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134 Mendeley
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Article details
Title
Maps of open chromatin highlight cell type–restricted patterns of regulatory sequence variation at hematological trait loci
Published in
Genome Research, April 2013
DOI 10.1101/gr.155127.113
Pubmed ID
Authors
Abstract

Nearly three-quarters of the 143 genetic signals associated with platelet and erythrocyte phenotypes identified by meta-analyses of genome-wide association (GWA) studies are located at non-protein-coding regions. Here, we assessed the role of candidate regulatory variants associated with cell type-restricted, closely related hematological quantitative traits in biologically relevant hematopoietic cell types. We used formaldehyde-assisted isolation of regulatory elements followed by next-generation sequencing (FAIRE-seq) to map regions of open chromatin in three primary human blood cells of the myeloid lineage. In the precursors of platelets and erythrocytes, as well as in monocytes, we found that open chromatin signatures reflect the corresponding hematopoietic lineages of the studied cell types and associate with the cell type-specific gene expression patterns. Dependent on their signal strength, open chromatin regions showed correlation with promoter and enhancer histone marks, distance to the transcription start site, and ontology classes of nearby genes. Cell type-restricted regions of open chromatin were enriched in sequence variants associated with hematological indices. The majority (63.6%) of such candidate functional variants at platelet quantitative trait loci (QTLs) coincided with binding sites of five transcription factors key in regulating megakaryopoiesis. We experimentally tested 13 candidate regulatory variants at 10 platelet QTLs and found that 10 (76.9%) affected protein binding, suggesting that this is a frequent mechanism by which regulatory variants influence quantitative trait levels. Our findings demonstrate that combining large-scale GWA data with open chromatin profiles of relevant cell types can be a powerful means of dissecting the genetic architecture of closely related quantitative traits.

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X Demographics

X Demographics

The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 134 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
United States 4 3%
United Kingdom 2 1%
South Africa 1 <1%
Norway 1 <1%
Netherlands 1 <1%
Mexico 1 <1%
Luxembourg 1 <1%
Italy 1 <1%
Greece 1 <1%
Other 5 4%
Unknown 116 87%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Researcher 49 37%
Student > Ph. D. Student 30 22%
Professor > Associate Professor 14 10%
Professor 10 7%
Student > Master 6 4%
Other 17 13%
Unknown 8 6%
Readers by discipline
Readers by discipline Count As %
Agricultural and Biological Sciences 67 50%
Biochemistry, Genetics and Molecular Biology 33 25%
Medicine and Dentistry 10 7%
Computer Science 3 2%
Arts and Humanities 2 1%
Other 6 4%
Unknown 13 10%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 25 May 2013.
All research outputs
#15,091,901
of 25,374,647 outputs
Outputs from Genome Research
#3,873
of 4,425 outputs
Outputs of similar age
#117,398
of 212,311 outputs
Outputs of similar age from Genome Research
#39
of 45 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. This one is in the 40th percentile – i.e., 40% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,425 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 17.3. This one is in the 12th percentile – i.e., 12% of its peers scored the same or lower than it.
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 212,311 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 45 others from the same source and published within six weeks on either side of this one. This one is in the 13th percentile – i.e., 13% of its contemporaries scored the same or lower than it.