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Bioinformatics Advance Access published online on March 3, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti365
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© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oupjournals.org
Received October 20, 2004
Revised February 17, 2005
Accepted February 28, 2005

Article

Prediction of the phenotypic effects of nonsynonymous single nucleotide polymorphisms using structural and evolutionary information

Lei Bao 1 and Yan Cui 1*

1 Department of Molecular Sciences, Center of Genomics and Bioinformatics, University of Tennessee Health Science Center, Memphis, TN 38163, USA

* To whom correspondence should be addressed.
Yan Cui, E-mail: ycui2{at}utmem.edu


  Abstract

Motivation: There has been great expectation that knowledge of an individual's genotype will provide a basis for assessing susceptibility to diseases and designing individualized therapy. Nonsynonymous single-nucleotide polymorphisms (nsSNP) that lead to an amino acid change in the protein product are of particular interest because they account for nearly half of the known genetic variations related to human inherited disease (Stenson et al., 2003). To facilitate identifying disease-associated nsSNPs from a large number of neutral nsSNPs, it is important to develop computational tools to predict the nsSNP's phenotypic effect.

Results: We prepared a training set based on the variant phenotypic annotation of the SwissProt database and focused our analysis on nsSNPs having homologous 3D structures. Structural environment parameters derived from the 3D homologous structure as well as evolutionary information derived from the multiple sequence alignment were used as predictors. Two machine learning methods, support vector machine and random forest, were trained and evaluated. We compared the performance of our method with that of the SIFT algorithm (Ng and Henikoff, 2003), which is one of the best predictive methods to date. An unbiased evaluation study shows that for nsSNPs with sufficient evolutionary information (e.g., with no fewer than 10 homologous sequences), the performance of our method is comparable to the SIFT algorithm, while for nsSNPs with insufficient evolutionary information (e.g., fewer than 10 homologous sequences), our method outperforms the SIFT algorithm significantly. These findings indicate that incorporating structural information is critical to achieving good prediction accuracy when sufficient evolutionary information is not available.

Availability: The codes and curated dataset are available at http://compbio.utmem.edu/snp/.


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