGA0010040, MARC0029355, MARC0058875, WU_10.2_10_49571394, M1GA0014659, and MARC0042106 had extreme phenotypic values that corresponded to the homozygous genotypes, while the intermediate values corresponded to the heterozygous genotypes. We screened for flanking regions ± 200 kb nearby the seven significant SNPs, and identified 38 genes in total. Among them, 28 of the candidates were involved in lactose metabolism, colostrum immunity, milk protein, and milk fat by functional enrichment analysis. Through the combined analysis between 28 candidate genes and transcriptome data of the sow mammary gland, we found nine commons (ANO3, MUC15, DISP3, FBXO6, CLCN6, HLA-DRA, SLA-DRB1, SLA-DQB1, and SLA-DQA1). Furthermore, by comparing the chromosome positions of the candidate genes with the quantitative trait locus (QTLs) as previously reported, a total of 17 genes were found to be within 0.86–94.02 Mb of the reported QTLs for sow milk production ability, in which, NAV2 was found to be located with 0.86 Mb of the QTL region ssc2: 40936355. In conclusion, we identified seven significant SNPs located on SSC2, 6, 7, 10, 11, and 15, and propose 28 candidate genes for the ability to produce milk in Yorkshire pigs, 10 of which were key candidates.

An Efficient Score Test Integrated with Empirical Bayes for Genome-Wide Association Studies

Many methods used in multi-locus genome-wide association studies (GWAS) have been developed to improve statistical power. However, most existing multi-locus methods are not quicker than single-locus methods. To address this concern, we proposed a fast score test integrated with Empirical Bayes (ScoreEB) for multi-locus GWAS. Firstly, a score test was conducted for each single nucleotide polymorphism (SNP) under a linear mixed model (LMM) framework, taking into account the genetic relatedness and population structure. Then, all of the potentially associated SNPs were selected with a less stringent criterion. Finally, Empirical Bayes in a multi-locus model was performed for all of the selected SNPs to identify the true quantitative trait nucleotide (QTN). Our new method ScoreEB adopts the similar strategy of multi-locus random-SNP-effect mixed linear model (mrMLM) and fast multi-locus random-SNP-effect EMMA (FASTmrEMMA), and the only difference is that we use the score test to select all the potentially associated markers. Monte Carlo simulation studies demonstrate that ScoreEB significantly improved the computational efficiency compared with the popular methods mrMLM, FASTmrEMMA, iterative modified-sure independence screening EM-Bayesian lasso (ISIS EM-BLASSO), hybrid of restricted and penalized maximum likelihood (HRePML) and genome-wide efficient mixed model association (GEMMA). In addition, ScoreEB remained accurate in QTN effect estimation and effectively controlled false positive rate. Subsequently, ScoreEB was applied to re-analyze quantitative traits in plants and animals. The results show that ScoreEB not only can detect previously reported genes, but also can mine new genes.

Mapping causal genes and genetic interactions for agronomic traits using a large F2 population in rice

Dissecting the genetic mechanisms underlying agronomic traits is of great importance for crop breeding. Agronomic traits are usually controlled by multiple quantitative trait loci (QTLs) and genetic interactions, and mapping the underlying causal genes is still labor-intensive and time-consuming. Here, we present a genetic tool for directly targeting the specific causal genes by using a single-gene resolution linkage map that was constructed from 3756 F2 rice plants via targeted sequencing technology and Tukey-Kramer multiple comparisons test. Three large- and moderate-effect QTLs, qHD6-2, qGL3-1, and qGW5-2, were successfully mapped to their specific causal genes, Hd1, GS3, and GW5, respectively. A complex genetic interaction network containing 30 QTL-QTL interactions was constructed, revealing that the alternative allele of hub QTL, qHD6-2, can hide or release the genetic contributions of the alleles at interacting loci. Moreover, arranging genetic interactions in the models lead to more accurate phenotypic predictions. These results provide a community resource and new feasible strategy for deciphering the genetic mechanisms of complex agronomic traits and accelerating crop breeding.