title: Identification and analysis of deleterious human SNPs.
abstract: We have developed two methods of identifying which non-synonomous single base changes have a deleterious effect on protein function in vivo. One method, described elsewhere, analyzes the effect of the resulting amino acid change on protein stability, utilizing structural information. The other method, introduced here, makes use of the conservation and type of residues observed at a base change position within a protein family. A machine learning technique, the support vector machine, is trained on single amino acid changes that cause monogenic disease, with a control set of amino acid changes fixed between species. Both methods are used to identify deleterious single nucleotide polymorphisms (SNPs) in the human population. After carefully controlling for errors, we find that approximately one quarter of known non-synonymous SNPs are deleterious by these criteria, providing a set of possible contributors to human complex disease traits.
authors.affiliation: Center for Advanced Research in Biotechnology, University of Maryland Biotechnology Institute, Rockville MD 20850, USA.
authors.forename: Peng
authors.initials: P
authors.lastname: Yue
authors.forename: John
authors.initials: J
authors.lastname: Moult
mesh_terms: D000818:Animals; D004252:DNA Mutational Analysis; D030562:Databases, Protein; D019143:Evolution, Molecular; D020022:Genetic Predisposition to Disease; D016678:Genome; D006801:Humans; D051379:Mice; D018345:Mice, Knockout; D020641:Polymorphism, Single Nucleotide; D011506:Proteins / Q000737:chemistry / Q000235:genetics; D012680:Sensitivity and Specificity
chemical_list: D011506:Proteins