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Population whole-genome bisulfite sequencing across two tissues highlights the environment as the principal source of human methylome variation

Overview of attention for article published in Genome Biology, December 2015
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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 (92nd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (52nd percentile)

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41 X users

Citations

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87 Dimensions

Readers on

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156 Mendeley
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5 CiteULike
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Title
Population whole-genome bisulfite sequencing across two tissues highlights the environment as the principal source of human methylome variation
Published in
Genome Biology, December 2015
DOI 10.1186/s13059-015-0856-1
Pubmed ID
Authors

Stephan Busche, Xiaojian Shao, Maxime Caron, Tony Kwan, Fiona Allum, Warren A. Cheung, Bing Ge, Susan Westfall, Marie-Michelle Simon, The Multiple Tissue Human Expression Resource, Amy Barrett, Jordana T. Bell, Mark I. McCarthy, Panos Deloukas, Mathieu Blanchette, Guillaume Bourque, Timothy D. Spector, Mark Lathrop, Tomi Pastinen, Elin Grundberg

Abstract

CpG methylation variation is involved in human trait formation and disease susceptibility. Analyses within populations have been biased towards CpG-dense regions through the application of targeted arrays. We generate whole-genome bisulfite sequencing data for approximately 30 adipose and blood samples from monozygotic and dizygotic twins for the characterization of non-genetic and genetic effects at single-site resolution. Purely invariable CpGs display a bimodal distribution with enrichment of unmethylated CpGs and depletion of fully methylated CpGs in promoter and enhancer regions. Population-variable CpGs account for approximately 15-20 % of total CpGs per tissue, are enriched in enhancer-associated regions and depleted in promoters, and single nucleotide polymorphisms at CpGs are a frequent confounder of extreme methylation variation. Differential methylation is primarily non-genetic in origin, with non-shared environment accounting for most of the variance. These non-genetic effects are mainly tissue-specific. Tobacco smoking is associated with differential methylation in blood with no evidence of this exposure impacting cell counts. Opposite to non-genetic effects, genetic effects of CpG methylation are shared across tissues and thus limit inter-tissue epigenetic drift. CpH methylation is rare, and shows similar characteristics of variation patterns as CpGs. Our study highlights the utility of low pass whole-genome bisulfite sequencing in identifying methylome variation beyond promoter regions, and suggests that targeting the population dynamic methylome of tissues requires assessment of understudied intergenic CpGs distal to gene promoters to reveal the full extent of inter-individual variation.

X Demographics

X Demographics

The data shown below were collected from the profiles of 41 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 156 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 1%
Japan 2 1%
Germany 1 <1%
Canada 1 <1%
New Zealand 1 <1%
Mexico 1 <1%
Norway 1 <1%
Russia 1 <1%
United Kingdom 1 <1%
Other 2 1%
Unknown 143 92%

Demographic breakdown

Readers by professional status Count As %
Researcher 36 23%
Student > Ph. D. Student 35 22%
Student > Bachelor 13 8%
Professor 11 7%
Student > Doctoral Student 11 7%
Other 32 21%
Unknown 18 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 55 35%
Biochemistry, Genetics and Molecular Biology 46 29%
Medicine and Dentistry 12 8%
Computer Science 7 4%
Psychology 3 2%
Other 9 6%
Unknown 24 15%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 22. 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 20 September 2016.
All research outputs
#1,711,390
of 25,371,288 outputs
Outputs from Genome Biology
#1,400
of 4,467 outputs
Outputs of similar age
#28,429
of 396,496 outputs
Outputs of similar age from Genome Biology
#34
of 72 outputs
Altmetric has tracked 25,371,288 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,467 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.6. This one has gotten more attention than average, scoring higher than 68% 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 396,496 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 92% of its contemporaries.
We're also able to compare this research output to 72 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 52% of its contemporaries.