↓ Skip to main content

Searching for an Accurate Marker-Based Prediction of an Individual Quantitative Trait in Molecular Plant Breeding

Overview of attention for article published in Frontiers in Plant Science, July 2017
Altmetric Badge

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 (81st percentile)
  • High Attention Score compared to outputs of the same age and source (90th percentile)

Mentioned by

blogs
1 blog
twitter
6 X users

Citations

dimensions_citation
27 Dimensions

Readers on

mendeley
102 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Searching for an Accurate Marker-Based Prediction of an Individual Quantitative Trait in Molecular Plant Breeding
Published in
Frontiers in Plant Science, July 2017
DOI 10.3389/fpls.2017.01182
Pubmed ID
Authors

Yong-Bi Fu, Mo-Hua Yang, Fangqin Zeng, Bill Biligetu

Abstract

Molecular plant breeding with the aid of molecular markers has played an important role in modern plant breeding over the last two decades. Many marker-based predictions for quantitative traits have been made to enhance parental selection, but the trait prediction accuracy remains generally low, even with the aid of dense, genome-wide SNP markers. To search for more accurate trait-specific prediction with informative SNP markers, we conducted a literature review on the prediction issues in molecular plant breeding and on the applicability of an RNA-Seq technique for developing function-associated specific trait (FAST) SNP markers. To understand whether and how FAST SNP markers could enhance trait prediction, we also performed a theoretical reasoning on the effectiveness of these markers in a trait-specific prediction, and verified the reasoning through computer simulation. To the end, the search yielded an alternative to regular genomic selection with FAST SNP markers that could be explored to achieve more accurate trait-specific prediction. Continuous search for better alternatives is encouraged to enhance marker-based predictions for an individual quantitative trait in molecular plant breeding.

X Demographics

X Demographics

The data shown below were collected from the profiles of 6 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 102 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 102 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 22 22%
Student > Ph. D. Student 13 13%
Student > Master 11 11%
Student > Doctoral Student 7 7%
Student > Postgraduate 7 7%
Other 12 12%
Unknown 30 29%
Readers by discipline Count As %
Agricultural and Biological Sciences 53 52%
Biochemistry, Genetics and Molecular Biology 13 13%
Environmental Science 1 <1%
Computer Science 1 <1%
Immunology and Microbiology 1 <1%
Other 2 2%
Unknown 31 30%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 05 September 2017.
All research outputs
#3,070,636
of 22,990,068 outputs
Outputs from Frontiers in Plant Science
#1,525
of 20,454 outputs
Outputs of similar age
#57,963
of 313,507 outputs
Outputs of similar age from Frontiers in Plant Science
#51
of 538 outputs
Altmetric has tracked 22,990,068 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 20,454 research outputs from this source. They receive a mean Attention Score of 4.0. 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 313,507 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 81% of its contemporaries.
We're also able to compare this research output to 538 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.