Effect of multi-allele combination on rice grain size based on prediction of regression equation model

Identification of quantitative trait loci for yield traits and fine-mapping of qGW4 using the chromosome segment substitution line-Z708 and dissected single-segment substitution lines

Identifying quantitative trait loci (QTL) for yield traits using single-segment substitution lines (SSSL) is essential for both targeted breeding and functional analysis of key genes. Here, a wide-grain rice chromosome segment substitution line (CSSL), Z708, carrying four substitution segments from Jinhui35 in the genetic background of Xihui18, was used to identify the QTL associated with grain size. Seven QTL for yield-related traits (qGW4, qRLW4, qGWT4, qGW5, qRLW5, qGWT5, and qGPP5) were identified on the substitution segments of the fourth and fifth chromosomes of Z708. Subsequently, four SSSLs (S1-S4), which harbored 16 QTL for yield traits, were constructed using molecular marker-assisted selection. These lines (S1-S4) exhibited a significant increase in yield per plant compared to that of Xihui18. Among them, qGW4, which controls wide grains, belongs to a single dominant gene action in S1 based on the frequency distribution of grain width and chi-square test analysis. Finally, qGW4 was fine-mapped to the interval of 80-kb (minimum) and 310-kb (maximum) using both traditional fine mapping and overlapping substitution mapping of the newly constructed secondary SSSLs (S5-S8). Within this interval, four previously unreported candidate genes were predicted.

Moving beyond the arabidopsis-centric view of G-protein signaling in plants

Heterotrimeric G-protein-mediated signaling is a key mechanism to transduce a multitude of endogenous and environmental signals in diverse organisms. The scope and expectations of plant G-protein research were set by pioneering work in metazoans. Given the similarity of the core constituents, G-protein-signaling mechanisms were presumed to be universally conserved. However, because of the enormous diversity of survival strategies and endless forms among eukaryotes, the signal, its interpretation, and responses vary even among different plant groups. Earlier G-protein research in arabidopsis (Arabidopsis thaliana) has emphasized its divergence from Metazoa. Here, we compare recent evidence from diverse plant lineages with the available arabidopsis G-protein model and discuss the conserved and novel protein components, signaling mechanisms, and response regulation.

SLG2 specifically regulates grain width through WOX11-mediated cell expansion control in rice

Grain size is specified by three dimensions of length, width and thickness, and slender grain is a desirable quality trait in rice. Up to now, many grain size regulators have been identified. However, most of these molecules show influence on multi-dimensions of grain development, and only a few of them function specifically in grain width, a key factor determining grain yield and appearance quality. In this study, we identify the SLG2 (SLENDER GUY2) gene that specifically regulates grain width by affecting cell expansion in the spikelet hulls. SLG2 encodes a WD40 domain containing protein, and our biochemical analyses show that SLG2 acts as a transcription activator of its interacting WOX family protein WOX11. We demonstrate that the SLG2-associated WOX11 binds directly to the promoter of OsEXPB7, one of the downstream cell expansion genes. We show that knockout of WOX11 results in plants with a slender grain phenotype similar to the slg2 mutant. We also present that finer grains with different widths could be produced by combining SLG2 with the grain width regulator GW8. Collectively, we uncover the crucial role of SLG2 in grain width control, and provide a promising route to design rice plants with better grain shape and quality.

Overexpression of Nitrate Transporter 1/Peptide Gene OsNPF7.6 Increases Rice Yield and Nitrogen Use Efficiency

Overuse of nitrogen fertilizer in fields has raised production costs, and caused environmental problems. Improving nitrogen use efficiency (NUE) of rice is essential for sustainable agriculture. Here we report the cloning, characterization and roles for rice of OsNPF7.6, a member of the nitrate transporter 1/peptide transporter family (NPF). The OsNPF7.6 protein is located in the plasma membrane, expressed in each tissue at all stages and is significantly regulated by nitrate in rice. Our study shows that the overexpression of OsNPF7.6 can increase the nitrate uptake rate of rice. Additionally, field experiments showed that OsNPF7.6 overexpression increased the total tiller number per plant and the grain weight per panicle, thereby improving grain yield and agronomic NUE in rice. Thus, OsNPF7.6 can be applied to be a novel target gene for breeding rice varieties with high NUE, and provide a reference for breeding higher yielding rice.

Research on Rice Yield Prediction Model Based on Deep Learning

Food is the paramount necessity of the people. With the progress of society and the improvement of social welfare system, the living standards of people all over the world are constantly improving. The development of medical industry improves people's health level constantly, and the world population is constantly climbing to a new peak. With the continuous development of deep learning in recent years, its advantages are constantly displayed, especially in the aspect of image recognition and processing, it drives into the distance. Thanks to the superiority of deep learning in image processing, the combination of remote sensing images and deep learning has attracted more attention. To simulate the four key factors of rice yield, this article tries a regression model with a combination of various characteristic independent variables. In this article, the selection of the best linear and nonlinear regression models is discussed, the prediction performance and significance of each regression model are analyzed, and some thoughts are given on estimation of actual rice yield.

Bin mapping-based QTL analyses using three genetic populations derived from indica-japonica crosses uncover multiple grain shape heterosis-related loci in rice

Exploitation of heterosis between indica and japonica has important significance in scientific research and agriculture application. However, the molecular mechanism of grain shape heterosis in indica-japonica hybrid remains unknown in rice (Oryza sativa L.). To reveal the genetic mechanism of grain shape in indica--japonica hybrid, we constructed a high-generation recombinant inbred line (RIL) population and two testcross hybrid populations derived from the cross of RILs and two cytoplasmic male sterile material (YTA and Z7A) and then performed a bin mapping-based quantitative trait locus (QTL) mapping of multiple grain shape traits, such as grain length (GL), grain width (GW), and grain length-to-width ratio (GLWR). A total of sixteen QTLs and 30 heterosis-related QTLs of grain shape traits were detected. We found that GS3, GS5, and OsPPKL2 were also correlated with grain shape both in RILs and two testcross hybrid populations. Homologous gene analysis emphasized two candidate grain shape-associated genes (LOC_Os06g14260 and LOC_Os04g51950). Our findings uncover multiple grain shape heterosis-related loci and provides a new insight into heterosis mechanism of grain shape in rice.

Multi-locus genome-wide association studies for five yield-related traits in rice

BACKGROUND

Improving the overall production of rice with high quality is a major target of breeders. Mining potential yield-related loci have been geared towards developing efficient rice breeding strategies. In this study, one single-locus genome-wide association studies (SL-GWAS) method (MLM) in conjunction with five multi-locus genome-wide association studies (ML-GWAS) approaches (mrMLM, FASTmrMLM, pLARmEB, pKWmEB, and ISIS EM-BLASSO) were conducted in a panel consisting of 529 rice core varieties with 607,201 SNPs.

RESULTS

A total of 152, 106, 12, 111, and 64 SNPs were detected by the MLM model associated with the five yield-related traits, namely grain length (GL), grain width (GW), grain thickness (GT), thousand-grain weight (TGW), and yield per plant (YPP), respectively. Furthermore, 74 significant quantitative trait nucleotides (QTNs) were presented across at least two ML-GWAS methods to be associated with the above five traits successively. Finally, 20 common QTNs were simultaneously discovered by both SL-GWAS and ML-GWAS methods. Based on genome annotation, gene expression analysis, and previous studies, two candidate key genes (LOC_Os09g02830 and LOC_Os07g31450) were characterized to affect GW and TGW, separately.

CONCLUSIONS

These outcomes will provide an indication for breeding high-yielding rice varieties in the immediate future.

Uncovering the genetic mechanisms regulating panicle architecture in rice with GPWAS and GWAS

Background

The number of panicles per plant, number of grains per panicle, and 1000-grain weight are important factors contributing to the grain yield per plant in rice. The Rice Diversity Panel 1 (RDP1) contains a total of 421 purified, homozygous rice accessions representing diverse genetic variations within O. sativa. The release of High-Density Rice Array (HDRA, 700 k SNPs) dataset provides a new opportunity to discover the genetic variants of panicle architectures in rice.

Results

In this report, a new method genome-phenome wide association study (GPWAS) was performed with 391 individuals and 27 traits derived from RDP1 to scan the relationship between the genes and multi-traits. A total of 1985 gene models were linked to phenomic variation with a p-value cutoff of 4.49E-18. Besides, 406 accessions derived from RDP1 with 411,066 SNPs were used to identify QTLs associated with the total spikelets number per panicle (TSNP), grain number per panicle (GNP), empty grain number per panicle (EGNP), primary branch number (PBN), panicle length (PL), and panicle number per plant (PN) by GLM, MLM, FarmCPU, and BLINK models for genome-wide association study (GWAS) analyses. A total of 18, 21, 18, 17, 15, and 17 QTLs were identified tightly linked with TSNP, GNP, EGNP, PBN, PL, and PN, respectively. Then, a total of 23 candidate genes were mapped simultaneously using both GWAS and GPWAS methods, composed of 6, 4, 5, 4, and 4 for TSNP, GNP, EGNP, PBN, and PL. Notably, one overlapped gene (Os01g0140100) were further investigated based on the haplotype and gene expression profile, indicating this gene might regulate the TSNP or panicle architecture in rice.

Conclusions

Nearly 30 % (30/106) QTLs co-located with the previous published genes or QTLs, indicating the power of GWAS. Besides, GPWAS is a new method to discover the relationship between genes and traits, especially the pleiotropy genes. Through comparing the results from GWAS and GPWAS, we identified 23 candidate genes related to panicle architectures in rice. This comprehensive study provides new insights into the genetic basis controlling panicle architectures in rice, which lays a foundation in rice improvement.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07391-x.