Fine mapping of a major quantitative trait locus, &lt;i&gt;qgnp7(t)&lt;/i&gt;, controlling grain number per panicle in African rice (&lt;i&gt;Oryza glaberrima&lt;/i&gt; S.)

Hu, Zejun; Cao, Liming; Sun, Xuejun; Yu, Zhao; TianYu, Zhang; Jiang, Lin; Liu, Yahui; Dong, Shaojun; Sun, Dayun; Yang, Jingshui; He, Haohua; Luo, Xin

doi:10.1270/jsbbs.18084

Cited by 11 publications

(6 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ashikari et al and other researchers studied Gn1a , which is a gene for cytokinin oxidase/dehydrogenase. It increases the number of reproductive organs, resulting in the enhancement of grain productivity [ 3 , 4 , 5 ]. This Gn1a came from a high-yielding indica variety Habataki and was introduced into a Japanese low yield variety, Koshihikari.…”

Section: Introductionmentioning

confidence: 99%

The Selection of Gamma-Ray Irradiated Higher Yield Rice Mutants by Directed Evolution Method

Katô

Shimizu

2020

Plants

View full text Add to dashboard Cite

We have succeeded in selecting four higher yield mutants from five gamma-ray irradiated high-yielding Japanese rice varieties using a novel approach. A total of 464 M2 plants which had heavier total panicle weights per plant were first selected from 9801 irradiated M2 plants. Their higher yields were confirmed by yield trials conducted for three years with a six to ten-pairwise replicated plot design. FukuhibikiH6 and FukuhibikiH8 were selected from an irradiated high-yielding variety Fukuhibiki and showed 1.2% to 22.5% higher yield than their original significantly. YamadawaraH3 was selected from an irradiated high-yielding variety Yamadawara and its yield advantages were 2.7% to 3.9%. However, there was no difference in the genotypes of the 96 SNP (single nucleotide polymorphism) markers between the higher yield mutants and their respective original varieties. The differences in the measured phenotypical traits between each mutant and its original variety were not constant and the actual differences were marginal. Therefore, the higher yields of the selected mutants were likely to have been caused by physiological traits rather than phenotypical traits. The selection method used in this study is an application of the directed evolution method which has long been commonly used in the substantial improvements of microorganisms and their proteins.

show abstract

Section: Introductionmentioning

confidence: 99%

The Selection of Gamma-Ray Irradiated Higher Yield Rice Mutants by Directed Evolution Method

Katô

Shimizu

2020

Plants

View full text Add to dashboard Cite

show abstract

“…Most of them were detected in different mapping populations such as F 2 , doubled haploid lines, and recombinant inbred lines (RILs) [5][6][7][8][9][10]. Several dozens of QTLs for NGP and NSP have been fine-mapped [11][12][13][14][15][16][17]. So far, eight QTLs have been cloned.…”

Section: Introductionmentioning

confidence: 99%

Identification and Validation of Quantitative Trait Loci for Grain Number in Rice (Oryza sativa L.)

et al. 2020

View full text Add to dashboard Cite

Grains number is one of the most important agronomic traits in the determination of rice productivity. To explore the underlying genetic basis of grain number in rice, quantitative trait locus (QTL) analysis was performed using three recombinant inbred line populations derived from indica rice crosses of Teqing/IRBB lines, Zhenshan 97/Milyang 46, and Xieqingzao/Milyang 46, respectively. A total of 58 QTLs distributed on all 12 rice chromosomes were identified, including 22 for number of grains per panicle (NGP), 17 for number of spikelets per panicle, and 19 for spikelet fertility. The individual QTL counted for 1.5 to 22.1% of phenotypic variation. Among them, 15 QTLs shared by two or three populations and eight QTLs showed large effects with R2 larger than 10%. Furthermore, three QTLs with minor effects for NGP, qNGP5.5, qNGP9.1, and qNGP12.1, were detected and validated by eliminating the segregation of major-effect QTL using four residual heterozygote-derived populations. These results not only enrich our understanding of the mechanism of grain number, but also provide a foundation for cloning and selecting candidate for marker-assisted selection breeding in rice.

show abstract

“…The following is a strategy by which numerous genes have been mapped and cloned. The most relevant include qGN1c mapped using a set of chromosomal segment substitution lines (CSSLs) [ 20 ], SPP1 identified through NILs controlling the number of spikelets [ 21 ], qTGW3.2 for grain weight in rice using RIL [ 22 ], GNP1 [ 19 ], and qgnp7 ( t ) [ 23 ] governing GNPP revealed through map-based cloning, and two steps substitution mapping, respectively. However, traditional gene mapping is tedious and time-consuming; most QTLs are inconsistent and span large genomic regions, making identifying candidate genes for a trait more challenging.…”

Section: Introductionmentioning

confidence: 99%

Combined Analysis of BSA-Seq Based Mapping, RNA-Seq, and Metabolomic Unraveled Candidate Genes Associated with Panicle Grain Number in Rice (Oryza sativa L.)

Mackon

et al. 2022

Biomolecules

View full text Add to dashboard Cite

Rice grain yield is a complex and highly variable quantitative trait consisting of several key components, including the grain weight, the effective panicles per unit area, and the grain number per panicle (GNPP). The GNPP is a significant contributor to grain yield controlled by multiple genes (QTL) and is crucial for improvement. Attempts have been made to find genes for this trait, which has always been a challenging and arduous task through conventional methods. We combined a BSA analysis, RNA profiling, and a metabolome analysis in the present study to identify new candidate genes involved in the GNPP. The F2 population from crossing R4233 (high GNPP) and Ce679 (low GNPP) revealed a frequency distribution fitting two segregated genes. Three pools, including low, middle, and high GNPP, were constructed and a BSA analysis revealed six candidate regions spanning 5.38 Mb, containing 739 annotated genes. Further, a conjunctive analysis of BSA-Seq and RNA-Seq showed 31 differentially expressed genes (DEGs) in the candidate intervals. Subsequently, a metabolome analysis showed 1024 metabolites, with 71 significantly enriched, including 44 up and 27 downregulated in Ce679 vs. R4233. A KEGG enrichment analysis of these 31 DEGs and 71 differentially enriched metabolites (DEMs) showed two genes, Os12g0102100 and Os01g0580500, significantly enriched in the metabolic pathways’ biosynthesis of secondary metabolites, cysteine and methionine metabolism, and fatty acid biosynthesis. Os12g0102100, which encodes for the alcohol dehydrogenase superfamily and a zinc-containing protein, is a novel gene whose contribution to the GNPP is not yet elucidated. This gene coding for mitochondrial trans-2-enoyl-CoA reductase is involved in the biosynthesis of myristic acid, also known as tetradecanoic acid. The Os01g0580500 coding for the enzyme 1-aminoclopropane-1-carboxylate oxidase (OsACO7) is responsible for the final step of the ethylene biosynthesis pathway through the conversion of 1-aminocyclopropane-1-carboxylic acid (ACC) into ethylene. Unlike Os12g0102100, this gene was significantly upregulated in R4233, downregulated in Ce679, and significantly enriched in two of the three metabolite pathways. This result pointed out that these two genes are responsible for the difference in the GNPP in the two cultivars, which has never been identified. Further validation studies may disclose the physiological mechanisms through which they regulate the GNPP in rice.

show abstract

Fine mapping of a major quantitative trait locus, <i>qgnp7(t)</i>, controlling grain number per panicle in African rice (<i>Oryza glaberrima</i> S.)

Cited by 11 publications

References 28 publications

The Selection of Gamma-Ray Irradiated Higher Yield Rice Mutants by Directed Evolution Method

The Selection of Gamma-Ray Irradiated Higher Yield Rice Mutants by Directed Evolution Method

Identification and Validation of Quantitative Trait Loci for Grain Number in Rice (Oryza sativa L.)

Combined Analysis of BSA-Seq Based Mapping, RNA-Seq, and Metabolomic Unraveled Candidate Genes Associated with Panicle Grain Number in Rice (Oryza sativa L.)

Contact Info

Product

Resources

About