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Gene Expression Variability within and between Human Populations and Implications toward Disease Susceptibility

Overview of attention for article published in PLoS Computational Biology, August 2010
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Readers on

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140 Mendeley
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Title
Gene Expression Variability within and between Human Populations and Implications toward Disease Susceptibility
Published in
PLoS Computational Biology, August 2010
DOI 10.1371/journal.pcbi.1000910
Pubmed ID
Authors

Jingjing Li, Yu Liu, TaeHyung Kim, Renqiang Min, Zhaolei Zhang

Abstract

Variations in gene expression level might lead to phenotypic diversity across individuals or populations. Although many human genes are found to have differential mRNA levels between populations, the extent of gene expression that could vary within and between populations largely remains elusive. To investigate the dynamic range of gene expression, we analyzed the expression variability of ∼18, 000 human genes across individuals within HapMap populations. Although ∼20% of human genes show differentiated mRNA levels between populations, our results show that expression variability of most human genes in one population is not significantly deviant from another population, except for a small fraction that do show substantially higher expression variability in a particular population. By associating expression variability with sequence polymorphism, intriguingly, we found SNPs in the untranslated regions (5' and 3'UTRs) of these variable genes show consistently elevated population heterozygosity. We performed differential expression analysis on a genome-wide scale, and found substantially reduced expression variability for a large number of genes, prohibiting them from being differentially expressed between populations. Functional analysis revealed that genes with the greatest within-population expression variability are significantly enriched for chemokine signaling in HIV-1 infection, and for HIV-interacting proteins that control viral entry, replication, and propagation. This observation combined with the finding that known human HIV host factors show substantially elevated expression variability, collectively suggest that gene expression variability might explain differential HIV susceptibility across individuals.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 140 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 5 4%
Switzerland 2 1%
Netherlands 1 <1%
Malaysia 1 <1%
Canada 1 <1%
Brazil 1 <1%
Unknown 129 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 38 27%
Researcher 38 27%
Student > Bachelor 9 6%
Student > Master 9 6%
Professor > Associate Professor 7 5%
Other 21 15%
Unknown 18 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 68 49%
Biochemistry, Genetics and Molecular Biology 21 15%
Medicine and Dentistry 8 6%
Immunology and Microbiology 4 3%
Computer Science 4 3%
Other 14 10%
Unknown 21 15%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 22 October 2015.
All research outputs
#7,356,550
of 25,374,917 outputs
Outputs from PLoS Computational Biology
#4,995
of 8,960 outputs
Outputs of similar age
#33,500
of 103,405 outputs
Outputs of similar age from PLoS Computational Biology
#28
of 59 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 8,960 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one is in the 42nd percentile – i.e., 42% 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 103,405 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 65% of its contemporaries.
We're also able to compare this research output to 59 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 50% of its contemporaries.