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Genetic dissection and validation of candidate genes for flag leaf size in rice (Oryza sativa L.)

Overview of attention for article published in Theoretical and Applied Genetics, December 2017
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Title
Genetic dissection and validation of candidate genes for flag leaf size in rice (Oryza sativa L.)
Published in
Theoretical and Applied Genetics, December 2017
DOI 10.1007/s00122-017-3036-8
Pubmed ID
Authors

Xinxin Tang, Rong Gong, Wenqiang Sun, Chaopu Zhang, Sibin Yu

Abstract

Two major loci with functional candidate genes were identified and validated affecting flag leaf size, which offer desirable genes to improve leaf architecture and photosynthetic capacity in rice. Leaf size is a major determinant of plant architecture and yield potential in crops. However, the genetic and molecular mechanisms regulating leaf size remain largely elusive. In this study, quantitative trait loci (QTLs) for flag leaf length and flag leaf width in rice were detected with high-density single nucleotide polymorphism genotyping of a chromosomal segment substitution line (CSSL) population, in which each line carries one or a few chromosomal segments from the japonica cultivar Nipponbare in a common background of the indica variety Zhenshan 97. In total, 14 QTLs for flag leaf length and nine QTLs for flag leaf width were identified in the CSSL population. Among them, qFW4-2 for flag leaf width was mapped to a 37-kb interval, with the most likely candidate gene being the previously characterized NAL1. Another major QTL for both flag leaf width and length was delimited by substitution mapping to a small region of 13.5 kb that contains a single gene, Ghd7.1. Mutants of Ghd7.1 generated using CRISPR/CAS9 approach showed reduced leaf size. Allelic variation analyses also validated Ghd7.1 as a functional candidate gene for leaf size, photosynthetic capacity and other yield-related traits. These results provide useful genetic information for the improvement of leaf size and yield in rice breeding programs.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 37 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 19%
Researcher 5 14%
Student > Master 3 8%
Student > Bachelor 1 3%
Other 1 3%
Other 2 5%
Unknown 18 49%
Readers by discipline Count As %
Agricultural and Biological Sciences 15 41%
Biochemistry, Genetics and Molecular Biology 3 8%
Unknown 19 51%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 09 December 2017.
All research outputs
#21,141,111
of 23,794,258 outputs
Outputs from Theoretical and Applied Genetics
#3,320
of 3,565 outputs
Outputs of similar age
#379,337
of 443,940 outputs
Outputs of similar age from Theoretical and Applied Genetics
#50
of 51 outputs
Altmetric has tracked 23,794,258 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 3,565 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 51 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.