Molecular mapping of QTLs for yield related traits in recombinant inbred line (RIL) population derived from the popular rice hybrid KRH-2 and their validation through SNP genotyping

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

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

Hybrid Rice Production: A Worldwide Review of Floral Traits and Breeding Technology, with Special Emphasis on China

Rice is an important diet source for the majority of the world's population, and meeting the growing need for rice requires significant improvements at the production level. Hybrid rice production has been a significant breakthrough in this regard, and the floral traits play a major role in the development of hybrid rice. In grass species, rice has structural units called florets and spikelets and contains different floret organs such as lemma, palea, style length, anther, and stigma exsertion. These floral organs are crucial in enhancing rice production and uplifting rice cultivation at a broader level. Recent advances in breeding techniques also provide knowledge about different floral organs and how they can be improved by using biotechnological techniques for better production of rice. The rice flower holds immense significance and is the primary focal point for researchers working on rice molecular biology. Furthermore, the unique genetics of rice play a significant role in maintaining its floral structure. However, to improve rice varieties further, we need to identify the genomic regions through mapping of QTLs (quantitative trait loci) or by using GWAS (genome-wide association studies) and their validation should be performed by developing user-friendly molecular markers, such as Kompetitive allele-specific PCR (KASP). This review outlines the role of different floral traits and the benefits of using modern biotechnological approaches to improve hybrid rice production. It focuses on how floral traits are interrelated and their possible contribution to hybrid rice production to satisfy future rice demand. We discuss the significance of different floral traits, techniques, and breeding approaches in hybrid rice production. We provide a historical perspective of hybrid rice production and its current status and outline the challenges and opportunities in this field.

Identification of a Branch Number Locus in Soybean Using BSA-Seq and GWAS Approaches

The determination of the soybean branch number plays a pivotal role in plant morphogenesis and yield components. This polygenic trait is subject to environmental influences, and despite its significance, the genetic mechanisms governing the soybean branching number remain incompletely understood. To unravel these mechanisms, we conducted a comprehensive investigation employing a genome-wide association study (GWAS) and bulked sample analysis (BSA). The GWAS revealed 18 SNPs associated with the soybean branch number, among which qGBN3 on chromosome 2 emerged as a consistently detected locus across two years, utilizing different models. In parallel, a BSA was executed using an F2 population derived from contrasting cultivars, Wandou35 (low branching number) and Ruidou1 (high branching number). The BSA results pinpointed a significant quantitative trait locus (QTL), designated as qBBN1, located on chromosome 2 by four distinct methods. Importantly, both the GWAS and BSA methods concurred in co-locating qGBN3 and qBBN1. In the co-located region, 15 candidate genes were identified. Through gene annotation and RT-qPCR analysis, we predicted that Glyma.02G125200 and Glyma.02G125600 are candidate genes regulating the soybean branch number. These findings significantly enhance our comprehension of the genetic intricacies regulating the branch number in soybeans, offering promising candidate genes and materials for subsequent investigations aimed at augmenting the soybean yield. This research represents a crucial step toward unlocking the full potential of soybean cultivation through targeted genetic interventions.

Unravelling the genetic framework associated with grain quality and yield-related traits in maize (Zea mays L.)

Maize serves as a crucial nutrient reservoir for a significant portion of the global population. However, to effectively address the growing world population's hidden hunger, it is essential to focus on two key aspects: biofortification of maize and improving its yield potential through advanced breeding techniques. Moreover, the coordination of multiple targets within a single breeding program poses a complex challenge. This study compiled mapping studies conducted over the past decade, identifying quantitative trait loci associated with grain quality and yield related traits in maize. Meta-QTL analysis of 2,974 QTLs for 169 component traits (associated with quality and yield related traits) revealed 68 MQTLs across different genetic backgrounds and environments. Most of these MQTLs were further validated using the data from genome-wide association studies (GWAS). Further, ten MQTLs, referred to as breeding-friendly MQTLs (BF-MQTLs), with a significant phenotypic variation explained over 10% and confidence interval less than 2 Mb, were shortlisted. BF-MQTLs were further used to identify potential candidate genes, including 59 genes encoding important proteins/products involved in essential metabolic pathways. Five BF-MQTLs associated with both quality and yield traits were also recommended to be utilized in future breeding programs. Synteny analysis with wheat and rice genomes revealed conserved regions across the genomes, indicating these hotspot regions as validated targets for developing biofortified, high-yielding maize varieties in future breeding programs. After validation, the identified candidate genes can also be utilized to effectively model the plant architecture and enhance desirable quality traits through various approaches such as marker-assisted breeding, genetic engineering, and genome editing.

Mapping of QTLs associated with yield and related traits under reproductive stage drought stress in rice using SNP linkage map

BACKGROUND

Drought stress is a major constraint for rice production worldwide. Reproductive stage drought stress (RSDS) leads to heavy yield losses in rice. The prospecting of new donor cultivars for identification and introgression of QTLs of major effect (Quantitative trait locus) for drought tolerance is crucial for the development of drought-resilient rice varieties.

METHODS AND RESULTS

Our study aimed to map QTLs associated with yield and its related traits under RSDS conditions. A saturated linkage map was constructed using 3417 GBS (Genotyping by sequencing) derived SNP (Single nucleotide polymorphism) markers spanning 1924.136 cM map length with an average marker density of 0.56 cM, in the F3 mapping population raised via cross made between the traditional ahu rice cultivar, Koniahu (drought tolerant) and a high-yielding variety, Disang (drought susceptible). Using the Inclusive composite interval mapping approach, 35 genomic regions governing yield and related traits were identified in pooled data from 198 F3 and F4 segregating lines evaluated for two consecutive seasons under both RSDS and irrigated control conditions. Of the 35 QTLs, 23 QTLs were identified under RSDS with LOD (Logarithm of odds) values ranging between 2.50 and 7.83 and PVE (phenotypic variance explained) values of 2.95-12.42%. Two major QTLs were found to be linked to plant height (qPH1.29) and number of filled grains per panicle (qNOG5.12) under RSDS. Five putative QTLs for grain yield namely, qGY2.00, qGY5.05, qGY6.16, qGY9.19, and qGY10.20 were identified within drought conditions. Fourteen QTL regions having ≤ 10 Mb QTL interval size were further analysed for candidate gene identification and a total of 4146 genes were detected out of these 2263 (54.63%) genes were annotated to at least one gene ontology (GO) term.

CONCLUSION

Several QTLs associated with grain yield and yield components and putative candidate genes were identified. The putative QTLs and candidate genes identified could be employed to augment drought resilience in rice after further validation through MAS strategies.

Integrated Genomic Selection for Accelerating Breeding Programs of Climate-Smart Cereals

Rapidly rising population and climate changes are two critical issues that require immediate action to achieve sustainable development goals. The rising population is posing increased demand for food, thereby pushing for an acceleration in agricultural production. Furthermore, increased anthropogenic activities have resulted in environmental pollution such as water pollution and soil degradation as well as alterations in the composition and concentration of environmental gases. These changes are affecting not only biodiversity loss but also affecting the physio-biochemical processes of crop plants, resulting in a stress-induced decline in crop yield. To overcome such problems and ensure the supply of food material, consistent efforts are being made to develop strategies and techniques to increase crop yield and to enhance tolerance toward climate-induced stress. Plant breeding evolved after domestication and initially remained dependent on phenotype-based selection for crop improvement. But it has grown through cytological and biochemical methods, and the newer contemporary methods are based on DNA-marker-based strategies that help in the selection of agronomically useful traits. These are now supported by high-end molecular biology tools like PCR, high-throughput genotyping and phenotyping, data from crop morpho-physiology, statistical tools, bioinformatics, and machine learning. After establishing its worth in animal breeding, genomic selection (GS), an improved variant of marker-assisted selection (MAS), has made its way into crop-breeding programs as a powerful selection tool. To develop novel breeding programs as well as innovative marker-based models for genetic evaluation, GS makes use of molecular genetic markers. GS can amend complex traits like yield as well as shorten the breeding period, making it advantageous over pedigree breeding and marker-assisted selection (MAS). It reduces the time and resources that are required for plant breeding while allowing for an increased genetic gain of complex attributes. It has been taken to new heights by integrating innovative and advanced technologies such as speed breeding, machine learning, and environmental/weather data to further harness the GS potential, an approach known as integrated genomic selection (IGS). This review highlights the IGS strategies, procedures, integrated approaches, and associated emerging issues, with a special emphasis on cereal crops. In this domain, efforts have been taken to highlight the potential of this cutting-edge innovation to develop climate-smart crops that can endure abiotic stresses with the motive of keeping production and quality at par with the global food demand.

Genomic prediction of hybrid performance in grain sorghum (Sorghum bicolor L.)

Genomic selection is expected to improve selection efficiency and genetic gain in breeding programs. The objective of this study was to assess the efficacy of predicting the performance of grain sorghum hybrids using genomic information of parental genotypes. One hundred and two public sorghum inbred parents were genotyped using genotyping-by-sequencing. Ninty-nine of the inbreds were crossed to three tester female parents generating a total of 204 hybrids for evaluation at two environments. The hybrids were sorted in to three sets of 77,59 and 68 and evaluated along with two commercial checks using a randomized complete block design in three replications. The sequence analysis generated 66,265 SNP markers that were used to predict the performance of 204 F1 hybrids resulted from crosses between the parents. Both additive (partial model) and additive and dominance (full model) were constructed and tested using various training population (TP) sizes and cross-validation procedures. Increasing TP size from 41 to 163 increased prediction accuracies for all traits. With the partial model, the five-fold cross validated prediction accuracies ranged from 0.03 for thousand kernel weight (TKW) to 0.58 for grain yield (GY) while it ranged from 0.06 for TKW to 0.67 for GY with the full model. The results suggest that genomic prediction could become an effective tool for predicting the performance of sorghum hybrids based on parental genotypes.

Genome-wide association study reveals novel genomic regions governing agronomic and grain quality traits and superior allelic combinations for Basmati rice improvement

BACKGROUND

Basmati is a speciality segment in the rice genepool characterised by explicit grain quality. For the want of suitable populations, genome-wide association study (GWAS) in Basmati rice has not been attempted.

MATERIALS

To address this gap, we have performed a GWAS on a panel of 172 elite Basmati multiparent population comprising of potential restorers and maintainers. Phenotypic data was generated for various agronomic and grain quality traits across seven different environments during two consecutive crop seasons. Based on the observed phenotypic variation, three agronomic traits namely, days to fifty per cent flowering, plant height and panicle length, and three grain quality traits namely, kernel length before cooking, length breadth ratio and kernel length after cooking were subjected to GWAS. Genotyped with 80K SNP array, the population was subjected to principal component analysis to stratify the underlying substructure and subjected to the association analysis using Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK) model.

RESULTS

We identified 32 unique MTAs including 11 robust MTAs for the agronomic traits and 25 unique MTAs including two robust MTAs for the grain quality traits. Six out of 13 robust MTAs were novel. By genome annotation, six candidate genes associated with the robust MTAs were identified. Further analysis of the allelic combinations of the robust MTAs enabled the identification of superior allelic combinations in the population. This information was utilized in selecting 77 elite Basmati rice genotypes from the panel.

CONCLUSION

This is the first ever GWAS study in Basmati rice which could generate valuable information usable for further breeding through marker assisted selection, including enhancing of heterosis.

Integrating plant and fungal quantitative genetics to improve the ecological and agricultural applications of mycorrhizal symbioses

Finding and targeting genes that quantitatively contribute to agricultural and ecological processes progresses food production and conservation efforts. Typically, quantitative genetic approaches link variants in a single organism's genome with a trait of interest. Recently, genome-to-genome mapping has found genome variants interacting between species to produce the result of a multiorganism (including multikingdom) interaction. These were plant and bacterial pathogen genome interactions; plant-fungal coquantitative genetics have not yet been applied. Plant-mycorrhizae symbioses exist across most biomes, for a majority of land plants, including crop plants, and manipulate many traits from single organisms to ecosystems for which knowing the genetic basis would be useful. The availability of Rhizophagus irregularis mycorrhizal isolates, with genomic information, makes dual-genome methods with beneficial mutualists accessible and imminent.

A meta-quantitative trait loci analysis identified consensus genomic regions and candidate genes associated with grain yield in rice

Improving grain yield potential in rice is an important step toward addressing global food security challenges. The meta-QTL analysis offers stable and robust QTLs irrespective of the genetic background of mapping populations and phenotype environment and effectively narrows confidence intervals (CI) for candidate gene (CG) mining and marker-assisted selection improvement. To achieve these aims, a comprehensive bibliographic search for grain yield traits (spikelet fertility, number of grains per panicle, panicles number per plant, and 1000-grain weight) QTLs was conducted, and 462 QTLs were retrieved from 47 independent QTL research published between 2002 and 2022. QTL projection was performed using a reference map with a cumulative length of 2,945.67 cM, and MQTL analysis was conducted on 313 QTLs. Consequently, a total of 62 MQTLs were identified with reduced mean CI (up to 3.40 fold) compared to the mean CI of original QTLs. However, 10 of these MQTLs harbored at least six of the initial QTLs from diverse genetic backgrounds and environments and were considered the most stable and robust MQTLs. Also, MQTLs were compared with GWAS studies and resulted in the identification of 16 common significant loci modulating the evaluated traits. Gene annotation, gene ontology (GO) enrichment, and RNA-seq analyses of chromosome regions of the stable MQTLs detected 52 potential CGs including those that have been cloned in previous studies. These genes encode proteins known to be involved in regulating grain yield including cytochrome P450, zinc fingers, MADs-box, AP2/ERF domain, F-box, ubiquitin ligase domain protein, homeobox domain, DEAD-box ATP domain, and U-box domain. This study provides the framework for molecular dissection of grain yield in rice. Moreover, the MQTLs and CGs identified could be useful for fine mapping, gene cloning, and marker-assisted selection to improve rice productivity.

Whole-Genome Sequencing of KMR3 and Oryza rufipogon-Derived Introgression Line IL50-13 (Chinsurah Nona 2/Gosaba 6) Identifies Candidate Genes for High Yield and Salinity Tolerance in Rice

The genomes of an elite rice restorer line KMR3 (salinity-sensitive) and its salinity-tolerant introgression line IL50-13, a popular variety of coastal West Bengal, India, were sequenced. High-quality paired-end reads were obtained for KMR3 (147.6 million) and IL50-13 (131.4 million) with a sequencing coverage of 30X-39X. Scaffolds generated from the pre-assembled contigs of each sequenced genome were mapped separately onto the reference genome of Oryza sativa ssp. japonica cultivar Nipponbare to identify genomic variants in terms of SNPs and InDels. The SNPs and InDels identified for KMR3 and IL50-13 were then compared with each other to identify polymorphic SNPs and InDels unique and common to both the genomes. Functional enrichment analysis of the protein-coding genes with unique InDels identified GO terms involved in protein modification, ubiquitination, deubiquitination, peroxidase activity, and antioxidant activity in IL50-13. Linoleic acid metabolism, circadian rhythm, and alpha-linolenic acid metabolism pathways were enriched in IL50-13. These GO terms and pathways are involved in reducing oxidative damage, thus suggesting their role in stress responses. Sequence analysis of QTL markers or genes known to be associated with grain yield and salinity tolerance showed polymorphism in 20 genes, out of which nine were not previously reported. These candidate genes encoded Nucleotide-binding adaptor shared by APAF-1, R proteins, and CED-4 (NB-ARC) domain-containing protein, cyclase, receptor-like kinase, topoisomerase II-associated protein PAT1 domain-containing protein, ion channel regulatory protein, UNC-93 domain-containing protein, subunit A of the heteromeric ATP-citrate lyase, and three conserved hypothetical genes. Polymorphism was observed in the coding, intron, and untranslated regions of the genes on chromosomes 1, 2, 4, 7, 11, and 12. Genes showing polymorphism between the two genomes were considered as sequence-based new candidates derived from Oryza rufipogon for conferring high yield and salinity tolerance in IL50-13 for further functional studies.

Genome-wide identification of quantitative trait loci for morpho-agronomic and yield-related traits in foxtail millet (Setaria italica) across multi-environments

Foxtail millet (Setaria italica) is an ideal model of genetic system for functional genomics of the Panicoideae crop. Identification of QTL responsible for morpho-agronomic and yield-related traits facilitates dissection of genetic control and breeding in cereal crops. Here, based on a Yugu1 × Longgu7 RIL population and genome-wide resequencing data, an updated linkage map harboring 2297 bin and 74 SSR markers was constructed, spanning 1315.1 cM with an average distance of 0.56 cM between adjacent markers. A total of 221 QTL for 17 morpho-agronomic and yield-related traits explaining 5.5 ~ 36% of phenotypic variation were identified across multi-environments. Of these, 109 QTL were detected in two to nine environments, including the most stable qLMS6.1 harboring a promising candidate gene Seita.6G250500, of which 70 were repeatedly identified in different trials in the same geographic location, suggesting that foxtail millet has more identical genetic modules under the similar ecological environment. One hundred-thirty QTL with overlapping intervals formed 22 QTL clusters. Furthermore, six superior recombinant inbred lines, RIL35, RIL48, RIL77, RIL80, RIL115 and RIL125 with transgressive inheritance and enrichment of favorable alleles in plant height, tiller, panicle morphology and yield related-traits were screened by hierarchical cluster. These identified QTL, QTL clusters and superior lines lay ground for further gene-trait association studies and breeding practice in foxtail millet.

Mapping novel QTLs for yield related traits from a popular rice hybrid KRH-2 derived doubled haploid (DH) population

A doubled haploid (DH) population consisting of 125 DHLs derived from the popular rice hybrid, KRH-2 (IR58025A/KMR3R) was utilized for Quantitative Trait Loci (QTL) mapping to identify novel genomic regions associated with yield related traits. A genetic map was constructed with 126 polymorphic SSR and EST derived markers, which were distributed across rice genome. QTL analysis using inclusive composite interval mapping (ICIM) method identified a total of 24 major and minor effect QTLs. Among them, twelve major effect QTLs were identified for days to fifty percent flowering (qDFF12-1), total grain yield/plant (qYLD3-1 and qYLD6-1), test (1,000) grain weight (qTGW6-1 and qTGW7-1), panicle weight (qPW9-1), plant height (qPH12-1), flag leaf length (qFLL6-1), flag leaf width (qFLW4-1), panicle length (qPL3-1 and qPL6-1) and biomass (qBM4-1), explaining 29.95-56.75% of the phenotypic variability with LOD scores range of 2.72-16.51. Chromosomal regions with gene clusters were identified on chromosome 3 for total grain yield/plant (qYLD3-1) and panicle length (qPL3-1) and on chromosome 6 for total grain yield/plant (qYLD6-1), flag leaf length (qFLL6-1) and panicle length (qPL6-1). Majority of the QTLs identified were observed to be co-localized with the previously reported QTL regions. Five novel, major effect QTLs associated with panicle weight (qPW9-1), plant height (qPH12-1), flag leaf width (qFLW4-1), panicle length (qPL3-1) and biomass (qBM4-1) and three novel minor effect QTLs for panicle weight (qPW3-1 and qPW8-1) and fertile grains per panicle (qFGP5-1) were identified. These QTLs can be used in breeding programs aimed to yield improvement after their validation in alternative populations.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s13205-021-03045-7.

Drought Resistance Loci in Recombinant Lines of Iranian Oryza sativa L. in Germination Stage

In order to locate control genes related to Oryza sativa L. traits at the germination stage under normal conditions and at drought stress levels (−4.5 and −9.0 bar), we evaluated 120 F8 generation offspring from the cross between two cultivars Neda × Ahlemitarum in a factorial experiment in a completely randomized block design with three replications in 2013 in the botanical laboratory of Gonbad Kavous University. A linkage map was prepared using 90 Simple Sequence Repeats (SSR) markers and 28 Inter Simple Sequence Repeats (ISSR), and 6 iPBS and 9 IRAP markers (265 polymorphic alleles). The results of the analysis of variance showed that all of the evaluated traits had a significant difference at the probability level of 1%. Hence, it can be noted that the desired genetic diversity can be found between genotypes. The results of the stepwise regression analysis for the germination percentage as a dependent variable and other traits as independent variables in the studied treatments showed that under normal conditions, there was variable coleoptile length, but under drought stress of −4.5 and −9.0 bar, the variable plumule dry weight entered the model. In this study, the markers included in RM1-RM490 and ISSR2-3-RM133 of chromosomes 1 and 6 of Oryza sativa were identified as the main regulators of traits associated with Oryza sativa drought resistance. In particular, they present the quantitative trait loci (QTL) that control the first stages of germination of Oryza sativa in water stress conditions.

Toward Integrated Multi-Omics Intervention: Rice Trait Improvement and Stress Management

Rice (Oryza sativa) is an imperative staple crop for nearly half of the world's population. Challenging environmental conditions encompassing abiotic and biotic stresses negatively impact the quality and yield of rice. To assure food supply for the unprecedented ever-growing world population, the improvement of rice as a crop is of utmost importance. In this era, "omics" techniques have been comprehensively utilized to decipher the regulatory mechanisms and cellular intricacies in rice. Advancements in omics technologies have provided a strong platform for the reliable exploration of genetic resources involved in rice trait development. Omics disciplines like genomics, transcriptomics, proteomics, and metabolomics have significantly contributed toward the achievement of desired improvements in rice under optimal and stressful environments. The present review recapitulates the basic and applied multi-omics technologies in providing new orchestration toward the improvement of rice desirable traits. The article also provides a catalog of current scenario of omics applications in comprehending this imperative crop in relation to yield enhancement and various environmental stresses. Further, the appropriate databases in the field of data science to analyze big data, and retrieve relevant information vis-à-vis rice trait improvement and stress management are described.

Automated predictive analytics tool for rainfall forecasting

Australia faces a dryness disaster whose impact may be mitigated by rainfall prediction. Being an incredibly challenging task, yet accurate prediction of rainfall plays an enormous role in policy making, decision making and organizing sustainable water resource systems. The ability to accurately predict rainfall patterns empowers civilizations. Though short-term rainfall predictions are provided by meteorological systems, long-term prediction of rainfall is challenging and has a lot of factors that lead to uncertainty. Historically, various researchers have experimented with several machine learning techniques in rainfall prediction with given weather conditions. However, in places like Australia where the climate is variable, finding the best method to model the complex rainfall process is a major challenge. The aim of this paper is to: (a) predict rainfall using machine learning algorithms and comparing the performance of different models. (b) Develop an optimized neural network and develop a prediction model using the neural network (c) to do a comparative study of new and existing prediction techniques using Australian rainfall data. In this paper, rainfall data collected over a span of ten years from 2007 to 2017, with the input from 26 geographically diverse locations have been used to develop the predictive models. The data was divided into training and testing sets for validation purposes. The results show that both traditional and neural network-based machine learning models can predict rainfall with more precision.