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Nucleic Acids Research 2005 33(5):1710-1721; doi:10.1093/nar/gki311
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Published online 22 March 2005

© The Author 2005. Published by Oxford University Press. All rights reserved
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Article

Sequence variation in G-protein-coupled receptors: analysis of single nucleotide polymorphisms

Suganthi Balasubramanian1, Yu Xia1, Elizaveta Freinkman1 and Mark Gerstein1,2,*

1Department of Molecular Biophysics and Biochemistry, Yale University 266 Whitney Avenue, New Haven, CT 06520-8114, USA 2Department of Computer Science, Yale University 266 Whitney Avenue, New Haven, CT 06520-8114, USA

*To whom correspondence should be addressed. Tel: +1 203 432 6105; Fax: +1 360 838 7861; Email: mark.gerstein{at}yale.edu

Received October 12, 2004. Revised January 18, 2005. Accepted March 1, 2005.

We assessed the disease-causing potential of single nucleotide polymorphisms (SNPs) based on a simple set of sequence-based features. We focused on SNPs from the dbSNP database in G-protein-coupled receptors (GPCRs), a large class of important transmembrane (TM) proteins. Apart from the location of the SNP in the protein, we evaluated the predictive power of three major classes of features to differentiate between disease-causing mutations and neutral changes: (i) properties derived from amino-acid scales, such as volume and hydrophobicity; (ii) position-specific phylogenetic features reflecting evolutionary conservation, such as normalized site entropy, residue frequency and SIFT score; and (iii) substitution-matrix scores, such as those derived from the BLOSUM62, GRANTHAM and PHAT matrices. We validated our approach using a control dataset consisting of known disease-causing mutations and neutral variations. Logistic regression analyses indicated that position-specific phylogenetic features that describe the conservation of an amino acid at a specific site are the best discriminators of disease mutations versus neutral variations, and integration of all our features improves discrimination power. Overall, we identify 115 SNPs in GPCRs from dbSNP that are likely to be associated with disease and thus are good candidates for genotyping in association studies.


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