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Human Monogenic Disease Genes Have Frequently Functionally Redundant Paralogs

Overview of attention for article published in PLoS Computational Biology, May 2013
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  • Good Attention Score compared to outputs of the same age (72nd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (62nd percentile)

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82 Mendeley
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Title
Human Monogenic Disease Genes Have Frequently Functionally Redundant Paralogs
Published in
PLoS Computational Biology, May 2013
DOI 10.1371/journal.pcbi.1003073
Pubmed ID
Authors

Wei-Hua Chen, Xing-Ming Zhao, Vera van Noort, Peer Bork

Abstract

Mendelian disorders are often caused by mutations in genes that are not lethal but induce functional distortions leading to diseases. Here we study the extent of gene duplicates that might compensate genes causing monogenic diseases. We provide evidence for pervasive functional redundancy of human monogenic disease genes (MDs) by duplicates by manifesting 1) genes involved in human genetic disorders are enriched in duplicates and 2) duplicated disease genes tend to have higher functional similarities with their closest paralogs in contrast to duplicated non-disease genes of similar age. We propose that functional compensation by duplication of genes masks the phenotypic effects of deleterious mutations and reduces the probability of purging the defective genes from the human population; this functional compensation could be further enhanced by higher purification selection between disease genes and their duplicates as well as their orthologous counterpart compared to non-disease genes. However, due to the intrinsic expression stochasticity among individuals, the deleterious mutations could still be present as genetic diseases in some subpopulations where the duplicate copies are expressed at low abundances. Consequently the defective genes are linked to genetic disorders while they continue propagating within the population. Our results provide insight into the molecular basis underlying the spreading of duplicated disease genes.

X Demographics

X Demographics

The data shown below were collected from the profiles of 5 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 82 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 4%
United Kingdom 2 2%
Korea, Republic of 1 1%
Israel 1 1%
France 1 1%
Canada 1 1%
Germany 1 1%
Japan 1 1%
Spain 1 1%
Other 0 0%
Unknown 70 85%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 25 30%
Researcher 12 15%
Student > Bachelor 8 10%
Student > Master 8 10%
Professor 7 9%
Other 10 12%
Unknown 12 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 35 43%
Biochemistry, Genetics and Molecular Biology 17 21%
Medicine and Dentistry 6 7%
Computer Science 5 6%
Pharmacology, Toxicology and Pharmaceutical Science 1 1%
Other 7 9%
Unknown 11 13%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 10 April 2016.
All research outputs
#7,009,134
of 26,017,215 outputs
Outputs from PLoS Computational Biology
#4,665
of 9,038 outputs
Outputs of similar age
#55,874
of 211,144 outputs
Outputs of similar age from PLoS Computational Biology
#39
of 107 outputs
Altmetric has tracked 26,017,215 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 9,038 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 47th percentile – i.e., 47% 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 211,144 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 72% of its contemporaries.
We're also able to compare this research output to 107 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 62% of its contemporaries.