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Amino Acid Changes in Disease-Associated Variants Differ Radically from Variants Observed in the 1000 Genomes Project Dataset

Overview of attention for article published in PLoS Computational Biology, December 2013
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Title
Amino Acid Changes in Disease-Associated Variants Differ Radically from Variants Observed in the 1000 Genomes Project Dataset
Published in
PLoS Computational Biology, December 2013
DOI 10.1371/journal.pcbi.1003382
Pubmed ID
Authors

Tjaart A. P. de Beer, Roman A. Laskowski, Sarah L. Parks, Botond Sipos, Nick Goldman, Janet M. Thornton

Abstract

The 1000 Genomes Project data provides a natural background dataset for amino acid germline mutations in humans. Since the direction of mutation is known, the amino acid exchange matrix generated from the observed nucleotide variants is asymmetric and the mutabilities of the different amino acids are very different. These differences predominantly reflect preferences for nucleotide mutations in the DNA (especially the high mutation rate of the CpG dinucleotide, which makes arginine mutability very much higher than other amino acids) rather than selection imposed by protein structure constraints, although there is evidence for the latter as well. The variants occur predominantly on the surface of proteins (82%), with a slight preference for sites which are more exposed and less well conserved than random. Mutations to functional residues occur about half as often as expected by chance. The disease-associated amino acid variant distributions in OMIM are radically different from those expected on the basis of the 1000 Genomes dataset. The disease-associated variants preferentially occur in more conserved sites, compared to 1000 Genomes mutations. Many of the amino acid exchange profiles appear to exhibit an anti-correlation, with common exchanges in one dataset being rare in the other. Disease-associated variants exhibit more extreme differences in amino acid size and hydrophobicity. More modelling of the mutational processes at the nucleotide level is needed, but these observations should contribute to an improved prediction of the effects of specific variants in humans.

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Geographical breakdown

Country Count As %
United Kingdom 4 3%
United States 4 3%
Netherlands 2 2%
Spain 2 2%
Austria 1 <1%
Canada 1 <1%
Sweden 1 <1%
Germany 1 <1%
Sri Lanka 1 <1%
Other 0 0%
Unknown 113 87%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 38 29%
Researcher 30 23%
Student > Postgraduate 8 6%
Professor > Associate Professor 8 6%
Student > Master 8 6%
Other 27 21%
Unknown 11 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 68 52%
Biochemistry, Genetics and Molecular Biology 33 25%
Computer Science 5 4%
Medicine and Dentistry 2 2%
Engineering 2 2%
Other 7 5%
Unknown 13 10%