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Bioinformatics. 2009 Apr 15;25(8):1048-55. doi: 10.1093/bioinformatics/btp103. Epub 2009 Feb 19.

An integrative scoring system for ranking SNPs by their potential deleterious effects.

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Computational Biology and Machine Learning Lab, School of Computing, Queen's University, Kingston, ON, Canada.



Identifying single nucleotide polymorphisms (SNPs) that underlie common and complex human diseases, such as cancer, is of major interest in current molecular epidemiology. Nevertheless, the tremendous number of SNPs on the human genome requires computational methods for prioritizing SNPs according to their potentially deleterious effects to human health, and as such, for expediting genotyping and analysis. As of yet, little has been done to quantitatively assess the possible deleterious effects of SNPs for effective association studies.


We propose a new integrative scoring system for prioritizing SNPs based on their possible deleterious effects within a probabilistic framework. We applied our system to 580 disease-susceptibility genes obtained from the OMIM (Online Mendelian Inheritance in Man) database, which is one of the most widely used databases of human genes and genetic disorders. The scoring results clearly show that the distribution of the functional significance (FS) scores for already known disease-related SNPs is significantly different from that of neutral SNPs. In addition, we summarize distinct features of potentially deleterious SNPs based on their FS score, such as functional genomic regions where they occur or bio-molecular functions that they mainly affect. We also demonstrate, through a comparative study, that our system improves upon other function-assessment systems for SNPs, by assigning significantly higher FS scores to already known disease-related SNPs than to neutral SNPs.

[Indexed for MEDLINE]

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