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Diversity analysis of cotton (Gossypium hirsutum L.) germplasm using the CottonSNP63K Array

Overview of attention for article published in BMC Plant Biology, February 2017
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (52nd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (55th percentile)

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3 tweeters
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1 Facebook page

Citations

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24 Dimensions

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54 Mendeley
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Title
Diversity analysis of cotton (Gossypium hirsutum L.) germplasm using the CottonSNP63K Array
Published in
BMC Plant Biology, February 2017
DOI 10.1186/s12870-017-0981-y
Pubmed ID
Authors

Lori L. Hinze, Amanda M. Hulse-Kemp, Iain W. Wilson, Qian-Hao Zhu, Danny J. Llewellyn, Jen M. Taylor, Andrew Spriggs, David D. Fang, Mauricio Ulloa, John J. Burke, Marc Giband, Jean-Marc Lacape, Allen Van Deynze, Joshua A. Udall, Jodi A. Scheffler, Steve Hague, Jonathan F. Wendel, Alan E. Pepper, James Frelichowski, Cindy T. Lawley, Don C. Jones, Richard G. Percy, David M. Stelly

Abstract

Cotton germplasm resources contain beneficial alleles that can be exploited to develop germplasm adapted to emerging environmental and climate conditions. Accessions and lines have traditionally been characterized based on phenotypes, but phenotypic profiles are limited by the cost, time, and space required to make visual observations and measurements. With advances in molecular genetic methods, genotypic profiles are increasingly able to identify differences among accessions due to the larger number of genetic markers that can be measured. A combination of both methods would greatly enhance our ability to characterize germplasm resources. Recent efforts have culminated in the identification of sufficient SNP markers to establish high-throughput genotyping systems, such as the CottonSNP63K array, which enables a researcher to efficiently analyze large numbers of SNP markers and obtain highly repeatable results. In the current investigation, we have utilized the SNP array for analyzing genetic diversity primarily among cotton cultivars, making comparisons to SSR-based phylogenetic analyses, and identifying loci associated with seed nutritional traits. The SNP markers distinctly separated G. hirsutum from other Gossypium species and distinguished the wild from cultivated types of G. hirsutum. The markers also efficiently discerned differences among cultivars, which was the primary goal when designing the CottonSNP63K array. Population structure within the genus compared favorably with previous results obtained using SSR markers, and an association study identified loci linked to factors that affect cottonseed protein content. Our results provide a large genome-wide variation data set for primarily cultivated cotton. Thousands of SNPs in representative cotton genotypes provide an opportunity to finely discriminate among cultivated cotton from around the world. The SNPs will be relevant as dense markers of genome variation for association mapping approaches aimed at correlating molecular polymorphisms with variation in phenotypic traits, as well as for molecular breeding approaches in cotton.

Twitter Demographics

The data shown below were collected from the profiles of 3 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 54 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 54 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 12 22%
Student > Ph. D. Student 7 13%
Student > Bachelor 7 13%
Student > Doctoral Student 6 11%
Student > Postgraduate 5 9%
Other 10 19%
Unknown 7 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 33 61%
Biochemistry, Genetics and Molecular Biology 7 13%
Business, Management and Accounting 2 4%
Earth and Planetary Sciences 1 2%
Engineering 1 2%
Other 0 0%
Unknown 10 19%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 24 October 2017.
All research outputs
#6,772,957
of 12,043,827 outputs
Outputs from BMC Plant Biology
#546
of 1,460 outputs
Outputs of similar age
#149,876
of 328,514 outputs
Outputs of similar age from BMC Plant Biology
#13
of 29 outputs
Altmetric has tracked 12,043,827 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,460 research outputs from this source. They receive a mean Attention Score of 3.3. This one has gotten more attention than average, scoring higher than 60% 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 328,514 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 52% of its contemporaries.
We're also able to compare this research output to 29 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 55% of its contemporaries.