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Approaches in Characterizing Genetic Structure and Mapping in a Rice Multiparental Population

Overview of attention for article published in G3: Genes, Genomes, Genetics, June 2017
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (76th percentile)
  • Good Attention Score compared to outputs of the same age and source (69th percentile)

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Article details
Title
Approaches in Characterizing Genetic Structure and Mapping in a Rice Multiparental Population
Published in
G3: Genes, Genomes, Genetics, June 2017
DOI 10.1534/g3.117.042101
Pubmed ID
Authors
Abstract

Multi-parent Advanced Generation Intercross (MAGIC) populations are fast becoming mainstream tools for research and breeding, along with the technology and tools for analysis. This paper demonstrates the analysis of a rice MAGIC population from data filtering to imputation and processing of genetic data to characterizing genomic structure, and finally quantitative trait loci (QTL) mapping. In this study, 1316 S6:8 indica MAGIC (MI) lines and the eight founders were sequenced using Genotyping by Sequencing (GBS). As the GBS approach often includes missing data, the first step was to impute the missing SNPs. The observable number of recombinations in the population was then explored. Based on this case study, a general outline of procedures for a MAGIC analysis workflow is provided, as well as for QTL mapping of agronomic traits and biotic and abiotic stress, using the results from both association and interval mapping approaches. QTL for agronomic traits (yield, flowering time, and plant height), physical (grain length and grain width) and cooking properties (amylose content) of the rice grain, abiotic stress (submergence tolerance), and biotic stress (brown spot disease) were mapped. Through presenting this extensive analysis in the MI population in rice, we highlight important considerations when choosing analytical approaches. The methods and results reported in this paper will provide a guide to future genetic analysis methods applied to multi-parent populations.

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The data shown below were collected from the profile of 1 X user 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 62 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Geographical breakdown
Country Count As %
Unknown 62 100%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Ph. D. Student 16 26%
Researcher 10 16%
Student > Master 5 8%
Student > Bachelor 4 6%
Student > Doctoral Student 3 5%
Other 11 18%
Unknown 13 21%
Readers by discipline
Readers by discipline Count As %
Agricultural and Biological Sciences 38 61%
Biochemistry, Genetics and Molecular Biology 6 10%
Environmental Science 3 5%
Engineering 2 3%
Computer Science 1 2%
Other 1 2%
Unknown 11 18%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 June 2017.
All research outputs
#5,343,233
of 28,832,041 outputs
Outputs from G3: Genes, Genomes, Genetics
#1,011
of 3,654 outputs
Outputs of similar age
#77,632
of 339,864 outputs
Outputs of similar age from G3: Genes, Genomes, Genetics
#27
of 89 outputs
Altmetric has tracked 28,832,041 research outputs across all sources so far. Compared to these this one has done well and is in the 81st percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,654 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.3. This one has gotten more attention than average, scoring higher than 71% 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 339,864 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 76% of its contemporaries.
We're also able to compare this research output to 89 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 69% of its contemporaries.