A quantitative genomics map of rice provides genetic insights and guides breeding

Integrative multi-omics analysis reveals genetic and heterotic contributions to male fertility and yield in potato

The genetic analysis of potato is hampered by the complexity of tetrasomic inheritance. An ongoing effort aims to transform the clonally propagated tetraploid potato into a seed-propagated diploid crop, which would make genetic analyses much easier owing to disomic inheritance. Here, we construct and report the large-scale genetic and heterotic characteristics of a diploid F2 potato population derived from the cross of two highly homozygous inbred lines. We investigate 20,382 traits generated from multi-omics dataset and identify 25,770 quantitative trait loci (QTLs). Coupled with gene expression data, we construct a systems-genetics network for gene discovery in potatoes. Importantly, we explore the genetic basis of heterosis in this population, especially for yield and male fertility heterosis. We find that positive heterotic effects of yield-related QTLs and negative heterotic effects of metabolite QTLs (mQTLs) contribute to yield heterosis. Additionally, we identify a PME gene with a dominance heterotic effect that plays an important role in male fertility heterosis. This study provides genetic resources for the potato community and will facilitate the application of heterosis in diploid potato breeding.

Unlocking the mystery of heterosis opens the era of intelligent rice breeding

Heterosis refers to the phenomenon where the first filial offspring (F1) from genetically diverse parents displays advantages in growth rate, yield, and adaptability compared with its parents. The exploitation of heterosis in rice breeding has greatly increased the productivity, making a significant contribution to food security in the last half of the century. Conventional hybrid rice breeding highly relies on the breeder's experience on random crossing and comprehensive field selection. This process is time-consuming and labor-intensive. In recent years, rice hybrid breeding has encountered challenges stemming from limited germplasm resource, low breeding efficiency, and high uncertainty, which constrain the progress in yield increase, coupled with difficulties in balancing grain yield, quality, and resistance. Understanding the genetic basis of rice heterosis could lead to significant advancements in breeding concepts and methods. This will fully unleash the advantages of heterosis. In this review, we focus on the research progress of the genetic dissection of crop heterosis and briefly introduce some key advancements in modern intelligent breeding of rice hybrid.

Identification and analysis of low light responsive yield enhancing QTLs in rice

Rice is one of the major food crops grown globally. However, during the wet season, rice suffers significant yield loss due to reduced light intensity caused by overcast clouds when the light intensity is only around 450-500 µmol/m2/s, compared to 1400-1800 µmol/m2/s in summer. This reduction in light intensity leads to a decrease in seed yield, mainly by limiting tiller or panicle numbers. Yield and its attributing parameters were recorded in one hundred thirty RILs for four consecutive wet seasons in ambient light (AL) and low light (LL, 35% light-cut using white shade net). QTL analysis was performed using Inclusive Composite Interval Mapping (ICIM) with all the phenotypic data and 927 polymorphic SNPs identified by the 7 K Infinium chip. The study identified a large QTL influencing panicle numbers and yield exclusively in lowlight on chromosome 1 (qPNLL1.1, qGYLL1.1) in four consecutive seasons with LOD > 10 and PVE > 30%. The favourable alleles are from the tolerant parent, Swarnaprabha. Another grain yield improving QTL was identified on chromosome 6 (qGYLL6.1), with LOD > 3 in three consecutive seasons. In a diverse rice panel of one hundred seventeen genotypes with five different models, association analysis identified the associated marker for panicle numbers and grain yield in LL, which is also the left marker of the newly identified QTLs for the traits under LL condition. A shade-responsive gene, monoculm 2 (MOC2, LOC_Os01g64660) inside the QTL on chromosome 1, upregulated in the tolerant parent and its QTL-carrying RILs, whereas repressed in the susceptible one. Therefore, due to its significant additive effect and validation across various genotypes, the yield-improving QTL on chromosome 1 can be directly utilised in marker-assisted selection (MAS) for developing shade-tolerant rice. This can also help reduce the yield gap between wet and dry-season rice.

The era of panomics-driven gene discovery in plants

Panomics is an approach to integrate multiple 'omics' datasets, generated using different individuals or natural variations. Considering their diverse phenotypic spectrum, the phenome is inherently associated with panomics-based science, which is further combined with genomics, transcriptomics, metabolomics, and other omics techniques, either independently or collectively. Panomics has been accelerated through recent technological advancements in the field of genomics that enable the detection of population-wide structural variations (SVs) and hence offer unprecedented insights into the genetic variations contributing to important agronomic traits. The present review provides the recent trends of panomics-driven gene discovery toward various traits related to plant development, stress tolerance, accumulation of specialized metabolites, and domestication/dedomestication. In addition, the success stories are highlighted in the broader context of enhancing crop productivity.

Rapid breeding of an early maturing, high-quality, and high-y.ielding rice cultivar using marker‑assisted selection coupled with optimized anther culture

With the global shift towards healthier eating habits, the focus of the rice industry has evolved from quantity to quality. In China, the Yangtze River Basin is the main area consuming long-grain and high-quality indica rice. Hubei Province, a significant rice-producing area, currently cultivates a limited range of rice varieties, risking degradation and diminishing economic returns. Therefore, it is imperative to cultivate elite rice varieties tailored to the local production conditions and can significantly enhance the added value. This study bred the novel rice cultivar "Runxiangyu", characterized by early maturity, high quality, and high yield. It is a hybrid of Ezhong 5, known for its moderate height and excellent quality, albeit with a long growth period and lack of fragrance, and Yuzhenxiang, renowned for its high quality, short growth period, and fragrance but limited by its tall stature and poor tillering ability. The breeding process utilized optimized anther culture coupled with molecular marker-assisted selection (MAS) and phenotype analysis. In the field, the developed cultivar was 120.9 cm tall and had an entire growth period of 117.5 days, demonstrating moderate disease resistance and excellent heat tolerance. Its grains are fragrant, meeting the national standard of grade two high-quality rice set by the Food Quality Supervision and Inspection Center of the Ministry of Agriculture and Rural Areas). Exhibiting superior agronomic traits, such as plant type, height, growth period, and stress resistance, along with and quality attributes, including grain shape, chalkiness, fragrance, and taste, "Runxiangyu" was certified by the Agricultural Crop Variety Certification Commission of Hubei in 2022. These findings suggested that molecular MAS coupled with optimized anther culture and multi-site phenotype analysis is an efficient and rapid method for crop breeding.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-024-01495-4.

Comprehensive mapping and modelling of the rice regulome landscape unveils the regulatory architecture underlying complex traits

Unraveling the regulatory mechanisms that govern complex traits is pivotal for advancing crop improvement. Here we present a comprehensive regulome atlas for rice (Oryza sativa), charting the chromatin accessibility across 23 distinct tissues from three representative varieties. Our study uncovers 117,176 unique open chromatin regions (OCRs), accounting for ~15% of the rice genome, a notably higher proportion compared to previous reports in plants. Integrating RNA-seq data from matched tissues, we confidently predict 59,075 OCR-to-gene links, with enhancers constituting 69.54% of these associations, including many known enhancer-to-gene links. Leveraging this resource, we re-evaluate genome-wide association study results and discover a previously unknown function of OsbZIP06 in seed germination, which we subsequently confirm through experimental validation. We optimize deep learning models to decode regulatory grammar, achieving robust modeling of tissue-specific chromatin accessibility. This approach allows to predict cross-variety regulatory dynamics from genomic sequences, shedding light on the genetic underpinnings of cis-regulatory divergence and morphological disparities between varieties. Overall, our study establishes a foundational resource for rice functional genomics and precision molecular breeding, providing valuable insights into regulatory mechanisms governing complex traits.

Powerful QTL mapping and favorable allele mining in an all-in-one population: a case study of heading date

The multiparent advanced generation intercross (MAGIC) population is characterized with great potentials in power and resolution of quantitative trait locus (QTL) mapping, but single nucleotide polymorphism (SNP)-based GWAS does not fully reach its potential. In this study, a MAGIC population of 1021 lines was developed from four Xian and four Geng varieties from five subgroups of rice. A total of 44 000 genes showed functional polymorphisms among eight parents, including frameshift variations or premature stop codon variations, which provides the potential to map almost all genes of the MAGIC population. Principal component analysis results showed that the MAGIC population had a weak population structure. A high-density bin map of 24 414 bins was constructed. Segregation distortion occurred in the regions possessing the genes underlying genetic incompatibility and gamete development. SNP-based association analysis and bin-based linkage analysis identified 25 significant loci and 47 QTLs for heading date, including 14 known heading date genes. The mapping resolution of genes is dependent on genetic effects with offset distances of <55 kb for major effect genes and <123 kb for moderate effect genes. Four causal variants and noncoding structure variants were identified to be associated with heading date. Three to four types of alleles with strong, intermediate, weak, and no genetic effects were identified from eight parents, providing flexibility for the improvement of rice heading date. In most cases, japonica rice carries weak alleles, and indica rice carries strong alleles and nonfunctional alleles. These results confirm that the MAGIC population provides the exceptional opportunity to detect QTLs, and its use is encouraged for mapping genes and mining favorable alleles for breeding.

Computational tools for plant genomics and breeding

Plant genomics and crop breeding are at the intersection of biotechnology and information technology. Driven by a combination of high-throughput sequencing, molecular biology and data science, great advances have been made in omics technologies at every step along the central dogma, especially in genome assembling, genome annotation, epigenomic profiling, and transcriptome profiling. These advances further revolutionized three directions of development. One is genetic dissection of complex traits in crops, along with genomic prediction and selection. The second is comparative genomics and evolution, which open up new opportunities to depict the evolutionary constraints of biological sequences for deleterious variant discovery. The third direction is the development of deep learning approaches for the rational design of biological sequences, especially proteins, for synthetic biology. All three directions of development serve as the foundation for a new era of crop breeding where agronomic traits are enhanced by genome design.

Mass spectrometry-based proteomic landscape of rice reveals a post-transcriptional regulatory role of N6-methyladenosine

Rice is one of the most important staple food and model species in plant biology, yet its quantitative proteomes are largely uncharacterized. Here we quantify the relative protein levels of over 15,000 genes across major rice tissues using a tandem mass tag strategy followed by intensive fractionation and mass spectrometry. We identify tissue-specific and tissue-enriched proteins that are linked to the functional specificity of individual tissues. Proteogenomic comparison of rice and Arabidopsis reveals conserved proteome expression, which differs from mammals in that there is a strong separation of species rather than tissues. Notably, profiling of N6-methyladenosine (m6A) across the rice major tissues shows that m6A at untranslated regions is negatively correlated with protein abundance and contributes to the discordance between RNA and protein levels. We also demonstrate that our data are valuable for identifying novel genes required for regulating m6A methylation. Taken together, this study provides a paradigm for further research into rice proteogenome.

Engineering the future cereal crops with big biological data: toward intelligence-driven breeding by design

How to feed 10 billion human populations is one of the challenges that need to be addressed in the following decades, especially under an unpredicted climate change. Crop breeding, initiating from the phenotype-based selection by local farmers and developing into current biotechnology-based breeding, has played a critical role in securing the global food supply. However, regarding the changing environment and ever-increasing human population, can we breed outstanding crop varieties fast enough to achieve high productivity, good quality, and widespread adaptability? This review outlines the recent achievements in understanding cereal crop breeding, including the current knowledge about crop agronomic traits, newly developed techniques, crop big biological data research, and the possibility of integrating them for intelligence-driven breeding by design, which ushers in a new era of crop breeding practice and shapes the novel architecture of future crops. This review focuses on the major cereal crops, including rice, maize, and wheat, to explain how intelligence-driven breeding by design is becoming a reality.

Genomic investigation of 18,421 lines reveals the genetic architecture of rice

Understanding how numerous quantitative trait loci (QTL) shape phenotypic variation is an important question in genetics. To address this, we established a permanent population of 18,421 (18K) rice lines with reduced population structure. We generated reference-level genome assemblies of the founders and genotyped all 18K-rice lines through whole-genome sequencing. Through high-resolution mapping, 96 high-quality candidate genes contributing to variation in 16 traits were identified, including OsMADS22 and OsFTL1 verified as causal genes for panicle number and heading date, respectively. We identified epistatic QTL pairs and constructed a genetic interaction network with 19 genes serving as hubs. Overall, 170 masking epistasis pairs were characterized, serving as an important factor contributing to genetic background effects across diverse varieties. The work provides a basis to guide grain yield and quality improvements in rice.

A genome-wide association study of panicle blast resistance to Magnaporthe oryzae in rice

Rice blast, caused by Magnaporthe oryzae (M. oryzae), is one of the most serious diseases worldwide. Developing blast-resistant rice varieties is an effective strategy to control the spread of rice blast and reduce the reliance on chemical pesticides. In this study, 477 sequenced rice germplasms from 48 countries were inoculated and assessed at the booting stage. We found that 23 germplasms exhibited high panicle blast resistance against M. oryzae. Genome-wide association analysis (GWAS) identified 43 quantitative trait loci (QTLs) significantly associated (P < 1.0 × 10-4) with resistance to rice panicle blast. These QTL intervals encompass four genes (OsAKT1, OsRACK1A, Bsr-k1 and Pi25/Pid3) previously reported to contribute to rice blast resistance. We selected QTLs with -Log10 (P-value) greater than 6.0 or those detected in two-year replicates, amounting to 12 QTLs, for further candidate gene analysis. Three blast resistance candidate genes (Os06g0316800, Os06g0320000, Pi25/Pid3) were identified based on significant single nucleotide polymorphisms (SNP) distributions within annotated gene sequences across these 12 QTLs and the differential expression levels among blast-resistant varieties after 72 h of inoculation. Os06g0316800 encodes a glycine-rich protein, OsGrp6, an important component of plant cell walls involved in cellular stress responses and signaling. Os06g0320000 encodes a protein with unknown function (DUF953), part of the thioredoxin-like family, which is crucial for maintaining reactive oxygen species (ROS) homeostasis in vivo, named as OsTrxl1. Lastly, Pi25/Pid3 encodes a disease resistance protein, underscoring its potential importance in plant biology. By analyzing the haplotypes of these three genes, we identified favorable haplotypes for blast resistance, providing valuable genetic resources for future rice blast resistance breeding programs.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-024-01486-5.

Epistasis and pleiotropy-induced variation for plant breeding

Epistasis refers to nonallelic interaction between genes that cause bias in estimates of genetic parameters for a phenotype with interactions of two or more genes affecting the same trait. Partitioning of epistatic effects allows true estimation of the genetic parameters affecting phenotypes. Multigenic variation plays a central role in the evolution of complex characteristics, among which pleiotropy, where a single gene affects several phenotypic characters, has a large influence. While pleiotropic interactions provide functional specificity, they increase the challenge of gene discovery and functional analysis. Overcoming pleiotropy-based phenotypic trade-offs offers potential for assisting breeding for complex traits. Modelling higher order nonallelic epistatic interaction, pleiotropy and non-pleiotropy-induced variation, and genotype × environment interaction in genomic selection may provide new paths to increase the productivity and stress tolerance for next generation of crop cultivars. Advances in statistical models, software and algorithm developments, and genomic research have facilitated dissecting the nature and extent of pleiotropy and epistasis. We overview emerging approaches to exploit positive (and avoid negative) epistatic and pleiotropic interactions in a plant breeding context, including developing avenues of artificial intelligence, novel exploitation of large-scale genomics and phenomics data, and involvement of genes with minor effects to analyse epistatic interactions and pleiotropic quantitative trait loci, including missing heritability.

Dominance complementation of parental heading date alleles of Hd1, Ghd7, DTH8, and PRR37 confers transgressive late maturation in hybrid rice

Rice (Oryza sativa L.) is a short-day plant whose heading date is largely determined by photoperiod sensitivity (PS). Many parental lines used in hybrid rice breeding have weak PS, but their F1 progenies have strong PS and exhibit an undesirable transgressive late-maturing phenotype. However, the genetic basis for this phenomenon is unclear. Therefore, effective methods are needed for selecting parents to create F1 hybrid varieties with the desired PS. In this study, we used bulked segregant analysis with F1 Ningyou 1179 (strong PS) and its F2 population, and through analyzing both parental haplotypes and PS data for 918 hybrid rice varieties, to identify the genetic basis of transgressive late maturation which is dependent on dominance complementation effects of Hd1, Ghd7, DTH8, and PRR37 from both parents rather than from a single parental genotype. We designed a molecular marker-assisted selection system to identify the genotypes of Hd1, Ghd7, DTH8, and PRR37 in parental lines to predict PS in F1 plants prior to crossing. Furthermore, we used CRISPR/Cas9 technique to knock out Hd1 in Ning A (sterile line) and Ning B (maintainer line) and obtained an hd1-NY material with weak PS while retaining the elite agronomic traits of NY. Our findings clarified the genetic basis of transgressive late maturation in hybrid rice and developed effective methods for parental selection and gene editing to facilitate the breeding of hybrid varieties with the desired PS for improving their adaptability.

A higher-yield hybrid rice is achieved by assimilating a dominant heterotic gene in inbred parental lines

The exploitation of heterosis to integrate parental advantages is one of the fastest and most efficient ways of rice breeding. The genomic architecture of heterosis suggests that the grain yield is strongly correlated with the accumulation of numerous rare superior alleles with positive dominance. However, the improvements in yield of hybrid rice have shown a slowdown or even plateaued due to the limited availability of complementary superior alleles. In this study, we achieved a considerable increase in grain yield of restorer lines by inducing an alternative splicing event in a heterosis gene OsMADS1 through CRISPR-Cas9, which accounted for approximately 34.1%-47.5% of yield advantage over their corresponding inbred rice cultivars. To achieve a higher yield in hybrid rice, we crossed the gene-edited restorer parents harbouring OsMADS1GW3p6 with the sterile lines to develop new rice hybrids. In two-line hybrid rice Guang-liang-you 676 (GLY676), the yield of modified hybrids carrying the homozygous heterosis gene OsMADS1GW3p6 significantly exceeded that of the original hybrids with heterozygous OsMADS1. Similarly, the gene-modified F1 hybrids with heterozygous OsMADS1GW3p6 increased grain yield by over 3.4% compared to the three-line hybrid rice Quan-you-si-miao (QYSM) with the homozygous genotype of OsMADS1. Our study highlighted the great potential in increasing the grain yield of hybrid rice by pyramiding a single heterosis gene via CRISPR-Cas9. Furthermore, these results demonstrated that the incomplete dominance of heterosis genes played a major role in yield-related heterosis and provided a promising strategy for breeding higher-yielding rice varieties above what is currently achievable.

Real-time emulation of future global warming reveals realistic impacts on the phenological response and quality deterioration in rice

Decreased production of crops due to climate change has been predicted scientifically. While climate-resilient crops are necessary to ensure food security and support sustainable agriculture, predicting crop growth under future global warming is challenging. Therefore, we aimed to assess the impact of realistic global warming conditions on rice cultivation. We developed a crop evaluation platform, the agro-environment (AE) emulator, which generates diverse environments by implementing the complexity of natural environmental fluctuations in customized, fully artificial lighting growth chambers. We confirmed that the environmental responsiveness of rice obtained in the fluctuation of artificial environments is similar to those exhibited in natural environments by validating our AE emulator using publicly available meteorological data from multiple years at the same location and multiple locations in the same year. Based on the representative concentration pathway, real-time emulation of severe global warming unveiled dramatic advances in the rice life cycle, accompanied by a 35% decrease in grain yield and an 85% increase in quality deterioration, which is higher than the recently reported projections. The transcriptome dynamism showed that increasing temperature and CO2 concentrations synergistically changed the expression of various genes and strengthened the induction of flowering, heat stress adaptation, and CO2 response genes. The predicted severe global warming greatly alters rice environmental adaptability and negatively impacts rice production. Our findings offer innovative applications of artificial environments and insights for enhancing varietal potential and cultivation methods in the future.

GWAS analysis reveals the genetic basis of blast resistance associated with heading date in rice

Rice blast is a destructive fungal disease affecting rice plants at various growth stages, significantly threatening global yield stability. Development of resistant rice cultivars stands as a practical means of disease control. Generally, association mapping with a diversity panel powerfully identifies new alleles controlling trait of interest. On the other hand, utilization of a breeding panel has its advantage that can be directly applied in a breeding program. In this study, we conducted a genome-wide association study (GWAS) for blast resistance using 296 commercial rice cultivars with low population structure but large phenotypic diversity. We attempt to answer the genetic basis behind rice blast resistance among early maturing cultivars by subdividing the population based on its Heading date 1 (Hd1) functionality. Subpopulation-specific GWAS using the mixed linear model (MLM) based on blast nursery screening conducted in three years revealed a total of 26 significant signals, including three nucleotide-binding site leucine-rich repeat (NBS-LRR) genes (Os06g0286500, Os06g0286700, and Os06g0287500) located at Piz locus on chromosome 6, and one at the Pi-ta locus (Os12g0281300) on chromosome 12. Haplotype analysis revealed blast resistance associated with Piz locus was exclusively specific to Type 14 hd1 among japonica rice. Our findings provide valuable insights for breeding blast resistant rice and highlight the applicability of our elite cultivar panel to detect superior alleles associated with important agronomic traits.

Omics-driven advances in the understanding of regulatory landscape of peanut seed development

Peanuts (Arachis hypogaea) are an essential oilseed crop known for their unique developmental process, characterized by aerial flowering followed by subterranean fruit development. This crop is polyploid, consisting of A and B subgenomes, which complicates its genetic analysis. The advent and progression of omics technologies-encompassing genomics, transcriptomics, proteomics, epigenomics, and metabolomics-have significantly advanced our understanding of peanut biology, particularly in the context of seed development and the regulation of seed-associated traits. Following the completion of the peanut reference genome, research has utilized omics data to elucidate the quantitative trait loci (QTL) associated with seed weight, oil content, protein content, fatty acid composition, sucrose content, and seed coat color as well as the regulatory mechanisms governing seed development. This review aims to summarize the advancements in peanut seed development regulation and trait analysis based on reference genome-guided omics studies. It provides an overview of the significant progress made in understanding the molecular basis of peanut seed development, offering insights into the complex genetic and epigenetic mechanisms that influence key agronomic traits. These studies highlight the significance of omics data in profoundly elucidating the regulatory mechanisms of peanut seed development. Furthermore, they lay a foundational basis for future research on trait-related functional genes, highlighting the pivotal role of comprehensive genomic analysis in advancing our understanding of plant biology.

Progress in Rice Breeding Based on Genomic Research

The role of rice genomics in breeding progress is becoming increasingly important. Deeper research into the rice genome will contribute to the identification and utilization of outstanding functional genes, enriching the diversity and genetic basis of breeding materials and meeting the diverse demands for various improvements. Here, we review the significant contributions of rice genomics research to breeding progress over the last 25 years, discussing the profound impact of genomics on rice genome sequencing, functional gene exploration, and novel breeding methods, and we provide valuable insights for future research and breeding practices.

Natural uORF variation in plants

Taking advantage of natural variation promotes our understanding of phenotypic diversity and trait evolution, ultimately accelerating plant breeding, in which the identification of causal variations is critical. To date, sequence variations in the coding region and transcription level polymorphisms caused by variations in the promoter have been prioritized. An upstream open reading frame (uORF) in the 5' untranslated region (5' UTR) regulates gene expression at the post-transcription or translation level. In recent years, studies have demonstrated that natural uORF variations shape phenotypic diversity. This opinion article highlights recent researches and speculates on future directions for natural uORF variation in plants.

Innovative computational tools provide new insights into the polyploid wheat genome

Bread wheat (Triticum aestivum) is an important crop and serves as a significant source of protein and calories for humans, worldwide. Nevertheless, its large and allopolyploid genome poses constraints on genetic improvement. The complex reticulate evolutionary history and the intricacy of genomic resources make the deciphering of the functional genome considerably more challenging. Recently, we have developed a comprehensive list of versatile computational tools with the integration of statistical models for dissecting the polyploid wheat genome. Here, we summarize the methodological innovations and applications of these tools and databases. A series of step-by-step examples illustrates how these tools can be utilized for dissecting wheat germplasm resources and unveiling functional genes associated with important agronomic traits. Furthermore, we outline future perspectives on new advanced tools and databases, taking into consideration the unique features of bread wheat, to accelerate genomic-assisted wheat breeding.

Transcription factor encoding gene OsC1 regulates leaf sheath color through anthocyanidin metabolism in Oryza rufipogon and Oryza sativa

Carbohydrates, proteins, lipids, minerals and vitamins are nutrient substances commonly seen in rice grains, but anthocyanidin, with benefit for plant growth and animal health, exists mainly in the common wild rice but hardly in the cultivated rice. To screen the rice germplasm with high intensity of anthocyanidins and identify the variations, we used metabolomics technique and detected significant different accumulation of anthocyanidins in common wild rice (Oryza rufipogon, with purple leaf sheath) and cultivated rice (Oryza sativa, with green leaf sheath). In this study, we identified and characterized a well-known MYB transcription factor, OsC1, through phenotypic (leaf sheath color) and metabolic (metabolite profiling) genome-wide association studies (pGWAS and mGWAS) in 160 common wild rice (O. rufipogon) and 151 cultivated (O. sativa) rice varieties. Transgenic experiments demonstrated that biosynthesis and accumulation of cyanidin-3-Galc, cyanidin 3-O-rutinoside and cyanidin O-syringic acid, as well as purple pigmentation in leaf sheath were regulated by OsC1. A total of 25 sequence variations of OsC1 constructed 16 functional haplotypes (higher accumulation of the three anthocyanidin types within purple leaf sheath) and 9 non-functional haplotypes (less accumulation of anthocyanidins within green leaf sheath). Three haplotypes of OsC1 were newly identified in our germplasm, which have potential values in functional genomics and molecular breeding of rice. Gene-to-metabolite analysis by mGWAS and pGWAS provides a useful and efficient tool for functional gene identification and omics-based crop genetic improvement.

Toward understanding and utilizing crop heterosis in the age of biotechnology

Heterosis, a universal phenomenon in nature, mainly reflected in the superior productivity, quality, and fitness of F1 hybrids compared with their inbred parents, has been exploited in agriculture and greatly benefited human society in terms of food security. However, the flexible and efficient utilization of heterosis has remained a challenge in hybrid breeding systems because of the limitations of "three-line" and "two-line" methods. In the past two decades, rapidly developed biotechnologies have provided unprecedented conveniences for both understanding and utilizing heterosis. Notably, "third-generation" (3G) hybrid breeding technology together with high-throughput sequencing and gene editing greatly promoted the efficiency of hybrid breeding. Here, we review emerging ideas about the genetic or molecular mechanisms of heterosis and the development of 3G hybrid breeding system in the age of biotechnology. In addition, we summarized opportunities and challenges for optimal heterosis utilization in the future.

Identification of qGL4.1 and qGL4.2, two closely linked QTL controlling grain length in rice

Grain size is an important appearance quality trait in rice, which also affects grain yield. In this study, a recombinant inbred line (RIL) population derived from a cross between indica variety 9311 and japonica variety Cypress was constructed. And 181 out of 600 RILs were sequenced, and a high-density genetic map containing 2842 bin markers was constructed, with a total map length of 1500.6 cM. A total of 10 quantitative trait loci (QTL) related to grain length (GL), grain width (GW), grain length-to-width ratio (LWR), and 1000-grain weight (TGW) were detected under two environments. The genetic effect of qGL4, a minor QTL for GL and TGW, was validated using three heterogeneous inbred family (HIF) segregation populations. It was further dissected into two closed linked QTL, qGL4.1 and qGL4.2. By progeny testing, qGL4.1 and qGL4.2 were successfully delimited to intervals of 1304-kb and 423-kb, respectively. Our results lay the foundation for the map-based cloning of qGL4.1 and qGL4.2 and provide new gene resources for the improvement of grain yield and quality in rice.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-024-01447-y.

Fine mapping of the panicle length QTL qPL5 in rice

Panicle length is a crucial trait tightly associated with spikelets per panicle and grain yield in rice. To dissect the genetic basis of panicle length, a population of 161 recombinant inbred lines (RILs) was developed from the cross between an aus variety Chuan 7 (C7) and a tropical Geng variety Haoboka (HBK). C7 has a panicle length of 30 cm, 7 cm longer than that of HBK, and the panicle length was normally distributed in the RIL population. A total of six quantitative trait loci (QTLs) for panicle length were identified, and single QTLs explained the phenotypic variance from 4.9 to 18.1%. Among them, three QTLs were mapped to the regions harbored sd1, DLT, and Ehd1, respectively. To validate the genetic effect of a minor QTL qPL5, a near-isogenic F2 (NIF2) population segregated at qPL5 was developed. Interestingly, panicle length displayed bimodal distribution, and heading date also exhibited significant variation in the NIF2 population. qPL5 accounted for 66.5% of the panicle length variance. The C7 allele at qPL5 increased panicle length by 2.4 cm and promoted heading date by 5 days. Finally, qPL5 was narrowed down to an 80-kb region flanked by markers M2197 and M2205 using a large NIF2 population of 7600 plants. LOC_Os05g37540, encoding a phytochrome signal protein whose homolog in Arabidopsis enlarges panicle length, is regarded as the candidate gene because a single-nucleotide mutation (C1099T) caused a premature stop codon in HBK. The characterization of qPL5 with enlarging panicle length but promoting heading date makes its great value in breeding early mature varieties without yield penalty in rice.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-024-01443-2.

A high-performance computational workflow to accelerate GATK SNP detection across a 25-genome dataset

BACKGROUND

Single-nucleotide polymorphisms (SNPs) are the most widely used form of molecular genetic variation studies. As reference genomes and resequencing data sets expand exponentially, tools must be in place to call SNPs at a similar pace. The genome analysis toolkit (GATK) is one of the most widely used SNP calling software tools publicly available, but unfortunately, high-performance computing versions of this tool have yet to become widely available and affordable.

RESULTS

Here we report an open-source high-performance computing genome variant calling workflow (HPC-GVCW) for GATK that can run on multiple computing platforms from supercomputers to desktop machines. We benchmarked HPC-GVCW on multiple crop species for performance and accuracy with comparable results with previously published reports (using GATK alone). Finally, we used HPC-GVCW in production mode to call SNPs on a "subpopulation aware" 16-genome rice reference panel with ~ 3000 resequenced rice accessions. The entire process took ~ 16 weeks and resulted in the identification of an average of 27.3 M SNPs/genome and the discovery of ~ 2.3 million novel SNPs that were not present in the flagship reference genome for rice (i.e., IRGSP RefSeq).

CONCLUSIONS

This study developed an open-source pipeline (HPC-GVCW) to run GATK on HPC platforms, which significantly improved the speed at which SNPs can be called. The workflow is widely applicable as demonstrated successfully for four major crop species with genomes ranging in size from 400 Mb to 2.4 Gb. Using HPC-GVCW in production mode to call SNPs on a 25 multi-crop-reference genome data set produced over 1.1 billion SNPs that were publicly released for functional and breeding studies. For rice, many novel SNPs were identified and were found to reside within genes and open chromatin regions that are predicted to have functional consequences. Combined, our results demonstrate the usefulness of combining a high-performance SNP calling architecture solution with a subpopulation-aware reference genome panel for rapid SNP discovery and public deployment.

CropGS-Hub: a comprehensive database of genotype and phenotype resources for genomic prediction in major crops

The explosive amount of multi-omics data has brought a paradigm shift both in academic research and further application in life science. However, managing and reusing the growing resources of genomic and phenotype data points presents considerable challenges for the research community. There is an urgent need for an integrated database that combines genome-wide association studies (GWAS) with genomic selection (GS). Here, we present CropGS-Hub, a comprehensive database comprising genotype, phenotype, and GWAS signals, as well as a one-stop platform with built-in algorithms for genomic prediction and crossing design. This database encompasses a comprehensive collection of over 224 billion genotype data and 434 thousand phenotype data generated from >30 000 individuals in 14 representative populations belonging to 7 major crop species. Moreover, the platform implemented three complete functional genomic selection related modules including phenotype prediction, user model training and crossing design, as well as a fast SNP genotyper plugin-in called SNPGT specifically built for CropGS-Hub, aiming to assist crop scientists and breeders without necessitating coding skills. CropGS-Hub can be accessed at https://iagr.genomics.cn/CropGS/.

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).

Navigating rice seedling cold resilience: QTL mapping in two inbred line populations and the search for genes

Due to global climate change resulting in extreme temperature fluctuations, it becomes increasingly necessary to explore the natural genetic variation in model crops such as rice to facilitate the breeding of climate-resilient cultivars. To uncover genomic regions in rice involved in managing cold stress tolerance responses and to identify associated cold tolerance genes, two inbred line populations developed from crosses between cold-tolerant and cold-sensitive parents were used for quantitative trait locus (QTL) mapping of two traits: degree of membrane damage after 1 week of cold exposure quantified as percent electrolyte leakage (EL) and percent low-temperature seedling survivability (LTSS) after 1 week of recovery growth. This revealed four EL QTL and 12 LTSS QTL, all overlapping with larger QTL regions previously uncovered by genome-wide association study (GWAS) mapping approaches. Within the QTL regions, 25 cold-tolerant candidate genes were identified based on genomic differences between the cold-tolerant and cold-sensitive parents. Of those genes, 20% coded for receptor-like kinases potentially involved in signal transduction of cold tolerance responses; 16% coded for transcription factors or factors potentially involved in regulating cold tolerance response effector genes; and 64% coded for protein chaperons or enzymes potentially serving as cold tolerance effector proteins. Most of the 25 genes were cold temperature regulated and had deleterious nucleotide variants in the cold-sensitive parent, which might contribute to its cold-sensitive phenotype.

10k-level integrated rice database shows power for exploiting rare variants

This Highlight features a recent study by Shang Lianguang and Qian Qian's groups, who re-analyzed published resequencing data covering 10,548 accessions of Asian cultivated rice Oryza sativa and wild rice Oryza rufipogon from 98 countries worldwide to generate a super-large rice genomic variation dataset.

Genetic interrogation of phenotypic plasticity informs genome-enabled breeding in cotton

Phenotypic plasticity, or the ability to adapt to and thrive in changing climates and variable environments, is essential for developmental programs in plants. Despite its importance, the genetic underpinnings of phenotypic plasticity for key agronomic traits remain poorly understood in many crops. In this study, we aim to fill this gap by using genome-wide association studies to identify genetic variations associated with phenotypic plasticity in upland cotton (Gossypium hirsutum L.). We identified 73 additive quantitative trait loci (QTLs), 32 dominant QTLs, and 6799 epistatic QTLs associated with 20 traits. We also identified 117 additive QTLs, 28 dominant QTLs, and 4691 epistatic QTLs associated with phenotypic plasticity in 19 traits. Our findings reveal new genetic factors, including additive, dominant, and epistatic QTLs, that are linked to phenotypic plasticity and agronomic traits. Meanwhile, we find that the genetic factors controlling the mean phenotype and phenotypic plasticity are largely independent in upland cotton, indicating the potential for simultaneous improvement. Additionally, we envision a genomic design strategy by utilizing the identified QTLs to facilitate cotton breeding. Taken together, our study provides new insights into the genetic basis of phenotypic plasticity in cotton, which should be valuable for future breeding.

QTL analysis of traits related to seed size and shape in sesame (Sesamum indicum L.)

Seed size and shape are important traits that determine seed yield in sesame. Understanding the genetic basis of seed size and shape is essential for improving the yield of sesame. In this study, F2 and BC1 populations were developed by crossing the Yuzhi 4 and Bengal small-seed (BS) lines for detecting the quantitative trait loci (QTLs) of traits related to seed size and shape. A total of 52 QTLs, including 13 in F2 and 39 in BC1 populations, for seed length (SL), seed width (SW), and length to width ratio (L/W) were identified, explaining phenotypic variations from 3.68 to 21.64%. Of these QTLs, nine stable major QTLs were identified in the two populations. Notably, three major QTLs qSL-LG3-2, qSW-LG3-2, and qSW-LG3-F2 that accounted for 4.94-16.34% of the phenotypic variations were co-localized in a 2.08 Mb interval on chromosome 1 (chr1) with 279 candidate genes. Three stable major QTLs qSL-LG6-2, qLW-LG6, and qLW-LG6-F2 that explained 8.14-33.74% of the phenotypic variations were co-localized in a 3.27 Mb region on chr9 with 398 candidate genes. In addition, the stable major QTL qSL-LG5 was co-localized with minor QTLs qLW-LG5-3 and qSW-LG5 to a 1.82 Mb region on chr3 with 195 candidate genes. Gene annotation, orthologous gene analysis, and sequence analysis indicated that three genes are likely involved in sesame seed development. These results obtained herein provide valuable in-formation for functional gene cloning and improving the seed yield of sesame.

Crop adaptation to climate change: An evolutionary perspective

The disciplines of evolutionary biology and plant and animal breeding have been intertwined throughout their development, with responses to artificial selection yielding insights into the action of natural selection and evolutionary biology providing statistical and conceptual guidance for modern breeding. Here we offer an evolutionary perspective on a grand challenge of the 21st century: feeding humanity in the face of climate change. We first highlight promising strategies currently under way to adapt crops to current and future climate change. These include methods to match crop varieties with current and predicted environments and to optimize breeding goals, management practices, and crop microbiomes to enhance yield and sustainable production. We also describe the promise of crop wild relatives and recent technological innovations such as speed breeding, genomic selection, and genome editing for improving environmental resilience of existing crop varieties or for developing new crops. Next, we discuss how methods and theory from evolutionary biology can enhance these existing strategies and suggest novel approaches. We focus initially on methods for reconstructing the evolutionary history of crops and their pests and symbionts, because such historical information provides an overall framework for crop-improvement efforts. We then describe how evolutionary approaches can be used to detect and mitigate the accumulation of deleterious mutations in crop genomes, identify alleles and mutations that underlie adaptation (and maladaptation) to agricultural environments, mitigate evolutionary trade-offs, and improve critical proteins. Continuing feedback between the evolution and crop biology communities will ensure optimal design of strategies for adapting crops to climate change.

Structure and function of rice hybrid genomes reveal genetic basis and optimal performance of heterosis

Exploitation of crop heterosis is crucial for increasing global agriculture production. However, the quantitative genomic analysis of heterosis was lacking, and there is currently no effective prediction tool to optimize cross-combinations. Here 2,839 rice hybrid cultivars and 9,839 segregation individuals were resequenced and phenotyped. Our findings demonstrated that indica-indica hybrid-improving breeding was a process that broadened genetic resources, pyramided breeding-favorable alleles through combinatorial selection and collaboratively improved both parents by eliminating the inferior alleles at negative dominant loci. Furthermore, we revealed that widespread genetic complementarity contributed to indica-japonica intersubspecific heterosis in yield traits, with dominance effect loci making a greater contribution to phenotypic variance than overdominance effect loci. On the basis of the comprehensive dataset, a genomic model applicable to diverse rice varieties was developed and optimized to predict the performance of hybrid combinations. Our data offer a valuable resource for advancing the understanding and facilitating the utilization of heterosis in rice.

A simulation study comparing advanced marker-assisted selection with genomic selection in tree breeding programs

Advances in DNA sequencing technologies allow the sequencing of whole genomes of thousands of individuals and provide several million single nucleotide polymorphisms (SNPs) per individual. These data combined with precise and high-throughput phenotyping enable genome-wide association studies (GWAS) and the identification of SNPs underlying traits with complex genetic architectures. The identified causal SNPs and estimated allelic effects could then be used for advanced marker-assisted selection (MAS) in breeding programs. But could such MAS compete with the broadly used genomic selection (GS)? This question is of particular interest for the lengthy tree breeding strategies. Here, with our new software "SNPscan breeder," we simulated a simple tree breeding program and compared the impact of different selection criteria on genetic gain and inbreeding. Further, we assessed different genetic architectures and different levels of kinship among individuals of the breeding population. Interestingly, apart from progeny testing, GS using gBLUP performed best under almost all simulated scenarios. MAS based on GWAS results outperformed GS only if the allelic effects were estimated in large populations (ca. 10,000 individuals) of unrelated individuals. Notably, GWAS using 3,000 extreme phenotypes performed as good as the use of 10,000 phenotypes. GS increased inbreeding and thus reduced genetic diversity more strongly compared to progeny testing and GWAS-based selection. We discuss the practical implications for tree breeding programs. In conclusion, our analyses further support the potential of GS for forest tree breeding and improvement, although MAS may gain relevance with decreasing sequencing costs in the future.

Integrated genome-wide differentiation and association analyses identify causal genes underlying breeding-selected grain quality traits in japonica rice

Improving grain quality is a primary objective in contemporary rice breeding. Japanese modern rice breeding has developed two different types of rice, eating and sake-brewing rice, with different grain characteristics, indicating the selection of variant gene alleles during the breeding process. Given the critical importance of promptly and efficiently identifying genes selected in past breeding for future molecular breeding, we conducted genome scans for divergence, genome-wide association studies, and map-based cloning. Consequently, we successfully identified two genes, OsMnS and OsWOX9D, both contributing to rice grain traits. OsMnS encodes a mannan synthase that increases the white core frequency in the endosperm, a desirable trait for sake brewing but decreases the grain appearance quality. OsWOX9D encodes a grass-specific homeobox-containing transcription factor, which enhances grain width for better sake brewing. Furthermore, haplotype analysis revealed that their defective alleles were selected in East Asia, but not Europe, during modern improvement. In addition, our analyses indicate that a reduction in grain mannan content during African rice domestication may also be caused a defective OsMnS allele due to breeding selection. This study not only reveals the delicate balance between grain appearance quality and nutrition in rice but also provides a new strategy for isolating causal genes underlying complex traits, based on the concept of "breeding-assisted genomics" in plants.

Plant genome resequencing and population genomics: Current status and future prospects

Advances in DNA sequencing technology have sparked a genomics revolution, driving breakthroughs in plant genetics and crop breeding. Recently, the focus has shifted from cataloging genetic diversity in plants to exploring their functional significance and delivering beneficial alleles for crop improvement. This transformation has been facilitated by the increasing adoption of whole-genome resequencing. In this review, we summarize the current progress of population-based genome resequencing studies and how these studies affect crop breeding. A total of 187 land plants from 163 countries have been resequenced, comprising 54 413 accessions. As part of resequencing efforts 367 traits have been surveyed and 86 genome-wide association studies have been conducted. Economically important crops, particularly cereals, vegetables, and legumes, have dominated the resequencing efforts, leaving a gap in 49 orders, including Lycopodiales, Liliales, Acorales, Austrobaileyales, and Commelinales. The resequenced germplasm is distributed across diverse geographic locations, providing a global perspective on plant genomics. We highlight genes that have been selected during domestication, or associated with agronomic traits, and form a repository of candidate genes for future research and application. Despite the opportunities for cross-species comparative genomics, many population genomic datasets are not accessible, impeding secondary analyses. We call for a more open and collaborative approach to population genomics that promotes data sharing and encourages contribution-based credit policy. The number of plant genome resequencing studies will continue to rise with the decreasing DNA sequencing costs, coupled with advances in analysis and computational technologies. This expansion, in terms of both scale and quality, holds promise for deeper insights into plant trait genetics and breeding design.

Omics technologies towards sesame improvement: a review

Genetic improvement of sesame (Sesamum indicum L.), one of the most important oilseed crops providing edible oil, proteins, minerals, and vitamins, is important to ensure a balanced diet for the growing world population. Increasing yield, seed protein, oil, minerals, and vitamins is urgently needed to meet the global demand. The production and productivity of sesame is very low due to various biotic and abiotic stresses. Therefore, various efforts have been made to combat these constraints and increase the production and productivity of sesame through conventional breeding. However, less attention has been paid to the genetic improvement of the crop through modern biotechnological methods, leaving it lagging behind other oilseed crops. Recently, however, the scenario has changed as sesame research has entered the era of "omics" and has made significant progress. Therefore, the purpose of this paper is to provide an overview of the progress made by omics research in improving sesame. This review presents a number of efforts that have been made over past decade using omics technologies to improve various traits of sesame, including seed composition, yield, and biotic and abiotic resistant varieties. It summarizes the advances in genetic improvement of sesame using omics technologies, such as germplasm development (web-based functional databases and germplasm resources), gene discovery (molecular markers and genetic linkage map construction), proteomics, transcriptomics, and metabolomics that have been carried out in the last decade. In conclusion, this review highlights future directions that may be important for omics-assisted breeding in sesame genetic improvement.

Validation of a metabolite-GWAS network for Populus trichocarpa family 1 UDP-glycosyltransferases

Metabolite genome-wide association studies (mGWASs) are increasingly used to discover the genetic basis of target phenotypes in plants such as Populus trichocarpa, a biofuel feedstock and model woody plant species. Despite their growing importance in plant genetics and metabolomics, few mGWASs are experimentally validated. Here, we present a functional genomics workflow for validating mGWAS-predicted enzyme-substrate relationships. We focus on uridine diphosphate-glycosyltransferases (UGTs), a large family of enzymes that catalyze sugar transfer to a variety of plant secondary metabolites involved in defense, signaling, and lignification. Glycosylation influences physiological roles, localization within cells and tissues, and metabolic fates of these metabolites. UGTs have substantially expanded in P. trichocarpa, presenting a challenge for large-scale characterization. Using a high-throughput assay, we produced substrate acceptance profiles for 40 previously uncharacterized candidate enzymes. Assays confirmed 10 of 13 leaf mGWAS associations, and a focused metabolite screen demonstrated varying levels of substrate specificity among UGTs. A substrate binding model case study of UGT-23 rationalized observed enzyme activities and mGWAS associations, including glycosylation of trichocarpinene to produce trichocarpin, a major higher-order salicylate in P. trichocarpa. We identified UGTs putatively involved in lignan, flavonoid, salicylate, and phytohormone metabolism, with potential implications for cell wall biosynthesis, nitrogen uptake, and biotic and abiotic stress response that determine sustainable biomass crop production. Our results provide new support for in silico analyses and evidence-based guidance for in vivo functional characterization.

Diamonds in the not-so-rough: Wild relative diversity hidden in crop genomes

Crop production is becoming an increasing challenge as the global population grows and the climate changes. Modern cultivated crop species are selected for productivity under optimal growth environments and have often lost genetic variants that could allow them to adapt to diverse, and now rapidly changing, environments. These genetic variants are often present in their closest wild relatives, but so are less desirable traits. How to preserve and effectively utilize the rich genetic resources that crop wild relatives offer while avoiding detrimental variants and maladaptive genetic contributions is a central challenge for ongoing crop improvement. This Essay explores this challenge and potential paths that could lead to a solution.

Genetic Diversity and Breeding Signatures for Regional Indica Rice Improvement in Guangdong of Southern China

As the pioneer of the Green Revolution in China, Guangdong province witnessed the improvement and spread of semi-dwarf Xian/Indica rice cultivars and possessed diverse rice germplasm of landrace and cultivars. A total of 517 accessions containing a core germplasm of 479 newly sequenced landraces and modern cultivars were used to reveal breeding signatures and key variations for regional genetic improvement of indica rice from Guangdong. Four subpopulations were identified in the collection, which including Ind IV as a novel subpopulation that not covered by previously released accessions. Modern cultivars of subpopulation Ind II were inferred to have less deleterious variations, especially in yield related genes. About 15 Mb genomic segments were identified as potential breeding signatures by cross-population likelihood method (XP-CLR) of modern cultivars and landraces. The selected regions spanning multiple yield related QTLs (quantitative trait locus) which identified by GWAS (genome-wide association studies) of the same population, and specific variations that fixed in modern cultivars of Ind II were characterized. This study highlights genetic differences between traditional landraces and modern cultivars, which revealed the potential molecular basis of regional genetic improvement for Guangdong indica rice from southern China.

Dissecting the genetic basis of heterosis in elite super-hybrid rice

Y900 is one of the top hybrid rice (Oryza sativa) varieties, with its yield exceeding 15 t·hm-2. To dissect the mechanism of heterosis, we sequenced the male parent line R900 and female parent line Y58S using long-read and Hi-C technology. High-quality reference genomes of 396.41 Mb and 398.24 Mb were obtained for R900 and Y58S, respectively. Genome-wide variations between the parents were systematically identified, including 1,367,758 single-nucleotide polymorphisms, 299,149 insertions/deletions, and 4,757 structural variations. The level of variation between Y58S and R900 was the lowest among the comparisons of Y58S with other rice genomes. More than 75% of genes exhibited variation between the two parents. Compared with other two-line hybrids sharing the same female parent, the portion of Geng/japonica (GJ)-type genetic components from different male parents increased with yield increasing in their corresponding hybrids. Transcriptome analysis revealed that the partial dominance effect was the main genetic effect that constituted the heterosis of Y900. In the hybrid, both alleles from the two parents were expressed, and their expression patterns were dynamically regulated in different tissues. The cis-regulation was dominant for young panicle tissues, while trans-regulation was more common in leaf tissues. Overdominance was surprisingly prevalent in stems and more likely regulated by the trans-regulation mechanism. Additionally, R900 contained many excellent GJ haplotypes, such as NARROW LEAF1, Oryza sativa SQUAMOSA PROMOTER BINDING PROTEIN-LIKE13, and Grain number, plant height, and heading date8, making it a good complement to Y58S. The fine-tuned mechanism of heterosis involves genome-wide variation, GJ introgression, key functional genes, and dynamic gene/allele expression and regulation pattern changes in different tissues and growth stages.

Novel insights into maize (Zea mays) development and organogenesis for agricultural optimization

In maize, intrinsic hormone activities and sap fluxes facilitate organogenesis patterning and plant holistic development; these hormone movements should be a primary focus of developmental biology and agricultural optimization strategies. Maize (Zea mays) is an important crop plant with distinctive life history characteristics and structural features. Genetic studies have extended our knowledge of maize developmental processes, genetics, and molecular ecophysiology. In this review, the classical life cycle and life history strategies of maize are analyzed to identify spatiotemporal organogenesis properties and develop a definitive understanding of maize development. The actions of genes and hormones involved in maize organogenesis and sex determination, along with potential molecular mechanisms, are investigated, with findings suggesting central roles of auxin and cytokinins in regulating maize holistic development. Furthermore, investigation of morphological and structural characteristics of maize, particularly node ubiquity and the alternate attachment pattern of lateral organs, yields a novel regulatory model suggesting that maize organ initiation and subsequent development are derived from the stimulation and interaction of auxin and cytokinin fluxes. Propositions that hormone activities and sap flow pathways control organogenesis are thoroughly explored, and initiation and development processes of distinctive maize organs are discussed. Analysis of physiological factors driving hormone and sap movement implicates cues of whole-plant activity for hormone and sap fluxes to stimulate maize inflorescence initiation and organ identity determination. The physiological origins and biogenetic mechanisms underlying maize floral sex determination occurring at the tassel and ear spikelet are thoroughly investigated. The comprehensive outline of maize development and morphogenetic physiology developed in this review will enable farmers to optimize field management and will provide a reference for de novo crop domestication and germplasm improvement using genome editing biotechnologies, promoting agricultural optimization.

Effective use of legacy data in a genome-wide association studies improves the credibility of quantitative trait loci detection in rice

Genome-wide association studies (GWASs) are used to detect quantitative trait loci (QTL) using genomic and phenotypic data as inputs. While genomic data are obtained with high throughput and low cost, obtaining phenotypic data requires a large amount of effort and time. In past breeding programs, researchers and breeders have conducted a large number of phenotypic surveys and accumulated results as legacy data. In this study, we conducted a GWAS using phenotypic data of temperate japonica rice (Oryza sativa) varieties from a public database. The GWAS using the legacy data detected several known agriculturally important genes, indicating reliability of the legacy data for GWAS. By comparing the GWAS using legacy data (L-GWAS) and a GWAS using phenotypic data that we measured (M-GWAS), we detected reliable QTL for agronomically important traits. These results suggest that an L-GWAS is a strong alternative to replicate tests to confirm the reproducibility of QTL detected by an M-GWAS. In addition, because legacy data have often been accumulated for many traits, it is possible to evaluate the pleiotropic effect of the QTL identified for the specific trait that we focused on with respect to various other traits. This study demonstrates the effectiveness of using legacy data for GWASs and proposes the use of legacy data to accelerate genomic breeding.

Identification of subspecies-divergent genetic loci responsible for mineral accumulation in rice grains

Rice (Oryza sativa L.) is a major staple food that provides not only dietary calories but also trace elements for the global inhabitants. The insufficiency of mineral nutrients and the potential accumulation of excessive toxic elements in grains pose risks to human health. The substantial natural variations in mineral accumulation in rice grains presents potentials for genetic improvements of rice via biofortifications of essential mineral nutrients and eliminations of toxic elements in grains. However, the genetic mechanisms underlying the natural variations in mineral accumulation have not been fully explored to date owing to unstable phenotypic variations, which are attributed to poor genetic performance and strong environmental effects. In this study, we first compared the genetic performance of different normalization approaches in determining the grain-Cd, grain-Mn, and grain-Zn variations in rice in different genetic populations. Then through quantitative trait loci (QTLs) identification in two rice inter-ectype populations, three QTLs, including qCd7, qMn3, and qZn7, were identified and the QTLs were found to exhibit allelic differentiation in the different ecotypes. Our results were expected to broaden our understanding for mineral accumulation in rice and propose the potential functional alleles that can be explored for further genetic improvement of rice.

OsDPE2 Regulates Rice Panicle Morphogenesis by Modulating the Content of Starch

Starch is a carbon sink for most plants, and its biological role changes with response to the environment and during plant development. Disproportionating Enzyme 2 (DPE2) is a 4-α-glycosyltransferase involved in starch degradation in plants at night. LAX1 plays a vital role in axillary meristem initiation in rice. Herein, results showed that Oryza sativa Disproportionating Enzyme 2 (OsDPE2) could rescue the mutant phenotype of lax1-6, LAX1 mutant. OsDPE2 encodes rice DPE2 located in the cytoplasm. In this study, OsDPE2 affected the vegetative plant development of rice via DPE2 enzyme. Additionally, OsDPE2 regulated the reproductive plant development of rice by modulating starch content in young panicles. Furthermore, haplotype OsDPE2(AQ) with higher DPE2 enzyme activity increased the panicle yield of rice. In summary, OsDPE2 can regulate vegetative and reproductive plant development of rice by modulating starch content. Furthermore, DPE2 activities of OsDPE2 haplotypes are associated with the panicle yield of rice. This study provides guidance for rice breeding to improve panicle yield traits.

Integration of Auxin, Brassinosteroid and Cytokinin in the Regulation of Rice Yield

Crop varieties with a high yield are most desirable in the present context of the ever-growing human population. Mostly, the yield traits are governed by a complex of numerous molecular and genetic facets modulated by various quantitative trait loci (QTLs). With the identification and molecular characterizations of yield-associated QTLs over recent years, the central role of phytohormones in regulating plant yield is becoming more apparent. Most often, different groups of phytohormones work in close association to orchestrate yield attributes. Understanding this cross talk would thus provide new venues for phytohormone pyramiding by editing a single gene or QTL(s) for yield improvement. Here, we review a few important findings to integrate the knowledge on the roles of auxin, brassinosteroid and cytokinin and how a single gene or a QTL could govern cross talk among multiple phytohormones to determine the yield traits.

The Pyramiding of Elite Allelic Genes Related to Grain Number Increases Grain Number per Panicle Using the Recombinant Lines Derived from Indica-japonica Cross in Rice

Indica(xian)-japonica(geng) hybrid rice has many heterosis traits that can improve rice yield. However, the traditional hybrid technology will struggle to meet future needs for the development of higher-yield rice. Available genomics resources can be used to efficiently understand the gene-trait association trait for rice breeding. Based on the previously constructed high-density genetic map of 272 high-generation recombinant inbred lines (RILs) originating from the cross of Luohui 9 (indica, as female) and RPY geng (japonica, as male) and high-quality genomes of parents, here, we further explore the genetic basis for an important complex trait: possible causes of grain number per panicle (GNPP). A total of 20 genes related to grains number per panicle (GNPP) with the differences of protein amino acid between LH9 and RPY were used to analyze genotype combinations, and PCA results showed a combination of PLY1, LAX1, DTH8 and OSH1 from the RPY geng with PYL4, SP1, DST and GNP1 from Luohui 9 increases GNPP. In addition, we also found that the combination of LAX1-T2 and GNP1-T3 had the most significant increase in GNPP. Notably, Molecular Breeding Knowledgebase (MBK) showed a few aggregated rice cultivars, LAX1-T2 and GNP1-T3, which may be a result of the natural geographic isolation between the two gene haplotypes. Therefore, we speculate that the pyramiding of japonica-type LAX-T2 with indica-type GNP1-T3 via hybridization can significantly improve rice yield by increasing GNPP.

Plant pan-genomics and its applications

Plant genomes are so highly diverse that a substantial proportion of genomic sequences are not shared among individuals. The variable DNA sequences, along with the conserved core sequences, compose the more sophisticated pan-genome that represents the collection of all non-redundant DNA in a species. With rapid progress in genome sequencing technologies, pan-genome research in plants is now accelerating. Here we review recent advances in plant pan-genomics, including major driving forces of structural variations that constitute the variable sequences, methodological innovations for representing the pan-genome, and major successes in constructing plant pan-genomes. We also summarize recent efforts toward decoding the remaining dark matter in telomere-to-telomere or gapless plant genomes. These new genome resources, which have remarkable advantages over numerous previously assembled less-than-perfect genomes, are expected to become new references for genetic studies and plant breeding.