title: SNP web resources and their potential applications in personalized medicine.
abstract: Single nucleotide polymorphisms (SNPs) are the commonest genetic variant in the human genome and have been associated with inter-individual differences in drug response. Finding the causative SNPs underlying variations in drug response has been a cornerstone of personalized medicine. However, as there are over 19 million SNPs, the task of finding causative SNPs underlying differences in drug response using in vitro and in vivo methods can be intimidating. SNP related web resources can be invaluable in the search for SNPs relevant to drug response phenotypes as they represent relatively cheaper yet efficient ways of prioritizing relevant SNPs for further study. These resources serve as repositories of SNP information or contain in silico tools that can predict the functionality of a SNP. More sophisticated resources integrate the information repository function with the predictive function to create a one stop SNP resource for researchers. SNP related web resources can also aid researchers in planning and analyzing different types of genetic association studies by aiding in selecting SNPs for genotyping in these studies. The focus of this mini review is to outline the SNP related web resources that are available to researchers and how these resources may aid researchers studying SNP-drug response phenotype associations. Through efficient utilization of SNP related web resources, researchers will hopefully be able accelerate the pace of SNP related research in pharmacogenomics by identifying high risk SNP variants contributing to drug response as well as developing novel therapeutic targets based on understanding how SNPs alter drug response pathways.
authors.affiliation: Division of Medical Sciences, National Cancer Center, 11 Hospital Drive, Singapore.
authors.forename: Jingbo
authors.initials: J
authors.lastname: Wang
authors.forename: Grace S Y
authors.initials: GS
authors.lastname: Pang
authors.forename: Samuel S
authors.initials: SS
authors.lastname: Chong
authors.forename: Caroline G L
authors.initials: CG
authors.lastname: Lee
mesh_terms: D030541:Databases, Genetic / Q000639:trends; D015894:Genome, Human / Q000235:genetics; D006801:Humans; D020407:Internet / Q000639:trends; D010597:Pharmacogenetics / Q000379:methods / Q000639:trends; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*; D057285:Precision Medicine / Q000379:methods* / Q000639:trends