title: Ranking single nucleotide polymorphisms by potential deleterious effects.
abstract: Identifying single nucleotide polymorphisms (SNPs) that are responsible for common and complex diseases, such as cancer, is of major interest in current molecular epidemiology. However, due to the tremendous number of SNPs on the human genome, to expedite genotyping and analysis, there is a clear need to prioritize SNPs according to their potentially deleterious effects to human health. As of yet, there have been few efforts to quantitatively assess the possible deleterious effects of SNPs for effective association studies. Here we propose a new integrative scoring system for prioritizing SNPs based on their possible deleterious effects in a probabilistic framework. We also provide the evaluation result of our system on the OMIM (Online Mendelian Inheritance in Man) database, which is one of the most widely-used databases of human genes and genetic disorders.
authors.affiliation: Computational Biology and Machine Learning Lab,School of Computing, Queen's University, Kingston, ON, Canada.
authors.forename: Phil Hyoun
authors.initials: PH
authors.lastname: Lee
authors.forename: Hagit
authors.initials: H
authors.lastname: Shatkay
mesh_terms: D002874:Chromosome Mapping / Q000379:methods*; D004252:DNA Mutational Analysis / Q000379:methods*; D020022:Genetic Predisposition to Disease / Q000453:epidemiology* / Q000235:genetics*; D006801:Humans; D015994:Incidence; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*; D016016:Proportional Hazards Models; D018570:Risk Assessment / Q000379:methods; D012307:Risk Factors; D017422:Sequence Analysis, DNA / Q000379:methods*