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Bioinformatics Advance Access published online on January 29, 2004
Bioinformatics, doi:10.1093/bioinformatics/bth029
Bioinformatics © Oxford University Press 2004; all rights reserved
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Received February 9, 2003
Revised October 15, 2003
Accepted November 10, 2003

Article

Large-scale analysis of non-synonymous coding region single nucleotide polymorphisms

Robert J. Clifford 1, Michael N. Edmonson 1, Cu Nguyen 1, Kenneth H. Buetow 1*

1 Laboratory of Population Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA

* To whom correspondence should be addressed. E-mail: buetowk{at}nih.gov.


  Abstract

Motivation: Single nucleotide polymorphisms (SNPs) are the most common form of genetic variant in humans. SNPs causing amino acid substitutions are of particular interest as candidates for loci affecting susceptibility to complex diseases, such as diabetes and hypertension. To efficiently screen SNPs for disease association, it is important to distinguish neutral variants from deleterious ones.

Results: We describe the use of Pfam protein motif models (Bateman et al., 1999, 2002) and the HMMER program (Eddy, 1998) to predict whether amino acid changes in conserved domains are likely to affect protein function. We find that the magnitude of the change in the HMMER E-value caused by an amino acid substitution is a good predictor of whether it is deleterious. We provide internet-accessible display tools for a genomewide collection of SNPs, including 7391 distinct non-synonymous coding region SNPs in 2683 genes.

Availability: http://lpgws.nci.nih.gov/cgi-bin/GeneViewer.cgi.


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