title: A systematic assessment of linking gene expression with genetic variants for prioritizing candidate targets.
abstract: Gene expression and disease-associated variants are often used to prioritize candidate genes for target validation. However, the success of these gene features alone or in combination in the discovery of therapeutic targets is uncertain. Here we evaluated the effectiveness of the differential expression (DE), the disease-associated single nucleotide polymorphisms (SNPs) and the combination of the two in recovering and predicting known therapeutic targets across 56 human diseases. We demonstrate that the performance of each feature varies across diseases and generally the features have more recovery power than predictive power. The combination of the two features, however, has significantly higher predictive power than each feature alone. Our study provides a systematic evaluation of two common gene features, DE and SNPs, for prioritization of candidate targets and identified an improved predictive power of coupling these two features.
authors.affiliation: Stanford University, Division of System Medicine, Department of Pediatrics, Stanford, CA 94305, USA. fminogue@stanford.edu.
authors.forename: Hua
authors.initials: H
authors.lastname: Fan-Minogue
authors.forename: Bin
authors.initials: B
authors.lastname: Chen
authors.forename: Weronika
authors.initials: W
authors.lastname: Sikora-Wohlfeld
authors.forename: Marina
authors.initials: M
authors.lastname: Sirota
authors.forename: Atul J
authors.initials: AJ
authors.lastname: Butte
mesh_terms: D019295:Computational Biology; D030541:Databases, Genetic / Q000706:statistics & numerical data; D004194:Disease / Q000235:genetics; D005260:Female; D015870:Gene Expression; D014644:Genetic Variation; D006801:Humans; D008297:Male; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data; D020641:Polymorphism, Single Nucleotide