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Understanding rice adaptation to varying agro-ecosystems: trait interactions and quantitative trait loci

Overview of attention for article published in BMC Genetics, August 2015
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (86th percentile)
  • High Attention Score compared to outputs of the same age and source (90th percentile)

Mentioned by

1 blog
7 tweeters


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63 Mendeley
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Understanding rice adaptation to varying agro-ecosystems: trait interactions and quantitative trait loci
Published in
BMC Genetics, August 2015
DOI 10.1186/s12863-015-0249-1
Pubmed ID

Shalabh Dixit, Alexandre Grondin, Cheng-Ruei Lee, Amelia Henry, Thomas-Mitchell Olds, Arvind Kumar


Interaction and genetic control for traits influencing the adaptation of the rice crop to varying environments was studied in a mapping population derived from parents (Moroberekan and Swarna) contrasting for drought tolerance, yield potential, lodging resistance, and adaptation to dry direct seeding. A BC2F3-derived mapping population for traits related to these four trait groups was phenotyped to understand the interactions among traits and to map and align QTLs using composite interval mapping (CIM). The study also aimed to identify QTLs for the four trait groups as composite traits using multivariate least square interval mapping (MLSIM) to further understand the genetic control of these traits. Significant correlations between drought- and yield-related traits at seedling and reproductive stages respectively with traits for adaptation to dry direct-seeded conditions were observed. CIM and MLSIM methods were applied to identify QTLs for univariate and composite traits. QTL clusters showing alignment of QTLs for several traits within and across trait groups were detected at chromosomes 3, 4, and 7 through CIM. The largest number of QTLs related to traits belonging to all four trait groups were identified on chromosome 3 close to the qDTY 3.2 locus. These included QTLs for traits such as bleeding rate, shoot biomass, stem strength, and spikelet fertility. Multivariate QTLs were identified at loci supported by univariate QTLs such as on chromosomes 3 and 4 as well as at distinctly different loci on chromosome 8 which were undetected through CIM. Rice requires better adaptation across a wide range of environments and cultivation practices to adjust to climate change. Understanding the genetics and trade-offs related to each of these environments and cultivation practices thus becomes highly important to develop varieties with stability of yield across them. This study provides a wider picture of the genetics and physiology of adaptation of rice to wide range of environments. With a complete understanding of the processes and relationships between traits and trait groups, marker-assisted breeding can be used more efficiently to develop plant types that can combine all or most of the beneficial traits and show high stability across environments, ecosystems, and cultivation practices.

Twitter Demographics

The data shown below were collected from the profiles of 7 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 63 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Indonesia 1 2%
Chile 1 2%
Benin 1 2%
Unknown 60 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 21 33%
Student > Ph. D. Student 12 19%
Student > Master 7 11%
Student > Bachelor 6 10%
Professor > Associate Professor 3 5%
Other 5 8%
Unknown 9 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 40 63%
Biochemistry, Genetics and Molecular Biology 3 5%
Environmental Science 2 3%
Medicine and Dentistry 2 3%
Psychology 1 2%
Other 3 5%
Unknown 12 19%

Attention Score in Context

This research output has an Altmetric Attention Score of 11. 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 12 February 2017.
All research outputs
of 11,293,566 outputs
Outputs from BMC Genetics
of 776 outputs
Outputs of similar age
of 235,683 outputs
Outputs of similar age from BMC Genetics
of 44 outputs
Altmetric has tracked 11,293,566 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 776 research outputs from this source. They receive a mean Attention Score of 3.3. This one has done particularly well, scoring higher than 92% 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 235,683 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 86% of its contemporaries.
We're also able to compare this research output to 44 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 90% of its contemporaries.