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Substantial contribution of genetic variation in the expression of transcription factors to phenotypic variation revealed by eRD-GWAS

Overview of attention for article published in Genome Biology (Online Edition), October 2017
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (94th percentile)
  • Good Attention Score compared to outputs of the same age and source (75th percentile)

Mentioned by

blogs
2 blogs
twitter
52 tweeters
facebook
1 Facebook page

Citations

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20 Dimensions

Readers on

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75 Mendeley
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Title
Substantial contribution of genetic variation in the expression of transcription factors to phenotypic variation revealed by eRD-GWAS
Published in
Genome Biology (Online Edition), October 2017
DOI 10.1186/s13059-017-1328-6
Pubmed ID
Authors

Hung-ying Lin, Qiang Liu, Xiao Li, Jinliang Yang, Sanzhen Liu, Yinlian Huang, Michael J. Scanlon, Dan Nettleton, Patrick S. Schnable

Abstract

There are significant limitations in existing methods for the genome-wide identification of genes whose expression patterns affect traits. The transcriptomes of five tissues from 27 genetically diverse maize inbred lines were deeply sequenced to identify genes exhibiting high and low levels of expression variation across tissues or genotypes. Transcription factors are enriched among genes with the most variation in expression across tissues, as well as among genes with higher-than-median levels of variation in expression across genotypes. In contrast, transcription factors are depleted among genes whose expression is either highly stable or highly variable across genotypes. We developed a Bayesian-based method for genome-wide association studies (GWAS) in which RNA-seq-based measures of transcript accumulation are used as explanatory variables (eRD-GWAS). The ability of eRD-GWAS to identify true associations between gene expression variation and phenotypic diversity is supported by analyses of RNA co-expression networks, protein-protein interaction networks, and gene regulatory networks. Genes associated with 13 traits were identified using eRD-GWAS on a panel of 369 maize inbred lines. Predicted functions of many of the resulting trait-associated genes are consistent with the analyzed traits. Importantly, transcription factors are significantly enriched among trait-associated genes identified with eRD-GWAS. eRD-GWAS is a powerful tool for associating genes with traits and is complementary to SNP-based GWAS. Our eRD-GWAS results are consistent with the hypothesis that genetic variation in transcription factor expression contributes substantially to phenotypic diversity.

Twitter Demographics

The data shown below were collected from the profiles of 52 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 75 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 75 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 19 25%
Researcher 18 24%
Professor 6 8%
Student > Master 6 8%
Professor > Associate Professor 5 7%
Other 13 17%
Unknown 8 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 44 59%
Biochemistry, Genetics and Molecular Biology 20 27%
Immunology and Microbiology 1 1%
Physics and Astronomy 1 1%
Medicine and Dentistry 1 1%
Other 1 1%
Unknown 7 9%

Attention Score in Context

This research output has an Altmetric Attention Score of 46. 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 21 November 2018.
All research outputs
#456,670
of 15,275,955 outputs
Outputs from Genome Biology (Online Edition)
#412
of 3,301 outputs
Outputs of similar age
#17,614
of 322,563 outputs
Outputs of similar age from Genome Biology (Online Edition)
#59
of 241 outputs
Altmetric has tracked 15,275,955 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,301 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 24.9. This one has done well, scoring higher than 87% 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 322,563 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 94% of its contemporaries.
We're also able to compare this research output to 241 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 75% of its contemporaries.