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Omic personality: implications of stable transcript and methylation profiles for personalized medicine

Overview of attention for article published in Genome Medicine, August 2015
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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 (88th percentile)
  • Good Attention Score compared to outputs of the same age and source (68th percentile)

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18 X users
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2 Wikipedia pages

Citations

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13 Dimensions

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67 Mendeley
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Title
Omic personality: implications of stable transcript and methylation profiles for personalized medicine
Published in
Genome Medicine, August 2015
DOI 10.1186/s13073-015-0209-4
Pubmed ID
Authors

Rubina Tabassum, Ambily Sivadas, Vartika Agrawal, Haozheng Tian, Dalia Arafat, Greg Gibson

Abstract

Personalized medicine is predicated on the notion that individual biochemical and genomic profiles are relatively constant in times of good health and to some extent predictive of disease or therapeutic response. We report a pilot study quantifying gene expression and methylation profile consistency over time, addressing the reasons for individual uniqueness, and its relation to N = 1 phenotypes. Whole blood samples from four African American women, four Caucasian women, and four Caucasian men drawn from the Atlanta Center for Health Discovery and Well Being study at three successive 6-month intervals were profiled by RNA-Seq, miRNA-Seq, and Illumina Methylation 450 K arrays. Standard regression approaches were used to evaluate the proportion of variance for each type of omic measure among individuals, and to quantify correlations among measures and with clinical attributes related to wellness. Longitudinal omic profiles were in general highly consistent over time, with an average of 67 % variance in transcript abundance, 42 % in CpG methylation level (but 88 % for the most differentiated CpG per gene), and 50 % in miRNA abundance among individuals, which are all comparable to 74 % variance among individuals for 74 clinical traits. One third of the variance could be attributed to differential blood cell type abundance, which was also fairly stable over time, and a lesser amount to expression quantitative trait loci (eQTL) effects. Seven conserved axes of covariance that capture diverse aspects of immune function explained over half of the variance. These axes also explained a considerable proportion of individually extreme transcript abundance, namely approximately 100 genes that were significantly up-regulated or down-regulated in each person and were in some cases enriched for relevant gene activities that plausibly associate with clinical attributes. A similar fraction of genes had individually divergent methylation levels, but these did not overlap with the transcripts, and fewer than 20 % of genes had significantly correlated methylation and gene expression. People express an "omic personality" consisting of peripheral blood transcriptional and epigenetic profiles that are constant over the course of a year and reflect various types of immune activity. Baseline genomic profiles can provide a window into the molecular basis of traits that might be useful for explaining medical conditions or guiding personalized health decisions.

X Demographics

X Demographics

The data shown below were collected from the profiles of 18 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 67 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 3%
Indonesia 1 1%
Germany 1 1%
Luxembourg 1 1%
Unknown 62 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 21 31%
Student > Ph. D. Student 13 19%
Student > Bachelor 5 7%
Other 5 7%
Professor > Associate Professor 3 4%
Other 8 12%
Unknown 12 18%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 19 28%
Agricultural and Biological Sciences 17 25%
Medicine and Dentistry 9 13%
Computer Science 4 6%
Mathematics 1 1%
Other 5 7%
Unknown 12 18%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 15. 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 January 2024.
All research outputs
#2,444,534
of 25,339,932 outputs
Outputs from Genome Medicine
#556
of 1,574 outputs
Outputs of similar age
#30,554
of 270,902 outputs
Outputs of similar age from Genome Medicine
#11
of 32 outputs
Altmetric has tracked 25,339,932 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,574 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 26.9. This one has gotten more attention than average, scoring higher than 64% 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 270,902 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 88% of its contemporaries.
We're also able to compare this research output to 32 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 68% of its contemporaries.