Journal
JOURNAL OF MOLECULAR BIOLOGY
Volume 356, Issue 5, Pages 1263-1274Publisher
ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.jmb.2005.12.025
Keywords
single nucleotide polymorphisms (SNPs); monogenic disease; human disease; complex traits; support vector machine
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Funding
- NLM NIH HHS [LM07174] Funding Source: Medline
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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. (c) 2006 Elsevier Ltd. All rights reserved.
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