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Finding a Needle in a Haystack: Distinguishing Mexican Maize Landraces Using a Small Number of SNPs

Overview of attention for article published in Frontiers in Genetics, April 2017
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (80th percentile)
  • High Attention Score compared to outputs of the same age and source (80th percentile)

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Title
Finding a Needle in a Haystack: Distinguishing Mexican Maize Landraces Using a Small Number of SNPs
Published in
Frontiers in Genetics, April 2017
DOI 10.3389/fgene.2017.00045
Pubmed ID
Authors

Jose L. Caldu-Primo, Alicia Mastretta-Yanes, Ana Wegier, Daniel Piñero

Abstract

In Mexico's territory, the center of origin and domestication of maize (Zea mays), there is a large phenotypic diversity of this crop. This diversity has been classified into "landraces." Previous studies have reported that genomic variation in Mexican maize is better explained by environmental factors, particularly those related with altitude, than by landrace. Still, landraces are extensively used by agronomists, who recognize them as stable and discriminatory categories for the classification of samples. In order to investigate the genomic foundation of maize landraces, we analyzed genomic data (35,909 SNPs from Illumina MaizeSNP50 BeadChip) obtained from 50 samples representing five maize landraces (Comiteco, Conejo, Tehua, Zapalote Grande, and Zapalote Chico), and searched for markers suitable for landrace assignment. Landrace clusters could not be identified taking all the genomic information, but they become manifest taking only a subset of SNPs with high FST among landraces. Discriminant analysis of principal components was conducted to classify samples using SNP data. Two classification analyses were done, first classifying samples by landrace and then by altitude category. Through this classification method, we identified 20 landrace-informative SNPs and 14 altitude-informative SNPs, with only 6 SNPs in common for both analyses. These results show that Mexican maize phenotypic diversity can be classified in landraces using a small number of genomic markers, given the fact that landrace genomic diversity is influenced by environmental factors as well as artificial selection due to bio-cultural practices.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 72 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 1 1%
Unknown 71 99%

Demographic breakdown

Readers by professional status Count As %
Researcher 16 22%
Student > Master 15 21%
Student > Doctoral Student 6 8%
Student > Bachelor 5 7%
Student > Ph. D. Student 4 6%
Other 11 15%
Unknown 15 21%
Readers by discipline Count As %
Agricultural and Biological Sciences 42 58%
Biochemistry, Genetics and Molecular Biology 7 10%
Social Sciences 3 4%
Earth and Planetary Sciences 1 1%
Medicine and Dentistry 1 1%
Other 2 3%
Unknown 16 22%
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 18 June 2019.
All research outputs
#3,459,908
of 24,703,339 outputs
Outputs from Frontiers in Genetics
#1,002
of 13,311 outputs
Outputs of similar age
#60,630
of 315,188 outputs
Outputs of similar age from Frontiers in Genetics
#11
of 50 outputs
Altmetric has tracked 24,703,339 research outputs across all sources so far. Compared to these this one has done well and is in the 85th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 13,311 research outputs from this source. They receive a mean Attention Score of 3.8. 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 315,188 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 80% of its contemporaries.
We're also able to compare this research output to 50 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 80% of its contemporaries.