GeneTIER: prioritization of candidate disease genes using tissue-specific gene expression profiles

Motivation: In attempts to determine the genetic causes of human disease, researchers are often faced with a large number of candidate genes. Linkage studies can point to a genomic region containing hundreds of genes, while the high-throughput sequencing approach will often identify a great number of non-synonymous genetic variants. Since systematic experimental verification of each such candidate gene is not feasible, a method is needed to decide which genes are worth investigating further. Computational gene prioritization presents itself as a solution to this problem, systematically analyzing and sorting each gene from the most to least likely to be the disease-causing gene, in a fraction of the time it would take a researcher to perform such queries manually. Results: Here, we present Gene TIssue Expression Ranker (GeneTIER), a new web-based application for candidate gene prioritization. GeneTIER replaces knowledge-based inference traditionally used in candidate disease gene prioritization applications with experimental data from tissue-specific gene expression datasets and thus largely overcomes the bias toward the better characterized genes/diseases that commonly afflict other methods. We show that our approach is capable of accurate candidate gene prioritization and illustrate its strengths and weaknesses using case study examples.