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Single nucleotide variations: Biological impact and theoretical interpretation
Author(s) -
Katsonis Panagiotis,
Koire Amanda,
Wilson Stephen Joseph,
Hsu TengKuei,
Lua Rhonald C.,
Wilkins Angela Dawn,
Lichtarge Olivier
Publication year - 2014
Publication title -
protein science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.353
H-Index - 175
eISSN - 1469-896X
pISSN - 0961-8368
DOI - 10.1002/pro.2552
Subject(s) - computational biology , biology , genome wide association study , exome , function (biology) , genetic association , single nucleotide polymorphism , genetics , exome sequencing , phenotype , gene , genotype
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.