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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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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.

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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%