title: regSNPs: a strategy for prioritizing regulatory single nucleotide substitutions.
abstract: BACKGROUND
  One of the fundamental questions in genetics study is to identify functional DNA variants that are responsible to a disease or phenotype of interest. Results from large-scale genetics studies, such as genome-wide association studies (GWAS), and the availability of high-throughput sequencing technologies provide opportunities in identifying causal variants. Despite the technical advances, informatics methodologies need to be developed to prioritize thousands of variants for potential causative effects.
  
  
  RESULTS
  We present regSNPs, an informatics strategy that integrates several established bioinformatics tools, for prioritizing regulatory SNPs, i.e. the SNPs in the promoter regions that potentially affect phenotype through changing transcription of downstream genes. Comparing to existing tools, regSNPs has two distinct features. It considers degenerative features of binding motifs by calculating the differences on the binding affinity caused by the candidate variants and integrates potential phenotypic effects of various transcription factors. When tested by using the disease-causing variants documented in the Human Gene Mutation Database, regSNPs showed mixed performance on various diseases. regSNPs predicted three SNPs that can potentially affect bone density in a region detected in an earlier linkage study. Potential effects of one of the variants were validated using luciferase reporter assay.
authors.affiliation: School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China.
authors.forename: Mingxiang
authors.initials: M
authors.lastname: Teng
authors.forename: Shoji
authors.initials: S
authors.lastname: Ichikawa
authors.forename: Leah R
authors.initials: LR
authors.lastname: Padgett
authors.forename: Yadong
authors.initials: Y
authors.lastname: Wang
authors.forename: Matthew
authors.initials: M
authors.lastname: Mort
authors.forename: David N
authors.initials: DN
authors.lastname: Cooper
authors.forename: Daniel L
authors.initials: DL
authors.lastname: Koller
authors.forename: Tatiana
authors.initials: T
authors.lastname: Foroud
authors.forename: Howard J
authors.initials: HJ
authors.lastname: Edenberg
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Econs
authors.forename: Yunlong
authors.initials: Y
authors.lastname: Liu
mesh_terms: D019540:Area Under Curve; D001665:Binding Sites; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D008040:Genetic Linkage; D015894:Genome, Human; D055106:Genome-Wide Association Study; D057809:HEK293 Cells; D006801:Humans; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide; D011401:Promoter Regions, Genetic; D012372:ROC Curve; D014157:Transcription Factors / Q000235:genetics*
chemical_list: D014157:Transcription Factors