Single nucleotide variations: Biological impact and theoretical interpretation. (20th October 2014)

• Record Type: Journal Article
• Authors: Katsonis, Panagiotis; Koire, Amanda; Wilson, Stephen Joseph; Hsu, Teng‐Kuei; Lua, Rhonald C.; Wilkins, Angela Dawn; Lichtarge, Olivier
• Is Part Of: Protein science. Volume 23:Number 12(2014:Dec.)
• Page Start: 1650
• Page End: 1666
• Publication Date: 2014-10-20
• Subjects: functional impact prediction methods -- disease causing SNV (single nucleotide variation) -- single nucleotide polymorphism prioritization -- missense variant classification -- non‐synonymous protein mutations; Proteins -- Periodicals; 572.6
• DOI: 10.1002/pro.2552
• Languages: English
• ISSNs: 0961-8368

Abstract

Abstract: Genome‐wide association studies (GWAS) and whole‐exome sequencing (WES) generate massive amounts of genomic variant information, and a major challenge is to identify which variations drive disease or contribute to phenotypic traits. Because the majority of known disease‐causing mutations are exonic non‐synonymous single nucleotide variations (nsSNVs), most studies focus on whether these nsSNVs affect protein function. Computational studies show that the impact of nsSNVs on protein function reflects sequence homology and structural information and predict the impact through statistical methods, machine learning techniques, or models of protein evolution. Here, we review impact prediction methods and discuss their underlying principles, their advantages and limitations, and how they compare to and complement one another. Finally, we present current applications and future directions for these methods in biological research and medical genetics.