QTL analysis on rice grain appearance quality, as exemplifying the typical events of transgenic or backcrossing breeding

Genetic Diversity and Association Mapping of Grain-Size Traits in Rice Landraces from the Honghe Hani Rice Terraces System in Yunnan Province

The Honghe Hani Rice Terraces System (HHRTS) of Yunnan Province is an important agricultural and cultural heritage landscape. Until now, a large number of local rice landraces have been planted. Mining excellent genes contained in these landraces provides a reference for variety improvement and new variety breeding. In this study, 96 rice landraces collected from the Hani terraces were planted in Honghe Mengzi, Yunnan Province, in 2013, 2014, 2015, and 2021, and five major grain traits were measured and analyzed. The genomic variation of 96 rice landraces was scanned by 201 simple sequence repeat (SSR) markers. The genetic diversity, population structure, and genetic relationships of the natural population were analyzed. The mixed linear model (MLM) method of the TASSEL software was used to analyze the associations between markers and traits. A total of 936 alleles were amplified by 201 pairs of SSR primers. The average number of observed alleles (Na), the effective number of alleles (Ne), Shannon's information index (I), heterozygosity (H), and the polymorphism information content (PIC) per marker were 4.66, 2.71, 1.08, 0.15, and 0.55, respectively. Ninety-six landraces were divided into two groups by population structure, clustering, and principal component analysis, and indica rice was the main group. The coefficients of variation of the five traits ranged from 6.80 to 15.24%, and their broad heritabilities were more than 70%. In addition, there were positive correlations among the same grain traits between different years. Through MLM analysis, 2, 36, 7, 7, and 4 SSR markers were significantly associated with grain length (GL), grain width (GW), grain thickness (GT), grain length-width ratio (LWR), and thousand-grain weight (TGW), respectively. The explanation rates of phenotypic variation were 16.31 (RM449, Chr. 1)-23.51% (RM316, Chr. 9), 10.84 (RM523, Chr. 3; RM161/RM305, Chr. 5)-43.01% (RM5496, Chr. 1), 11.98 (RM161/RM305, Chr. 5)-24.72% (RM275, Chr. 6), 12.68 (RM126, Chr. 8)-36.96% (RM5496, Chr. 1), and 17.65 (RM4499, Chr. 2)-26.32% (RM25, Chr. 8), respectively. The associated markers were distributed on 12 chromosomes of the genome.

Mapping combined with principal component analysis identifies excellent lines with increased rice quality

Quality-related traits are some of the most important traits in rice, and screening and breeding rice lines with excellent quality are common ways for breeders to improve the quality of rice. In this study, we used 151 recombinant inbred lines (RILs) obtained by crossing the northern cultivated japonica rice variety ShenNong265 (SN265) with the southern indica rice variety LuHui99 (LH99) and simplified 18 common rice quality-related traits into 8 independent principal components (PCs) by principal component analysis (PCA). These PCs included peak and hot paste viscosity, chalky grain percentage and chalkiness degree, brown and milled rice recovery, width length rate, cooked taste score, head rice recovery, milled rice width, and cooked comprehensive score factors. Based on the weight ratio of each PC score, the RILs were classified into five types from excellent to poor, and five excellent lines were identified. Compared with SN265, these 5 lines showed better performance regarding the chalky grain percentage and chalkiness degree factor. Moreover, we performed QTL localization on the RIL population and identified 94 QTLs for quality-related traits that formed 6 QTL clusters. In future research, by combining these QTL mapping results, we will be using backcrossing to aggregate excellent traits and achieve quality improvement of SN265.

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.

Dissection of three quantitative trait loci for grain size on the long arm of chromosome 10 in rice (Oryza sativa L.)

Background

Thousand grain weight is a key component of grain yield in rice, and a trait closely related to grain length (GL) and grain width (GW) that are important traits for grain quality. Causal genes for 16 quantitative trait loci (QTL) affecting these traits have been cloned, but more QTL remain to be characterized for establishing a genetic regulating network. A QTL controlling grain size in rice, qGS10, was previously mapped in the interval RM6100–RM228 on chromosome 10. This study aimed to delimitate this QTL to a more precise location.

Method

A total of 12 populations were used. The ZC9 population comprised 203 S_1:2 families derived from a residual heterozygous (RH) plant in the F₉ generation of the indica rice cross Teqing (TQ)/IRBB52, segregating the upper region of RM6100–RM228 and three more regions on chromosomes 1, 9, and 11. The Ti52-1 population comprised 171 S₁ plants derived from one RH plant in F₇ of TQ/IRBB52, segregating a single interval that was in the lower portion of RM6100–RM228. The other ten populations were all derived from Ti52-1, including five S₁ populations with sequential segregating regions covering the target region and five near isogenic line (NIL) populations maintaining the same segregating pattern. QTL analysis for 1,000-grain weight, GL, and GW was performed using QTL IciMapping and SAS procedure GLM.

Result

Three QTL were separated in the original qGS10 region. The qGL10.1 was located in the upper region RM6704–RM3773, shown to affect GL only. The qGS10.1 was located within a 207.1-kb interval flanked by InDel markers Te20811 and Te21018, having a stable and relatively high effect on all the three traits analyzed. The qGS10.2 was located within a 1.2-Mb interval flanked by simple sequence repeat markers RM3123 and RM6673. This QTL also affected all the three traits but the effect was inconsistent across different experiments. QTL for grain size were also detected in all the other three segregating regions.

Conclusion

Three QTL for grain size that were tightly linked on the long arm of chromosome 10 of rice were separated using NIL populations with sequential segregating regions. One of them, qGS10.1, had a stable and relatively high effect on grain weight, GL, and GW, providing a good candidate for gene cloning. Another QTL, qGS10.2, had a significant effect on all the three traits but the effect was inconsistent across different experiments, providing an example of genotype-by-environmental interaction.

Down-Regulation of SSSII-2 Gene Expression Results in Novel Low-Amylose Rice with Soft, Transparent Grains

Although soft rice, with low amylose content (AC), has high eating and cooking quality (ECQ), its appearance is poor due to the opaque endosperm. Here, a novel soft rice with low AC but a transparent appearance was generated by knocking-down the expression of SSSII-2, a gene encoding one isoform of soluble starch synthase (SSS). The physicochemical properties of the SSSII-2 RNAi rice are quite different from the control but more like the popular soft rice "Nanjing 46". The taste value assay further demonstrated that the ECQ of SSSII-2 RNAi rice was as high as "Nanjing 46", but only SSSII-2 RNAi rice retained the transparent endosperm under low moisture conditions. Further examination showed that the different morphologies and fine structures of the starch granules may contribute to the specific properties of SSSII-2 RNAi rice. Therefore, SSSII-2 has potential application in future high quality rice breeding programs.

Genetic loci simultaneously controlling lignin monomers and biomass digestibility of rice straw

Lignin content and composition are crucial factors affecting biomass digestibility. Exploring the genetic loci simultaneously affecting lignin-relevant traits and biomass digestibility is a precondition for lignin genetic manipulation towards energy crop breeding. In this study, a high-throughput platform was employed to assay the lignin content, lignin composition and biomass enzymatic digestibility of a rice recombinant inbred line population. Correlation analysis indicated that the absolute content of lignin monomers rather than lignin content had negative effects on biomass saccharification, whereas the relative content of p-hydroxyphenyl unit and the molar ratio of p-hydroxyphenyl unit to guaiacyl unit exhibited positive roles. Eight QTL clusters were identified and four of them affecting both lignin composition and biomass digestibility. The additive effects of clustered QTL revealed consistent relationships between lignin-relevant traits and biomass digestibility. Pyramiding rice lines containing the above four positive alleles for increasing biomass digestibility were selected and showed comparable lignin content, decreased syringyl or guaiacyl unit and increased molar percentage of p-hydroxyphenyl unit, the molar ratio of p-hydroxyphenyl unit to guaiacyl unit and sugar releases. More importantly, the lodging resistance and eating/cooking quality of pyramiding lines were not sacrificed, indicating the QTL information could be applied to select desirable energy rice lines.

Examining two sets of introgression lines reveals background-independent and stably expressed QTL that improve grain appearance quality in rice (Oryza sativa L.)

A novel QTL cluster for appearance quality on Chr07 was identified using reciprocal introgression populations in different locations in China. Two secondary F ₂ populations validated QTL with significant effect on appearance quality. Appearance quality (AQ) is the main determinants of market value of rice. Identification of QTL affecting AQ is the prerequisite for efficient improvement of AQ through marker-assisted selection (MAS). Two sets of reciprocal introgression lines derived from indica Minghui 63 and japonica 02428 were used to dissect the stability of QTL affecting five AQ traits, including grain length, grain width, length to width ratio, percentage of grains with chalkiness, and degree of endosperm chalkiness using 4568 bin genotype produced from 58,000 SNPs across five different environments. A total of 41 and 30 main-effect QTL were identified in MH63 and 02428 backgrounds, respectively. Among them, 9 background-independent QTL (BI-QTL) were found. There were also 13 and 10 stable-expressed QTL (SE-QTL) across at least two environments in MH63 and 02428 backgrounds, respectively. Two important BI- and SE-QTL regions (BISERs) including BISER-I harboring qPGWC5, qDEC5, qGW5.1, and qLWR5 on chromosome 5 and BISER-II harboring qGL7, qLWR7, qPGWC7, and qDEC7 on chromosome 7 were identified. The BISER-II was newly reported and validated by two secondary F₂ populations in the reciprocal backgrounds. Among 59 epistatic QTL (E-QTL) detected in this study, there were only four SE- but no BI-E-QTL detected in different environments, indicating that genetic background has stronger effect on AQ traits than the environmental factors, especially for percentage of grains with chalkiness (PGWC) and degree of endosperm chalkiness (DEC) with lower heritability. BISER-I and BISER-II harboring many BI- and SE-QTL with favorable alleles from slender grain rice are much important for improvement of rice AQ by MAS.

Detection of Novel QTLs Regulating Grain Size in Extra-Large Grain Rice (Oryza sativa L.) Lines

BACKGROUND

Grain size is an important trait that affects rice yield. Although many genes that contribute to grain size have been cloned from mutants or by quantitative trait locus (QTL) analysis based on bi-parental mapping, the molecular mechanisms underlying grain-size determination remain poorly understood. In this study, we identified the lines with the largest grain size and detected novel QTLs affecting the grain size.

RESULTS

We screened the National Institute for Agrobiological Sciences Genebank database and identified two rice lines, BG23 with the widest grain and LG10 with the longest grain. Using these two lines, we performed QTL analysis for grain size. Eight QTLs were detected during the QTL analyses using F2 populations derived from crosses between the large-grain lines BG23 or LG10 and the middle-size grain cultivars Nipponbare and Kasalath. Both BG23 and LG10 possessed large-grain alleles of four major QTLs: GW2, GS3, qSW5/GW5, and GW8. Other three minor QTLs were derived from BG23. However, these QTLs did not explain the differences in grain size between these two lines. Additionally, four QTLs for grain length or width were detected in an F2 population derived from a cross between BG23 and LG10; this population lacked the strong effects of the four major QTLs shared by both parent plants. Of these newly detected QTLs, the effects of two QTLs, GL3b and GL6, were confirmed by progeny testing. Comparison of the length of inner epidermal cells in plants homozygous for BG23 and LG10 alleles indicated that GL3b and GL6 genes regulate cell elongation and cell division, respectively.

CONCLUSIONS

In this study, we detected 12 loci including 14 QTLs regulating grain size from two lines with largest grains available in Japanese stock. Of these loci, we confirmed the effect of two gene loci and mapped their candidate region. Identification of novel genes regulating grain size will contribute to our understanding of the molecular mechanisms controlling grain size.

Multi-environment QTL analysis of grain morphology traits and fine mapping of a kernel-width QTL in Zheng58 × SK maize population

Sixteen major QTLs regulating maize kernel traits were mapped in multiple environments and one of them, qKW - 9.2 , was restricted to 630 Kb, harboring 28 putative gene models. To elucidate the genetic basis of kernel traits, a quantitative trait locus (QTL) analysis was conducted in a maize recombinant inbred line population derived from a cross between two diverse parents Zheng58 and SK, evaluated across eight environments. Construction of a high-density linkage map was based on 13,703 single-nucleotide polymorphism markers, covering 1860.9 cM of the whole genome. In total, 18, 26, 23, and 19 QTLs for kernel length, width, thickness, and 100-kernel weight, respectively, were detected on the basis of a single-environment analysis, and each QTL explained 3.2-23.7 % of the phenotypic variance. Sixteen major QTLs, which could explain greater than 10 % of the phenotypic variation, were mapped in multiple environments, implying that kernel traits might be controlled by many minor and multiple major QTLs. The major QTL qKW-9.2 with physical confidence interval of 1.68 Mbp, affecting kernel width, was then selected for fine mapping using heterogeneous inbred families. At final, the location of the underlying gene was narrowed down to 630 Kb, harboring 28 putative candidate-gene models. This information will enhance molecular breeding for kernel traits and simultaneously assist the gene cloning underlying this QTL, helping to reveal the genetic basis of kernel development in maize.

Deep sequencing transcriptional fingerprinting of rice kernels for dissecting grain quality traits

BACKGROUND

Rice represents one the most important foods all over the world. In Europe, Italy is the first rice producer and Italian production is driven by tradition and quality. All main rice grain quality traits, like cooking properties, texture, gelatinization temperature, chalkiness and yield, are related to the content and composition of starch and seed-storage proteins in the endosperm and to grain shape. In addition, a number of nutraceutical compounds and allergens are known to have a significant effect on grain quality determination. To investigate the genetic bases underlying the qualitative differences that characterize traditional Italian rice cultivars, a comparative RNA-Seq-based transcriptomic analysis of developing caryopsis was conducted at 14 days after flowering on six popular Italian varieties (Carnaroli, Arborio, Balilla, Vialone Nano, Gigante Vercelli and Volano) phenotypically differing for qualitative grain-related traits.

RESULTS

Co-regulation analyses of differentially expressed genes showing the same expression patterns in the six genotypes highlighted clusters of loci up or down-regulated in specific varieties, with respect to the others. Among them, we detected loci involved in cell wall biosynthesis, protein metabolism and redox homeostasis, classes of genes affecting in chalkiness determination. Moreover, loci encoding for seed-storage proteins, allergens or involved in the biosynthesis of specific nutraceutical compounds were also present and specifically regulated in the different clusters. A wider investigation of all the DEGs detected in pair-wise comparisons revealed transcriptional variation, among the six genotypes, for quality-related loci involved in starch biosynthesis (e.g. GBSSI, starch synthases and AGPase), genes encoding for transcription factors, additional seed storage proteins, allergens or belonging to additional nutraceutical compounds biosynthetic pathways and loci affecting grain size. Putative functional SNPs associated to amylose content in starch, gelatinization temperature and grain size were also identified.

CONCLUSIONS

The present work represents a more extended phenotypic characterization of a set of rice accessions that present a wider genetic variability than described nowadays in literature. The results provide the first transcriptional picture for several of the grain quality differences observed among the Italian rice varieties analyzed and reveal that each variety is characterized by the over-expression of a peculiar set of loci affecting grain appearance and quality. A list of candidates and SNPs affecting specific grain properties has been identified offering a starting point for further works aimed to characterize genes and molecular markers for breeding programs.