Understanding complex genetic architecture of rice grain weight through QTL-meta analysis and candidate gene identification

Candidate Gene for Kernel-Related Traits in Maize Revealed by a Combination of GWAS and Meta-QTL Analyses

Maize kernel traits represent crucial agronomic characteristics that significantly determine yield potential. Analyzing the genetic basis of these traits is essential for yield improvement. In this study, we utilized 1283 maize inbred lines to investigate three kernel-related characteristics: kernel length (KL), kernel width (KW), and 100-kernel weight (HKW). We conducted a genome-wide association study (GWAS) on three kernel-related traits, resulting in the identification of 29 significantly associated SNPs and six candidate genes. Additionally, we compiled quantitative trait loci (QTL) information for 765 maize kernel-related traits from 56 studies, conducted a meta-analysis of QTL, and identified 65 meta-QTLs (MQTLs). Among the 23 MQTLs, we found 25 functional genes and reported candidate genes related to kernel traits. We identified 26 maize homologs across 19 MQTLs by utilizing 25 genes that affect rice grain traits. We compared the 29 significant SNPs detected with the physical locations of 65 MQTLs and found that 3 significant SNPs were located within these MQTL intervals, and another 10 significant SNPs were in proximity to these intervals, being less than 2 Mb away, although they were not included within the MQTL intervals. The results of this study provide a theoretical foundation for elucidating the genetic basis of maize kernel-related traits and advancing molecular marker-assisted breeding selection.

Identification of a novel locus qGW12/OsPUB23 regulating grain shape and weight in rice (Oryza sativa L.)

KEY MESSAGE

Key message A major quantitative trait locus (qGW12) for grain shape and weight has been isolated in rice, corresponding to LOC_Os12g17900/OsPUB23, and its encoded protein interacts with OsMADS1. Grain shape in rice is an important trait that influences both yield and quality. The primary determinants of grain shape are quantitative trait loci (QTLs) inherited from natural variation in crops. In recent years, much attention has been paid to the molecular role of QTLs in regulating grain shape and weight. In this study, we report the cloning and characterization of qGW12, a major QTL regulating grain shape and weight in rice, using a series of chromosome fragment substitution lines (CSSLs) derived from Oryza sativa indica cultivar 9311 (acceptor) and Oryza rufipogon Griff (donor). One CSSL line, Q187, harboring the introgression of qGW12, exhibited a significant decrease in grain-shape-related traits (including grain length and width) and thousand-grain weight compared to the cultivar 9311. Subsequent backcrossing of Q187 with 9311 resulted in the generation of secondary segregating populations, which were used to fine-map qGW12 to a 24-kb region between markers Seq-44 and Seq-48. Our data indicated that qGW12 encodes a previously unreported U-box type E3 ubiquitin ligase, designated OsPUB23, which exhibited E3 ubiquitin ligase activity. Overexpression of OsPUB23 in rice resulted in higher plant yield than the wild type due to an increase in grain size and weight. Conversely, loss of OsPUB23 function resulted in the opposite tendency. Yeast two-hybrid screening and split luciferase complementation assays revealed that OsPUB23 interacts with OsMADS1. The functional characterization of OsPUB23 provides new genetic resources for improving of grain yield and quality in crops.

Ustilaginoidea virens, an emerging pathogen of rice: the dynamic interplay between the pathogen virulence strategies and host defense

MAIN CONCLUSION

The Ustilaginoidea virens -rice pathosystem has been used as a model for flower-infecting fungal pathogens. The molecular biology of the interactions between U. virens and rice, with an emphasis on the attempt to get a deeper comprehension of the false smut fungus's genomes, proteome, host range, and pathogen biology, has been investigated. Meta-QTL analysis was performed to identify potential QTL hotspots for use in marker-assisted breeding. The Rice False Smut (RFS) caused by the fungus Ustilaginoidea virens currently threatens rice cultivators across the globe. RFS infects rice panicles, causing a significant reduction in grain yield. U. virens can also parasitize other hosts though they play only a minor role in its life cycle. Furthermore, because it produces mycotoxins in edible rice grains, it puts both humans and animals at risk of health problems. Although fungicides are used to control the disease, some fungicides have enabled the pathogen to develop resistance, making its management challenging. Several QTLs have been reported but stable gene(s) that confer RFS resistance have not been discovered yet. This review offers a comprehensive overview of the pathogen, its virulence mechanisms, the genome and proteome of U. virens, and its molecular interactions with rice. In addition, information has been compiled on reported resistance QTLs, facilitating the development of a consensus genetic map using meta-QTL analysis for identifying potential QTL hotspots. Finally, this review highlights current developments and trends in U. virens-rice pathosystem research while identifying opportunities for future investigations.

Refinement of rice blast disease resistance QTLs and gene networks through meta-QTL analysis

Rice blast disease is the most devastating disease constraining crop productivity. Vertical resistance to blast disease is widely studied despite its instability. Clusters of genes or QTLs conferring blast resistance that offer durable horizontal resistance are important in resistance breeding. In this study, we aimed to refine the reported QTLs and identify stable meta-QTLs (MQTLs) associated with rice blast resistance. A total of 435 QTLs were used to project 71 MQTLs across all the rice chromosomes. As many as 199 putative rice blast resistance genes were identified within 53 MQTL regions. The genes included 48 characterized resistance gene analogs and related proteins, such as NBS-LRR type, LRR receptor-like kinase, NB-ARC domain, pathogenesis-related TF/ERF domain, elicitor-induced defense and proteins involved in defense signaling. MQTL regions with clusters of RGA were also identified. Fifteen highly significant MQTLs included 29 candidate genes and genes characterized for blast resistance, such as Piz, Nbs-Pi9, pi55-1, pi55-2, Pi3/Pi5-1, Pi3/Pi5-2, Pikh, Pi54, Pik/Pikm/Pikp, Pb1 and Pb2. Furthermore, the candidate genes (42) were associated with differential expression (in silico) in compatible and incompatible reactions upon disease infection. Moreover, nearly half of the genes within the MQTL regions were orthologous to those in O. sativa indica, Z. mays and A. thaliana, which confirmed their significance. The peak markers within three significant MQTLs differentiated blast-resistant and susceptible lines and serve as potential surrogates for the selection of blast-resistant lines. These MQTLs are potential candidates for durable and broad-spectrum rice blast resistance and could be utilized in blast resistance breeding.

Fine mapping of interspecific secondary CSSL populations revealed key regulators for grain weight at qTGW3.1 locus from Oryza nivara

Grain weight (GW) is the most important stable trait that directly contributes to crop yield in case of cereals. A total of 105 backcross introgression lines (BC2F10 BILs) derived from Swarna/O. nivara IRGC81848 (NPS) and 90 BILs from Swarna/O. nivara IRGC81832 (NPK) were evaluated for thousand-grain weight (TGW) across four years (wet seasons 2014, 2015, 2016 and 2018) and chromosome segment substitution lines (CSSLs) were selected. From significant pair- wise mean comparison with Swarna, a total of 77 positively and 29 negatively significant NPS lines and 62 positively and 29 negatively significant NPK lines were identified. In all 4 years, 14 NPS lines and 9 NPK lines were positively significant and one-line NPS69 (IET22161) was negatively significant for TGW over Swarna consistently. NPS lines and NPK lines were genotyped using 111 and 140 polymorphic SSRs respectively. Quantitative trait locus (QTL) mapping using ICIM v4.2 software showed 13 QTLs for TGW in NPS. Three major effect QTLs qTGW2.1, qTGW8.1 and qTGW11.1 were identified in NPS for two or more years with PVE ranging from 8 to 14%. Likewise, 10 QTLs were identified in NPK and including two major effect QTL qTGW3.1 and qTGW12.1 with 6 to 32% PVE. In all QTLs, O. nivara alleles increased TGW. These consistent QTLs are very suitable for fine mapping and functional analysis of grain weight. Further in this study, CSSLs NPS1 (10-2S) and NPK61 (158 K) with significantly higher grain weight than the recurrent parent, Swarna cv. Oryza sativa were selected from each population and secondary F2 mapping populations were developed. Using Bulked Segregant QTL sequencing, a grain weight QTL, designated as qTGW3.1 was fine mapped from the cross between NPK61 and Swarna. This QTL explained 48% (logarithm of odds = 32.2) of the phenotypic variations and was fine mapped to a 31 kb interval using recombinant analysis. GRAS transcription factor gene (OS03go103400) involved in plant growth and development located at this genomic locus might be the candidate gene for qTGW3.1. The results of this study will help in further functional studies and improving the knowledge related to the molecular mechanism of grain weight in Oryza and lays a solid foundation for the breeding for high yield.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12298-024-01483-0.

Genome-wide screening of meta-QTL and candidate genes controlling yield and yield-related traits in barley (Hordeum vulgare L.)

Increasing yield is an important goal of barley breeding. In this study, 54 papers published from 2001-2022 on QTL mapping for yield and yield-related traits in barley were collected, which contained 1080 QTLs mapped to the barley high-density consensus map for QTL meta-analysis. These initial QTLs were integrated into 85 meta-QTLs (MQTL) with a mean confidence interval (CI) of 2.76 cM, which was 7.86-fold narrower than the CI of the initial QTL. Among these 85 MQTLs, 68 MQTLs were validated in GWAS studies, and 25 breeder's MQTLs were screened from them. Seventeen barley orthologs of yield-related genes in rice and maize were identified within the hcMQTL region based on comparative genomics strategy and were presumed to be reliable candidates for controlling yield-related traits. The results of this study provide useful information for molecular marker-assisted breeding and candidate gene mining of yield-related traits in barley.

A meta-QTL analysis highlights genomic hotspots associated with phosphorus use efficiency in rice (Oryza sativa L.)

Phosphorus use efficiency (PUE) is a complex trait, governed by many minor quantitative trait loci (QTLs) with small effects. Advances in molecular marker technology have led to the identification of QTLs underlying PUE. However, their practical use in breeding programs remains challenging due to the unstable effects in different genetic backgrounds and environments, interaction with soil status, and linkage drag. Here, we compiled PUE QTL information from 16 independent studies. A total of 192 QTLs were subjected to meta-QTL (MQTL) analysis and were projected into a high-density SNP consensus map. A total of 60 MQTLs, with significantly reduced number of initial QTLs and confidence intervals (CI), were identified across the rice genome. Candidate gene (CG) mining was carried out for the 38 MQTLs supported by multiple QTLs from at least two independent studies. Genes related to amino and organic acid transport and auxin response were found to be abundant in the MQTLs linked to PUE. CGs were cross validated using a root transcriptome database (RiceXPro) and haplotype analysis. This led to the identification of the eight CGs (OsARF8, OsSPX-MFS3, OsRING141, OsMIOX, HsfC2b, OsFER2, OsWRKY64, and OsYUCCA11) modulating PUE. Potential donors for superior PUE CG haplotypes were identified through haplotype analysis. The distribution of superior haplotypes varied among subspecies being mostly found in indica but were largely scarce in japonica. Our study offers an insight on the complex genetic networks that modulate PUE in rice. The MQTLs, CGs, and superior CG haplotypes identified in our study are useful in the combination of beneficial alleles for PUE in rice.

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.

Unraveling genomic regions and candidate genes for multiple disease resistance in upland cotton using meta-QTL analysis

Verticillium wilt (VW), Fusarium wilt (FW) and Root-knot nematode (RKN) are the main diseases affecting cotton production. However, many reported quantitative trait loci (QTLs) for cotton resistance have not been used for agricultural practices because of inconsistencies in the cotton genetic background. The integration of existing cotton genetic resources can facilitate the discovery of important genomic regions and candidate genes involved in disease resistance. Here, an improved and comprehensive meta-QTL analysis was conducted on 487 disease resistant QTLs from 31 studies in the last two decades. A consensus linkage map with genetic overall length of 3006.59 cM containing 8650 markers was constructed. A total of 28 Meta-QTLs (MQTLs) were discovered, among which nine MQTLs were identified as related to resistance to multiple diseases. Candidate genes were predicted based on public transcriptome data and enriched in pathways related to disease resistance. This study used a method based on the integration of Meta-QTL, known genes and transcriptomics to reveal major genomic regions and putative candidate genes for resistance to multiple diseases, providing a new basis for marker-assisted selection of high disease resistance in cotton breeding.

Gene based markers improve precision of genome-wide association studies and accuracy of genomic predictions in rice breeding

It is hypothesized that the genome-wide genic markers may increase the prediction accuracy of genomic selection for quantitative traits. To test this hypothesis, a set of candidate gene-based markers for yield and grain traits-related genes cloned across the rice genome were custom-designed. A multi-model, multi-locus genome-wide association study (GWAS) was performed using new genic markers developed to test their effectiveness for gene discovery. Two multi-locus models, FarmCPU and mrMLM, along with a single-locus mixed linear model (MLM), identified 28 significant marker-trait associations. These associations revealed novel causative alleles for grain weight and pleiotropic associations with other traits. For instance, the marker YD91 derived from the gene OsAAP3 on chromosome 1 was consistently associated with grain weight, while the gene has a significant effect on grain yield. Furthermore, nine genomic selection methods, including regression-based and machine learning-based models, were used to predict grain weight using a leave-one-out five-fold cross-validation approach to optimize the genomic selection model with genic markers. Among nine prediction models, Kernel Hilbert Space Regression (RKHS) is the best among regression-based models, and Random Forest Regression (RFR) is the best among machine learning-based models. Genomic prediction accuracies with and without GWAS significant markers were compared to assess the effectiveness of markers. The rapid decreases in prediction accuracy upon dropping GWAS significant markers indicate the effectiveness of new genic markers in genomic selection. Apart from that, the candidate gene-based markers were found to be more effective in genomic selection programs for better accuracy.

Advances in integrated genomic selection for rapid genetic gain in crop improvement: a review

Genomic selection and its importance in crop breeding. Integration of GS with new breeding tools and developing SOP for GS to achieve maximum genetic gain with low cost and time. The success of conventional breeding approaches is not sufficient to meet the demand of a growing population for nutritious food and other plant-based products. Whereas, marker assisted selection (MAS) is not efficient in capturing all the favorable alleles responsible for economic traits in the process of crop improvement. Genomic selection (GS) developed in livestock breeding and then adapted to plant breeding promised to overcome the drawbacks of MAS and significantly improve complicated traits controlled by gene/QTL with small effects. Large-scale deployment of GS in important crops, as well as simulation studies in a variety of contexts, addressed G × E interaction effects and non-additive effects, as well as lowering breeding costs and time. The current study provides a complete overview of genomic selection, its process, and importance in modern plant breeding, along with insights into its application. GS has been implemented in the improvement of complex traits including tolerance to biotic and abiotic stresses. Furthermore, this review hypothesises that using GS in conjunction with other crop improvement platforms accelerates the breeding process to increase genetic gain. The objective of this review is to highlight the development of an appropriate GS model, the global open source network for GS, and trans-disciplinary approaches for effective accelerated crop improvement. The current study focused on the application of data science, including machine learning and deep learning tools, to enhance the accuracy of prediction models. Present study emphasizes on developing plant breeding strategies centered on GS combined with routine conventional breeding principles by developing GS-SOP to achieve enhanced genetic gain.