Current computational methods for prioritizing candidate regulatory polymorphisms.
Discovery of DNA sequence variants responsible for human phenotypic variation is key to advances in molecular diagnostics and medicines. Historically, variants that alter the protein-coding sequence of genes have been targeted when attempting to identify a trait's etiology; this is done because the rules governing these regions are generally well-understood and candidate variants can be easily selected. However, the effects of variants on gene regulation are increasingly regarded as being as important as protein-coding variation in uncovering the nature of phenotypic variation. I discuss resources and methodology that have recently been developed to computationally prioritize variants that may alter gene expression.
Montgomery
Stephen
S
Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK.
D000483:Alleles; D001665:Binding Sites / Q000235:genetics; D019295:Computational Biology; D003627:Data Interpretation, Statistical; D030541:Databases, Genetic; D005786:Gene Expression Regulation; D005787:Gene Frequency; D014644:Genetic Variation; D006801:Humans; D011110:Polymorphism, Genetic; D020641:Polymorphism, Single Nucleotide; D022661:RNA, Untranslated / Q000235:genetics; D012984:Software; D014157:Transcription Factors / Q000378:metabolism
D022661:RNA, Untranslated; D014157:Transcription Factors