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Effect of qGN4.1 QTL for Grain Number per Panicle in Genetic Backgrounds of Twelve Different Mega Varieties of Rice

Overview of attention for article published in Rice, January 2018
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (74th percentile)
  • Good Attention Score compared to outputs of the same age and source (66th percentile)

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Title
Effect of qGN4.1 QTL for Grain Number per Panicle in Genetic Backgrounds of Twelve Different Mega Varieties of Rice
Published in
Rice, January 2018
DOI 10.1186/s12284-017-0195-9
Pubmed ID
Authors

Vijay Kumar Singh, Ranjith Kumar Ellur, Ashok Kumar Singh, M. Nagarajan, Brahma Deo Singh, Nagendra Kumar Singh

Abstract

Rice is a major source of food, particularly for the growing Asian population; hence, the utilization of genes for enhancing its yield potential is important for ensuring food security. Earlier, we have mapped a major quantitative trait loci (QTL) for the grain number per panicle, qGN4.1, using biparental recombinant inbred line (RIL) populations involving a new plant type Indica rice genotype Pusa 1266. Later, three independent studies have confirmed the presence of a major QTL for spikelet number by three different names (SPIKE, GPS and LSCHL4) in the same chromosomal region, and have implicated the overexpression of Nal1 gene as the causal factor for high spikelet number. However, the effect of qGN4.1 in different rice genetic backgrounds and expression levels of the underlying candidate genes is not known. Here, we report the effect of qGN4.1 QTL in the genetic backgrounds of 12 different high-yielding mega varieties of rice, introgressed by marker assisted-backcross breeding (MABB) using two QTL positive markers for foreground selection and two QTL negative flanking markers for recombinant selection together with phenotypic selection for the recovery of recipient parent genetic background. Analysis of the performance of BC2F3 plants showed a significant increase in the average number of well-filled grains per panicle in all the backgrounds, ranging from 21.6 in CSR 30-GN4.1 to 147.6 in Samba Mahsuri-GN4.1. Furthermore, qGN4.1 caused a significant increase in flag leaf width and panicle branching in most backgrounds. We identified BC3F3 qGN4.1 near-isogenic lines (NILs) with 92.0-98.0% similarity to the respective recipient parent by background analysis using a 50 K rice SNP genotyping chip. Three of the NILs, namely Pusa Basmati 1121-GN4.1, Samba Mahsuri-GN4.1 and Swarna-GN4.1, showed a significant yield superiority to their recipient parents. Analysis of differential gene expression revealed that high grain number in these QTL-NILs was unlikely due to the overexpression of Nal1 gene (LOC_Os04g52479). Instead, another tightly linked gene (LOC_Os04g52590) coding for a protein kinase domain-containing protein was consistently overexpressed in the high grain number NILs. We have successfully introgressed the qGN4.1 QTL for high grain number per panicle into 12 different mega varieties of rice using marker-assisted backcross breeding. The advanced near-isogenic lines are promising for the development of even higher yielding versions of these high-yielding mega varieties of rice.

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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 9 24%
Student > Master 7 19%
Researcher 5 14%
Lecturer 1 3%
Other 1 3%
Other 1 3%
Unknown 13 35%
Readers by discipline Count As %
Agricultural and Biological Sciences 17 46%
Biochemistry, Genetics and Molecular Biology 4 11%
Arts and Humanities 1 3%
Computer Science 1 3%
Economics, Econometrics and Finance 1 3%
Other 0 0%
Unknown 13 35%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 27 January 2018.
All research outputs
#5,635,194
of 23,018,998 outputs
Outputs from Rice
#57
of 388 outputs
Outputs of similar age
#113,220
of 441,076 outputs
Outputs of similar age from Rice
#3
of 9 outputs
Altmetric has tracked 23,018,998 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 388 research outputs from this source. They receive a mean Attention Score of 3.8. This one has done well, scoring higher than 85% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 441,076 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 74% of its contemporaries.
We're also able to compare this research output to 9 others from the same source and published within six weeks on either side of this one. This one has scored higher than 6 of them.