Sequence variations in G-protein-coupled receptors:
analysis of single nucleotide polymorphisms
Suganthi Balasubramanian,
Yu Xia, Elizaveta Freinkman and Mark Gerstein
___________________________________________________________________________________________________
Abstract
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 dbSNP
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: (1) Properties derived from amino-acid scales,
such as volume and hydrophobicity; (2)
Position-specific phylogenetic features reflecting
evolutionary conservation, such as normalized site entropy, residue frequency
and SIFT score; and (3) Substitution-matrix scores such as those from
the BLOSUM62, GRANTHAM and PHAT matrices. We
validated this 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 the 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.
___________________________________________________________________________________________________
___________________________________________________________________________________________________