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Non-Synonymous and Synonymous Coding SNPs Show Similar Likelihood and Effect Size of Human Disease Association

Overview of attention for article published in PLOS ONE, October 2010
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Title
Non-Synonymous and Synonymous Coding SNPs Show Similar Likelihood and Effect Size of Human Disease Association
Published in
PLOS ONE, October 2010
DOI 10.1371/journal.pone.0013574
Pubmed ID
Authors

Rong Chen, Eugene V. Davydov, Marina Sirota, Atul J. Butte

Abstract

Many DNA variants have been identified on more than 300 diseases and traits using Genome-Wide Association Studies (GWASs). Some have been validated using deep sequencing, but many fewer have been validated functionally, primarily focused on non-synonymous coding SNPs (nsSNPs). It is an open question whether synonymous coding SNPs (sSNPs) and other non-coding SNPs can lead to as high odds ratios as nsSNPs. We conducted a broad survey across 21,429 disease-SNP associations curated from 2,113 publications studying human genetic association, and found that nsSNPs and sSNPs shared similar likelihood and effect size for disease association. The enrichment of disease-associated SNPs around the 80(th) base in the first introns might provide an effective way to prioritize intronic SNPs for functional studies. We further found that the likelihood of disease association was positively associated with the effect size across different types of SNPs, and SNPs in the 3' untranslated regions, such as the microRNA binding sites, might be under-investigated. Our results suggest that sSNPs are just as likely to be involved in disease mechanisms, so we recommend that sSNPs discovered from GWAS should also be examined with functional studies.

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The data shown below were compiled from readership statistics for 269 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 10 4%
United Kingdom 3 1%
Mexico 2 <1%
Belgium 2 <1%
Spain 2 <1%
Hong Kong 1 <1%
Russia 1 <1%
Germany 1 <1%
Japan 1 <1%
Other 1 <1%
Unknown 245 91%

Demographic breakdown

Readers by professional status Count As %
Researcher 67 25%
Student > Ph. D. Student 61 23%
Professor > Associate Professor 21 8%
Student > Bachelor 21 8%
Other 18 7%
Other 47 17%
Unknown 34 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 133 49%
Biochemistry, Genetics and Molecular Biology 45 17%
Medicine and Dentistry 20 7%
Computer Science 9 3%
Immunology and Microbiology 5 2%
Other 20 7%
Unknown 37 14%