Fine mapping and candidate gene analysis of the quantitative trait locus gw8.1 associated with grain length in rice

Kang, Yun-Joo; Shim, Kyu-Chan; Lee, Hyun‐Sook; Jeon, Yun-A; Kim, Sun-Ha; Kang, Ju-Won; Yun, Yeo-Tae; Park, Inkyu; Ahn, Sang-Nag

doi:10.1007/s13258-017-0640-6

Cited by 17 publications

(12 citation statements)

References 28 publications

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“…It is of great significance to clone the genes controlling grain weight and then to clarify their molecular genetic mechanism. Great achievements have been made in genes/QTLs identification and dissection for grain size and grain weight in many crops, such as rice 16–20 , soybean 21,22 , wheat 23,24 . Especially many genes related to grain weight or grain size in rice, including GS3 25 , GS5 16 , qGL3 26 , GW2 18 , GW8 27 , GS2 28 , qGW7/GL7 29 , have been successfully cloned.…”

Section: Introductionmentioning

confidence: 99%

Mapping quantitative trait loci for yield-related traits and predicting candidate genes for grain weight in maize

Zhao

2019

Sci Rep

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Quantitative trait loci (QTLs) mapped in different genetic populations are of great significance for marker-assisted breeding. In this study, an F2:3 population were developed from the crossing of two maize inbred lines SG-5 and SG-7 and applied to QTL mapping for seven yield-related traits. The seven traits included 100-kernel weight, ear length, ear diameter, cob diameter, kernel row number, ear weight, and grain weight per plant. Based on an ultra-high density linkage map, a total of thirty-three QTLs were detected for the seven studied traits with composite interval mapping (CIM) method, and fifty-four QTLs were indentified with genome-wide composite interval mapping (GCIM) methods. For these QTLs, Fourteen were both detected by CIM and GCIM methods. Besides, eight of the thirty QTLs detected by CIM were identical to those previously mapped using a F2 population (generating from the same cross as the mapping population in this study), and fifteen were identical to the reported QTLs in other recent studies. For the fifty-four QTLs detected by GCIM, five of them were consistent with the QTLs mapped in the F2 population of SG-5 × SG-7, and twenty one had been reported in other recent studies. The stable QTLs associated with grain weight were located on maize chromosomes 2, 5, 7, and 9. In addition, differentially expressed genes (DEGs) between SG-5 and SG-7 were obtained from the transcriptomic profiling of grain at different developmental stages and overlaid onto the stable QTLs intervals to predict candidate genes for grain weight in maize. In the physical intervals of confirmed QTLs qKW-7, qEW-9, qEW-10, qGWP-6, qGWP-8, qGWP-10, qGWP-11 and qGWP-12, there were 213 DEGs in total. Finally, eight genes were predicted as candidate genes for grain size/weight. In summary, the stable QTLs would be reliable and the candidate genes predicted would be benefit for maker assisted breeding or cloning.

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Section: Introductionmentioning

confidence: 99%

Mapping quantitative trait loci for yield-related traits and predicting candidate genes for grain weight in maize

Zhao

2019

Sci Rep

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“…In monocotyledonous endospermic seeds such as rice, the formation of grain size, and weight is largely determined by the development of endosperm and the spikelet hull. For example, the key genes GS5 and GW8 have been identified as critical regulators for controlling grain width and weight through regulating cell number during seed development; while the regulators GL7 and SPL13 determine rice grain size through the regulation of cell size [48,49,50,51]. In dicotyledonous non-endospermic seeds such as Arabidopsis, the formation of seed size and weight is usually determined by the development of cotyledon.…”

Section: Discussionmentioning

confidence: 99%

Global Gene Expression of Seed Coat Tissues Reveals a Potential Mechanism of Regulating Seed Size Formation in Castor Bean

Wang

Zhang

et al. 2019

IJMS

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The physiological and molecular basis of seed size formation is complex, and the development of seed coat (derived from integument cells) might be a critical factor that determines seed size formation for many endospermic seeds. Castor bean (Ricinus communis L.), a model system of studying seed biology, has large and persistent endosperm with a hard seed coat at maturity. Here, we investigated the potential molecular mechanisms underlying seed size formation in castor bean by comparing the difference between global gene expression within developing seed coat tissues between the large-seed ZB107 and small-seed ZB306. First, we observed the cell size of seed coat and concluded that the large seed coat area of ZB107 resulted from more cell numbers (rather than cell size). Furthermore, we found that the lignin proportion of seed coat was higher in ZB306. An investigation into global gene expression of developing seed coat tissues revealed that 815 genes were up-regulated and 813 were down-regulated in ZB306 relative to ZB107. Interestingly, we found that many genes involved in regulating cell division were up-regulated in ZB107, whereas many genes involved in regulating lignin biosynthesis (including several NAC members, as well as MYB46/83 and MYB58/63) and in mediating programmed cell death (such as CysEP1 and βVPE) were up-regulated in ZB306. Furthermore, the expression patterns of the genes mentioned above indicated that the lignification of seed coat tissues was enhanced and occurred earlier in the developing seeds of ZB306. Taken together, we tentatively proposed a potential scenario for explaining the molecular mechanisms of seed coat governing seed size formation in castor bean by increasing the cell number and delaying the onset of lignification in seed coat tissues in large-seed ZB107. This study not only presents new information for possible modulation of seed coat related genes to improve castor seed yield, but also provides new insights into understanding the molecular basis of seed size formation in endospermic seeds with hard seed coat.

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“…The GW8 bearing genotypes were reported to have a higher grain length and grain length-width ratio [20]. The scanning results of the electron microscopy analysis of the lemma in GW8 .1 carrying NILs showed that the inner epidermal cell length was higher than the lines without this gene, indicating that GW8 .1 might be responsible for regulating cell elongation [64]. GW8 (OsSPL16) encodes a protein that is positively associated with cell proliferation [31].…”

Section: Discussionmentioning

confidence: 99%

InDel Marker Based Estimation of Multi-Gene Allele Contribution and Genetic Variations for Grain Size and Weight in Rice (Oryza sativa L.)

Gull

Haider²,

et al. 2019

IJMS

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The market success of any rice cultivar is exceedingly dependent on its grain appearance, as well as its grain yield, which define its demand by consumers as well as growers. The present study was undertaken to explore the contribution of nine major genes, qPE9~1, GW2, SLG7, GW5, GS3, GS7, GW8, GS5, and GS2, in regulating four size and weight related traits, i.e., grain length (GL), grain width (GW), grain thickness (GT), and thousand grain weight (TGW) in 204 diverse rice germplasms using Insertion/Deletion (InDel) markers. The studied germplasm displayed wide-ranging variability in the four studied traits. Except for three genes, all six genes showed considerable association with these traits with varying strengths. Whole germplasm of 204 genotypes could be categorized into three major clusters with different grain sizes and weights that could be utilized in rice breeding programs where grain appearance and weight are under consideration. The study revealed that TGW was 24.9% influenced by GL, 37.4% influenced by GW, and 49.1% influenced by GT. Hence, assuming the trend of trait selection, i.e., GT > GW > GL, for improving TGW in the rice yield enhancement programs. The InDel markers successfully identified a total of 38 alleles, out of which 27 alleles were major and were found in more than 20 genotypes. GL was associated with four genes (GS3, GS7, GW8, and GS2). GT was also found to be regulated by four different genes (GS3, GS7, GW8, and GS2) out of the nine studied genes. GW was found to be under the control of three studied genes (GW5, GW8, and GS2), whereas TGW was found to be under the influence of four genes (SLG7, GW5, GW8, and GS5) in the germplasm under study. The Unweighted Pair Group Method with Arithmetic means (UPGMA) tree based on the studied InDel marker loci segregated the whole germplasm into three distinct clusters with dissimilar grain sizes and weights. A two-dimensional scatter plot constructed using Principal Coordinate Analysis (PCoA) based on InDel markers further separated the 204 rice germplasms into four sub-populations with prominent demarcations of extra-long, long, medium, and short grain type germplasms that can be utilized in breeding programs accordingly. The present study could help rice breeders to select a suitable InDel marker and in formulation of breeding strategies for improving grain appearance, as well as weight, to develop rice varieties to compete international market demands with higher yield returns. This study also confirms the efficient application of InDel markers in studying diverse types of rice germplasm, allelic frequencies, multiple-gene allele contributions, marker-trait associations, and genetic variations that can be explored further.

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Fine mapping and candidate gene analysis of the quantitative trait locus gw8.1 associated with grain length in rice

Cited by 17 publications

References 28 publications

Mapping quantitative trait loci for yield-related traits and predicting candidate genes for grain weight in maize

Mapping quantitative trait loci for yield-related traits and predicting candidate genes for grain weight in maize

Global Gene Expression of Seed Coat Tissues Reveals a Potential Mechanism of Regulating Seed Size Formation in Castor Bean

InDel Marker Based Estimation of Multi-Gene Allele Contribution and Genetic Variations for Grain Size and Weight in Rice (Oryza sativa L.)

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